Chasing the Cut: A Measurement Approach for Machine Tool Condition Monitoring

Huchel, Lukasz; Krause, Thomas C.; Ługowski, Tomasz; Leeb, Steven B.; Helsen, Jan

doi:10.1109/tim.2020.3047939

Cited by 11 publications

(4 citation statements)

References 22 publications

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“…efficiency [1]. A CNC machine tool comprises functional components and foundational elements, with the well-being of the former exerting a direct influence on production efficiency [2,3].…”

Section: Introductionmentioning

confidence: 99%

Optimised LightGBM-based health condition evaluation method for the functional components in CNC machine tools under strong noise background

Jia,

Jialong,

Wanghao

et al. 2024

Meas. Sci. Technol.

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The accurate health condition evaluation of the functional components in computer numerical control (CNC) machine tools is an important prerequisite for predictive maintenance and fault warning. The vibration signals of the functional components in CNC machine tools often contain substantial noise, impeding the extraction of relevant health condition information from the vibration signals. This work presents an approach that leverages the variational mode decomposition (VMD) enhanced by the Artificial Hummingbird Algorithm (AHA) alongside the Light Gradient Boosting Machine (LightGBM) optimised through particle swarm optimisation (PSO) to evaluate the health condition of the functional components in CNC machine tools amidst pervasive noise. Initially, the AHA optimised the penalty factor (α) and the decomposition layer (K) within the VMD. This optimised VMD was subsequently applied to denoise the original vibration signals. After this denoising process, PSO was employed to optimise the learning rate and maximum tree depth within LightGBM. Health condition evaluation experiments were executed on the feed system and spindle of the CNC machine tool to validate the proposed methodology. Comparative analysis indicates that the proposed method attains paramount accuracy and computational efficiency, which are crucial for accurately evaluating the health condition of the functional components in CNC machine tools.

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“…efficiency [1]. A CNC machine tool comprises functional components and foundational elements, with the well-being of the former exerting a direct influence on production efficiency [2,3].…”

Section: Introductionmentioning

confidence: 99%

Optimised LightGBM-based health condition evaluation method for the functional components in CNC machine tools under strong noise background

Jia,

Jialong,

Wanghao

et al. 2024

Meas. Sci. Technol.

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show abstract

“…Alternatively, the indirect methods rely on the changes in sensor signals to monitor tool wear in machining processes [13]. The widely used sensor signals for TCM include cutting force, torque, temperature, acoustic emission and vibration [14], [15]. These approaches directly extract features from collected signals to reveal the physical states of cutting tools.…”

Section: Introductionmentioning

confidence: 99%

“…This implies that the techniques may not be suitable for real-world applications where the machining processes are often much more complicated. Secondly, different signal features must be designed for different milling processes, which makes the feature design a significantly demanding task to be completed every time [14].…”

Section: Introductionmentioning

confidence: 99%

Vibration Signal-Based Tool Condition Monitoring Using Regularized Sensor Data Modeling and Model Frequency Analysis

Liu,

Lang,

Gui

et al. 2024

IEEE Trans. Instrum. Meas.

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“…A piecewise linear assumption is applied in this situation to interpolate the wear value between samples.In 2020, several researches were conducted on tool condition monitoring. Łukasz Huchel et al carried out an experiment using carbide end mills and aluminum as the stock material[32]. Yuqing Zhou and Weifang Sun carried out an experiment in which motor current sensors were used because they are cheap, easy to install, and have no effect on the milling process[33].…”

mentioning

confidence: 99%

Neural Networks and Deep learning approach to predict the Tool life during turning of Nimonic C-263 Alloy

Sen

Ottakath

Rajyalakshmi

et al. 2023

Preprint

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In the manufacturing industry, tool wear prediction is critical for increased productivity and product quality. The focus of this study is to compare the results obtained via an artificial neural network (ANN) models for tool wear prediction on machining a nickel based super alloy: Nimonic C-263 alloy with other prediction method and the effects different parameters have on the efficiency of prediction. In this study, flank wear area (in micron2) is used as the wear indication variable during machining since it influences the precision, stability, and reliability of the machine, whereas cutting speed, feed, and depth of cut are used as input factors. Other variables include cutting force, temperature and MRR. The experimentally determined values will be utilised to train an artificial neural network (ANN) for tool wear prediction. To minimise extraneous variables, correlations between the parameters and wear are determined. The ANN model's error of forecasted response values is compared to other models such as linear regression, as well as prediction values without the elimination of variables. A GUI will also be created that lets the user input the necessary parameters to trained neural network model which then predicts the flank wear area. Results indicate that eliminating parameters resulted in reduction of prediction error (from 11–8%) and also it was found that ANN produced better results than linear regression, decision tree and random forest. Hence, more precise estimation of tool wear can be performed in a lesser time-frame. Number of experimental trials had to be limited due to unfavourable circumstances. The dataset size had been increased by assuming linearity between two results. However, this is justified as the objective is to compare prediction accuracies before and after elimination of variables after a correlation study.

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Chasing the Cut: A Measurement Approach for Machine Tool Condition Monitoring

Cited by 11 publications

References 22 publications

Optimised LightGBM-based health condition evaluation method for the functional components in CNC machine tools under strong noise background

Optimised LightGBM-based health condition evaluation method for the functional components in CNC machine tools under strong noise background

Vibration Signal-Based Tool Condition Monitoring Using Regularized Sensor Data Modeling and Model Frequency Analysis

Neural Networks and Deep learning approach to predict the Tool life during turning of Nimonic C-263 Alloy

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