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Cloud-Based Parallel Machine Learning for Tool Wear Prediction
... Tool condition classification were implement by a machine ensemble technique, using support vector machine, multilayer perceptron and radial basis function neural network. Wu et al. (2018a) combined cloud computing and random forest to predict tool wear in milling process. Thousands regression trees of random forest were built to predict tool wear respectively, where MapReduce technique was introduced to obtain the finally prediction of tool wear. ...
... For the experiment with randomized training data set selection, 90% of all samples in the three different set are randomly selected as training set, and the remainder is used for model test. Results of tool wear prediction are shown in Fig. 9. Wu et al. (2018a) has achieved a very accurate prediction on this dataset using MapReduce-based parallel random forests (PRFs), which consists of 10,000 regression trees. For comparison, the same mean square error (MSE) and coefficient of determination (R 2 ) as Wu et al. (2018a) are calculated, as shown in Table 6. ...
... Results of tool wear prediction are shown in Fig. 9. Wu et al. (2018a) has achieved a very accurate prediction on this dataset using MapReduce-based parallel random forests (PRFs), which consists of 10,000 regression trees. For comparison, the same mean square error (MSE) and coefficient of determination (R 2 ) as Wu et al. (2018a) are calculated, as shown in Table 6. Although the R 2 metrics of two method are close, the hybrid information model is still worse than MapReduce-based PRFs based on MSE. ...
Excessive tool wear leads to the damage and eventual breakage of the tool, workpiece, and machining center. Therefore, it is crucial to monitor the condition of tools during processing so that appropriate actions can be taken to prevent catastrophic tool failure. This paper presents a hybrid information system based on a long short-term memory network (LSTM) for tool wear prediction. First, a stacked LSTM is used to extract the abstract and deep features contained within the multi-sensor time series. Subsequently, the temporal features extracted are combined with process information to form a new input vector. Finally, a nonlinear regression model is designed to predict tool wear based on the new input vector. The proposed method is validated on both NASA Ames milling data set and the 2010 PHM Data Challenge data set. Results show the outstanding performance of the hybrid information model in tool wear prediction, especially when the experiments are run under various operating conditions.
... Terminal devices typically have limited storage space and computing power; therefore, only simple signal processing algorithms can be employed, resulting in low monitoring accuracy [12]. With the emergence of IIoT and cloud computing, some cloud computing-based monitoring and prognostics systems using the powerful computing and storage capabilities of cloud centers are developed for intelligent manufacturing [13][14][15]. In these cloud-based systems, a large volume of raw sensor data needs to be transmitted to the cloud center and large deep learning models are deployed in the cloud to improve the monitoring accuracy. ...
... Cloud computing provides an opportunity to transfer advanced monitoring and prediction algorithms from the research lab to industry owing to the enhanced computing efficiency and data storage capability in the cloud center. Wu et al. [14] implemented the Map Reduce-based parallel random forests algorithm on a scalable cloud computing system to predict tool wear in milling operations and achieved an increased model training speed and a higher prediction accuracy. A cloud-based manufacturing process monitoring framework for tool condition monitoring during machining is proposed in [15]. ...
Tool condition monitoring (TCM) during the manufacturing process is of great significance for ensuring product quality and plays an important role in intelligent manufacturing. Current TCM systems deployed in the local device or cloud computing environment unable meet the requirements of low response latency and high accuracy at the same time. The emerging fog computing provides new solutions for the above problem. This paper presents a tool wear monitoring and prediction (TWMP) system based on deep learning models and fog computing. In order to improve monitoring and prediction accuracy, we propose a multiscale convolutional long short-term memory model (MCLSTM) to complete the tool wear monitoring task and a bi-directional LSTM model (BiLSTM) to complete the tool wear prediction task. To reduce the response latency of the TWMP system, we deploy the MCLSTM model and the BiLSTM model in a fog computing architecture. The fog computing architecture consists of an edge computing layer, a fog computing layer, and a cloud computing layer. The edge computing layer undertakes real-time signal collection task. The fog computing layer undertakes real-time tool wear monitoring task. The cloud computing layer with powerful computing resources undertakes intensive computing and latency-insensitive tasks such as data storage, tool wear prediction, and model training. A twist drill wear monitoring and prediction experiment is conducted to test the performance of the proposed system in terms of accuracy, response time, and network bandwidth consumption.
... In mechanical engineering domain, with the advent of automation and due to the need of carrying out certain operations independently without human involvement, Machine learning has been extensively used for assisting certain types of engineering processes as follows. For example, (i) Fault diagnosis of a reciprocating diesel engine is carried out by selecting the most appropriate case from the database after calculating the similarity between the given case and the previous cases in the database [2], (ii) Prediction of flank tool wear in high speed machines is carried out using Random forests model and a set of statistical data created from parameters like cutting force, vibration and acoustic emissions collected from milling tests [3], (iii) Planning of production processes is done using Machine learning models which recognize features such as holes, grooves, etc. on the parts [4], (iv) Characterization of materials using Unsupervised clustering attempted by [5] where thermal, electrical, physical, mechanical and chemical compositions of the materials were the features considered for the study. ...
... It was observed that in general the mistake in identifying number of sides is within +/-1 side as seen from a typical confusion matrix (Fig. 7) for POLY-3. The rows and columns of the matrix correspond to actual and predicted number of sides (3,4,5,6,7). The diagonal elements indicate number of perfect matches between actual and predicted number of sides. ...
... In addition, users can observe the real-time processed signal on the visualisation dashboard. Another notable study by Wu et al. [64,65] compared the relative speed-ups and training time using parallel cloud computations for real-time monitoring tool wear of a CNC milling machine. ...
... In other words, enabling ML in real-time becomes a primary issue because it is pointless to adopt ML-CPMS if the predictions take too long to arrive at the decision-makers. In facing this challenge, many methods have been proposed such as reducing the latency by adopting edge computing which is closer to the data source as compared to cloud computing which is higher in latency [55], speeding [64,65], adopting agent-based systems to handle the real-time execution logic among others [69]. ...
First year report for PhD at Institute for Manufacturing, Cambridge
... In the Prognostics and Health Management (PHM) community specifically, recent developments aim to build fully aware and resilient manufacturing systems that react quickly and appropriately to environmental signals through cloud-based resources (Lee et al. 2014). Wu et al. (2018) demonstrated the use of the cloud for performing advanced data analytics to predict tool wear during machining operations (Wu et al. 2018). However, lack of standard guidelines for the underlying architecture, such as sensor selection, data transmission and database creation, limit cloud-based architectures' scalability, reproducibility and interoperability (Gao et al. 2015;Lee 2003). ...
... In the Prognostics and Health Management (PHM) community specifically, recent developments aim to build fully aware and resilient manufacturing systems that react quickly and appropriately to environmental signals through cloud-based resources (Lee et al. 2014). Wu et al. (2018) demonstrated the use of the cloud for performing advanced data analytics to predict tool wear during machining operations (Wu et al. 2018). However, lack of standard guidelines for the underlying architecture, such as sensor selection, data transmission and database creation, limit cloud-based architectures' scalability, reproducibility and interoperability (Gao et al. 2015;Lee 2003). ...
This paper reports on the development of Factory Optima, a web-based system that allows manufacturing process engineers to compose, optimise and perform trade-off analysis of manufacturing and contract service networks based on a reusable repository of performance models. Performance models formally describe process feasibility constraints and metrics of interest, such as cost, throughput and CO 2 emissions, as a function of fixed and control parameters, such as equipment and contract properties and settings. The repository contains performance models representing (1) unit manufacturing processes, (2) base contract services and (3) a composite steady-state service network. The proposed framework allows process engineers to hierarchically compose model instances of service networks, which can represent production cells, lines, factory facilities and supply chains, and perform deterministic optimisation based on mathematical programming and Pareto-optimal trade-off analysis. Factory Optima is demonstrated using a case study of a service network for a heat sink product which involves contract vendors and manufacturing activities, including cutting, shearing, Computer Numerical Control (CNC) machining with milling and drilling operations, quality inspection, finishing, and assembly. ARTICLE HISTORY
... Wu et al. [17] proposed a cloud-based parallel machine learning system to deal with monitoring systems where large volumes of training data are required to make accurate predictions. The proposed system showed a MapReduce-based parallel random forests (PRFs) algorithm to predict tool wear in milling operations. ...
Tool condition monitoring (TCM) systems are key technologies for ensuring machining efficiency. Despite the large number of TCM solutions, these systems have not been implemented in industry, especially in small- and medium-sized enterprises (SMEs), mainly because of the need for invasive sensors, time-consuming deployment solutions and a lack of straightforward, scalable solutions from the laboratory. The implementation of TCM solutions for the new era of the Industry 4.0 is encouraging practitioners to look for systems based on IoT (Internet of Things) platforms with plug and play capabilities, minimum interruption time during setup and minimal experimental tests. In this paper, we propose a TCM system based on low-cost and non-invasive sensors that are plug and play devices, an IoT platform for fast deployment and a mobile app for receiving operator feedback. The system is based on a sensing node by Arduino Uno Wi-Fi that acts as an edge-computing node to extract a similarity index for tool wear classification; a machine learning node based on a BeagleBone Black board that builds the machine learning model using a Python script; and an IoT platform to provide the communication infrastructure and register all data for future analytics. Experimental results on a CNC lathe show that a logistic regression model applied on the machine learning node can provide a low-cost and straightforward solution with an accuracy of 88% in tool wear classification. The complete solution has a cost of EUR 170 and only a few hours are required for deployment. Practitioners in SMEs can find the proposed approach interesting since fast results can be obtained and more complex analysis could be easily incorporated while production continues using the operator’s feedback from the mobile app.
... Parallel machine learning algorithms [1,2] are a class of algorithms that can be run on multiple processors or computers in parallel in order to speed up the training process. These algorithms are important in today's data-driven world because they allow machine learning models to be trained on large datasets more efficiently, enabling organizations to extract valuable insights from data in a timely manner [3]. ...
Parallel machine learning algorithms are a class of algorithms that can be run on multiple processors or computers in parallel in order to speed up the training process. These algorithms are becoming increasingly important as the volume and complexity of data continue to grow, and as organizations seek to extract valuable insights from data in a timely and cost-effective manner. In this review, we provide an overview of the various approaches that have been proposed for parallelizing machine learning algorithms, including data parallelism, model parallelism, and hybrid approaches. We also discuss the challenges and opportunities of parallel machine learning, including issues related to data partitioning, communication, and scalability. We evaluate the performance of different approaches on a range of machine learning tasks and datasets, and discuss the limitations and trade-offs of different approaches. Finally, we provide insights on the future direction of research in this area and identify areas where further work is needed. Overall, this review provides a comprehensive overview of the field of parallel machine learning and highlights the importance of this area for organizations seeking to extract insights from large datasets.
... As a powerful pattern recognition tool, the application of AI in fault diagnosis has attracted much attention from many researchers. There are a series of traditional machine learning classification algorithms, such as k-nearest neighbor (k-NN), artificial neural networks (ANN), support vector machines (SVM), and decision trees [80][81][82]. In recent years, deep learning has seen increasing use in fault diagnosis tasks with better real-time and generalization capabilities [83][84][85][86]. ...
In the era of Industry 4.0, highly complex production equipment is becoming increasingly integrated and intelligent, posing new challenges for data-driven process monitoring and fault diagnosis. Technologies such as IIoT, CPS, and AI are seeing increasing use in modern industrial smart manufacturing. Cloud computing and big data storage greatly facilitate the processing and management of industrial information flow, which helps the development of real-time fault diagnosis (RTFD) technology. This paper provides a comprehensive review of the latest RTFD technologies in the field of industrial process monitoring and machine condition monitoring. The RTFD process is introduced in detail, starting with the data acquisition process. The current RTFD methods are divided into methods based on independent feature extraction, methods based on “end-to-end” neural networks, and methods based on qualitative knowledge reasoning from a new perspective. In addition, this paper discusses the challenges and potential trends of RTFD in future development to provide a reference for researchers focusing on this field.
... Machine learning-based in-situ batch detection of materials during metal cutting operations is developed to increase the product quality and decrease manufacturing costs [74]. Tool wear estimation utilizing cloud-based parallel machine learning is developed in order to increase cutting tool life during machining operations [75]. A comparative study on machine learning algorithms for smart factories is implemented in order to predict the tool wear during machining operations using random forests [76]. ...
Artificial Intelligence (AI) and Machine learning (ML) represents an important evolution in computer science and data processing systems which can be used in order to enhance almost every technology-enabled service, products, and industrial applications. A subfield of artificial intelligence and computer science is named machine learning which focuses on using data and algorithms to simulate learning process of machines and enhance the accuracy of the systems. Machine learning systems can be applied to the cutting forces and cutting tool wear prediction in CNC machine tools in order to increase cutting tool life during machining operations. Optimized machining parameters of CNC machining operations can be obtained by using the advanced machine learning systems in order to increase efficiency during part manufacturing processes. Moreover, surface quality of machined components can be predicted and improved using advanced machine learning systems to improve the quality of machined parts. In order to analyze and minimize power usage during CNC machining operations, machine learning is applied to prediction techniques of energy consumption of CNC machine tools. In this paper, applications of machine learning and artificial intelligence systems in CNC machine tools is reviewed and future research works are also recommended to present an overview of current research on machine learning and artificial intelligence approaches in CNC machining processes. As a result, the research filed can be moved forward by reviewing and analysing recent achievements in published papers to offer innovative concepts and approaches in applications of artificial Intelligence and machine learning in CNC machine tools.
... It was found that Random Forests outperformed Neural Networks and Support Vector Regression. Wu et al. [21] demonstrated that the Random Forests algorithm was capable of predicting tool wear in milling with high accuracy. They further improved the calculation efficiency by applying the MapReduce-based parallel Random Forests algorithm. ...
Insufficient data is always a challenge for developing an accurate machine learning or deep learning model in manufacturing processes, especially in tool wear monitoring under varied cutting conditions. This paper presents a Random Forest model for predicting tool wear under varied cutting conditions as well as studies extracted signal features. The Random Forest algorithm was chosen as the machine learning model, rather than the novel deep learning model. This was due to the feature importance investigation, which was embedded in the Random Forest algorithm, thereby making it easier to study the physical meanings of signal features. The frequency domain signals were rearranged as features related to spindle speeds and machine tool structure based on domain knowledge. This is the first paper to rearrange the frequency domain signals for observing the physical meanings of selected features. When data normalization was adopted, frequency domain signals related to spindle speeds were excluded from important features. Only spectrum energy related to structure vibration and time domain signals were important features. Data normalization enhanced the weighting of structure vibration features in a machine learning model. This study showed that feature normalization made the machine learning model more adaptable to different cutting conditions. Furthermore, prediction accuracy for cutting condition of spindle speed = 42,000 rpm and feed = 1.5 μm/rev (lowest prediction accuracy among cutting tests in this study) showed an increase from 68.0 to 84.1%. In addition, spindle speed had a more significant effect than feed on classification accuracy in tool wear monitoring based on experimental results. As a result, at least two data sets of the same spindle speed as in tool wear prediction were recommended to be used for model training. When there were at least two data sets in training data with the same spindle speed as in testing data, the study showed prediction accuracies were greater than 75% without data normalization and 81% with data normalization.
... Compared with the manual offline measurement by a microscope, this method has higher cost-effectiveness. Wu et al. [19] proposed a machine learning algorithm based on cloud computing and random forest, which effectively used real-time sensing data to predict tool wear during the actual processing. This method improved the processing speed of the algorithm and the predictive capability of the model. ...
A major problem in the high-speed cutting process of machine tools is tool wear. Tool wear directly affects the surface quality and machining accuracy of the workpiece. However, the limits of fusing multiple sensing signals to indirectly monitor tool wear are rarely concerned in real manufacturing environments. In this paper, a tool wear identification method based on a single sensor signal is proposed. To solve the limits of less obtained information and poor anti-interference ability of single sensor, multi-domain feature fusion strategy is established. By establishing a hybrid model of deep convolutional neural network and stacked long short-term memory network, the complex mapping relationship between fusion features and tool wear is constructed. Specifically, the spatial features of the input data set are extracted by the convolution kernel of the deep convolutional neural network. Then, a stacked double-layer long short-term memory neural network is established to capture sequence features with long-term dependence, thereby identifying tool wear. Finally, the superiority of the developed method is verified by tool wear experiments. The results show that the method can be effectively applied to tool wear identification from single sensor signals, and the mean RMSE and MAE of the identification results are 9.43 and 7.15, respectively. Compared with four other traditional multiple regression methods, RMSE and MAE are reduced by 73.0% and 78.7% on average. This study provides a reference value for the industrial implementation of tool wear monitoring system.
... It is simpler to write programmes to retrieve information from these structures since they are more clearly defined. In [2] and [3], several ways of extracting CADD data from CADD designs are explored. Extracting data from Engineering Drawings, which are often made up of numerous entities, such as Drawing views, text, symbols that convey information about manufacturing or materials, and measurements, requires segmentation. ...
... Wu et al. [10,11] introduced a random forest-based approach and developed parallel algorithms based on the MapReduce framework, which significantly improved the computation speed. Zhang and Zhang [12] established a tool wear model for a ball-end milling cutter based on a least squares SVM. ...
The accuracy and quality of the workpiece significantly depends on the degree of tool wear. The monitoring and prediction of tool wear are very important. Regretfully, most of references study on the tool wear based on the model of plane milling. It is well known that the cutting force and tool wear are great difference on the sculpture surfaces. So the accuracy of existing methods need be further improved. Considering that tool wear is related to time series signals and the sculpture surface has the different curvature, a new method based on the fusion of a temporal convolutional network and self-attention mechanism (TCNA) is proposed to predict the tool wear both in sculpture surface and plane surface. A temporal convolutional network ensures the causality between the output and input data and the self-attention mechanism strengthens the connection between the current output and input data from past moments. Compared with the traditional deep learning algorithms such as convolutional neural network (CNN) and long short-term memory (LSTM), TCNA has an excellent result on tool wear prediction either in plane milling or sculpture surface milling; the mean squared error (MSE) of TCNA model is reduced by more than 26.59% and 37.95%. Therefore, TCNA could be more accurately used for the tool wear prediction in any arbitrary surface cutting.
... Finally, the generalized multi-classification SVM was exploited as the tool monitoring model. Wu et al. [12] used the random forest (RF) algorithm as the tool wear prediction model, which can significantly improve the prediction model's recognition speed. Rad et al. [13] used short-time Fourier transform to decompose the acoustic emission signal and used two-dimensional principal component analysis to reduce the dimension of the feature. ...
Tool wear monitoring plays a vital role in improving product quality and reducing production costs. Aiming at the problems of low prediction accuracy and poor generalization caused by the difference in tool wear data distribution and the fixation of single global model parameters, a hybrid prediction modeling method for tool wear based on joint distribution adaptation (JDA) is proposed. Firstly, JDA is exploited to adapt the data features under different data distributions. Then, the adapted data features are identified by the KNN classifier. Finally, according to the tool state classification results, different regression prediction models are assigned to different wear stages to complete the whole tool wear prediction task. The results of milling experiments show that the maximum prediction accuracy of this method can reach 91.15%, and it has good recognition accuracy and generalization performance. Through the analysis of the tool wear hybrid prediction modeling method, the research can improve the prediction accuracy and generalization performance of the model and realize tool on-line monitoring. The research results can provide solutions and a theoretical basis for the application of tool wear monitoring technology in practical production.
... value obtained after the process through regression analysis. Dazhong Wu [21] used vibration signals, acoustic emission signals, and force signals as inputs to train large-scale tool prediction models effectively by cloud-based parallel machine learning algorithm, which verifies the speed and accuracy of the tool monitoring method. ...
An adaptive learning method for tool wear monitoring, taking the time of one machining process step as a monitoring period, is proposed, which aims to provide new ideas for tool life research and improve the reliability of machine tool operation. First, the motor current signal is collected as the original signal, and the RMS (root mean square) value of the current signal is extracted as the characteristic quantity. Statistical analysis of characteristic quantity RMS using SPSS (Statistical Product and Service Solutions) method shows that the RMS value of current signal obeys normal distribution approximately in the monitoring period with process step as the unit. Then, a monitoring method based on ± 3σ principle of normal distribution is proposed. Although RMS does not follow normal distribution completely, it is still possible to estimate the dispersion range of RMS by introducing distribution coefficient K through ± 3σ principle of statistical mathematics. Finally, a self-learning algorithm for the boundary mathematical model of tool wear monitoring is presented. According to the analysis of tool wear, the tool failure time could be calculated, and the tool life can be predicted. The experimental results show that the monitoring model can be formed quickly during semi-finishing and finishing machining and can get the satisfactory monitoring results.
... RF was used to select the features by measuring the importance of features. More details about feature selection using RF can be found in Wu et al. (2018). The number of selected features was determined by balancing the trade-off between prediction accuracy and training time. ...
Chemical mechanical polishing (CMP) has been widely used in the semiconductor sector for creating planar surfaces with the combination of chemical and mechanical forces. CMP is very complex because several chemical and mechanical phenomena (e.g., surface kinetics, contact mechanics, stress mechanics, and tribochemistry) are involved. Due to the complexity of the CMP process, it is very challenging to predict material removal rate (MRR) with sufficient accuracy. While physics-based methods have been introduced to predict MRR, little research has been reported on data-driven predictive modeling of MRR in the CMP process. This paper presents a novel decision tree-based ensemble learning algorithm that trains a predictive model of MRR on condition monitoring data. A stacking technique is used to combine three decision tree-based learning algorithms, including the random forests (RF), gradient boosting trees (GBT), and extremely randomized trees (ERT). The proposed method is demonstrated on the data collected from a wafer CMP tool that removes material from the surface of the wafer. Experimental results have shown that the decision tree-based ensemble learning algorithm can predict MRR in the CMP process with very high accuracy.
... In the field of machining, Wu et al. (2018) shows the possibility of using cloud-based parallel machine learning algorithms to predict tool wear depending on the condition monitoring data. Also in the field of machine tools, the management of the manufacturing system's events and alarms is accomplished by a cyberphysical system in Villalonga et al. (2018a,b) by using the cloud and its services to process CNCs monitored conditions data. ...
Smart manufacturing is the modern form of manufacturing that utilises Industry 4.0 enablers for decision making and resources planning by taking advantage of the available data. Therefore, the state of the art technologies are either replaced or improved using the newly introduced manufacturing paradigm. In practice, condition monitoring is an ongoing activity that preserves the manufacturing facility capability to deliver its production aims and decrease the production discontinuity as much as possible. Against this background, this paper discusses the state of the art condition monitoring and proposes a framework of fault detection and decision making at different levels namely component and station. The introduced framework relies on Virtual Engineering (VE) and Discrete Event Simulation (DES) in smart manufacturing environments. The application of the suggested methodology and its implementation is demonstrated in a case study of a battery module assembly line.
... Mohammadi et al. [36] utilized the SVR classification algorithm and developed a prognostic model to forecast quality in the abrasion-resistant production method. In analogous work, Wu et al. [37] unified RFs and MapReduce data processing schemes to forecast tool wear in milling experiments. With this new approach, they attained a significant enhancement in processing speed and excellent prediction accuracy. ...
The actualization of the befitting sampling strategy and the application of an appropriate evaluation algorithm have been elementary issues in the coordinate metrology. The decisions regarding their choice for a given geometrical feature customarily rely upon the user’s instinct or experience. As a consequence, the measurement results have to be accommodated between the accuracy and the inspection time. Certainly, a reliable and efficient sampling plan is imperative to accomplish a dependable inspection in minimal time, effort, and cost. This paper deals with the determination of an optimal sampling plan that minimizes the inspection cost, while still promising a measurement quality. A cylindrical-shaped component has been utilized in this work to achieve the desired objective. The inspection quality of the cylinder using a coordinate measuring machine (CMM) can be enhanced by controlling the three main parameters, which are used as input variables in the data file, namely, point distribution schemes, total number of points, and form evaluation algorithms. These factors affect the inspection output, in terms of cylindricity and measurement time, which are considered as target variables. The dataset, which comprises input and intended parameters, has been acquired through experimentation on the CMM machine. This work has utilized state-of-the-art machine learning algorithms to establish predictive models, which can predict the inspection output. The different algorithms have been examined and compared to seek for the most relevant machine learning regression method. The best performance has been observed using the support vector regression for cylindricity, with a mean absolute error of 0.000508 mm and a root-mean-squared error of 0.000885 mm. Likewise, the best prediction performance for measuring time has been demonstrated by the decision trees. Finally, the optimal parameters are estimated by employing the grey relational analysis (GRA) and the fuzzy technique for order performance by similarity to ideal solution (FTOPSIS). It has been approved that the values obtained from GRA are comparable with those of the FTOPSIS. Moreover, the quality of the optimal results is bettered by incorporating the measurement uncertainty in the outcome.
1. Introduction
Coordinate measuring machine (CMM) has revolutionized the manufacturing industries, owing to its high accuracy and precision capabilities. It is being widely used in the automotive, aerospace, and medical industries to perform the part inspection. It is a complex machine, where different components and subcomponents with their varied performance influence the measurement outcome. Indeed, the CMM inspection process must be well defined and designed for successful and efficient performance [1]. With growing intricacy of parts and tighter tolerances, intelligible approaches and techniques are needed to effectively plan measurement on the CMM. According to Baldwin et al. [2] and Weckenmann et al. [3], numerous variables as depicted in Figure 1 can influence the output of the CMM measurement. These factors encompass the sampling strategy, evaluation algorithm, workpiece position and orientation, surface conditions, sensor type and configuration, environment conditions, etc.
... Dazhong Wu used Cloud computing, Industrial Internet of Things (IIoT) and machine learning to estimate the tool wear characteristics of a cutting tool. Random forests (RF) algorithm was used alongside 'MapReduce' data processing scheme and the training time is reduced by 14.7 times along with a high prediction accuracy [11]. In addition to machine learning algorithms, signal and image processing were also used to predict tool wear. ...
In automated manufacturing systems, most of the manufacturing processes, including machining, are automated. Automatic tool change is one of the important parameters for reducing manufacturing lead time. Machining studies on Martensitic Stainless Steel was conducted using Ti[C,N] mixed alumina ceramic cutting tool. Tool life was evaluated using flank wear criterion. The tool life obtained from experimental machining process was taken as training dataset and test dataset for machine learning. Tool life model was developed using Gradient Descent Algorithm. The accuracy of the machine learning model was tested using the test data, and 99.83% accuracy was obtained.
... In the field of machining, [10] shows the possibility of using cloud-based parallel machine learning algorithms to predict tool wear depending on the condition monitoring data. Also in the field of machine tools, the management of the manufacturing system's events and alarms is accomplished by a cyber-physical system in [11,12] by using the cloud and its services to process CNCs monitored conditions data. ...
Smart manufacturing is the modern form of manufacturing that utilises Industry 4.0 enablers for decision making and resources planning by taking advantage of the available data. Therefore, the state of the art technologies are either replaced or improved using the newly introduced manufacturing paradigm. In practice, condition monitoring is an ongoing activity that preserves the manufacturing facility capability to deliver its production aims and decrease the production discontinuity as much as possible. Against this background, this paper discusses the state of the art condition monitoring and proposes a framework of fault detection and decision making at different levels namely component and station. The introduced framework relies on Virtual Engineering (VE) and Discrete Event Simulation (DES) in smart manufacturing environments. The application of the suggested methodology and its implementation is demonstrated in a case study of a battery module assembly line. c 2020 The Authors, Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer review under the responsibility of the scientific committee of CIRP.
... Under excessive wear, an increase was also simultaneously seen in the cutting force, which can be interpreted as tool wear or breakage. Table 3 shows other studies Table 2 Monitoring techniques using vibration signals References Types of machining operation Measurement [24] Turning Tool failure detection using sensor fusion [25] Milling process on stainless steel Tool wear prediction using AE and vibration signals [26] Milling Online tool condition monitoring using sensor fusion [27] Milling operation on S45C carbon steel Tool condition prediction using vibration signals [28] Turning process on Inconel 718 Tool wear condition monitoring using sensor fusion system with cutting force and vibration signals [29] Milling process Tool wear monitoring using accelerometer and dynamometer [30] Turning process on gray cast iron (FGL 250) ...
Tool condition monitoring and machine tool diagnostics are performed using advanced sensors and computational intelligence to predict and avoid adverse conditions for cutting tools and machinery. Undesirable conditions during machining cause chatter, tool wear, and tool breakage, directly affecting the tool life and consequently the surface quality, dimensional accuracy of the machined parts, and tool costs. Tool condition monitoring is, therefore, extremely important for manufacturing efficiency and economics. Acoustic emission, vibration, power, and temperature sensors monitor the stability and efficiency of the machining process, collecting large amounts of data to detect tool wear, breakage, and chatter. Studies on monitoring the vibrations and acoustic emissions from machine tools have provided information and data regarding the detection of undesirable conditions. Herein, studies on tool condition monitoring are reviewed and classified. As Industry 4.0 penetrates all manufacturing sectors, the amount of manufacturing data generated has reached the level of big data, and classical artificial intelligence analyses are no longer adequate. Nevertheless, recent advances in deep learning methods have achieved revolutionary success in numerous industries. Deep multi-layer perceptron (DMLP), long-short-term memory (LSTM), convolutional neural network (CNN), and deep reinforcement learning (DRL) are among the most preferred methods of deep learning in recent years. As data size increases, these methods have shown promising performance improvement in prediction and learning, compared to classical artificial intelligence methods. This paper summarizes tool condition monitoring first, then presents the underlying theory of some of the most recent deep learning methods, and finally, attempts to identify new opportunities in tool condition monitoring, toward the realization of Industry 4.0.
... [Saadallah 2018] trained an ensemble of deep learning (DL)-algorithms to predict the stability of a milling process. Furthermore, random forests with 10,000 trees were applied for a condition monitoring system in order to predict the tool wear [Khorasani 2015, Saadallah 2018Wu 2018]. ...
... Excessive tool wear could result in substantial decreases in dimensional accuracy, significant increases in energy consumption, and eventually total breakage of cutting tools due to excessive cutting forces and vibrations, intensive stresses and temperature, as well as massive fracture at cutting edges. Health monitoring and predictive analytics techniques are crucial to monitoring the health conditions of cutting tools as well as predicting tool wear [1][2][3][4]. ...
Tool wear in machining could result in poor surface finish, excessive vibration and energy consumption. Monitoring tool wear in real-time is crucial to improve manufacturing productivity and quality. While numerous sensor-based tool wear monitoring techniques have been demonstrated in laboratory environments, few tool wear monitoring systems have been deployed in factories because it is not realistic to install some of the important sensors such as dynamometers on manufacturing machines. To address this issue, a novel audio signal processing approach is introduced. This technique does not require expensive sensors but audio sensors only. A blind source separation method is used to separate source signals from noise. An extended principal component analysis is used for dimensionality reduction. Real-time multi-channel audio signals are collected during a set of milling tests under varying cutting conditions. The experimental data are used to develop and validate a predictive model. Experimental results have shown that the predictive model is capable of classifying tool wear conditions with high accuracy.
... As with humans who use their senses to acquire a variety of information on the state of a machining process, an automated tool condition monitoring system may also use an assortment of sensors to learn about the process and the tool condition. Sensor signals have been used in a variety of different ways to estimate the condition of a tool, e.g., artificial neural network (Segreto et al. 2013;Cho et al. 2010), support vector machine (Kothuru et al. 2018;Lee et al. 2019), fuzzy system (Mesina and Langari 2000), and random forest (Wu et al. 2018). ...
Tool wear is one of the consequences of a machining process. Excessive tool wear can lead to poor surface finish, and result in a defective product. It can also lead to premature tool failure, and may result in process downtime and damaged components. With this in mind, it has long been desired to monitor tool wear/tool condition. Kernel principal component analysis (KPCA) is proposed as an effective and efficient method for monitoring the tool condition in a machining process. The KPCA-based method may be used to identify faults (abnormalities) in a process through the fusion of multi-sensor signals. The method employs a control chart monitoring approach that uses Hotelling’s T²-statistic and Q-statistic to identify the faults in conjunction with control limits, which are computed by kernel density estimation (KDE). KDE is a non-parametric technique to approximate a probability density function. Four performance metrics, abnormality detection rate, false detection rate, detection delay, and prediction accuracy, are employed to test the reliability of the monitoring system and are used to compare the KPCA-based method with PCA-based method. Application of the proposed monitoring system to experimental data shows that the KPCA based method can effectively monitor the tool wear.
... To build an intelligent tool condition monitoring system, various sensing technologies (e.g., vibration, acoustic emission, force, and power) are incorporated in the manufacturing process to acquire information about the condition of the machine tool [5]. With the availability of sensor signals, data-driven models can be developed using artificial intelligence techniques (artificial neural networks [6], support vector machines [7], fuzzy systems [8], and random forest methods [9]) to monitor and predict the condition of the tool. ...
Smart manufacturing has leveraged the evolution of a sensor-based and data-driven platform to improve manufacturing outcomes. As a result of increased use of sensors and networked machines in manufacturing operations, artificial intelligence techniques play a key role to derive meaningful value from big data infrastructure. These techniques can inform decision making and can enable the implementation of more sustainable practices in the manufacturing industry. In machining processes, a considerable amount of waste (scrap) is generated as a result of failure to monitor a tool condition. Therefore, an intelligent tool condition monitoring system is developed in this paper to identify sustainability-related manufacturing tradeoffs and a set of optimal machining conditions by monitoring the status of the machine tool. An evolutionary algorithm-based multi-objective optimization is used to find the optimal operating conditions, and the solutions are visualized using a Pareto optimal front.
... There has been a recent push for cloud-based services and technologies to offload some of the stress and inhouse analysis requirements for manufacturers [5]. Specific examples of technologies are developed for asset monitoring of distributed factories and environments [6]. There are also some works exploring the required architecture of the data for cloud-based software services [7]. ...
Manufacturing environments face many unique challenges with regard to balancing high standards of both product quality and production efficiency. Proper diagnostic health assessment is essential for maximizing uptime and maintaining product and process quality. Information for diagnostic assessments, and reliability information in general, can come from a myriad of sources that can be processed and managed through numerous algorithms that range from simplistic to hypercomplex. One area that typifies the assortment of information sources in a modern manufacturing setting is found with the use of industrial robotics and automated manipulators. Although several monitoring methods and technologies have been previously proposed for this and other assets, adoption has been sporadic with returns on investment not always meeting expectations. Practical concerns regarding data limitations, variability of setup, and scarcity of ground truth points of validation from active industrial sites have contributed to this. This paper seeks to provide an overview of barriers and offer a feasible action plan for developing a practical condition monitoring information utilization program, matching available capabilities and assets to maximize knowledge gain. Observations are made on a real-world case study involving industrial 6 degrees of freedom (DOF) robots actively deployed in a manufacturing facility with a variety of operational tasks.
... The limitation of model-based methods is that certain distributions and assumptions made when developing close-form analytical solution do not always hold true [8,9]. Recent advances in artificial intelligence and machine learn- ing enable predictive modeling of the complex CMP process by analyzing large volumes of condition monitoring data and identi- fying patterns [10][11][12]. Therefore, the objective of this study is to develop an ensemble learning approach to predicting the MRR of a wafer CMP process using large volumes of real-time condition monitoring data and a stacking technique. ...
Chemical mechanical planarization (CMP) has been widely used in the semiconductor industry to create planar surfaces with a combination of chemical and mechanical forces. A CMP process is very complex because several chemical and mechanical phenomena (e.g., surface kinetics, electrochemical interfaces, contact mechanics, stress mechanics, hydrodynamics, and tribochemistry) are involved. Predicting the material removal rate (MRR) in a CMP process with sufficient accuracy is essential to achieving uniform surface finish. While physics-based methods have been introduced to predict material removal rates, little research has been reported on monitoring and predictive modeling of the MRR in CMP. This paper presents a novel decision tree-based ensemble learning algorithm that can train the predictive model of the MRR. The stacking technique is used to combine three decision tree-based learning algorithms, including the random forests (RF), gradient boosting trees (GBT), and extremely randomized trees (ERT), via a meta-regressor. The proposed method is demonstrated on the data collected from a CMP tool that removes material from the surface of wafers. Experimental results have shown that the decision tree-based ensemble learning algorithm using stacking can predict the MRR in the CMP process with very high accuracy.
... Due to this, operator intervention is decreasing in production systems, the goal being the achievement self-sustaining machining processes. In this regard, it is important to obtain reliable information about the state of the machining process and how it is developing in real time, in order to maintain the process in optimum conditions [3]. ...
The evolution of the manufacturing industry has favored the use of new technologies that increase the level of autonomy in production systems. The work presented shows a methodology that allows for online estimation of cutting parameters based on the analysis of the cutting force signal pattern. The dynamic response of the tool is taken into account through a function that relates the response time to the input variables in the process. The force signal is obtained with a dynamometric platform based on piezoelectric sensors. The final section of the paper shows the experimental validation where machining tests with variable machining conditions were carried out. The results reveal high precision in the estimation of depths of cut in end milling. © 2018 American Society of Mechanical Engineers (ASME). All rights reserved.
Interaction of wear debris at the tool-workpiece interface in micro-grinding are quite random which leads to considerable variability in the working life of similar tools. It is not possible to capture the effect of wear debris entrapment on process signals using available physics-based model, which makes it difficult to identify the tool life stages. The present study highlights the wear pattern of a polycrystalline diamond tool (PCD) during micro-grinding of BK7 glass. Based on the time and frequency domain cutting force features and tool surface morphology, life of a PCD tool could be divided into three stages viz., abrasion stage (0-23% of total tool life), loading stage (23-77% of total tool life) and chipping stage (77-100% of total tool life). A machine learning model utilizing support vector machine (SVM) could predict the life stages of a tool with a prediction accuracy of around 80.5%, and the wear pattern of a new tool coming into service becomes more deterministic on using more datasets for model training. A new modified textured PCD tool, which provided better tool-work interaction and improved debris disposal, shows little variation in cutting force features across many similar design tools which enabled identify the life stages with higher confidence. Prognosis of tool redressing criterion enabled timely redressing of the tool which led to refined tool surface condition, such as increased number of available chip pockets and greater protrusion height of the abrasives, and lowered roughness of the machined surface.
This keynote paper mainly focuses on advancements of machining technology and systems for enhanced performance, increased system integration and augmented machine intelligence, critically hinging on new sensors, sensor systems and sensing methodologies being robust, reconfigurable and intelligent, while providing direct adoption and plug-and-play use in industrial practice. One chief novelty is given by the key enabling technologies of Industry 4.0 where integration of sensing systems in manufacturing plants is a cornerstone for transforming conventional manufacturing concepts into digital manufacturing paradigms. Application examples to industrial processes, future challenges and coming trends in machining monitoring are shown.
This book presents recent advancements in research, a review of new methods and techniques, and applications in decision support systems (DSS) with Machine Learning and Probabilistic Graphical Models, which are very effective techniques in gaining knowledge from Big Data and in interpreting decisions. It explores Bayesian network learning, Control Chart, Reinforcement Learning for multicriteria DSS, Anomaly Detection in Smart Manufacturing with Federated Learning, DSS in healthcare, DSS for supply chain management, etc. Researchers and practitioners alike will benefit from this book to enhance the understanding of machine learning, Probabilistic Graphical Models, and their uses in DSS in the context of decision making with uncertainty. The real-world case studies in various fields with guidance and recommendations for the practical applications of these studies are introduced in each chapter.
Thanks to the applications of advanced technologies like cloud computing, the Internet of Things, Big data analytics, and Artificial Intelligence, we are now transferring to smart manufacturing in which the process of manufacturing becomes more intelligent. The system of machines in production lines requires higher accuracy in operation. Any interruption due to failures or malfunction of machinery can result in significant losses for businesses. One of the important methods to keep the machine system safe and reliable is predictive maintenance. This chapter presents a study on machine learning-based methods for predictive maintenance (PdM). To be more specific, we suggest designing a decision support system using a long short-term memory network with Bayesian optimization to estimate the remaining useful life of the machines, an important method in PdM. We provide a case study to show the performance of our proposed method. Several studies related to the problem of PdM and perspectives for further researches are also reviewed and discussed.
Tribology research mainly focuses on the friction, wear, and lubrication between interacting surfaces. With the continuous increase in the industrialization of human society, tribology research objects have become increasingly extensive. Tribology research methods have also gone through the stages of empirical science based on phenomena, theoretical science based on models, and computational science based on simulations. Tribology research has a strong engineering background. Owing to the intense coupling characteristics of tribology, tribological information includes subject information related to mathematics, physics, chemistry, materials, machinery, etc. Constantly emerging data and models are the basis for the development of tribology. The development of information technology has provided new and more efficient methods for generating, collecting, processing, and analyzing tribological data. As a result, the concept of “tribo-informatics (triboinformatics)” has been introduced. In this paper, guided by the framework of tribo-informatics, the application of tribo-informatics methods in tribology is reviewed. This article aims to provide helpful guidance for efficient and scientific tribology research using tribo-informatics approaches.
Conventional machine learning based predictive modeling methods require large volumes of training data; however, collecting large training data is very labor-intensive and expensive in real-world applications such as manufacturing. Therefore, small data is a common challenge for machine learning approaches. To address this issue, we introduce a meta domain generalization method and demonstrate its effectiveness for tool wear prediction when a small set of training data are collected under different operating conditions. Domain generalization aims to generalize a machine learning model in a source domain to a target domain where there are small or no data. Meta-learning aims to optimize the initial hyper-parameters of a model for improving prediction accuracy. In this paper, we employ meta-learning to improve model pre-training for domain generalization for tool wear prediction. The proposed meta domain generalization involves three phases: source data split, meta-learning pre-training, and model fine-tuning. First, a training dataset from source domain is divided into pre-training and fine-tuning subsets based on the prior knowledge about target domain, i.e. the cutting conditions, to address the domain shift problem. Then, meta-learning is used to optimize model pre-training to better adapt to fine-tuning subset and improve model fine-tuning. Finally, model fine-tuning is carried out to enhance model generalization to the target domain. We conduct extensive experiments and justify the approach. The results show that meta domain generalization can predict tool wear under different operating conditions accurately with small data. We also find that data split optimization significantly affects the performance of meta domain generalization.
Manufacturers are implementing sensors, Internet of Things (IoT)-based automation, and communication devices on shop floors for connecting machine tools to a network-connected system for achieving “smart” functionality. The existing installation of legacy machines that offer either no or limited adaptability to these changes is a big obstacle to realizing the potential benefits of smart manufacturing. This research paper presents a sensor-based dimension monitoring system to capture and digitize component dimensions during machining operations. The capabilities are attained by developing an integrated framework consisting of sensors, data acquisition systems, feature extraction modules, and digital interfaces. The framework is implemented on legacy equipment such as lathes, milling, and drilling machines for component dimension monitoring while performing common operations. The proposed system functions at the edge level to improve man (operator)–machine–material interactions by displaying component dimensions and graphical visualization of the operations. The system also helps the operator recognize the resulting cutting forces and thereby achieve guided process control. The data generated at an edge level can be transmitted to the enterprise layer for performing tasks such as machine performance evaluation, man–machine utilization, process optimization, operator feedback, etc. The proposed framework provides a potential solution for integrating a vast base of the existing legacy machines into the Industry 4.0 framework.
Prognostics and health management (PHM) has become a crucial aspect of the management of engineering systems and structures, where sensor hardware and decision support tools are deployed to detect anomalies, diagnose faults and predict remaining useful lifetime (RUL). Methodologies for PHM are either model-driven, data-driven or a fusion of both approaches. Data-driven approaches make extensive use of large-scale datasets collected from physical assets to identify underlying failure mechanisms and root causes. In recent years, many data-driven PHM models have been developed to evaluate system’s health conditions using artificial intelligence (AI) and machine learning (ML) algorithms applied to condition monitoring data. The field of AI is fast gaining acceptance in various areas of applications such as robotics, autonomous vehicles and smart devices. With advancements in the use of AI technologies in Industry 4.0, where systems consist of multiple interconnected components in a cyber–physical space, there is increasing pressure on industries to move towards more predictive and proactive maintenance practices. In this paper, a thorough state-of-the-art review of the AI techniques adopted for PHM of engineering systems is conducted. Furthermore, given that the future of inspection and maintenance will be predominantly AI-driven, the paper discusses the soft issues relating to manpower, cyber-security, standards and regulations under such a regime. The review concludes that the current systems and methodologies for maintenance will inevitably become incompatible with future designs and systems; as such, continued research into AI-driven prognostics systems is expedient as it offers the best promise of bridging the potential gap.
Fueled by increasing data availability and the rise of technological advances for data processing and communication, business analytics is a key driver for smart manufacturing. However, due to the multitude of different local advances as well as its multidisciplinary complexity, both researchers and practitioners struggle to keep track of the progress and acquire new knowledge within the field, as there is a lack of a holistic conceptualization. To address this issue, we performed an extensive structured literature review, yielding 904 relevant hits, to develop a quadripartite taxonomy as well as to derive archetypes of business analytics in smart manufacturing. The taxonomy comprises the following meta-characteristics: application domain, orientation as the objective of the analysis, data origins, and analysis techniques. Collectively, they comprise eight dimensions with a total of 52 distinct characteristics. Using a cluster analysis, we found six archetypes that represent a synthesis of existing knowledge on planning, maintenance (reactive, offline, and online predictive), monitoring, and quality management. A temporal analysis highlights the push beyond predictive approaches and confirms that deep learning already dominates novel applications. Our results constitute an entry point to the field but can also serve as a reference work and a guide with which to assess the adequacy of one's own instruments.
Reliable prediction of assembly precision is important for quality control of customized mechanical products characterized by individual customization, small batch size, and multiple varieties, resulting in insufficient samples for predicting assembly performance. A customized mechanical product assembly precision prediction method based on generative adversarial networks and feature transfer learning (GAN-FTL) is proposed in this paper. A GAN is built based on high quality data (source domain) to generate auxiliary samples with high fidelity and large sample size. A support vector machine is used to generate pseudo-tags for auxiliary samples. Features of source domain, target domain and auxiliary samples from different distributions are transferred to the same distribution to achieve multi-source fusion of measured and simulated data using FTL. Data after FTL is used to train the assembly precision prediction model. The elevator guide rail assembly is taken as the case study. T70/B and T90/B guide rail assembly are selected as the source and target domains, respectively. FTL was performed between the source and target domains, with different sample sets for comparison and compared with five different methods. Experimental results show that the prediction accuracy of the target domain is improved when the auxiliary sample size is 300, 400, and 500, and the accuracy improvement of the five methods are 15.37%, 12.17%, 9.68%, 6.29%, and 4.31%, respectively, which verified the effectiveness and usability of the proposed assembly precision prediction method based on GAN-FTL.
Prognostic Health Management (PHM) is a predictive maintenance strategy, which is based on Condition Monitoring (CM) data and aims to predict the future states of machinery. The existing literature reports the PHM at two levels: methodological and applicative. From the methodological point of view, there are many publications and standards of a PHM system design. From the applicative point of view, many papers address the improvement of techniques adopted for realizing PHM tasks without covering the whole process. In these cases, most applications rely on a large amount of historical data to train models for diagnostic and prognostic purposes. Industries, very often, are not able to obtain these data. Thus, the most adopted approaches, based on batch and off-line analysis, cannot be adopted. In this paper, we present a novel framework and architecture that support the initial application of PHM from the machinery producers’ perspective. The proposed framework is based on an edge-cloud infrastructure that allows performing streaming analysis at the edge to reduce the quantity of the data to store in permanent memory, to know the health status of the machinery at any point in time, and to discover novel and anomalous behaviors. The collection of the data from multiple machines into a cloud server allows training more accurate diagnostic and prognostic models using a higher amount of data, whose results will serve to predict the health status in real-time at the edge. The so-built PHM system would allow industries to monitor and supervise a machinery network placed in different locations and can thus bring several benefits to both machinery producers and users. After a brief literature review of signal processing, feature extraction, diagnostics, and prognostics, including incremental and semi-supervised approaches for anomaly and novelty detection applied to data streams, a case study is presented. It was conducted on data collected from a test rig and shows the potential of the proposed framework in terms of the ability to detect changes in the operating conditions and abrupt faults and storage memory saving. The outcomes of our work, as well as its major novel aspect, is the design of a framework for a PHM system based on specific requirements that directly originate from the industrial field, together with indications on which techniques can be adopted to achieve such goals.
In recent years, significant advancements have been achieved in the domain of machine monitoring as witnessed from the beginning of the new industrial revolution (IR) known as IR 4.0. This new revolution is characterized by complete automation, and an increase in the technological deployments and advanced devices used by various systems. As a result, considerable advancement has been reported by researchers and academia around the world by adopting and adapting the new technology related to the machine condition monitoring problem. For these reasons, it is important to highlight new findings and approaches in machine condition monitoring based on the wireless sensor solution and machine learning signal processing methodology that are relevant to assist in advancing this new revolution. This article presents a comprehensive review on tool condition monitoring (TCM), tool wear, and chatter based on vibration, cutting force, temperature, surface image, and smart label monitoring parameters from signal acquisition, signal processing methodology, and decision-making, particularly for the milling process. The paper also provides a brief introduction to the manufacturing industries and computer numerical control (CNC) machine tool demand and a review of machine monitoring and countries involvement in machine monitoring, as well as the approaches and a survey of machine monitoring. The aim is to contribute to this rapidly growing field of machine condition monitoring research by exploring the latest research findings on the solution approach for milling machine process monitoring, to help expedite future research, and to give some future direction that needs to be considered as a path to produce a standardized CNC machine platform.
Aerosol jet printing (AJP) is a direct-write additive manufacturing method, emerging as the process of choice for the fabrication of electronics, such as sensors, transistors, and optoelectronics. However, AJP is a highly complex process, prone to gradual drifts. Consequently, in situ process monitoring and control in AJP is a bourgeoning need. The goal of this work is to forward a framework for in situ monitoring of the functional properties of AJ-printed electronic devices. In pursuit of this goal, the objective of the work is to develop and apply a supervised machine learning model to estimate the device functional properties in near real-time. Accordingly, this work is dedicated to the development of a multiple-input, single-output (MISO) model with the aim to classify the line resistance as a function of process parameters and surface morphology traits of the printed test artifacts. To realize this objective, line morphology is captured: (i) via an in-process integrated high-resolution imaging system, and (ii) in real-time via the AJP standard process monitor camera. Utilizing image processing algorithms, a wide range of morphology features are quantified, constituting the primary source of data for the training, validation, and testing of the classification model. The four-point probe method is used to measure the line resistance and thus, to define a priori class labels. The results exhibited that using the presented approach, the class of printed line resistance is identified both in situ and in real-time with an accuracy of = 90%.
Detailed manufacturing process data and sensor signals are typically disregarded in production scheduling. However, they have strong relations since a longer processing time triggers a change in schedule. Although promising approaches already exist for mapping the influence of manufacturing processes on production scheduling, the variability of the production environment, including changing process conditions, technological parameters and the status of current orders, is usually ignored. For this reason, this paper presents a novel, data-driven approach that adaptively refines the production schedule by applying Machine Learning (ML)-models during the manufacturing process in order to predict the process-dependent parameters that influence the schedule. With the proper prediction of these parameters based on the process conditions, the production schedule is proactively adjusted to changing conditions not only to ensure the sufficient product quality but also to reduce the negative effects and losses that delayed rescheduling would cause. The proposed approach aims on minimizing the overall lateness by utilizing an active data exchange between the scheduling system and the predictive ML-models on the process level. The efficiency of the solution is demonstrated by a realistic case study using discrete event simulation.
The manufacturing industry has difficulties with the question of how advanced analytics, can be integrated into production. This paper describes the algorithm selection step of an overall methodology for the systematic implementation of data mining projects in production. This is intended to provide users with a guideline to what a basic procedure may look like and what steps should be considered. First, this procedure is explained, which is then performed and illustrated on an application of high-frequency machine data.
Manufacturers have faced an increasing need for the development of predictive models that predict mechanical failures and the remaining useful life (RUL) of manufacturing systems or components. Classical model-based or physics-based prognostics often require an in-depth physical understanding of the system of interest to develop closed-form mathematical models. However, prior knowledge of system behavior is not always available, especially for complex manufacturing systems and processes. To complement model-based prognostics, data-driven methods have been increasingly applied to machinery prognostics and maintenance management, transforming legacy manufacturing systems into smart manufacturing systems with artificial intelligence. While previous research has demonstrated the effectiveness of data-driven methods, most of these prognostic methods are based on classical machine learning techniques, such as artificial neural networks (ANNs) and support vector regression (SVR). With the rapid advancement in artificial intelligence, various machine learning algorithms have been developed and widely applied in many engineering fields. The objective of this research is to introduce a random forests (RFs)-based prognostic method for tool wear prediction as well as compare the performance of RFs with feed-forward back propagation (FFBP) ANNs and SVR. Specifically, the performance of FFBP ANNs, SVR, and RFs are compared using an experimental data collected from 315 milling tests. Experimental results have shown that RFs can generate more accurate predictions than FFBP ANNs with a single hidden layer and SVR.
Disturbances on manufacturing shop-floors and the increasing number of product variants necessitate adaptive and flexible process planning methods. This paper proposes a service-oriented Cloud-based software framework comprising two services. The first service generates non-linear process plans using event-driven function blocks and a genetic algorithm. The second service, gathers data from shop-floor machine tools through sensors, input from operators, and machine schedules. An information fusion technique processes the monitoring data in order to feed the process planning service with the status, specifications, and availability time windows of machine tools. The methodology is validated in a case study of a machining SME.
3D printing features highly digitized interfaces and automated processes for rapid prototyping and product customization. When distributed 3D printing resources are shared, gathered, and applied in a cloud platform, this will be a promising globalized and time-effective environment for customized production. However, how to intelligently and effectively manage and schedule distributed 3D printing services, such as dynamic evaluation, service intelligent matching, planning, and scheduling, in a cloud platform requires further in-depth study. In order to address this issue, this paper proposes a framework for a 3D printing service platform for cloud manufacturing (CMfg). In addition, 3D printing service online integration and 3D model library construction are analyzed. Moreover, some technologies of distributed 3D printing service management are discussed. Finally, some of the application tools and preliminary practices implemented by our team are introduced.
Monitoring tool wear in machining processes is one of the critical factors in reducing downtime and maximizing profitability and productivity. A worn out tool can deteriorate the surface finish or dimensional accuracy of the part. Due to the uncertainties that originate from machining, workpiece material composition, and measurement, predicting tool wear is a challenging task in modern manufacturing processes. Low cost sensing technology for measuring spindle current is commonly deployed in the CNC machine to measure spindle power consumption for predicting tool wear. In this study, spindle power information was integrated into a Kalman filter methodology to predict tool flank wear in cutting hard-to-machine gamma-prime strengthened alloys. Results show a maximum of 18% error in estimation, which indicates a good potential of using Kalman filter in predicting tool flank wear.
Advanced manufacturing depends on the timely acquisition, distribution, and utilization of information from machines and processes across spatial boundaries. These activities can improve accuracy and reliability in predicting resource needs and allocation, maintenance scheduling, and remaining service life of equipment. As an emerging infrastructure, cloud computing provides new opportunities to achieve the goals of advanced manufacturing. This paper reviews the historical development of prognosis theories and techniques and projects their future growth enabled by the emerging cloud infrastructure. Techniques for cloud computing are highlighted, as well as the influence of these techniques on the paradigm of cloud-enabled prognosis for manufacturing. Finally, this paper discusses the envisioned architecture and associated challenges of cloud-enabled prognosis for manufacturing.
Timely assessment and prediction of tool wear is essential to ensuring part quality, minimizing material waste, and contributing to sustainable manufacturing. This paper presents a probabilistic method based on particle filtering to account for uncertainties in the tool wear process. Tool wear state is predicted by recursively updating a physics-based tool wear rate model with online measurement, following a Bayesian inference scheme. For long term prediction where online measurement is not available, regression analysis methods such as autoregressive model and support vector regression are investigated by incorporating predicted measurement into particle filter. The effectiveness of the developed method is demonstrated using experiments performed on a CNC milling machine.
Recent advances in manufacturing industry has paved way for a systematical deployment of Cyber-Physical Systems (CPS), within which information from all related perspectives is closely monitored and synchronized between the physical factory floor and the cyber computational space. Moreover, by utilizing advanced information analytics, networked machines will be able to perform more efficiently, collaboratively and resiliently. Such trend is transforming manufacturing industry to the next generation, namely Industry 4.0. At this early development phase, there is an urgent need for a clear definition of CPS. In this paper, a unified 5-level architecture is proposed as a guideline for implementation of CPS.
Recently, cloud manufacturing (CMfg) as a new service-oriented manufacturing mode has been paid wide attention around the world. However, one of the key technologies for implementing CMfg is how to realize manufacturing resource intelligent perception and access. In order to achieve intelligent perception and access of various manufacturing resources, the applications of IoT technologies in CMfg has been investigated in this paper. The classification of manufacturing resources and services, as well as their relationships, are presented. A five-layered structure (i.e., resource layer, perception layer, network layer, service layer, and application layer) resource intelligent perception and access system based on IoT is designed and presented. The key technologies for intelligent perception and access of various resources (i.e., hard manufacturing resources, computational resources, and intellectual resources) in CMfg are described. A prototype application system is developed to valid the proposed method.
Design and operation of a manufacturing enterprise involve numerous types of decision-making at various levels and domains. A complex system has a large number of design variables and decision-making requires real-time data collected from machines, processes, and business environments. Enterprise systems (ESs) are used to support data acquisition, communication, and all decision-making activities. Therefore, information technology (IT) infrastructure for data acquisition and sharing affects the performance of an ES greatly. Our objective is to investigate the impact of emerging Internet of Things (IoT) on ESs in modern manufacturing. To achieve this objective, the evolution of manufacturing system paradigms is discussed to identify the requirements of decision support systems in dynamic and distributed environments; recent advances in IT are overviewed and associated with next-generation manufacturing paradigms; and the relation of IT infrastructure and ESs is explored to identify the technological gaps in adopting IoT as an IT infrastructure of ESs. The future research directions in this area are discussed.
Recently, Internet of Things (IoT) and cloud computing (CC) have been widely studied and applied in many fields, as they can provide a new method for intelligent perception and connection from M2M (including man-to-man, man-to-machine, and machine-to-machine), and on-demand use and efficient sharing of resources, respectively. In order to realize the full sharing, free circulation, on-demand use, and optimal allocation of various manufacturing resources and capabilities, the applications of the technologies of IoT and CC in manufacturing are investigated in this paper first. Then, a CC- and IoT-based cloud manufacturing (CMfg) service system (i.e., CCIoT-CMfg) and its architecture are proposed, and the relationship among CMfg, IoT, and CC is analyzed. The technology system for realizing the CCIoT-CMfg is established. Finally, the advantages, challenges, and future works for the application and implementation of CCIoT-CMfg are discussed.
After the technologies of integrated circuits, personal computers, and the Internet, Internet of Things (IoT) is the latest information technology (IT) that is radically changing business paradigms. However, IoT’s influence in the manufacturing sector has yet been fully explored. On the other hand, existing computer-aided software tools are experiencing a bottleneck in dealing with complexity, dynamics, and uncertainties in their applications of modern enterprises. It is argued that the adoption of IoT and cloud computing in enterprise systems (ESs) would overcome the bottleneck. In this paper, the challenges in generating assembly plans of complex products are discussed. IoT and cloud computing are proposed to help a conventional assembly modeling system evolve into an advanced system, which is capable to deal with complexity and changes automatically. To achieve this goal, an assembly modeling system is automated, and the proposed system includes the following innovations: 1) the modularized architecture to make the system robust, reliable, flexible, and expandable; 2) the integrated object-oriented templates to facilitate interfaces and reuses of system components; and 3) the automated algorithms to retrieve relational assembly matrices for assembly planning. Assembly modeling for aircraft engines is used as examples to illustrate the system effectiveness.
Traditional data-driven prognostics often requires a large amount of failure data for the offline training in order to achieve good accuracy for the online prediction. However, in many engineered systems, failure data are fairly expensive and time-consuming to obtain while suspension data are readily available. In such cases, it becomes essentially critical to utilize suspension data, which may carry rich information regarding the degradation trend and help achieve more accurate remaining useful life (RUL) prediction. To this end, this paper proposes a co-training-based data-driven prognostic algorithm, denoted by Cop rog, which uses two individual data-driven algorithms with each predicting RULs of suspension units for the other. The confidence of an individual data-driven algorithm in predicting the RUL of a suspension unit is quantified by the extent to which the inclusion of that unit in the training data set reduces the sum square error (SSE) in RUL prediction on the failure units. After a suspension unit is chosen and its RUL is predicted by an individual algorithm, it becomes a virtual failure unit that is added to the training data set. Results obtained from two case studies suggest that Coprog gives more accurate RUL predictions compared to any individual algorithm without the consideration of suspension data and that Coprog can effectively exploit suspension data to improve the accuracy in data-driven prognostics.
Model-based prognostics approaches rely on physics-based models that describe the behavior of systems and their components. These models must account for the several different damage processes occurring simultaneously within a component. Each of these damage and wear processes contributes to the overall component degradation. We develop a model-based prognostics methodology that consists of a joint state-parameter estimation problem, in which the state of a system along with parameters describing the damage progression are estimated, followed by a prediction problem, in which the joint state-parameter estimate is propagated forward in time to predict end of life and remaining useful life. The state-parameter estimate is computed using a particle filter and is represented as a probability distribution, allowing the prediction of end of life and remaining useful life within a probabilistic framework that supports uncertainty management. We also develop a novel variance control algorithm that maintains an uncertainty bound around the unknown parameters to limit the amount of estimation uncertainty and, consequently, reduce prediction uncertainty. We construct a detailed physics-based model of a centrifugal pump that includes damage progression models, to which we apply our model-based prognostics algorithm. We illustrate the operation of the prognostic solution with a number of simulation-based experiments and demonstrate the performance of the approach when multiple damage mechanisms are active.
A novel fractal estimation methodology, that uses—for the first time in metal cutting literature—fractal properties of machining dynamics for online estimation of cutting tool flank wear, is presented. The fractal dimensions of the attractor of machining dynamics are extracted from a collection of sensor signals using a suite of signal processing meth- ods comprising wavelet representation and signal separation, and are related to the instantaneous flank wear using a recurrent neural network. The performance of the re- sulting estimator, evaluated using actual experimental data, establishes our methodology to be viable for online flank wear estimation. This methodology is adequately generic for sensor-based prediction of gradual damage in mechanical systems, specifically manufac- turing processes. @S0022-0434~00!02401-1# Online damage prediction and estimation are essential for en- forcing real-time control of mechanical systems, particularly manufacturing processes. Tool wear, especially flank wear, ad- versely impacts the accuracy and surface finish of machined prod- ucts @1#. Conventionally, flank wear is quantified by the height of the flank wear land hw , and hw.0.018 in. is considered to be a good indicator that the tool is fully worn. In many industrial sce- narios it takes between 5 to 20 min for a cutting tool to wear out. A viable continuous flank wear estimator must accurately estimate values between 0.0-0.018 in. over the above-specified time span. Accuracies of
The functionality and reliability of Li-ion batteries as major energy storage devices have received more and more attention from a wide spectrum of stakeholders, including federal/state policymakers, business leaders, technical researchers, environmental groups and the general public. Failures of Li-ion battery not only result in serious inconvenience and enormous replacement/repair costs, but also risk catastrophic consequences such as explosion due to overheating and short circuiting. In order to prevent severe failures from occurring, and to optimize Li-ion battery maintenance schedules, breakthroughs in prognostics and health monitoring of Li-ion batteries, with an emphasis on fault detection, correction and remaining-useful-life prediction, must be achieved. This paper reviews various aspects of recent research and developments in Li-ion battery prognostics and health monitoring, and summarizes the techniques, algorithms and models used for state-of-charge (SOC) estimation, current/voltage estimation, capacity estimation and remaining-useful-life (RUL) prediction.
Cloud manufacturing, a service oriented, customer centric, demand driven manufacturing model is explored in both its possible future and current states. A unique strategic vision for the field is docu-mented, and the current state of technology is presented from both industry and academic viewpoints. Key commercial implementations are presented, along with the state of research in fields critical to enablement of cloud manufacturing, including but not limited to automation, industrial control systems, service composition, flexibility, business models, and proposed implementation models and architec-tures. Comparison of the strategic vision and current state leads to suggestions for future work, including research in the areas of high speed, long distance industrial control systems, flexibility enablement, business models, cloud computing applications in manufacturing, and prominent implementation archi-tectures.
Flank wear is the most commonly observed and unavoidable phenomenon in metal cutting which is also a major source of economic loss resulting due to material loss and machine down time. A wide variety of monitoring techniques have been developed for the online detection of flank wear. In order to provide a broad view of flank wear monitoring techniques and their implementation in tool condition monitoring system (TCMS), this paper reviews three key features of a TCMS, namely (1) signal acquisition, (2) signal processing and feature extraction, and (3) artificial intelligence techniques for decision making.
1 The estimation of remaining useful life (RUL) of a faulty component is at the center of system prognostics and health management. It gives operators a potent tool in decision making by quantifying how much time is left until functionality is lost. RUL prediction needs to contend with multiple sources of errors like modeling inconsistencies, system noise and degraded sensor fidelity, which leads to unsatisfactory performance from classical techniques like Autoregressive Integrated Moving Average (ARIMA) and Extended Kalman Filtering (EKF). Bayesian theory of uncertainty management provides a way to contain these problems. The Relevance Vector Machine (RVM), the Bayesian treatment of the well known Support Vector Machine (SVM), a kernel-based regression/classification technique, is used for model development. This model is incorporated into a Particle Filter (PF) framework, where statistical estimates of noise and anticipated operational conditions are used to provide estimates of RUL in the form of a probability density function (PDF). We present here a comparative study of the above mentioned approaches on experimental data collected from Li-ion batteries. Batteries were chosen as an example for a complex system whose internal state variables are either inaccessible to sensors or hard to measure under operational conditions. In addition, battery performance is strongly influenced by ambient environmental and load conditions.
In-process monitoring of tool conditions is important in micro-machining due to the high precision requirement and high tool wear rate. Tool condition monitoring in micro-machining poses new challenges compared to conventional machining. In this paper, a multi-category classification approach is proposed for tool flank wear state identification in micro-milling. Continuous Hidden Markov models (HMMs) are adapted for modeling of the tool wear process in micro-milling, and estimation of the tool wear state given the cutting force features. For a noise-robust approach, the HMM outputs are connected via a medium filter to minimize the tool state before entry into the next state due to high noise level. A detailed study on the selection of HMM structures for tool condition monitoring (TCM) is presented. Case studies on the tool state estimation in the micro-milling of pure copper and steel demonstrate the effectiveness and potential of these methods.
Tool wear and tool life are the principle areas are focus in any machining activity. The production rate, surface finish of machined component and the machine condition are directly related to the tool condition. This work on tool condition monitoring delves into data mining approach to discover the hidden information available in the tool vibration signals. The use of statistical features derived from the vibration data is used as the primary feature and Principle Component Analysis (PCA) transformed statistical features are evaluated as an alternative. In order to increase the robustness of the classifier and to reduce the data processing load, feature reduction is necessary. The feature reduction using (a) decision tree and (b) feature transformation and reduction using PCA are evaluated independently and the results are compared. The effective combination of feature reducer and classifier for designing the expert system is studied and reported.
Over the past few decades, both small- and medium-sized manufacturers as well as large original equipment manufacturers (OEMs) have been faced with an increasing need for low cost and scalable intelligent manufacturing machines. Capabilities are needed for collecting and processing large volumes of real-time data generated from manufacturing machines and processes as well as for diagnosing the root cause of identified defects, predicting their progression, and forecasting maintenance actions proactively to minimize unexpected machine down times. Although cloud computing enables ubiquitous and instant remote access to scalable information and communication technology (ICT) infrastructures and high volume data storage, it has limitations in latency-sensitive applications such as high performance computing and real-time stream analytics. The emergence of fog computing, Internet of Things (IoT), and cyber-physical systems (CPS) represent radical changes in the way sensing systems, along with ICT infrastructures, collect and analyze large volumes of real-time data streams in geographically distributed environments. Ultimately, such technological approaches enable machines to function as an agent that is capable of intelligent behaviors such as automatic fault and failure detection, self-diagnosis, and preventative maintenance scheduling. The objective of this research is to introduce a fog-enabled architecture that consists of smart sensor networks, communication protocols, parallel machine learning software, and private and public clouds. The fog-enabled architecture will have the potential to enable large-scale, geographically distributed online machine and process monitoring, diagnosis, and prognosis that require low latency and high bandwidth in the context of data-driven cyber-manufacturing systems.
Cloud-based design manufacturing (CBDM) refers to a service-oriented networked product development model in which service consumers are enabled to configure, select, and utilize customized product realization resources and services ranging from computer-aided engineering software to reconfigurable manufacturing systems. An ongoing debate on CBDM in the research community revolves around several aspects such as definitions, key characteristics, computing architectures, communication and collaboration processes, crowdsourcing processes, information and communication infrastructure, programming models, data storage, and new business models pertaining to CBDM. One question, in particular, has often been raised: Is cloud-based design and manufacturing actually a new paradigm, or is it just “old wine in new bottles”? To answer this question, we discuss and compare the existing definitions for CBDM, identify the essential characteristics of CBDM, define a systematic requirements checklist that an idealized CBDM system should satisfy, and compare CBDM to other relevant but more traditional collaborative design and distributed manufacturing systems such as web- and agent-based design and manufacturing systems. To justify the conclusion that CBDM can be considered as a new paradigm that is anticipated to drive digital manufacturing and design innovation, we present the development of a smart delivery drone as an idealized CBDM example scenario and propose a corresponding CBDM system architecture that incorporates CBDM-based design processes, integrated manufacturing services, information and supply chain management in a holistic sense.
Trending analysis has been widely investigated for prediction of tool wear, which impacts not only tool life but also the quality of machined products. This paper presents a Bayesian approach to predicting the flank wear rate, linking vibration data measured during machining with the state of tool wear. Variations in the measured data are aggregated based on the Kullback–Leibler divergence, which provides a measure for the distance between the current and initial probability distributions of measurement, when no wear is present. Subsequently, state space estimation of the tool wear is realized by particle filtering (PF), a non-linear and non-Gaussian system estimation technique. To overcome the sample impoverishment problem in sequential importance resampling (SIR), a new resampling scheme is proposed, which has shown to more reliably quantify the confidence interval and improve the prediction accuracy of the remaining useful life (RUL) prediction of the tool as compared to standard SIR method. The developed method is experimentally evaluated using a set of benchmark data measured from a high speed CNC machine that performed milling operations. Good results are confirmed by the comparison between the predicted tool wear state and off-line tool wear measurement.
Cloud manufacturing as a trend of future manufacturing would provide cost-effective, flexible and scalable solutions to companies by sharing manufacturing resources as services with lower support and maintenance costs. Targeting the Cloud manufacturing, the objective of this research is to develop an Internet- and Web-based service-oriented system for machine availability monitoring and process planning. Particularly, this paper proposes a tiered system architecture and introduces IEC 61499 function blocks for prototype implementation. By connecting to a Wise-ShopFloor framework, it enables real-time machine availability and execution status monitoring during metal-cutting operations, both locally or remotely. The closed-loop information flow makes process planning and monitoring feasible services for the Cloud manufacturing.
Remaining useful life estimation (RUL) is an essential part in
prognostics and health management. This paper addresses the problem of
estimating the RUL from the observed degradation data. A
Wiener-process-based degradation model with a recursive filter algorithm
is developed to achieve the aim. A novel contribution made in this paper
is the use of both a recursive filter to update the drift coefficient in
the Wiener process and the expectation maximization (EM) algorithm to
update all other parameters. Both updating are done at the time that a
new piece of degradation data becomes available. This makes the model
depend on the observed degradation data history, which the conventional
Wiener-process-based models did not consider. Another contribution is to
take into account the distribution in the drift coefficient when
updating, rather than using a point estimate as an approximation. An
exact RUL distribution considering the distribution of the drift
coefficient is obtained based on the concept of the first hitting time.
A practical case study for gyros in an inertial navigation system is
provided to substantiate the superiority of the proposed model compared
with competing models reported in the literature. The results show that
our developed model can provide better RUL estimation accuracy.
This paper investigates a flank wear estimation technique in turning through wavelet representation of acoustic emission (AE) signals. It is known that the power spectral density? of AE signals in turning is sensitive to gradually increasing flank wear In previous methods, the power spectral density of AE signals is computed from Fourier transform based techniques. 70 overcome some of the limitations associated with the Fourier representation of AE signals for flank wear estimation, wavelet representation of AE signals is investigated This investigation is motivated by the superiority of the wavelet transform over the Fourier transform in analyzing rapidly changing signals such as AE, in which high frequency components are to be studied with sharper time resolution than low frequency components. The effectiveness of the wavelet representation of AE signals fbr flank wear estimation is investigated by conducting a set of turning experiments on AISI 6150 steel workpiece and K68 (C2) grade uncoated carbide inserts. In these experiments, flank wear is monitored through AE signals. A recurrent neural network of simple architecture is used to relate AE features to flank wear. Using this technique, accurate flank wear estimation results are obtained for the operating conditions that are within in the range of those used during neural network training. These results compared to those of Fourier transform representation are much superior. These findings indicate that the wavelet representation of AE signals is more effective in extracting the AE features sensitive to gradually increasing flank wear than the Fourier representation.
Cloud computing is changing the way industries and enterprises do their businesses in that dynamically scalable and virtualized resources are provided as a service over the Internet. This model creates a brand new opportunity for enterprises. In this paper, some of the essential features of cloud computing are briefly discussed with regard to the end-users, enterprises that use the cloud as a platform, and cloud providers themselves. Cloud computing is emerging as one of the major enablers for the manufacturing industry; it can transform the traditional manufacturing business model, help it to align product innovation with business strategy, and create intelligent factory networks that encourage effective collaboration. Two types of cloud computing adoptions in the manufacturing sector have been suggested, manufacturing with direct adoption of cloud computing technologies and cloud manufacturing—the manufacturing version of cloud computing. Cloud computing has been in some of key areas of manufacturing such as IT, pay-as-you-go business models, production scaling up and down per demand, and flexibility in deploying and customizing solutions. In cloud manufacturing, distributed resources are encapsulated into cloud services and managed in a centralized way. Clients can use cloud services according to their requirements. Cloud users can request services ranging from product design, manufacturing, testing, management, and all other stages of a product life cycle.Highlights► Cloud computing is emerging as a major enabler for the manufacturing industry. ► Cloud computing technologies can be adopted in manufacturing. ► Cloud manufacturing is a pay-as-you-go business model. ► Distributed resources are encapsulated into cloud services and managed centrally.
A survey of the research done on preventive maintenance is presented. The scope of the present survey is on the research published after the paper by W. P. Pierskalla and J. A. Voelker [ibid. 23, 353-388 (1976; Zbl 0358.90028)]. This article includes optimization models for repair, replacement, and inspection of systems subject to stochastic deterioration. A classical scheme is used that categorizes recent research into inspection models, minimal repair models, shock models, or miscellaneous replacement models.
As summarized by Atlas, Bernard, and Narayanan (1996), the sensing
of acoustic vibrations can remotely estimate the state of wear at the
tool edge. This form of monitoring offers the potential to characterize,
in real time, the efficiency of metal removal processes such as drilling
and milling. For example, information about sudden increases in tool
wear, if manifest as a change in acoustic vibration, could be valuable
to a machine operator. The nature of this monitoring problem has some
similarities to automatic speech recognition. For example, there is
significant tool-to-tool variation in details of vibration and lifetime.
Also, the easy adaptability of monitoring systems across manufacturing
processes is important. In this work we model the evolution of vibration
signals with the same technique which has shown to be successful in
speech recognition: hidden Markov models (HMMs). We focus on the
monitoring of milling processes at three different time scales and show
the how HMMs can give accurate wear prediction
The development of tool wear monitoring system for machining processes has been well recognised in industry due to the ever-increased demand for product quality and productivity improvement. This paper presents a new tool wear predictive model by combination of least squares support vector machines (LS-SVM) and principal component analysis (PCA) technique. The corresponding tool wear monitoring system is developed based on the platform of PXI and LabVIEW. PCA is firstly proposed to extract features from multiple sensory signals acquired from machining processes. Then, LS-SVM-based tool wear prediction model is constructed by learning correlation between extracted features and actual tool wear. The effectiveness of proposed predictive model and corresponding tool wear monitoring system is demonstrated by experimental results from broaching trials.
The supervision of tool wear is the most difficult task in the context of tool condition monitoring for metal-cutting processes. Based on a continuous acquisition of signals with multi-sensor systems it is possible to estimate or to classify certain wear parameters by means of neural networks. However, despite of more than a decade of intensive scientific research, the development of tool wear monitoring systems is an on-going attempt. This article aims to investigate, why it has not been possible to develop appropriate monitoring systems up to now. In order to describe the ‘state of the art’, 138 publications dealing with on-line and indirect tool wear monitoring in turning by means of artificial neural networks are evaluated. The article compares the methods applied in these publications as well as the methodologies used to select certain methods, to carry out simulation experiments, to evaluate and to present results, etc. As a conclusion, possible directions for future research in this area are pointed out. Many of the recommendations are valid for other machining processes using tools with or without defined cutting edges, too.
This paper presents an integrated platform for multi-sensor equipment diagnosis and prognosis. This integrated framework is based on hidden semi-Markov model (HSMM). Unlike a state in a standard hidden Markov model (HMM), a state in an HSMM generates a segment of observations, as opposed to a single observation in the HMM. Therefore, HSMM structure has a temporal component compared to HMM. In this framework, states of HSMMs are used to represent the health status of a component. The duration of a health state is modeled by an explicit Gaussian probability function. The model parameters (i.e., initial state distribution, state transition probability matrix, observation probability matrix, and health-state duration probability distribution) are estimated through a modified forward–backward training algorithm. The re-estimation formulae for model parameters are derived. The trained HSMMs can be used to diagnose the health status of a component. Through parameter estimation of the health-state duration probability distribution and the proposed backward recursive equations, one can predict the useful remaining life of the component. To determine the “value” of each sensor information, discriminant function analysis is employed to adjust the weight or importance assigned to a sensor. Therefore, sensor fusion becomes possible in this HSMM based framework.The validation of the proposed framework and methodology are carried out in real world applications: monitoring hydraulic pumps from Caterpillar Inc. The results show that the increase of correct diagnostic rate is indeed very promising. Furthermore, the equipment prognosis can be implemented in the same integrated framework.
Monitoring of tool wear condition for drilling is a very important economical consideration in automated manufacturing. Two techniques are proposed in this paper for the on-line identification of tool wear based on the measurement of cutting forces and power signals. These techniques use hidden Markov models (HMMs), commonly used in speech recognition. In the first method, bargraph monitoring of the HMM probabilities is used to track the progress of tool wear during the drilling operation. In the second method, sensor signals that correspond to various types of wear status, e.g., sharp, workable and dull, are classified using a multiple modeling method. Experimental results demonstrate the effectiveness of the proposed methods. Although this work focuses on on-line tool wear condition monitoring for drilling operations, the HMM monitoring techniques introduced in this paper can be applied to other cutting processes.
Incremental training is commonly applied to train- ing recurrent neural networks (RNNs) for applications involv- ing prognosis. As the number of prognostic time-step increases, the accuracy of prognosis generally decreases, as often seen in long-term prognosis. Revision of the training techniques is there- fore necessary to improve the accuracy in long-term prognosis. This paper presents a competitive learning-based approach to long-term prognosis of machine health status. Specifically, vibra- tion signals from a defect-seeded rolling bearing are preprocessed using continuous wavelet transform (CWT). Statistical parameters computed from both the raw data and the preprocessed data are then utilized as candidate inputs to an RNN. Based on the principle of competitive learning, input data were clustered for effective rep- resentation of similar stages of defect propagation of the bearing being monitored. Analysis has shown that the developed technique is more accurate in predicting bearing defect progression than the incremental training technique.
Random Forests were introduced as a Machine Learning tool in Breiman (2001) and have since proven to be very popular and powerful for high-dimensional regression and classifi- cation. For regression, Random Forests give an accurate approximation of the conditional mean of a response variable. It is shown here that Random Forests provide information about the full conditional distribution of the response variable, not only about the con- ditional mean. Conditional quantiles can be inferred with Quantile Regression Forests, a generalisation of Random Forests. Quantile Regression Forests give a non-parametric and accurate way of estimating conditional quantiles for high-dimensional predictor variables. The algorithm is shown to be consistent. Numerical examples suggest that the algorithm is competitive in terms of predictive power.
Random forests are a scheme proposed by Leo Breiman in the 2000's for
building a predictor ensemble with a set of decision trees that grow in
randomly selected subspaces of data. Despite growing interest and practical
use, there has been little exploration of the statistical properties of random
forests, and little is known about the mathematical forces driving the
algorithm. In this paper, we offer an in-depth analysis of a random forests
model suggested by Breiman in \cite{Bre04}, which is very close to the original
algorithm. We show in particular that the procedure is consistent and adapts to
sparsity, in the sense that its rate of convergence depends only on the number
of strong features and not on how many noise variables are present.
This paper presents an integrated approach for Web-based collaborative manufacturing, including distributed process planning, dynamic scheduling, real-time monitoring, and remote control. It is enabled by a Web-based integrated sensor-driven e-ShopFloor (Wise-ShopFloor) framework targeting distributed yet collaborative manufacturing environments. Utilizing the latest Java technologies (Java 3D and Java Servlet) for system implementation, this approach allows users to plan and control distant shop floor operations based on runtime information from the shop floor. The objective of this research is to develop methodology and algorithms for Web-based collaborative planning and control, supported by real-time monitoring for dynamic scheduling. Details on the principle of the Wise-ShopFloor framework, system architecture, and a proof-of-concept prototype are reported in this paper. An example of distributed process planning for remote machining is chosen as a case study to demonstrate the effectiveness of this approach toward Web-based collaborative manufacturing.
A Survey of Artificial Intelligence for Prognostics
- M Schwabacher
- K Goebel
Schwabacher, M., and Goebel, K., 2007, "A Survey of Artificial Intelligence for Prognostics," Proc.
AAAI fall symposium, pp. 107-114.
Fuzzy Neural Network Modelling for Tool Wear Estimation in Dry Milling Operation
- X Li
- B Lim
- J Zhou
- S Huang
- S Phua
- K Shaw
Li, X., Lim, B., Zhou, J., Huang, S., Phua, S., Shaw, K., and Er, M., 2009, "Fuzzy Neural Network
Modelling for Tool Wear Estimation in Dry Milling Operation," Annual Conference of the
Prognostics and Health Management Society, pp. 1-11.
Map-Reduce for Machine Learning on Multicore
- C Chu
- S K Kim
- Y.-A Lin
- Y Yu
- G Bradski
- A Y Ng
- K Olukotun
Chu, C., Kim, S. K., Lin, Y.-A., Yu, Y., Bradski, G., Ng, A. Y., and Olukotun, K., 2007, "Map-Reduce
for Machine Learning on Multicore," Advances in Neural Information Processing Systems, 19, p.
281.