迟玉伦,范志辉,葛爱丽,李怡霖.面向绿色高表面质量制造的硬态车削工艺多目标参数优化[J].表面技术,2023,52(9):278-293.
CHI Yu-lun,FAN Zhi-hui,GE Ai-li,LI Yi-lin.Multi-objective Parameter Optimization of Hard Turning Process for Green High Surface Quality Manufacturing[J].Surface Technology,2023,52(9):278-293 |
| 面向绿色高表面质量制造的硬态车削工艺多目标参数优化 |
| Multi-objective Parameter Optimization of Hard Turning Process for Green High Surface Quality Manufacturing |
| 投稿时间：2022-11-06 修订日期：2023-03-13 |
| DOI：10.16490/j.cnki.issn.1001-3660.2023.09.024 |
| 中文关键词: 硬态车削 绿色制造 表面粗糙度 碳排放量模型 广义回归神经网络 多目标参数优化 试验研究 |
| 英文关键词:hard turning green manufacturing surface roughness carbon emission model generalized regression neural network multi-objective parameter optimization experimental study |
| 基金项目:国家自然科学基金项目(51605294) |
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| 中文摘要: |
| 目的 为了进行硬态车削绿色制造与工艺性能协同优化研究，提出一种同时考虑碳排放量和表面粗糙度的多目标优化方法。方法 首先，通过分析硬态车削过程中切削参数、工件材料、刀具材料等因素对切削功率的影响建立碳排放目标函数，针对工件的表面粗糙度受到切削条件、工件材料、刀具材料等诸多因素的影响，利用正交试验和广义回归神经网络建立轴承硬态车削表面粗糙度目标函数。然后，考虑加工过程中机床特性和硬车实际工况等约束条件，建立以切削参数为优化变量，以碳排放量和表面粗糙度为优化目标的多目标优化模型，引入权重系数将其转化为单目标优化模型。最后，利用遗传算法对优化模型进行优化求解，深入分析切削参数对优化目标的影响。结果 在工厂实际轴承产品硬车试验中验证了优化模型的有效性，结果表明，切削速度为225 m/min、进给量为0.08 mm/r、背吃刀量为0.10 mm时，碳排放量和表面粗糙度的综合优化指标最低。相比优化前，虽然碳排放量上升了13.05%，但表面质量提升了34.44%。结论 研究结果对面向绿色制造的轴承硬车工艺参数优化提供理论方法有重要意义。 |
| 英文摘要: |
| In the production of manufacturing industry, the manufacturing process of workpiece produces a large amount of energy consumption and material consumption, which has a serious impact on the environment. With continuous emphasis of the government on energy conservation and emission reduction, enterprises have increasingly attached importance to the green optimization of product processing. Hard turning is a widely used processing technology in the bearing processing process at present. How to optimize the cutting parameters to improve product quality and control carbon emissions is a hot issue to be solved in the manufacturing industry. In order to study the collaborative optimization of hard turning green manufacturing and process performance, a multi-objective optimization model considering both carbon emissions and surface roughness is proposed in this paper. This model can provide an effective solution for improving product surface quality and controlling carbon emission. Firstly, the carbon emission target function was established by analyzing the influence of cutting parameters, workpiece materials, tool materials and other factors on the cutting power during hard turning. In view of the fact that the surface roughness of the workpiece was affected by many factors such as cutting conditions, workpiece materials, tool materials, etc., the objective function of bearing hard turning surface roughness was established by using orthogonal experiments and generalized regression neural network. The model could well deal with the nonlinear relationship between cutting parameters and surface roughness. The genetic algorithm was used to optimize the structural parameters of the generalized regression neural network surface roughness prediction model. The accuracy of the prediction model was improved. Then, considering the constraint conditions such as the characteristics of the machine tool and the actual working conditions of the hard turning during the machining process, a multi-objective optimization model with cutting parameters as the optimization variables and carbon emissions and surface roughness as the optimization objectives was established. The weight coefficient was introduced to convert it into a single-objective optimization model. Finally, the genetic algorithm was used to optimize the optimization model, and the influence of cutting parameters on the optimization objectives was analyzed in depth. The effectiveness of the optimization model was verified in the hard turning test of the actual bearing products in the factory. The results showed that when the cutting speed was 225 m/min, the feed rate was 0.08 mm/r, and the cutting depth was 0.10 mm, the comprehensive optimization index of carbon emissions and surface roughness was the lowest. Compared with the previous optimization, although the carbon emission was increased by 13.05%, the surface quality was improved by 34.44%. The bearing surface quality was improved and the increase of carbon emission during processing was controlled. Through the single factor influence analysis method, it was found that the impact of each cutting parameter on the comprehensive optimization index was from highness to lowness:feed rate, cutting speed, cutting depth. The conclusion of this paper is that the research results are of great significance for improving the surface quality of hard turning bearing and optimizing the process parameters of green manufacturing. |
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