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Finite element analysis and response surface method for robust multi-performance optimization of radial turning of hard 300M steel

机译:有限元分析及响应径向转动硬300m钢的多功能优化

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摘要

300M steel is commonly used in the automotive and aerospace industries due to its high strength and fatigue life. Residual stresses induced by machining of these materials can greatly affect the fatigue life. The present research aims mainly to develop an optimization strategy to identify optimal cutting parameters to improve machining characteristics and residual stresses induced by radial orthogonal turning 300M steel. To achieve this, first, a predictive finite element tool is developed to model cutting temperature, cutting and thrust forces, and residual stresses. Orthogonal turning experiment is conducted to measure machining forces, chip thickness, and residual stresses to validate the developed finite element model. The validated model is then used to construct response functions (meta models) using combined design of experiment and response surface method for practical and efficient implementation of the optimization problem. Finally, the developed response functions are utilized to formulate various multi-objective optimization problems. A hybrid optimization algorithm combining genetic algorithm and sequential quadratic programming method is employed to solve optimization problems in order to identify optimal cutting conditions and tool geometry. The results show that for unconstrained optimization problems, the percentage improvement in the total objective function is greater than that of constraint optimization ones. For multi-objective optimizations, it is found that weighting factors affect the optimum values of the total objective function more than those of the machining parameters. Since very few efforts were exerted to perform finite element modeling, experimental tests, and multi-performance optimization of machining characteristics and residual stresses induced by orthogonal turning 300M steel, the present results can be utilized as a reference for future works along this filed.
机译:由于其高强度和疲劳生活,汽车和航空航天行业常用300米钢。通过加工诱导的这些材料诱导的残余应力可以极大地影响疲劳寿命。本研究主要目的是开发一种优化策略,以确定最佳的切削参数,以提高径向正交转动300m钢的加工特性和残余应力。为此,首先,开发预测有限元工具以模拟切割温度,切割和推力力,以及残余应力。进行正交转动实验以测量加工力,芯片厚度和残余应力,以验证开发的有限元模型。然后使用验证的模型来使用组合设计来构建响应函数(元模型),用于实际和有效地实现优化问题。最后,利用开发的响应函数来制定各种多目标优化问题。一种结合遗传算法和顺序二次编程方法的混合优化算法来解决优化问题,以识别最佳切削条件和工具几何形状。结果表明,对于不受约束的优化问题,总目标函数的提高大于约束优化的百分比。对于多目标优化,发现加权因子影响总目标函数的最佳值,而不是加工参数的最佳值。由于施加了很少的努力来执行有限元建模,实验测试和通过正交转动300m钢诱导的加工特性和残余应力的多功能优化,因此可以使用本结果作为未来作品的参考。

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