A modular tool wear monitoring system in a metal cutting operation using MLP neural networks and multivariate process parameters

1998 ◽  
Author(s):  
D.E. Dimla
Keyword(s):  
Neural Networks ◽  
Tool Wear ◽  
Monitoring System ◽  
Process Parameters ◽  
Metal Cutting ◽  
Tool Wear Monitoring ◽  
Multivariate Process ◽  
Cutting Operation ◽  
Wear Monitoring ◽  
Mlp Neural Networks

scholarly journals Tool Wear Monitoring in Turning: A Neural Network Application

2001 ◽  
Vol 34 (7) ◽  
pp. 207-222 ◽  
Author(s):  
Bernhard Sick
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Tool Wear ◽  
Process Parameters ◽  
Production Costs ◽  
Cutting Conditions ◽  
Tool Wear Monitoring ◽  
Computing Technique ◽  
Wear Monitoring ◽  
Signal Changes

Tool wear monitoring is the most difficult task in the area of tool condition monitoring for metal-cutting manufacturing processes. The main objective is to improve the process reliability, but the production costs need to be reduced as well. This article summarises a new approach for online and indirect tool wear estimation or classification in turning using neural networks. This technique uses a physical process model describing the influence of cutting conditions (such as the feed rate) on measured process parameters (here: cutting force signals) in order to separate signal changes caused by variable cutting conditions from signal changes caused by tool wear. Features extracted from the normalised process parameters are taken as inputs of a dynamic, but nonrecurrent neural network that estimates the current state of the tool. It is shown that the estimation error can be reduced significantly with this combination of a hard computing and a soft computing technique. The article represents an extended summary of the author's investigations and publications in the area of online and indirect tool wear monitoring in turning by means of artificial neural networks.

scholarly journals Overview of Tool Wear Monitoring Methods Based on Convolutional Neural Network

2021 ◽  
Vol 11 (24) ◽  
pp. 12041
Author(s):  
Qun Wang ◽  
Hengsheng Wang ◽  
Liwei Hou ◽  
Shouhua Yi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Tool Wear ◽  
Network Architecture ◽  
Manufacturing Systems ◽  
Convolution Neural Network ◽  
Tool Wear Monitoring ◽  
Monitoring Methods ◽  
Deep Convolutional Neural Networks ◽  
Wear Monitoring

Tool wear monitoring is of great significance for the development of manufacturing systems and intelligent manufacturing. Online tool condition monitoring is a crucial technology for cost reduction, quality improvement, and manufacturing intelligence in modern manufacturing. However, it remains a difficult problem to monitor the status of tools online, in real-time and accurately in the industry. In the research status of mainstream technology, the convolution neural network may be a good solution to this problem, based on the appropriate sensor system and correct signal processing methods. Therefore, this paper outlines the state-of-the-art systems encountered in the open access literature, focusing on information collection, feature selection–extraction technologies based on deep convolutional neural networks, and monitoring network architecture and modeling methods. Based on typical cases, this paper focuses on the application of the convolution neural network in tool wear monitoring. From the application results, it is feasible and reliable to apply convolution neural networks in tool wear monitoring. Additionally, it can improve the prediction accuracy, which is of great significance for the future development of technology. This paper can be a guide for the researchers and manufacturers in the area of tool wear monitoring for explaining the latest trends and requirements.

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