On-Line and Indirect Tool Wear Monitoring in Turning With Artificial Neural Networks: A Review of More Than a Decade of Research

Sick, Bernhard

doi:10.1006/mssp.2001.1460

Cited by 377 publications

(164 citation statements)

References 118 publications

(323 reference statements)

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“…There has been significant interest in tool condition monitoring in the recent past (Byrne et al 1995;Chao and Hwang 1997;Jemielniak et al 1998;Sick 2002;Rehorn et al 2005;Audy 2006;Wang et al 2008;Purushothaman 2009). Various indirect methods for tool condition monitoring (TCM), which use a pattern in sensor data to detect a failure mode (Byrne et al 1995;Scheffer and Heyns 2004;Heyns 2007), have been tried and tested by modeling the correlation between tool wear and sensory signals, namely the cutting force, torque, current, power, vibration, acoustic emission and airborne sound pressure acquired in machining processes.…”

Section: Introductionmentioning

confidence: 99%

“…(Byrne et al 1995;Sick 2002;Rehorn et al 2005;Heyns 2007). In general, cutting force is considered one of the most significant variables in the turning process (Sick 2002;Scheffer and Heyns 2004). Worn tools generally cause an increase in the static as well as dynamic components of the three orthogonal cutting force components, because of increased friction.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Various kinds of ANNs, such as Multilayer Perceptron (MLP), Recurrent Neural Network, Learning Vector Quantization, Time Delay Neural Network, Adaptive Resonance Theory, Self-Organizing Map, Radial Basis Function (RBF) network (Sick 2002) etc., have been tested in tool wear prediction problems. One of these, MLP, generally trained with a back-propagation error algorithm, is the most popular (Sick 2002). Dornfeld 1990, who pioneered the application of ANN to TCM, used a multilayer feed-forward neural network to integrate the features from multiple sensors (acoustic emission and force) in order to predict the tool wear in a turning operation.…”

Section: Introductionmentioning

confidence: 99%

“…Much research has been done on TCM in turning operations using ANNs. The effectiveness and shortcomings of these studies have been reviewed by Sick (2002) and Rehorn et al (2005).…”

Section: Introductionmentioning

confidence: 99%

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Tool wear monitoring and selection of optimum cutting conditions with progressive tool wear effect and input uncertainties

et al. 2009

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One of the big challenges in machining is replacing the cutting tool at the right time. Carrying on the process with a dull tool may degrade the product quality. However, it may be unnecessary to change the cutting tool if it is still capable of continuing the cutting operation. Both of these cases could increase the production cost. Therefore, an effective tool condition monitoring system may reduce production cost and increase productivity. This paper presents a neural network based sensor fusion model for a tool wear monitoring system in turning operations. A wavelet packet tree approach was used for the analysis of the acquired signals, namely cutting strains in tool holder and motor current, and the extraction of wear-sensitive features. Once a list of possible features had been extracted, the dimension of the input feature space was reduced using principal component analysis. Novel strategies, such as the robustness of the developed ANN models against uncertainty in the input data, and the integration of the monitoring information to an optimization system in order to utilize the progressive tool wear information for selecting the optimum cutting conditions, are proposed and validated in manual turning operations. The approach is simple and flexible enough for online implementation.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Much research has been done on TCM in turning operations using ANNs. The effectiveness and shortcomings of these studies have been reviewed by Sick (2002) and Rehorn et al (2005).…”

Section: Introductionmentioning

confidence: 99%

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Tool wear monitoring and selection of optimum cutting conditions with progressive tool wear effect and input uncertainties

et al. 2009

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Indirect On‐Line Tool Wear Monitoring

Sick¹

2004

Computationally Intelligent Hybrid Systems

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Numerically Trained Ultrasound AI for Monitoring Tool Degradation

Jin

Wang

Fox

et al. 2022

Advanced Intelligent Systems

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Monitoring tool degradation during manufacturing can ensure product accuracy and reliability. However, due to variations in degradation conditions and complexity in signal analysis, effective and broadly applicable monitoring is still challenging to achieve. Herein, a novel monitoring method using ultrasound signals augmented with a numerically trained machine learning technique is reported to monitor the wear condition of friction stir welding and processing tools. Ultrasonic signals travel axially inside the tools, and even minor tool wear will change the time and amplitude of the reflected signal. An artificial intelligence (AI) algorithm is selected as a suitable referee to identify the small variations in the tool conditions based on the reflected ultrasound signals. To properly train the AI referee, a human‐error‐free data bank using numerical simulation is generated. The simulation models the experimental conditions with high fidelity and can provide comparable ultrasound signals. As a result, the trained AI model can recognize the tool wear from real experiments with subwavelength accuracy prediction of the worn amount on the tool pins.

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On-Line and Indirect Tool Wear Monitoring in Turning With Artificial Neural Networks: A Review of More Than a Decade of Research

Cited by 377 publications

References 118 publications

Tool wear monitoring and selection of optimum cutting conditions with progressive tool wear effect and input uncertainties

Tool wear monitoring and selection of optimum cutting conditions with progressive tool wear effect and input uncertainties

Indirect On‐Line Tool Wear Monitoring

Numerically Trained Ultrasound AI for Monitoring Tool Degradation

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