The complex structure of turning aggravates obtaining the desired results in terms of tool wear and surface roughness. The existence of high temperature and pressure make difficult to reach and observe the cutting area. In-direct tool condition, monitoring systems provide tracking the condition of cutting tool via several released or converted energy types, namely, heat, acoustic emission, vibration, cutting forces and motor current. Tool wear inevitably progresses during metal cutting and has a relationship with these energy types. Indirect tool condition monitoring systems use sensors situated around the cutting area to state the wear condition of the cutting tool without intervention to cutting zone. In this study, sensors mostly used in indirect tool condition monitoring systems and their correlations between tool wear are reviewed to summarize the literature survey in this field for the last two decades. The reviews about tool condition monitoring systems in turning are very limited, and relationship between measured variables such as tool wear and vibration require a detailed analysis. In this work, the main aim is to discuss the effect of sensorial data on tool wear by considering previous published papers. As a computer aided electronic and mechanical support system, tool condition monitoring paves the way for machining industry and the future and development of Industry 4.0.

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A Review of Indirect Tool Condition Monitoring Systems and Decision-Making Methods in Turning: Critical Analysis and Trends.

Data-driven methods provided smart manufacturing with unprecedented opportunities to facilitate the transition toward Industry 4.0–based production. Machine learning and deep learning play a critical role in developing intelligent systems for descriptive, diagnostic, and predictive analytics for machine tools and process health monitoring. This paper reviews the opportunities and challenges of deep learning (DL) for intelligent machining and tool monitoring. The components of an intelligent monitoring framework are introduced. The main advantages and disadvantages of machine learning (ML) models are presented and compared with those of deep models. The main DL models, including autoencoders, deep belief networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs), were discussed, and their applications in intelligent machining and tool condition monitoring were reviewed. The opportunities of data-driven smart manufacturing approach applied to intelligent machining were discussed to be (1) automated feature engineering, (2) handling big data, (3) handling high-dimensional data, (4) avoiding sensor redundancy, (5) optimal sensor fusion, and (6) offering hybrid intelligent models. Finally, the data-driven challenges in smart manufacturing, including the challenges associated with the data size, data nature, model selection, and process uncertainty, were discussed, and the research gaps were outlined.

A review on deep learning in machining and tool monitoring: methods, opportunities, and challenges

Development of tool condition monitoring system in end milling process using wavelet features and Hoelder’s exponent with machine learning algorithms

Future research directions in the machining of Inconel 718

Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review

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

A novel chatter detection method for milling using deep convolution neural networks

Detection and avoidance of regenerative chatter play a crucial role in ensuring the high quality and efficiency of machining operations. Predominant analytical approaches provide stability lobe diagrams for machining processes. Deep learning is a general term given to the most recent and successful group of machine learning methods that proved great promise in many areas of human life. This study purposes a novel transfer learning framework that combines analytical solutions and convolution neural network (CNN) under a novel transfer learning framework. Stability lobes and numerical time-domain solutions of analytical methods are used to train and label, arguably one of the most successful CNN architectures, AlexNet. This approach eliminates the need for a time-consuming and costly experimental data collection phase for training. Furthermore, an ensemble empirical mode decomposition based signal pre-processing method is developed. An IMF-based multi-band ensemble approach is proposed where only intrinsic mode functions relevant to each modal frequency of the system are selected based on their entropy increase and used in training multiple AlexNet instances. The measured data were collected during shoulder milling from a CNC-vertical milling machine. The results revealed considerable success in several scenarios ranging from 82 to 100%, without using any experimentally measured data in training.

A novel transfer learning framework for chatter detection using convolutional neural networks

Milling is a highly crucial machining process in the modern industry. With the recent trends of Industry 4.0, it is becoming more common to implement Artificial Intelligence (AI) methods to increase the performance of milling processes. As a significant limitation for the efficiency of the machining processes, chatter detection, and avoidance are critical. In this paper, a chatter detection method based on vibration data features for the slot milling process is proposed. This method benefits from a deep learning method, Deep Multi-Layer Perceptron (DMLP). Vibration data was acquired by attaching an accelerometer to the spindle housing during slot milling operations. Fast Fouries Transform (FFT) was applied to time-domain vibratory data. Frequency domain data achieved by FFT was investigated for labeling the occurrence of chatter. These labels were used to train the DMLP algorithm. Time-domain signal features such as root mean square, clearance factor, skewness, crest factor, and shape factor were selected as inputs for the chatter detection algorithm. Finally, validation cuttings were performed for verifying the results of the DMLP algorithm. The results prove that time-domain features can provide enough information about the chatter occurrence in slot milling operations, and the DMLP algorithm proposed in this research can successfully detect the chatter occurrence.

Batihan Sener

Papers

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

A novel chatter detection method for milling using deep convolution neural networks

A novel transfer learning framework for chatter detection using convolutional neural networks

Intelligent Chatter Detection in Milling using Vibration Data Features and Deep Multi-Layer Perceptron

Deep Multi-Layer Perceptron based Prediction of Energy Efficiency and Surface Quality for Milling in The Era of Sustainability and Big Data