Artificial intelligence enabled smart machining and machine tools

Author(s):  
Yu Sung Chuo ◽  
Ji Woong Lee ◽  
Chang Hyeon Mun ◽  
In Woong Noh ◽  
Sina Rezvani ◽  
...  
Inventions ◽  
2018 ◽  
Vol 3 (3) ◽  
pp. 41 ◽  
Author(s):  
Chih-Wen Chang ◽  
Hau-Wei Lee ◽  
Chein-Hung Liu

Author(s):  
Guofa Li ◽  
Hongxiang Zhu ◽  
Jialong He ◽  
Yongchao Huo ◽  
Jiancheng Zhang

2021 ◽  
Vol 45 (5) ◽  
pp. 401-408
Author(s):  
Sungjae Yoon ◽  
Munyoung Lee ◽  
Jeonghwan Lee ◽  
Seong-hee Lee ◽  
Jungchan Na

2012 ◽  
Vol 271-272 ◽  
pp. 488-492
Author(s):  
Ji Ming Yan ◽  
Zhi Ping Guo ◽  
Hong Li Gao ◽  
Bei Bei Zheng ◽  
Yong Hong Dai ◽  
...  

This paper proposes to establish a CNC machine tool security system using artificial intelligence-based, and discusses the key technologies: the dynamic fuzzy neural network(DFNN), and based on this, describes an established security system of CNC machine tool.


Author(s):  
C. W. McCutchen ◽  
Lois W. Tice

Ultramicrotomists live in a state of guerilla warfare with chatter. This situation is likely to be permanent. We can infer this from the history of machine tools. If set the wrong way for the particular combination of cutting tool and material, most if not all machine tools will chatter.In more than 100 years since machine tools became common, no one has evolved a practical recipe that guarantees avoiding chatter. Rather than follow some single very conservative rule to avoid chatter in all cases, machinists detect it when it happens, and change conditions until it stops. This is possible because they have no trouble telling when their cutting tool is chattering. They can see chatter marks, and they can also hear a sometimes deafening noise.


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