Multi-objective optimization using Taguchi based grey relational analysis for micro-milling of Al 7075 material with ball nose end mill

Tool condition monitoring and machine tool diagnostics are performed using advanced sensors and computational intelligence to predict and avoid adverse conditions for cutting tools and machinery. Undesirable conditions during machining cause chatter, tool wear, and tool breakage, directly affecting the tool life and consequently the surface quality, dimensional accuracy of the machined parts, and tool costs. Tool condition monitoring is, therefore, extremely important for manufacturing efficiency and economics. Acoustic emission, vibration, power, and temperature sensors monitor the stability and efficiency of the machining process, collecting large amounts of data to detect tool wear, breakage, and chatter. Studies on monitoring the vibrations and acoustic emissions from machine tools have provided information and data regarding the detection of undesirable conditions. Herein, studies on tool condition monitoring are reviewed and classified. As Industry 4.0 penetrates all manufacturing sectors, the amount of manufacturing data generated has reached the level of big data, and classical artificial intelligence analyses are no longer adequate. Nevertheless, recent advances in deep learning methods have achieved revolutionary success in numerous industries. Deep multi-layer perceptron (DMLP), long-short-term memory (LSTM), convolutional neural network (CNN), and deep reinforcement learning (DRL) are among the most preferred methods of deep learning in recent years. As data size increases, these methods have shown promising performance improvement in prediction and learning, compared to classical artificial intelligence methods. This paper summarizes tool condition monitoring first, then presents the underlying theory of some of the most recent deep learning methods, and finally, attempts to identify new opportunities in tool condition monitoring, toward the realization of Industry 4.0.

Review of tool condition monitoring in machining and opportunities for deep learning

Investigation of progressive tool wear for determining of optimized machining parameters in turning

Investigation of signal behaviors for sensor fusion with tool condition monitoring system in turning

Identification and online monitoring of the dresser wear are necessary to guarantee a desired wheel surface and improve the effectiveness of grinding process to a satisfactory level. However, tool wear is a complex phenomenon occurring in several and different ways in cutting processes, and there is a lack of analytical models that can represent the tool condition. On the other hand, neural networks are considered as a good approach to resolve the absence of an analytical or empirical model. This paper describes a method to characterize the dresser wear condition from acoustic emission (AE) signal. To achieve this, some neural network models are proposed. Initially, a study on the frequency content of the raw AE signal was carried out to determine features that correlate the signal and dresser wear. The features of the signal were obtained from the root mean square and ratio of power statistics at nine frequency bands selected from AE spectra. Combinations of two frequency bands were evaluated as inputs to eight neural networks models, which have been compared with their classification ability. It could be verified that the combination of the frequency bands of 28-33 and 42-50 kHz best characterized the dresser wear condition. Some of the models produced very good results and can therefore ensure the ground part will be within project specifications.

M. Boudraa

Papers

Detection process approach of tool wear in high speed milling