从政, 曹岩, 贺志昊, 楚治国. TC11钛合金力热耦合仿真分析及双目标参数优化[J]. 制造技术与机床, 2022, (1): 119-123. DOI: 10.19287/j.cnki.1005-2402.2022.01.022
引用本文: 从政, 曹岩, 贺志昊, 楚治国. TC11钛合金力热耦合仿真分析及双目标参数优化[J]. 制造技术与机床, 2022, (1): 119-123. DOI: 10.19287/j.cnki.1005-2402.2022.01.022
CONG Zheng, CAO Yan, HE Zhihao, CHU Zhiguo. Mechanical and thermal coupling simulation analysis and dual-objective parameter optimization of TC11 titanium alloy[J]. Manufacturing Technology & Machine Tool, 2022, (1): 119-123. DOI: 10.19287/j.cnki.1005-2402.2022.01.022
Citation: CONG Zheng, CAO Yan, HE Zhihao, CHU Zhiguo. Mechanical and thermal coupling simulation analysis and dual-objective parameter optimization of TC11 titanium alloy[J]. Manufacturing Technology & Machine Tool, 2022, (1): 119-123. DOI: 10.19287/j.cnki.1005-2402.2022.01.022

TC11钛合金力热耦合仿真分析及双目标参数优化

Mechanical and thermal coupling simulation analysis and dual-objective parameter optimization of TC11 titanium alloy

  • 摘要: 针对TC11钛合金材质的某型航天盘类零件难加工问题,采用ABAQUS仿真平台,基于实际切削参数,对车削过程中的切削力、热分布规律以及切削参数交互作用进行了研究。首先采用单因素实验,探究了切削力和热分布规律,其次采用正交实验法,研究了交互作用下的切削力和热分布规律,并拟合出切削力、热的多元线性回归模型,最后对回归模型进行双目标优化。实验结果表明,切削用量对切削力的影响排序为:切削深度>进给量>切削速度,对切削温度的影响排序为进给量>切削速度>切削深度。回归模型高度拟合试验,算法寻优可以有效地优化切削参数,降低切削力和切削热。

     

    Abstract: Aiming at the difficult machining problem of a certain type of space disk parts made of TC11 titanium alloy, ABAQUS simulation software was used. Based on the actual cutting parameters, the cutting force, heat distribution law and the interaction of cutting parameters during the turning process were analyzed. First, a single factor experiment was used to explore the law of cutting force and heat distribution. Secondly, the orthogonal experiment method is used to study the cutting force and heat distribution law under the interaction, and to fit the multiple linear regression model of the cutting force and heat. Finally, the regression model is optimized with two goals. The experimental results show that the order of the effect of cutting amount on cutting force is: depth of cut>feed>cutting speed, and the order of influence on cutting temperature is feed>cutting speed>depth of cut. The regression model is highly fitting test, and the algorithm optimization can effectively optimize the cutting parameters and reduce the cutting force and cutting heat.

     

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