An in-process tool wear assessment using Bayesian optimized machine learning algorithm

Cutting tool wear monitoring (TWM) plays a significant role because it guarantees the machined surface integrity. Therefore, the present article proposed a TWM system using Bayesian optimized-support vector regression (BO-SVR) analysis. This objective was realized by acquiring machined surface textu...

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Veröffentlicht in:International journal on interactive design and manufacturing Jg. 17; H. 4; S. 1823 - 1845
Hauptverfasser: Babu, Mulpur Sarat, Rao, Thella Babu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Paris Springer Paris 01.08.2023
Springer Nature B.V
Schlagworte:
Accelerometers
Accuracy
Acoustics
Algorithms
Artificial intelligence
Bayesian analysis
CAE) and Design
Computer-Aided Engineering (CAD
Cutting tools
Cutting wear
Deep learning
Electronics and Microelectronics
Engineering
Engineering Design
Feature extraction
Industrial Design
Instrumentation
Kernel functions
Machine learning
Machining
Mechanical Engineering
Model accuracy
Morlet wavelet
Neural networks
Optimization
Original Paper
Prediction models
Sensors
Support vector machines
Surface layers
Texture
Tool wear
Wavelet transforms
Waviness
Bayesian optimization (BO)
Support vector regression (SVR)
Root mean square error (RMSE)
In-process tool wear prediction
Grey level cooccurrence approach (GLCM)
Fisher discriminant ratio (FDR)
ISSN:1955-2513, 1955-2505
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Zusammenfassung:Cutting tool wear monitoring (TWM) plays a significant role because it guarantees the machined surface integrity. Therefore, the present article proposed a TWM system using Bayesian optimized-support vector regression (BO-SVR) analysis. This objective was realized by acquiring machined surface texture video during machining from an in-situ CMOS camera, and subsequently, analyzing it by feature extraction, selection, predictive model training, model hyperparameters optimization, and model testing and validation. To develop an in-process TWM system, machined surface video is acquired during the machining process, and analyzed using Gabor wavelet (GW) and grey level co-occurrence matrix (GLCM) to extract the information related to roughness, feed marks, and waviness of texture. The significant features are selected using the fisher discriminant ratio (FDR) analysis. The in-process TWM system is trained using the FDR selected features and the predictive model hyperparameters such as C, gamma, epsilon, and kernel type are optimized using the Bayesian optimization algorithm, and their optimized results are 99.51, 0.55, 0.01186, and RBF. An optimized hyperparameters are used to establish an accurate and reliable in-process TWM system. The prediction model accuracy is compared with experimentally measured tool wear, the proposed BO-SVR model can predict tool wear with an RMSE of 0.026494.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1955-2513
1955-2505
DOI:10.1007/s12008-023-01270-3