Self-adjusting on-line cutting condition for high-speed milling process
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DOI 10.1007/s12206-020-0726-y
Journal of Mechanical Science and Technology 34 (8) 2020 Original Article DOI 10.1007/s12206-020-0726-y Keywords: · ANOVA · Artificial neural network · High speed machining · Self-adjustment · Self-HSM
Correspondence to: Ngoc-Hien Tran [email protected]
Citation: Hoang, T.-D., Nguyen, Q.-V., Nguyen, V.C., Tran, N.-H. (2020). Self-adjusting online cutting condition for high-speed milling process. Journal of Mechanical Science and Technology 34 (8) (2020) ?~?. http://doi.org/10.1007/s12206-020-0726-y
Self-adjusting on-line cutting condition for high-speed milling process Tien-Dung Hoang1, Quang-Vinh Nguyen2, Van-Cuong Nguyen2 and Ngoc-Hien Tran2 1
2
Faculty of Mechanical Engineering, Hanoi University of Industry, Hanoi, Vietnam, Faculty of Mechanical Engineering, University of Transport and Communications, Hanoi, Vietnam
Abstract
The paper presents an intelligent control system for self-adjusting on-line cutting condition for high speed machining (self-HSM) with considering the tool-wear amount to keep the machined product’s quality in allowable limit. For realizing the self-HSM, the empirical analysis of variance (ANOVA) and artifical neural network (ANN) are used. The ANOVA is used for generating the empirical functions which are used as the boundary condition as well as constraint evaluation. The ANN is used for generating the new optimal cutting condition. Then, the self-HSM updates this cutting condition on the real machine - HS Super MC500. The new optimal cutting parameter is sent to the controller for updating the new machining condition to keep the machined part’s quality. The integration of the empirical analysis and ANN enables generating the optimal cutting parameters correctly and efficiently for high-speed milling.
Received July 28th, 2019 Revised
December 12th, 2019
Accepted February 5th, 2020 † Recommended by Editor Hyung Wook Park
© The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2020
1. Introduction High speed machining (HSM) has been used widely as a strategic weapon for manufacturing applications due to advanced characteristics such as high material removal rate as well as high machining productivity. In comparison with the traditional machining, the HSM process has great cutting speed as well as great feed rate [1]. These are many factors affecting the machined part’s quality, including the machine characteristics, workpiece properties, cutting tool, and cutting process parameters. The disturbances during machining are the changes of these factors which are not in the predetermined plan. Among these factors, cutting parameter or process parameter is the one that can be economically controlled in order to obtain the optimum machining outcome. However, it is impossible to build the theoretical equations that demonstrate the effects of machining factors on the cutting tool vibration, surface roughness, cutting force, and tool wear, which are considered as the machining responses. Therefore, in this
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