Artificial intelligence enabled smart machining and machine tools

被引:0
|
作者
Yu Sung Chuo
Ji Woong Lee
Chang Hyeon Mun
In Woong Noh
Sina Rezvani
Dong Chan Kim
Jihyun Lee
Sang Won Lee
Simon S. Park
机构
[1] University of Calgary,Department of Mechanical and Manufacturing Engineering
[2] Sungkyunk-wan University,Department of Mechanical Engineering, Graduate School
[3] Ulsan National Institute of Science and Technology,Department of Mechanical Engineering
关键词
Artificial intelligence; Industry 4.0; Machine learning; Machine tools; Machining;
D O I
暂无
中图分类号
学科分类号
摘要
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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页码:1 / 23
页数:22
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