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Fundamental Analysis of a Circular Metal Sawing Process
Abstract
The most appropriate separation technique for the processing of solid metal parts with large dimensions is sawing. The cutting tools used in this machining process are exposed to very high mechanical and thermal loads, yet the highest precision, prod-uct quality and process stability must be guaranteed. With regard to process optimisa-tion, the prediction of tool failure and the estimation of the remaining useful life is an important goal. In this paper, a first approach is presented to work towards the devel-opment of a degradation model for circular saw blades based on monitored process parameters. Such a degradation model could then be used to derive the key objec-tives presented above. The basis of this analysis is the recording of the sensor signals current, voltage, vibration and sound with a high sampling rate, whereby in the pre-sent work the focus will initially be on the first two signals mentioned - current and voltage. These signals were analysed in such a way that key indicators could be de-rived. These key indicators were then used to carry out initial analyses, which are intended both to increase the understanding of the process and to form the basis for future analyses.
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The complex structure of turning aggravates obtaining the desired results in terms of tool wear and surface roughness. The existence of high temperature and pressure make difficult to reach and observe the cutting area. In-direct tool condition, monitoring systems provide tracking the condition of cutting tool via several released or converted energy types, namely, heat, acoustic emission, vibration, cutting forces and motor current. Tool wear inevitably progresses during metal cutting and has a relationship with these energy types. Indirect tool condition monitoring systems use sensors situated around the cutting area to state the wear condition of the cutting tool without intervention to cutting zone. In this study, sensors mostly used in indirect tool condition monitoring systems and their correlations between tool wear are reviewed to summarize the literature survey in this field for the last two decades. The reviews about tool condition monitoring systems in turning are very limited, and relationship between measured variables such as tool wear and vibration require a detailed analysis. In this work, the main aim is to discuss the effect of sensorial data on tool wear by considering previous published papers. As a computer aided electronic and mechanical support system, tool condition monitoring paves the way for machining industry and the future and development of Industry 4.0.
Accurate tool condition monitoring (TCM) is essential for the development of fully automated milling processes. This is typically accomplished using indirect TCM methods that synthesize the information collected from one or more sensors to estimate tool condition based on machine learning approaches. Among the many sensor types available for conducting TCM, motor current sensors offer numerous advantages, in that they are inexpensive, easily installed, and have no effect on the milling process. Accordingly, this study proposes a new TCM method employing a few appropriate current sensor signal features based on the time, frequency, and time–frequency domains of the signals and an advanced monitoring model based on an improved kernel extreme learning machine (KELM). The selected multi-domain features are strongly correlated with tool wear condition and overcome the loss of useful information related to tool condition when employing a single domain. The improved KELM employs a two-layer network structure and an angle kernel function that includes no hyperparameter, which overcome the drawbacks of KELM in terms of the difficulty of learning the features of complex nonlinear data and avoiding the need for preselecting the kernel function and its hyperparameter. The performance of the proposed method is verified by its application to the benchmark NASA milling dataset and separate TCM experiments in comparison with existing TCM methods. The results indicate that the proposed TCM method achieves excellent monitoring performance using only a few key signal features of current sensors.
Knowledge about the technical state of a complex machine is crucial regarding the reliability and availability within the entire usage phase of a certain technical product. By gathering and gaining information about the exact health condition, production stops can be minimized while optimizing the efficiency of a machine leading to increased customer satisfaction.
Since more and more data is available and gets recorded by plenty of sensors, classical statistical methods are not applicable anymore or are just not designed to process such large amount of data. Due to that purpose, many methods from the field of machine learning emerged in the past years. Machine learning in general addresses the issue of recognizing patterns and rules within large amounts of data with the goal to make predictions.
Within this paper, the focus is laid upon a detailed step-by-step guide on the development of customized condition monitoring solutions in manufacturing since the application of these still poses a major problem for lots of users.
The entire concept of such solutions starting with data collection, through signal-interpretation and index development, finishing with reliable monitoring of the machine state during the manufacturing process is presented throughout. This is done on a real-life application of the concept using synthesized yet realistic data.
Moreover, an in-depth analysis of advantages, possibilities and opportunities of the presented concept as well as its weaknesses and boundaries is performed.
The safe and reliable operations in industrial manufacturing processes play a crucial role in the economic productivity. Machining process disturbances such as collision, overload, breakdown, and tool wear tend to cause production system failures. The current study aims at investigating the limitations of tool wear prediction on the milling of CGI 450 plates, through the simultaneous detection of acceleration and spindle drive current sensor signals. Tool wear prediction has been accomplished, by utilizing the experimental results that derived from third degree regression models and pattern recognition systems. These results indicate that predictability is affected by the mean signal energy, acquired from the vibration acceleration signals.
This paper presents a monitoring method for on-line detection and indication of the occurrence of a cutting tool failure during high-speed face milling. The method consists of processing of the vibration signal using a reconfigurable infinite impulse response (IIR) bandpass digital filter and statistical techniques. The healthy tool threshold and the filter passband are adjusted and configured based on the cutting parameters that were set up during the machining process. For this process, sets of filter coefficients are pre-calculated for a number of defined insert passing frequencies ranges. The method is verified on-line during machining tests that are carried out at different tool failure levels and using various cutting parameters. In all experimental tests, the method allows the tool condition to be detected and indicated correctly. The proposed method is therefore shown to be simple, fast, computationally efficient, and reliable for the detection and indication of the presence of several types of tool failures for various cutting parameters, and the use of this method does not require any modification of the machine tool structure.
In recent years, increasing demands for more efficient high speed milling (HSM) processes have propelled the application and development of more effective modeling methods and intelligent machining approaches. Hence, a desired reference model has to incorporate more efficient and sophisticated feature extraction and artificial intelligence (AI) techniques aiming for more repeatability and generalizability.In our work, wavelet analysis is applied for feature extraction to provide a better insight into time-frequency changes of the process. Considering the high dimension of extracted wavelet features, dimension reduction methods, such as clustering techniques are inevitable. They are applied as an interpretation layer between the feature extraction and artificial intelligence subsystems to form a new model structure for HSM with generalizability and sequential learning capability.We introduce a new architecture that incorporates the advantages of fuzzy clustering into well known dynamic fuzzy neural networks (DFNN) to form an online condition monitoring system which is tolerant to slight drifts in process dynamics and adaptable to variations in parameters and device. Sequential Fuzzy Clustering Dynamic based Fuzzy Neural Networks (SFCDFNN) is developed and successfully applied for monitoring of an HSM process. It is able to sequentially learn the model and adapt itself to variations and also provide an estimation or prediction on the status of the process. It facilitates for nonintrusive fault diagnosis and prognosis. Lastly, its performance on modeling of experimental data is practically illustrated and compared to its other counterparts.
Presented in this work is a novel tool-condition criterion dubbed as the current rise criterion (CRC). This criterion is based on the measured current values of the machine tool’s spindle and drive motors. The CRC comprises two components: (1) the current rise index (CRI) and (2) a sensitivity factor (SF) indicated as a subscript to the CRI. Current rise criterion is described mathematically as CRC = CRI
SF
. The CRI that accounts for the damage (including wear) suffered by the tool is calculated as the square root of the sum of the squared percent increase in the root mean square (RMS) current values of the spindle and drive motors. To indicate the relative contribution of each of the machine tool motors to the CRI, the sensitivity factor (SF) reflects the ratio of the drive motor current percent rise to that of the spindle motor. The reference current used in calculating the percent rise of the motor current for both CRI and SF is measured at the first cut of the fresh tool. The versatility of the CRC was demonstrated here using two different machining processes: milling and drilling. Quantitative polar maps of the CRI and the associated sensitivity factor of cutting tools as well as qualitative descriptions of the various modes of tool condition afflicting the cutting tools are presented. CRC is demonstrated to be capable of monitoring the tool condition for a variety of cutting parameters of speeds and feeds. Another study demonstrated the versatility of CRC as a discriminator of the quality of chisel drills. It was found that the criterion successfully tracks the tool condition along a variety of process levels. CRC may be used to monitor tool condition and prognostics across practically all machining operations and process parameters, thus rendering the criterion “process independent.” CRC can also be used to monitor the change in power consumption of machine tools while cutting with worn tools.
On the application of machine learning techniques in condition monitoring systems of complex machines
- M Hinz
- D Brüggemann
- S Bracke