FE modeling and optimization of cutting temperature in orthogonal turning

Abstract eng:
Cutting difficult-to-machine materials has always been a challenging task for the industry. It is known that that high cutting temperature leads to inaccuracies in component dimensions, phase transformation, and tensile residual stresses, while large cutting forces cause high tool wear and power consumption of turning machines. Accordingly, the present study aims at the design optimization of machining parameters including cutting speed, feed rate, edge radius and rake angle using a combination of Genetic Algorithm (GA) and Sequential Quadratic Programming (SQP) technique in order to accurately capture the global minimum value for the cutting temperature against the cutting force constraint. The results demonstrate that low feed and edge radius along with a roughly small magnitude of cutting speed and an intermediate value of rake angle lead to the smallest cutting temperature when the resultant cutting force is limited to a specific value.

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