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ABSTRACT
Cutting vibration is affected by many factors and is nonlinear, so it is difficult to accurately establish a cutting vibration model by mathematical methods. This study uses BP neural network to predict the vibration of hardened steel turning. The vibration data of cutting under five parameters, such as cutting speed, feed rate, cutting depth, corner radius, and workpiece hardness, were collected. A BP neural network model was constructed by considering the prediction accuracy and running time, the model was trained based on the experiments data, and the prediction accuracy of the model was verified. It was found that the relative error of the predicted results is less than 10%, which is basically consistent with the vibration displacement under working conditions. The prediction of vibration amplitude provides a basis for parameter selection to improve the accuracy of hardened steel turning.
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No potential conflict of interest was reported by the authors.
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Notes on contributors
Guang-Jun Chen
Guang-jun Chen received the BS degree in Machinery Manufacture and Equipment from Jiamusi University, China, in 1997, and his MS degree in Mechanical Manufacture and Automation from Jiamusi University, China, in 2005, and his PhD degrees in Mechanical Manufacture and Automation from Harbin University of Science and Technology University, China, in 2011. He is a Professor in School of Mechanical Engineering, Tianjin University of Technology and Education and school of mechanical engineering, Jiamusi University. His research interests include Precision machining technology, sensor and testing technology, electromechanical integration technology.
Shuai Hou
Shuai Hou graduated in Mechanism design, manufacturing and automatization. from Jiamusi University, China, in 2018. Presently he is a Current Master Student from Jiamusi University, China. Major is Mechanical Manufacture and Automation. His current research interest is Precision machining technology.
Bing Yan
Bing Yan received the BS degree in agricultural engineering from the Beijing Agricultuer Engineering University, China, in 1990, and his MS degree in mechanical engineering from Tianjin University, China, in 1996, and his Ph.D degrees in Mechanical engineering from Tianjin University, China, in 1999. He is a Professor in School of Mechanical Engineering, Tianjin University of Technology and Education. His current research interest is Virtual manufacturing and cutting dynamics.
Ren-Ping Guo
Ren-ping Guo graduated in Mechanism design, manufacturing and automatization. from Jiamusi University, China, in 2019. Presently she is a Current Master Student from Nanjing University of aeronautics and astronautics, China. Her current research interest is Cutting dynamics.
Song-Xin Han
Song-xin Han graduated in Mechanism design, manufacturing and automatization. from Jiamusi University, China, in 2017. Presently he is a Current Master Student from Jiamusi University, China. Major is Mechanical Manufacture and Automation. His current research interest is Precision machining technology.
Liang Wang
Liang Wang graduated in Mechanism design, manufacturing and automatization. from Jiamusi University, China, in 2013, and his MS degree in Mechanical Manufacture and Automation from Jiamusi University, China, in 2016. His current research interest is Precision machining technology.
Guang-Xing Sun
Guang-xing Sun received the MS degree in Mechanical Manufacture and Automation from Tianjin University of Technology and Education, China, in 2018, and now he works in TIANJIN BAOLAI Precision Machinery Industry Group. His current research interest is Micro feed technology.