A sensor fusion and support vector machine based approach for recognition of complex machining conditions
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DOI: 10.1007/s10845-016-1209-y
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Cited by:
- Yanxi Zhang & Deyong You & Xiangdong Gao & Congyi Wang & Yangjin Li & Perry P. Gao, 2020. "Real-time monitoring of high-power disk laser welding statuses based on deep learning framework," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 799-814, April.
- Anshuman Kumar Sahu & Siba Sankar Mahapatra, 2021. "Prediction and optimization of performance measures in electrical discharge machining using rapid prototyping tool electrodes," Journal of Intelligent Manufacturing, Springer, vol. 32(8), pages 2125-2145, December.
- Xiang Zhu & Yunqiu Zhang, 2020. "Co-word analysis method based on meta-path of subject knowledge network," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(2), pages 753-766, May.
- Danil Yu Pimenov & Andres Bustillo & Szymon Wojciechowski & Vishal S. Sharma & Munish K. Gupta & Mustafa Kuntoğlu, 2023. "Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2079-2121, June.
- Xin Tong & Qiang Liu & Shiwei Pi & Yao Xiao, 2020. "Real-time machining data application and service based on IMT digital twin," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1113-1132, June.
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Keywords
Intelligent machining; Machining condition recognition; Sensor fusion; Support vector machine; Wavelet decomposition;All these keywords.
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