Self-adjusting on-line cutting condition for high-speed milling process

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) an...

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Bibliographic Details
Published in:Journal of mechanical science and technology 2020, 34(8), , pp.3335-3343
Main Authors: Hoang, Tien-Dung, Nguyen, Quang-Vinh, Nguyen, Van-Cuong, Tran, Ngoc-Hien
Format: Article
Language:English
Subjects:
Boundary conditions
Control
Cutting parameters
Cutting speed
Cutting wear
Dynamical Systems
Empirical analysis
Engineering
High speed machining
Industrial and Production Engineering
Machine tools
Mechanical Engineering
Neural networks
Original Article
Tool wear
Variance analysis
Vibration
기계공학
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Summary: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.
ISSN:1738-494X
1976-3824
DOI:10.1007/s12206-020-0726-y