Proceedings of the
9th International Conference of Asian Society for Precision Engineering and Nanotechnology (ASPEN2022)
15 – 18 November 2022, Singapore
doi:10.3850/978-981-18-6021-8_OR-09-0138
A Tool Protection Strategy for Digital Turning Process Based on Simulation Data
1School of Mechanical Engineering, Yeungnam University, 280, Daehak-ro, Gyeongsan-si, Gyeongsangbuk-do, 712-749, Republic of Korea
ABSTRACT
This paper presents a tool protection strategy for turning titanium alloy. The orthogonal table (L53) is taken as the simulation sample point in the design space, and the actual cutting force is compared through experiments to verify the reliability of the simulation results. A reliable response surface model (Rf2=0.980, RMSf=0.047, Rt2=0.948, RMSt==0.024) was obtained by extracting the simulated cutting force and the simulated tool tip temperature as the inputs of the surrogate model. The embedded Non-dominated Sorting Genetic Algorithms (NSGA-II) takes the minimum force and temperature as the objective, so as to realize the optimization of cutting parameters input and the minimization control of cutting force and tool tip temperature under different material removal rates. The results show that the strategy is effective and can be used as a reference module to help the transformation of digital manufacturing industry.
Keywords: Turning titanium alloy, Simulated cutting force, Simulated temperature, Optimization algorithm
1School of Mechanical Engineering, Yeungnam University, 280, Daehak-ro, Gyeongsan-si, Gyeongsangbuk-do, 712-749, Republic of Korea
ABSTRACT
This paper presents a tool protection strategy for turning titanium alloy. The orthogonal table (L53) is taken as the simulation sample point in the design space, and the actual cutting force is compared through experiments to verify the reliability of the simulation results. A reliable response surface model (Rf2=0.980, RMSf=0.047, Rt2=0.948, RMSt==0.024) was obtained by extracting the simulated cutting force and the simulated tool tip temperature as the inputs of the surrogate model. The embedded Non-dominated Sorting Genetic Algorithms (NSGA-II) takes the minimum force and temperature as the objective, so as to realize the optimization of cutting parameters input and the minimization control of cutting force and tool tip temperature under different material removal rates. The results show that the strategy is effective and can be used as a reference module to help the transformation of digital manufacturing industry.
Keywords: Turning titanium alloy, Simulated cutting force, Simulated temperature, Optimization algorithm
