Adaptive control simulation to optimize metal removal for rough turning

Journal of Engineering Research

S. E. Oraby¹  A. M. Alaskari² 

[1] Training;Associate ProfessorDepartment of Manufacturing Engineering TechnologyCollege of Technical studiesPublic Authority for Applied Education &

关键词: Adaptive control;    consumed power;    cutting forces;    cutting tool wear;    metal removal rate.;   

DOI  :  

学科分类：社会科学、人文和艺术（综合）

来源: Kuwait University * Academic Publication Council

【 摘 要 】

In conventional numerical control machining, it is not possible to change the preset operating parameters in the program during the machining cycle. By contrast, the adaptive control technique uses real-time sensing to continuously and instantaneously adjust the operative feed and/or the speed parameters to their optimal levels in order to ensure a more productive operation. In this study, a model-based adaptive control simulation strategy is proposed to optimize metal removal during rough turning by efficiently utilizing the available power resources within a safe machining environment. The approach is based on recursive continuous iterations to predict the instantaneous level of edge wear, together with the corresponding cutting forces and the consumed power, by considering the relevant models. The best speed-feed pair that provides the maximum metal removal rate without violating the imposed forces and power constraints is selected. Procedures are repeated for subsequent cut intervals by considering cumulative edge wear from preceding intervals until the accumulated edge wear level reaches the specified criterion value. The performance of the proposed model-based method was verified through comparisons with several conventional fixed-parameter wear-time methods. Results proved the superiority of the proposed AC disparate-parameter procedures in terms of noticeable greater productivity as the entire available machine power was exploited with a safe machining environment with reduced cost of the replacement cycle.

【 授权许可】

Unknown