Artificial intelligence enabled smart machining and machine tools
Artificial intelligence (AI) in machine tools offers diverse advantages, including learning and optimizing machining processes, compensating errors, saving energy, and preventing failures. Various AI techniques have been proposed and applied; however, many challenges still exist that inhibit the use...
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| Published in | Journal of mechanical science and technology Vol. 36; no. 1; pp. 1 - 23 |
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| Main Authors | , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Seoul
Korean Society of Mechanical Engineers
01.01.2022
Springer Nature B.V 대한기계학회 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1738-494X 1976-3824 |
| DOI | 10.1007/s12206-021-1201-0 |
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| Abstract | Artificial intelligence (AI) in machine tools offers diverse advantages, including learning and optimizing machining processes, compensating errors, saving energy, and preventing failures. Various AI techniques have been proposed and applied; however, many challenges still exist that inhibit the use of AI for machining tasks. This paper deals with different types and usage of AI technologies in machining operations such as predictive modelling, parameter optimization and control, chatter stability, tool wear, and energy conservation. We discuss the challenges of AI technologies, such as data quality, transferability, explainability, and suggest future directions to overcome them. |
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| AbstractList | Artificial intelligence (AI) in machine tools offers diverse advantages, including learning and optimizing machining processes, compensating errors, saving energy, and preventing failures. Various AI techniques have been proposed and applied; however, many challenges still exist that inhibit the use of AI for machining tasks. This paper deals with different types and usage of AI technologies in machining operations such as predictive modelling, parameter optimization and control, chatter stability, tool wear, and energy conservation. We discuss the challenges of AI technologies, such as data quality, transferability, explainability, and suggest future directions to overcome them. Artificial intelligence (AI) in machine tools offers diverse advantages, including learning and optimizing machining processes, compensating errors, saving energy, and preventing failures. Various AI techniques have been proposed and applied; however, many challenges still exist that inhibit the use of AI for machining tasks. This paper deals with different types and usage of AI technologies in machining operations such as predictive modelling, parameter optimization and control, chatter stability, tool wear, and energy conservation. We discuss the challenges of AI technologies, such as data quality, transferability, explainability, and suggest future directions to overcome them. KCI Citation Count: 2 |
| Author | Kim, Dong Chan Lee, Jihyun Park, Simon S. Chuo, Yu Sung Noh, In Woong Mun, Chang Hyeon Rezvani, Sina Lee, Ji Woong Lee, Sang Won |
| Author_xml | – sequence: 1 givenname: Yu Sung surname: Chuo fullname: Chuo, Yu Sung organization: Department of Mechanical and Manufacturing Engineering, University of Calgary – sequence: 2 givenname: Ji Woong surname: Lee fullname: Lee, Ji Woong organization: Department of Mechanical Engineering, Graduate School, Sungkyunk-wan University – sequence: 3 givenname: Chang Hyeon surname: Mun fullname: Mun, Chang Hyeon organization: Department of Mechanical Engineering, Ulsan National Institute of Science and Technology – sequence: 4 givenname: In Woong surname: Noh fullname: Noh, In Woong organization: Department of Mechanical Engineering, Graduate School, Sungkyunk-wan University – sequence: 5 givenname: Sina surname: Rezvani fullname: Rezvani, Sina organization: Department of Mechanical and Manufacturing Engineering, University of Calgary – sequence: 6 givenname: Dong Chan surname: Kim fullname: Kim, Dong Chan organization: Department of Mechanical Engineering, Ulsan National Institute of Science and Technology – sequence: 7 givenname: Jihyun surname: Lee fullname: Lee, Jihyun organization: Department of Mechanical and Manufacturing Engineering, University of Calgary – sequence: 8 givenname: Sang Won surname: Lee fullname: Lee, Sang Won organization: Department of Mechanical Engineering, Graduate School, Sungkyunk-wan University – sequence: 9 givenname: Simon S. surname: Park fullname: Park, Simon S. email: simon.park@ucalgary.ca organization: Department of Mechanical and Manufacturing Engineering, University of Calgary |
| BackLink | https://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART002804482$$DAccess content in National Research Foundation of Korea (NRF) |
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| Copyright | The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2022 The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2022. |
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| SubjectTerms | Artificial intelligence Control Control stability Dynamical Systems Energy conservation Engineering Industrial and Production Engineering Invited Review Article Machine tools Machining Mechanical Engineering Optimization Prediction models Tool wear Vibration 기계공학 |
| Title | Artificial intelligence enabled smart machining and machine tools |
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