Smart Machining Process Using Machine Learning: A Review and Perspective on Machining Industry

Kim, Dong‐Hyeon; Kim, Thomas J. Y.; Wang, Xinlin; Kim, Min‐Cheol; Quan, Ying‐Jun; Oh, Jin Woo; Min, Soo‐Hong; Kim, Hyungjung; Bhandari, Binayak; Yang, Insoon; Ahn, Sung‐Hoon

doi:10.1007/s40684-018-0057-y

Cited by 223 publications

(84 citation statements)

References 79 publications

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“…In the area of machining processes, the impact of ML solutions was presented in Reference [20]. Several machining cases were listed and a brief presentation of ML-based tool wear monitoring and prediction was included, outlining its potential.…”

Section: Previous Survey Workmentioning

confidence: 99%

See 1 more Smart Citation

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

Angelopoulos

Michailidis

Νομικός

et al. 2019

Sensors

219

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The recent advancements in the fields of artificial intelligence (AI) and machine learning (ML) have affected several research fields, leading to improvements that could not have been possible with conventional optimization techniques. Among the sectors where AI/ML enables a plethora of opportunities, industrial manufacturing can expect significant gains from the increased process automation. At the same time, the introduction of the Industrial Internet of Things (IIoT), providing improved wireless connectivity for real-time manufacturing data collection and processing, has resulted in the culmination of the fourth industrial revolution, also known as Industry 4.0. In this survey, we focus on the vital processes of fault detection, prediction and prevention in Industry 4.0 and present recent developments in ML-based solutions. We start by examining various proposed cloud/fog/edge architectures, highlighting their importance for acquiring manufacturing data in order to train the ML algorithms. In addition, as faults might also occur from sources beyond machine degradation, the potential of ML in safeguarding cyber-security is thoroughly discussed. Moreover, a major concern in the Industry 4.0 ecosystem is the role of human operators and workers. Towards this end, a detailed overview of ML-based human–machine interaction techniques is provided, allowing humans to be in-the-loop of the manufacturing processes in a symbiotic manner with minimal errors. Finally, open issues in these relevant fields are given, stimulating further research.

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Section: Previous Survey Workmentioning

confidence: 99%

“…Supervised learning is a method where an expert inserts known outputs for specific inputs to train the algorithm and is widely used for classification and regression [15,19,20]. Thus, supervised ML is usually employed in scenarios with labeled data availability.…”

mentioning

confidence: 99%

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

Angelopoulos

Michailidis

Νομικός

et al. 2019

Sensors

219

View full text Add to dashboard Cite

show abstract

“…Machine learning and deep learning are accelerating the rapid development of intelligent applications in the industry [ 32 , 33 , 34 ]. Luo et al [ 35 ] described a deep convolutional neural network (CNN)-based technique for the detection of micro defects on metal screw surfaces and the experiment results showed that the proposed technique can achieve a detection accuracy of 98%; the average detection time per picture was 1.2 s. Comparisons with traditional machine vision techniques, e.g., template matching-based techniques, demonstrate the superiority of the proposed deep CNN-based one.…”

Section: Discussionmentioning

confidence: 99%

Computer Vision Tool-Setting System of Numerical Control Machine Tool

Hou

Zhang

Yang

2020

Sensors

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An automatic tool-setting and workpiece online detecting system was proposed to study the key technologies of next-generation intelligent vision computerized numerical control (CNC) machines. A computer vision automatic tool-setting system for a CNC machine was set up on the basis of the vision tool-setting principle. A rapid vision calibration method based on the position feedback from the CNC machine was proposed on the basis of the theory of traditional vision system calibration. The coordinate mapping relationship of the image and the CNC machine, the tool-setting mark point on the workpiece, and the tool tip were calibrated. The vision system performance testing and system calibration experiments were performed. Experimental results indicated that the time consumption was 128 ms in image processing. The precision of tool setting and measuring was less than 1 μm. The workpiece positioning and processing online detection function of the system can completely meet the requirements of visual CNC machine application, and the system has wide application prospects.

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“…This method is typically used for finding meaningful patterns (e.g. WBM defect patterns) or classifications within a large data set [360]. Clustering, adaptive resonance theory network (ART), Hopfield neural network (HNN), Cellular Neural Network, and self-organizing map (SOM) are all examples of unsupervised learning algorithms.…”

Section: ) Machine Learning Classifiersmentioning

confidence: 99%

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

2020

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Electronics industry is one of the fastest evolving, innovative, and most competitive industries. In order to meet the high consumption demands on electronics components, quality standards of the products must be well-maintained. Automatic optical inspection (AOI) is one of the non-destructive techniques used in quality inspection of various products. This technique is considered robust and can replace human inspectors who are subjected to dull and fatigue in performing inspection tasks. A fully automated optical inspection system consists of hardware and software setups. Hardware setup include image sensor and illumination settings and is responsible to acquire the digital image, while the software part implements an inspection algorithm to extract the features of the acquired images and classify them into defected and non-defected based on the user requirements. A sorting mechanism can be used to separate the defective products from the good ones. This article provides a comprehensive review of the various AOI systems used in electronics, microelectronics , and opto-electronics industries. In this review the defects of the commonly inspected electronic components, such as semiconductor wafers, flat panel displays, printed circuit boards and light emitting diodes, are first explained. Hardware setups used in acquiring images are then discussed in terms of the camera and lighting source selection and configuration. The inspection algorithms used for detecting the defects in the electronic components are discussed in terms of the preprocessing, feature extraction and classification tools used for this purpose. Recent articles that used deep learning algorithms are also reviewed. The article concludes by highlighting the current trends and possible future research directions.

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Smart Machining Process Using Machine Learning: A Review and Perspective on Machining Industry

Cited by 223 publications

References 79 publications

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

Computer Vision Tool-Setting System of Numerical Control Machine Tool

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

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