赵嫚,王荣,茅健,张立强.马氏体合金钢3J33b磨削力热载荷–晶粒尺寸演变的动态迭代作用机制研究[J].表面技术,2023,52(7):217-230.
ZHAO Man,WANG Rong,MAO Jian,ZHANG Li-qiang.Dynamic Iteration Mechanism of Force and Heat-Grain Size Evolution in Micro-grinding of Maraging Steel 3J33b[J].Surface Technology,2023,52(7):217-230 |
| 马氏体合金钢3J33b磨削力热载荷–晶粒尺寸演变的动态迭代作用机制研究 |
| Dynamic Iteration Mechanism of Force and Heat-Grain Size Evolution in Micro-grinding of Maraging Steel 3J33b |
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| DOI：10.16490/j.cnki.issn.1001-3660.2023.07.019 |
| 中文关键词: 磨削力 磨削热 晶粒尺寸演变 流动应力 循环迭代 灵敏度 |
| 英文关键词:grinding force grinding heat grain size evolution flow stress iterative sensitivity |
| 基金项目:上海浦江人才计划(20PJ1404700);上海工程技术大学青年科研团队培育计划(QNTD202112);国家自然科学基金青年项目(52205485) |
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| Author | Institution |
| ZHAO Man | School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China |
| WANG Rong | School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China |
| MAO Jian | School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China |
| ZHANG Li-qiang | School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China |
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| 中文摘要: |
| 目的 探究微磨削过程中微磨削力热–晶粒尺寸演变的动态迭代作用机制，构建考虑动态迭代作用的磨削力热模型，提高微磨削力热的预测精度。方法 基于金属材料再结晶理论，探究磨削各阶段弹塑性变形作用下材料晶粒尺寸的演变规律。基于微观组织增强理论，分析微磨削加工过程中材料的流动应力，揭示多颗磨粒重复加载作用下晶粒尺寸演变–微磨削力热的动态迭代作用机制，通过微磨削实验进行实验验证。最后基于灵敏度分析，探究不同工艺参数组合下马氏体合金钢塑性变形和材料去除机理。结果 考虑动态迭代作用的磨削力预测模型的预测值在切向力的平均相对误差为31.51%，法向上的平均误差为27.40%，传统模型磨削力预测值切向力平均相对误差为40.82%，法向力平均相对误差为39.54%。考虑微磨削力热–晶粒尺寸演变的动态迭代作用的磨削温度最大温度预测值的平均相对误差为12.97%，而传统的磨削温度预测值的平均相对误差为16.14%。磨削力随着线速度的增大而减小，随着进给量、磨削深度和晶粒尺寸的增大而增大。磨削温度随着线速度和磨削深度的增大而增大，随着进给量和晶粒尺寸的增大而减小。结论 考虑微磨削力热–晶粒尺寸演变的动态迭代作用的磨削力热预测模型比传统力热预测模型更接近于实验值，预测精度更高。 |
| 英文摘要: |
| The work aims to investigate the dynamic iteration mechanism between grain size evolution-force and heat during micro-grinding of maraging steel 3J33b to achieve the prediction and optimization of micro-grinding force and temperature. Firstly, the mathematical model between grain size and strain and strain rate after recrystallization was obtained based on the exponential model of the image-only theory. Then, the behavior of dynamic recrystallization was described based on the JAMK theory to obtain the volume fraction of recrystallization, and finally the average grain size of the material was established by assuming that the grains were randomly distributed with and without recrystallization. Based on the established grain size evolution model, the material flow stresses during micro-grinding were analyzed based on the microstructure enhancement theory. The grinding force model for each stage of grinding was analyzed from individual grains, containing slip friction force, plowing force and chip formation force. The grinding force model of the whole grinding bar was then established based on the wheel morphology of the micro-grinding bar. The grinding heat model was established based on the causes of grinding heat generation and the heat flow distribution principle. Further, the mathematical model between the grain size and grinding force and heat was established to reveal the coupling relationship between them. The experiments were conducted for 3J33 high-elastic alloy steel, and orthogonal grinding experiments were carried out by a micro-grinder, and the experimental results were used for the validation of the model. The grinding force values were obtained by Kistler 9256C2, and the signals were collected and analyzed by Labview data acquisition system. The grinding temperature was measured by a K-type thermocouple. Finally, the obtained experimental data were compared with the predicted data of the model. It was found that the average relative error between the predicted and experimental values of the grinding force prediction model considering the dynamic iterative effect of the heat and force-grain size evolution of the micro-grinding was 31.51% in the tangential direction and 27.40% in the normal direction, while the average error of the conventional grinding force prediction model was 40.82% in the tangential direction and 39.54% in the normal direction. The average relative error between the maximum temperature prediction of grinding temperature considering the dynamic iterative effect of heat and force-grain size evolution of micro-grinding and the experimental value was 12.97%, while the average relative error between the conventional prediction of grinding temperature and the experimental value was 16.14%. Therefore, the experimental validation shows that the values predicted by the grinding force and heat prediction model considering the dynamic iterative effect of micro-grinding force and heat -grain size evolution are closer to the experimental values than the conventional force and heat prediction model and had higher prediction accuracy. Finally, the effects of process parameters and material grain size on grinding force and heat are investigated by sensitivity analysis. The deformation and removal mechanism of the material in micro-grinding of maraging alloy steel is analyzed by response surface curves. It is found that the grinding force decreases with the increase of linear speed and increases with the increase of feed, grinding depth and grain size. The grinding temperature increases with the increase of linear speed and grinding depth, and decreases with the increase of feed and grain size. |
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