A Machine Learning Scheme for Tool Wear Monitoring and Replacement in IoT-Enabled Smart Manufacturing

2020 ◽  
pp. 439-447
Author(s):  
Zeel Bharatkumar Patel ◽  
Sreekumar Muthuswamy
Keyword(s):  
Machine Learning ◽  
Tool Wear ◽  
Smart Manufacturing ◽  
Tool Wear Monitoring ◽  
Learning Scheme ◽  
Wear Monitoring

Application of Audible Sound Signals for Tool Wear Monitoring and Workpiece Hardness Identification in Gear Milling Using Machine Learning Techniques

2017 ◽  
Author(s):  
Achyuth Kothuru ◽  
Sai Prasad Nooka ◽  
Patricia Iglesias Victoria ◽  
Rui Liu
Keyword(s):  
Machine Learning ◽  
Tool Wear ◽  
Learning Algorithm ◽  
Machining Process ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Process Conditions ◽  
Tool Wear Monitoring ◽  
Audible Sound ◽  
Wear Monitoring

The machining process monitoring, especially the tool wear monitoring, is very critical in modern automated gear machining environment which needs instant detection of cutting tool state and/or process conditions, quick final diagnosis and appropriate actions. It has been realized that the non-uniform hardness of the workpiece material due to the improper heat treatment can cause expedited tool wear and unexpected tool breakage, which greatly increases difficulties and complexities in monitoring the tool conditions in gear cutting. This paper provides a solution to detect the wear conditions of the gear milling cutter in the cutting of workpiece materials with hardness variations using the audible sound signals. In this study, cutting tools and workpieces are prepared to have different flank wear classes and hardness variations respectively. A series of gear milling experiments are operated with a broad range of cutting conditions to collect sound signals. A machine learning algorithm that incorporates support vector machine (SVM) approach coupled with the application of time and frequency domain analysis is developed to correlate observed sound signals’ signatures to specified tool wear classes and workpiece hardness levels. The performance evaluation results of the proposed monitoring system have shown accurate predictions in detecting tool wear conditions and workpiece hardness variations from the sound signals in gear milling.

scholarly journals Tool Wear Monitoring for Complex Part Milling Based on Deep Learning

2020 ◽  
Vol 10 (19) ◽  
pp. 6916 ◽  
Author(s):  
Xiaodong Zhang ◽  
Ce Han ◽  
Ming Luo ◽  
Dinghua Zhang
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Tool Wear ◽  
Learning Model ◽  
Cutting Condition ◽  
Force Model ◽  
Tool Wear Monitoring ◽  
Monitoring Method ◽  
Complex Part ◽  
Wear Monitoring

Tool wear monitoring is necessary for cost reduction and productivity improvement in the machining industry. Machine learning has been proven to be an effective means of tool wear monitoring. Feature engineering is the core of the machining learning model. In complex parts milling, cutting conditions are time-varying due to the variable engagement between cutting tool and the complex geometric features of the workpiece. In such cases, the features for accurate tool wear monitoring are tricky to select. Besides, usually few sensors are available in an actual machining situation. This causes a high correlation between the hand-designed features, leading to the low accuracy and weak generalization ability of the machine learning model. This paper presents a tool wear monitoring method for complex part milling based on deep learning. The features are pre-selected based on cutting force model and wavelet packet decomposition. The pre-selected cutting forces, cutting vibration and cutting condition features are input to a deep autoencoder for dimension reduction. Then, a deep multi-layer perceptron is developed to estimate the tool wear. The dataset is obtained with a carefully designed varying cutting depth milling experiment. The proposed method works well, with an error of 8.2% on testing samples, which shows an obvious advantage over the classic machine learning method.

scholarly journals On-line tool wear monitoring based on machine learning

2021 ◽  
Vol 1 (1) ◽  
pp. 11-20
Author(s):  
Dianfang MU ◽  
Xianli LIU ◽  
Caixu YUE ◽  
Qiang LIU ◽  
Zhengyan BAI ◽  
...  
Keyword(s):  
Machine Learning ◽  
Tool Wear ◽  
Tool Wear Monitoring ◽  
On Line ◽  
Wear Monitoring
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