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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 mathema...
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... Draw a bootstrap sample Z of size N from the training data. The framework of predicting flank wear using an RF is illustrated in Fig. 3. In this research, a random forest is constructed using B ¼ 500 regression trees. Given the labeled training dataset D ¼ ðx i ; y i Þ, a bootstrap sample of size N ¼ 630 is drawn from the training dataset. For each regression tree, m ¼ 9 ðm ¼ ðp=3Þ; p ¼ 28Þ variables are selected at random from the 28 variables/ features. The best ...
Context 2
... Draw a bootstrap sample Z of size N from the training data. The framework of predicting flank wear using an RF is illus- trated in Fig. 3. In this research, a random forest is constructed using B ¼ 500 regression trees. Given the labeled training dataset D ¼ ðx i ; y i Þ, a bootstrap sample of size N ¼ 630 is drawn from the training dataset. For each regression tree, m ¼ 9 ðm ¼ ðp=3Þ; p ¼ 28Þ variables are selected at random from the 28 variables/ features. The best ...
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Citations
... Chen et al. [8] proposed a life prognosis framework for engines based on similarity theory and support vector machines, which used full life-cycle data and a failure-free performance deterioration index. Wu et al. [9] proposed a random forest (RF)-based tool wear prediction method, which was validated using milling test data. Berghout et al. [10] proposed an online sequential extreme learning machine (OS-ELM) method for RUL prognosis and validated it using the C-MAPSS data set. ...
As a significant component of rotation machinery, bearing plays a role in supporting and transmitting power. However, bearings are subject to complex operating conditions and are prone to failure. To avoid ineffectiveness and improve the reliability of bearings, a data-driven method is used to predict the remaining useful life (RUL). However, this method is less stable and can only forecast the RUL of bearings under training sample conditions. An ensemble deep, long-term, and short-term memory (EDLSTM) method is proposed to solve this problem. First, the feature of the forecast-bearing RUL was extracted including time-domain features, frequency-domain energy features, and Shannon entropy. Then, a deep long- and short-term memory network prediction model of the bearing RUL was constructed. To resolve the instability of DLSTM predictions, multiple DLSTMs were ensembled using the maximum information component (MIC) criterion. The model i trained using bearing data with different failure modes under difficult operating conditions to improve the predictive stability of the model. Finally, an EDLSTM was constructed to achieve the bearing RUL prediction. In the prediction result of the training set, the cumulative relative accuracy (CRA) was above 0.9 for most of the bearings. According to the experimental results in the test set, the mean CRA was over 0.80. For some of the bearing’s RUL, the CRA was more than 0.90. The above results show that the proposed approach can effectively predict the RUL of a bearing and has a more stable prediction ability than the bagging integration method.
... Different regression algorithms are commonly used in prediction-making by evaluating the correlation between dependent and independent variables. A data-driven random forest (RF) algorithm is used for wear prediction in comparison with artificial neural networks (ANN), and support vector regression (SVR) with kernel comparison of Gaussian radial basis function (RBF) and sigmoid kernel [18]. While the result from RF is found to have a good fit in prediction making, the time needed for training is greater than with the other algorithms, and RF model applicability to real-time applications is not supported by the research results. ...
... Benkedjouh et al. [20] used SVR for tool wear assessment and RUL prediction by using cutting force, vibration, and acoustic emission signals. The SVR generalisation capabilities for unseen data are recognised in [21], and extensive studies for SVR prediction accuracy in [18,22,23]. The relationship between the SVR's independent and dependent variables is depicted in detail in [21,24]. ...
... However, defining an appropriate kernel function and hyperparameter settings is necessary to receive higher accuracy in the results [26]. The most used kernels include polynomial (POLY), Gaussian radial basis function (RBF), and sigmoid kernels, according to research made by [18]. In this research, the POLY and RBF kernels were adopted due to their better capability to accommodate nonlinear data. ...
Modern industrial machine applications often contain data collection functions through automation systems or external sensors. Yet, while the different data collection mechanisms might be effortless to construct, it is advised to have a well-balanced consideration of the possible data inputs based on the machine characteristics, usage, and operational environment. Prior consideration of the collected data parameters reduces the risk of excessive data, yet another challenge remains to distinguish meaningful features significant for the purpose. This research illustrates a peripheral milling machine data collection and data pre-processing approach to diagnose significant machine parameters relevant to milling blade wear. The experiences gained from this research encourage conducting pre-categorisation of data significant for the purpose, those being manual setup data, programmable logic controller (PLC) automation system data, calculated parameters, and measured parameters under this study. Further, the results from the raw data pre-processing phase performed with Pearson Correlation Coefficient and permutation feature importance methods indicate that the most dominant correlation to recognised wear characteristics in the case machine context is perceived with vibration excitation monitoring. The root mean square (RMS) vibration signal is further predicted by using the support vector regression (SVR) algorithm to test the SVR’s overall suitability for the asset’s health index (HI) approximation. It was found that the SVR algorithm has sufficient data parameter behaviour forecast capabilities to be used in the peripheral milling machine prognostic process and its development. The SVR with Gaussian radial basis function (RBF) kernel receives the highest scoring metrics; therefore, outperforming the linear and polynomial kernels compared as part of the study.
... Karandikar et al. [21] have monitored the tool wear by using a Naive Bayes classifier in the end-milling process. Wu et al. [22] have done a comparison of the performance of random forest (RF) with ANN and SVR for tool wear prediction, and the results show that the accuracy of RF was better than other algorithms. Wang and Huang et al. [23] have done tool wear modeling during hard turning based on RNN, and the extended Kalman filter (EKF) method is used to train the network. ...
Tool wear monitoring is regarded as an incredibly important aspect of improving the surface integrity of machined components in the manufacturing sector. This research study performed operations using twelve different types of drilling and milling tools. The worn tools ranging from grade-1 to grade-5 were categorized based on tool wear severity by measuring the flank wear land width of each tool. Advanced algorithms were designed based on short-time Fourier transform and continuous wavelet transform to convert time-series force signals’ data into spectrogram and scalogram images, respectively, to increase the number of shots with which the model can work based on the methodology of 2-shot learning. An algorithm for image augmentation was developed to increase the number of images to improve the training and overall performance of the model. L2 regularization along with the optimal hyper-parameters were utilized to avoid overfitting and to improve the model’s efficiency. Hyper-parameters were optimized by using the grid-search methodology. The milling and drilling data was collated into 12 classes which resulted in a 12-way learning model. Therefore, it will work for both milling and drilling operations. The model will determine whether the test tool is normal or worn. And if worn, it will determine the severity level of tool wear ranging from grade-1 to grade-5. The final results have shown that the model has worked efficiently during CNC machining and achieved 87.83% accuracy.
... Manufacturing has been benefited from the emerging growth of Machine Learning (ML) applications [5][6][7][8]. Specifically for tool wear analysis, several ML algorithms have been applied by prior studies in analyzing and predicting the tool wear [9]. In [10], an Artificial Neural Network (ANN) was adopted to predict the surface roughness and tool wear in turning processes, and the results showed that the ANN was capable of predicting the tool wear with an reasonable accuracy. ...
This research aims develop an Explainable Artificial Intelligence (XAI) framework to facilitate human-understandable solutions for tool wear prediction during turning. A random forest algorithm was used as the supervised Machine Learning (ML) classifier for training and binary classification using acceleration , acoustics, temperature, and spindle speed during the orthogonal tube turning process as input features. The ML classi-fier was used to predict the condition of the tool after the cutting process, which was determined in a binary class form indicating if the cutting tool was available or failed. After the training process , the Shapley criterion was used to explain the predictions of the trained ML classifier. Specifically, the significance of each input feature in the decision-making and classification was identified to explain the reasoning of the ML classifier predictions. After implementing the Shapley criterion on all testing datasets, the tool temperature was identified as the most significant feature in determining the classification of available versus failed cutting tools. Hence, this research demonstrates capability of XAI to provide machining operators the ability to diagnose and understand complex ML classifiers in prediction of tool wear.
... The results show that the current absorbed from the spindle motor has a high correlation with tool wear since it is associated with an increment of the drilling force. Wu et al. [16] used the statistical features, namely max value, median, mean, and standard deviation of cutting force signals and vibration along different directions (x, y, and z) and the same value for the acoustic emission signal to train and compared results in tool wear estimation of different algorithms such as Random Forest, Support Vector Machine and different ANN architectures. ...
The drilling of carbon fiber-reinforced plastic (CFRP) materials is a key process in the aerospace industry, where ensuring high product quality is a critical issue. Low-quality of final products may be caused by the occurrence of drilling-induced defects such as delamination, which can be highly affected by the tool conditions. The abrasive carbon fibers generally produce very fast tool wear with negative effects on the hole quality. This suggests the need to develop a method able to accurately monitor the tool wear development during the drilling process in order to set up optimal tool management strategies. Nowadays, different types of sensors can be employed to acquire relevant signals associated with process variables which are useful to monitor tool wear during drilling. Moreover, the increasing computational capacity of modern computers allows the successful development of procedures based on Artificial Intelligence (AI) techniques for signal processing and decision making aimed at online tool condition monitoring. In this work, an advanced tool condition monitoring method based on the employment of autoencoders and gated recurrent unit (GRU) recurrent neural networks (RNN) is developed and implemented to estimate tool wear in the drilling of CFRP/CFRP stacks. This method exploits the automatic feature extraction capability of autoencoders to obtain relevant features from the sensor signals acquired by a multiple sensor system during the drilling process and the memory abilities of GRU to estimate tool wear based on the extracted sensor signal features. The results obtained with the proposed method are compared with other neural network approaches, such as traditional feedforward neural networks, and considerations are made on the influence that memory-based hyperparameters have on tool wear estimation performance.
... These models can deal with complex problems without sophisticated and specialized knowledge, provide an effective classification technique, and deal with nonlinear systems and low operational response time after the learning phase [15]. ANN models have been applied to a wide range of fields [12][13][14][15][16][17], but it is still of interest to explore ANN models into PdM and especially with sensor data as the main input. ...
Possessing an efficient production line relies heavily on the availability of the production equipment. Thus, to ensure that the required function for critical equipment is in compliance, and unplanned downtime is minimized, succeeding with the field of maintenance is essential for industrialists. With the emergence of advanced manufacturing processes, incorporating predictive maintenance capabilities is seen as a necessity. Another field of interest is how modern value chains can support the maintenance function in a company. Accessibility to data from processes, equipment and products have increased significantly with the introduction of sensors and Industry 4.0 technologies. However, how to gather and utilize these data for enabling improved decision making within maintenance and value chain is still a challenge. Thus, the aim of this paper is to investigate on how maintenance and value chain data can collectively be used to improve value chain performance through prediction. The research approach includes both theoretical testing and industrial testing. The paper presents a novel concept for a predictive maintenance platform, and an artificial neural network (ANN) model with sensor data input. Further, a case of a company that has chosen to apply the platform, with the implications and determinants of this decision, is also provided. Results show that the platform can be used as an entry-level solution to enable Industry 4.0 and sensor data based predictive maintenance.
... Subsequently, data-driven models are built by performing feature engineering, model training, and validation (Kusiak 2018). Machine learning methods such as linear regression, Artificial Neural networks (ANN), and Gaussian processes are typical to construct data-driven models (Dazhong et al. 2017). In large amounts of data, deep-learning methods have superior performance in building the non-linear relationship (Jinjiang et al. 2018). ...
This paper presents a data-driven digital twin (DT) framework that predicts key performance indicators (KPIs) in a CNC machining environment. The decision-makers can use these predicted KPIs in the CNC machining process flow to better choose cutting parameters to accomplish the required KPIs. Those beneficiaries would be the process planner in the process planning stage and the machine operator in the machining stage. The cutting parameters affect major performance KPIs such as machining time, quality, and energy consumption. So, correctly selected cutting parameters can improve KPIs in CNC machining operations. In this paper, the two KPIs considered for building predictive models, and their application in the proposed DT with experimental data are energy and surface roughness. The data for building the predictive models for a CNC milling process are obtained through experiments. This work also illustrates the choice of predictive modelling methods in both the stages of CNC machining and its outcomes.
... Of primary importance has been the development of conceptual frameworks to deal assist the analyst in converting the volumes of data and associated multiplicity of approaches into useful information (Kozjek et al. 2020). Such approaches have been successfully employed in applied case studies featuring semiconductor manufacturing (Moyne and Iskandar 2017), tool wear (Wu et al. 2017), remanufacturing (Zhang et al. 2022), and affective design (Yan Chan et al. 2018). Big data has also motivated the development of new approaches, with the progression of research migrating from traditional machine learning models (Wuest et al. 2016) to artificial intelligence approaches such as digital twin implementations or reinforcement learning (Liu et al. 2019). ...
... This research makes a novel contribution that is of practical relevance to the smart manufacturing community by addressing a specific gap in the current body of knowledge. While it is true that some previous studies have compared multiple machine learning algorithms for specific problems in certain domains (Tao et al. 2018;Wu et al. 2017;Hansson et al. 2016;Kang, Kim, and Cho 2016), there is heretofore no developed architecture for auto-selecting the best classifier from running multiple algorithms within a framework for solving industrial problems. Similarly, studies have been performed employing a single algorithm such as Naïve Bayes (Brundage, Ademujimi, and Rakshith 2017) or using combinations of algorithms (Fathy, Jaber, and Brintrup 2020); however, this research is distinct in that it automates the machine learning model selection. ...
The emergence of the Internet of Things (IoT), cloud computing, cyber-physical systems, system integration, big data, and data analytics for Industry 4.0 have transformed the world of traditional manufacturing into an era of smart manufacturing (SM). Smart manufacturing’s central focus is to process real-time IoT data and leverage advanced analytical approaches to detect abnormal behaviors. Social smart manufacturing applies analytics tools to empower decision makers and minimize duplication by executing the repetitive data processing work more consistently and precisely than can be done by a human operator. In smart manufacturing, the majority of industrial data is imbalanced. However, most traditional machine learning algorithms tend to be biased toward the majority class and under-represent the minority class. This research proposes a model selection architecture to automate the procedure of preprocessing input data and selecting the best combination of algorithms for anomaly detection. This design will play an essential role in producing high-quality products and improving quality control and business processes in diverse applications including predictive maintenance and fault detection. The framework is transferrable to any smart manufacturing task in the supervised learning domain.
... Know-when, built on Know-why, involves timely predictions of events or prediction of key variables based on historical data, allowing the decision-maker can take actions at early stages. For instance, Know-when in manufacturing includes quality prediction based on relevant variables [26,27], predictive maintenance via detection of incipient anomalies before break-down [28,29], and predicting Remaining Useful Life (RUL) [30,31]. • Know-how, on the foundation of Know-when, can recommend decisions that help adapt to expected disturbance and can aid in self-optimization. ...
... For instance, examples of Know-why tasks with tree-based methods at the product and machine level include identifying the influencing factors that lead to quality defects [91] or machine failure [92], thereby allowing the manufacturer to diagnose problems effectively. In addition, the identified important factors when using tree-based methods can help in further predicting target values such as product quality [93](Know-when, product level) or events of interest before they happen, such as machine breakdown [31] (Know-when, machine level). ...
While attracting increasing research attention in science and technology, Machine Learning (ML) is playing a critical role in the digitalization of manufacturing operations towards Industry 4.0. Recently, ML has been applied in several fields of production engineering to solve a variety of tasks with different levels of complexity and performance. However, in spite of the enormous number of ML use cases, there is no guidance or standard for developing ML solutions from ideation to deployment. This paper aims to address this problem by proposing an ML application roadmap for the manufacturing industry based on the state-of-the-art published research on the topic. First, this paper presents two dimensions for formulating ML tasks, namely, ’Four-Know’ (Know-what, Know-why, Know-when, Know-how) and ’Four-Level’ (Product, Process, Machine, System). These are used to analyze ML development trends in manufacturing. Then, the paper provides an implementation pipeline starting from the very early stages of ML solution development and summarizes the available ML methods, including supervised learning methods, semi-supervised methods, unsupervised methods, and reinforcement methods, along with their typical applications. Finally, the paper discusses the current challenges during ML applications and provides an outline of possible directions for future developments.
... 19,20 Machine learning (ML) algorithms have been advocated to solve complex modeling and optimization problems in numerous engineering fields as numerical computational power has increased. 21 It is a subfield of artificial intelligence (AI) concerned with the creation of models (knowledge) that can efficiently learn from real data. 22−24 Over the last few decades, ML has evolved into a wide field of study, resulting in a variety of different algorithms, hypotheses, methods, implementation areas, etc. 25 However, learning/algorithms have been broadly classified into three categories: ...
Layer-by-layer film (LbL) coatings made of polyelectrolytes are a powerful tool for surface modification, including the applications in the biomedical field, for food packaging, and in many electrochemical systems. However, despite the number of publications related to LbL assembly, predicting LbL coating properties represents quite a challenge, can take a long time, and be very costly. Machine learning (ML) methodologies that are now emerging can accelerate and improve new coating development and potentially revolutionize the field. Recently, we have demonstrated a preliminary ML-based model for coating thickness prediction. In this paper, we compared several ML algorithms for optimizing a methodology for coating thickness prediction, namely, linear regression, Support Vector Regressor, Random Forest Regressor, and Extra Tree Regressor. The current research has shown that learning algorithms are effective in predicting the coating output value, with the Extra Tree Regressor algorithm demonstrating superior predictive performance, when used in combination with optimized hyperparameters and with missing data imputation. The best predictors of the coating thickness were determined, and they can be later used to accurately predict coating thickness, avoiding measurement of multiple parameters. The development of optimized methodologies will ensure different reliable predictive models for coating property/function relations. As a continuation, the methodology can be adapted and used for predicting the outputs connected to antimicrobial, anti-inflammatory, and antiviral properties in order to be able to respond to actual biomedical problems such as antibiotic resistance, implant rejection, or COVID-19 outbreak.
