房丽雄,杨方燕,朱伏平,等.基于V形槽磨损状态预测的精研参数多目标优化[J].精密成形工程,2024,16(12):253-263.
FANG Lixiong,YANG Fangyan,ZHU Fuping,et al.Multi-objective Optimization of Precision Lapping Parameters Based on V-groove Wear State Prediction[J].Journal of Netshape Forming Engineering,2024,16(12):253-263. |
| 基于V形槽磨损状态预测的精研参数多目标优化 |
| Multi-objective Optimization of Precision Lapping Parameters Based on V-groove Wear State Prediction |
| 投稿时间：2024-01-19 |
| DOI：10.3969/j.issn.1674-6457.2024.12.023 |
| 中文关键词: 轴承钢球 精密研磨 磨损状态预测 工艺参数优化 多目标粒子群(MOPSO) |
| 英文关键词: bearing steel ball precision lapping wear state prediction process parameters optimization multi-objective particle swarm optimization (MOPSO) |
| 基金项目:西南科技大学博士基金(19zx7164) |
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| 中文摘要: |
| 目的 预测精研过程中V形槽的磨损，并联合多目标求解出不同状态下的最佳工艺参数，提高精研质量。方法 以直径3.175 mm的440C轴承钢球为研究对象，基于磨损指标转换结果采集工况数据，建立BPNN磨损状态预测模型，通过BBD试验拟合精研质量指标与影响因素间的量化关系，构建钢球精研参数多目标模型，并基于MOPSO优化求解得到最佳工艺参数组合，并进行试验验证。结果 成功构建磨损状态预测模型，相关性系数达0.999 8，得到了多目标模型的Pareto最优边界，解集均匀分散在整个磨损周期，并且当压力为2 485 N、转速为13.7 r/min、磨损值为1.37 mm时的最优尺寸、球形误差和表面粗糙度分别为3.175 9 mm、0.108 μm和0.014 1 μm。验证试验的成品尺寸和球形误差标准达到G5级，球表面粗糙度及表面缺陷标准达到G10级，对质量指标的预测精度均大于94%。结论 所构建的模型能够实现磨损状态的准确预测和因素与指标间的量化表达，通过参数多目标模型求解出不同磨损状态的最佳工艺参数，能够有效提高全磨损周期的精研加工质量。 |
| 英文摘要: |
| The work aims to predict the wear of V-grooves during precision lapping and combine multi-objective solutions to determine the optimal process parameters under different states, in order to improve the quality of precision lapping. A 440C bearing steel ball with a diameter of 3.175 mm was taken as the research object. Firstly, based on the conversion results of wear indicators, working condition data were collected to establish a BPNN wear state prediction model. Then, the quantitative relationship between precision lapping quality indicators and affecting factors was fitted through BBD experiments, and a multi-objective model of steel ball precision lapping parameters was constructed. Based on MOPSO optimization, the optimal process parameter combination was obtained and verified through tests. The correlation coefficient of the constructed wear state prediction model reached 0.999 8, and the Pareto optimal boundary of the multi-objective model was obtained. The solution set was uniformly dispersed throughout the entire wear cycle, and the optimal lapping quality was achieved when the pressure was 2 485 N, the speed was 13.7 r/min, and the wear value was 1.37 mm. Specifically, the size was 3.175 9 mm, the spherical error was 0.108 μm, and the surface roughness was 0.014 1 μm. The finished product size and spherical error standards in verification tests reached G5 level, and the surface roughness and surface defect standards of the ball reached G10 level. The prediction accuracy of quality indicators was greater than 94%. The conclusion is that the constructed model can accurately predict the wear state and quantify the expression of factors and indicators. Solving the optimal process parameters for different wear states through a multi-objective model of parameters can effectively improve the precision lapping quality during the entire wear cycle. |
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