https://escholarship.org/content/qt4h56k1ch/qt4h56k1ch.pdf

Advanced monitoring of machining operations

Flank wear is the most commonly observed and unavoidable phenomenon in metal cutting which is also a major source of economic loss resulting due to material loss and machine down time. A wide variety of monitoring techniques have been developed for the online detection of flank wear. In order to provide a broad view of flank wear monitoring techniques and their implementation in tool condition monitoring system (TCMS), this paper reviews three key features of a TCMS, namely (1) signal acquisition, (2) signal processing and feature extraction, and (3) artificial intelligence techniques for decision making.

A review of flank wear prediction methods for tool condition monitoring in a turning process

Accurate tool condition monitoring (TCM) is essential for the development of fully automated milling processes. However, while considerable research has been conducted in industrial and academic settings, the complexity of milling processes continues to complicate the implementation of TCM. This paper presents a review of the state-of-the-art methods employed for conducting TCM in milling processes. The review includes three key components: (1) sensors, (2) feature extraction, and (3) monitoring models for the categorization of cutting tool states in the decision-making process. In addition, the primary strengths and weaknesses of current practices are presented for these three components. Finally, this paper concludes with a list of recommendations for future research.

Review of tool condition monitoring methods in milling processes

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

The paper presents acoustic emission and cutting force signals application in tool condition monitoring in micro-milling of cold-work tool steel. The results obtained revealed strong influence of tool wear on acoustic emission signal, providing acceptable results even while used separately. The signal was easy to register, and showed a very short reaction time to the tool–workpiece contact. As excitation frequency, equal to edge passing frequency, was much higher than in conventional milling, cutting forces, usually the best for this purpose, were strongly disturbed by resonance vibration of the table dynamometer. Despite these disturbances, the signals still show dependence on tool wear, making them useful for tool condition monitoring.

Application of AE and cutting force signals in tool condition monitoring in micro-milling

This paper presents a tool wear monitoring strategy based on a large number of signal features in the rough turning of Inconel 625. Signal features (SFs) were extracted from time domain signals as well as from frequency domain transforms and their wavelet coefficients (time–frequency domain). All of them were automatically evaluated regarding their relevancy for tool wear monitoring based on a determination coefficient between the feature and its low-pass-filtered course as well as the repeatability. The selected SFs were used for tool wear estimation. The accuracy of this estimation was then used to evaluate the sensor and signal usability.

https://link.springer.com/content/pdf/10.1007%2Fs00170-011-3504-2.pdf

Tool condition monitoring based on numerous signal features.

Tool condition monitoring in micromilling based on hierarchical integration of signal measures

Real Time Monitoring of the CNC Process in a Production Environment- the Data Collection & Analysis Phase

Sensor Signal Segmentation for Tool Condition Monitoring

The paper presents a comparison of efficiency of tool wear monitoring strategies based on one signal feature, on a single neural network with several input signals, and on a hierarchical algorithm and a large number of signal features. In the first stage of the hierarchical algorithms, the tool wear was estimated separately for each signal feature. This stage was carried out using either simple neural networks or polynomial approximation. In the second stage, the results obtained in the first one, were integrated into the final tool wear evaluation. The integration was carried out by the use of either a neural network or averaging. The paper shows a considerable advantage of the hierarchical models over conventional industrial solutions (single signal feature) and typical laboratory solutions (single, large neural network).

Sebastian Bombiński

Papers

Tool condition monitoring based on numerous signal features.

Tool condition monitoring in micromilling based on hierarchical integration of signal measures

Real Time Monitoring of the CNC Process in a Production Environment- the Data Collection & Analysis Phase

Sensor Signal Segmentation for Tool Condition Monitoring

Hierarchical Strategies in Tool Wear Monitoring