柳想,王成,汪森辉,孙坤,费树辉,李保坤,邓海顺,沈刚.GA-BP-ANN耦合RSM优化表面机械滚压1060铝表面粗糙度[J].表面技术,2024,53(24):154-164.
LIU Xiang,WANG Cheng,WANG Senhui,SUN Kun,FEI Shuhui,LI Baokun,DENG Haishun,SHEN Gang.Optimization of Surface Roughness Resulting from Surface Mechanical Rolling of 1060 Aluminum by Coupling GA-BP-ANN with RSM[J].Surface Technology,2024,53(24):154-164 |
| GA-BP-ANN耦合RSM优化表面机械滚压1060铝表面粗糙度 |
| Optimization of Surface Roughness Resulting from Surface Mechanical Rolling of 1060 Aluminum by Coupling GA-BP-ANN with RSM |
| 投稿时间：2024-03-07 修订日期：2024-06-23 |
| DOI：10.16490/j.cnki.issn.1001-3660.2024.24.014 |
| 中文关键词: 表面粗糙度 表面机械滚压 1060铝 响应面法 GA-BP-ANN |
| 英文关键词:surface roughness surface mechanical rolling treatment 1060 aluminum response surface methodology (RSM) GA-BP-ANN |
| 基金项目:国家自然科学基金(U21A20122, U21A20125);高端激光制造装备省部共建协同创新中心开放项目(JGKF-202202);安徽理工大学研究生创新创业项目(2023CX2078) |
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| Author | Institution |
| LIU Xiang | School of Mechanical and Electrical Engineering, Anhui University of Science and Technology, Anhui Huainan 232001, China |
| WANG Cheng | School of Mechanical and Electrical Engineering, Anhui University of Science and Technology, Anhui Huainan 232001, China |
| WANG Senhui | School of Mechanical and Electrical Engineering, Anhui University of Science and Technology, Anhui Huainan 232001, China |
| SUN Kun | School of Mechanical and Electrical Engineering, Anhui University of Science and Technology, Anhui Huainan 232001, China |
| FEI Shuhui | School of Mechanical and Electrical Engineering, Anhui University of Science and Technology, Anhui Huainan 232001, China |
| LI Baokun | School of Mechanical and Electrical Engineering, Anhui University of Science and Technology, Anhui Huainan 232001, China |
| DENG Haishun | School of Mechanical and Electrical Engineering, Anhui University of Science and Technology, Anhui Huainan 232001, China |
| SHEN Gang | School of Mechanical and Electrical Engineering, Anhui University of Science and Technology, Anhui Huainan 232001, China |
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| 中文摘要: |
| 目的 优化表面机械滚压工艺参数组合，获得最小表面粗糙度。方法 基于气压驱动表面机械滚压实验平台，以1060铝棒为研究对象，采用响应面法(RSM)设计试验研究驱动压力、滚压道次、试样转速对受滚压铝棒试样表面粗糙度的影响规律，并利用遗传算法结合反向传播人工神经网络(GA-BP-ANN)机器学习模型预测不同工况参数组合对应的表面粗糙度，并通过实验对该模型进行有效性验证。基于GA-BP-ANN预测结果，在给定参数范围内构造多个随机小范围响应面，通过分析这些随机小范围RSM优化结果的聚集程度，实现GA-BP-ANN耦合RSM优化。结果 单个响应面优化的最佳工艺参数组合为0.074 MPa的驱动压力、5个滚压道次、435.4 r/min的试样转速，预测的表面粗糙度(Ra)为0.45 µm，但该工况下实验测量的表面粗糙度为0.53 µm，且非最小值;而GA-BP-ANN耦合RSM优化的工况组合为0.073 MPa的驱动压力、4个滚压道次、286.9 r/min的试样转速，预测的表面粗糙度为0.31 µm，相同工况下实验测量结果为0.36 µm。结论 与单个RSM优化结果相比，采用GA-BP-ANN耦合RSM能够更加有效地优化气压驱动表面机械滚压工艺参数组合，获得更小的表面粗糙度。 |
| 英文摘要: |
| Surface mechanical rolling treatment (SMRT) can effectively reduce the surface roughness of workpiece, whereas it has always been a challenge in the field of surface engineering how to optimize the combination of process parameters to achieve the minimum surface roughness. Based on the experiment platform of air pressure-driven SMRT, and with 1060 aluminum rods as research objects, the influences of air pressure, rolling pass and rotation speed on the surface roughness of the SMRTed rod samples were investigated with the response surface methodology (RSM), and the genetic algorithm combined with back propagation artificial neural network (GA-BP-ANN) machine learning model was developed to predict the surface roughness corresponding to different combinations of process parameters, and the model was experimentally validated. With the GA-BP-ANN prediction results, multiple random small-scale response surfaces were constructed within the given range of process parameters to optimize the combination of the process parameters to obtain the minimum surface roughness. By analyzing the aggregation degree of these random small-scale RSM optimization results, a novel optimization approach by coupling GA-BP-ANN with RSM was proposed. Making use of a single response surface, the RSM-optimized combination of the process parameters included the air pressure of 0.074 MPa, five rolling passes and the rotation speed of 435.4 r/min, and the value of Ra predicted by RSM was 0.45 µm. Under the same condition, the experimentally measured value of Ra was 0.53 µm, and it was obvious not the minimum. The optimized combination of process parameters by coupling GA-BP-ANN with RSM included the air pressure of 0.073 MPa, 4 rolling passes and rotation speed of 286.9 r/min, and the resultantly-predicted surface roughness was 0.31 µm, which was slightly smaller than the experimental results of 0.36 µm. The surface roughness corresponding to the combination of process parameters optimized by coupling GA-BP-ANN with RSM was significantly smaller than that resulting from the single RSM, indicating that optimization method of coupling GA-BP-ANN with RSM was more effective in comparison with the conventional single RSM optimization. In other words, the optimization method of coupling GA-BP-ANN with RSM was feasible and had higher efficiency. The main research ideas of this paper are as follows:Firstly, within the given parameter range, a single response surface method is used to optimize the parameters of surface mechanical rolling treatment process, predict the minimum surface roughness of 1060 aluminum rods, and carry out experimental verification. Secondly, based on the limited experimental results, the GA-BP-ANN model was developed to effectively predict the surface roughness of aluminum rods with different process parameters. Then, the optimization method of coupling GA-BP-ANN with RSM is proposed. Within the given range of process parameters, several small-scale response surfaces are randomly constructed, and the surface mechanical rolling process parameters are optimized by these small-scale response surfaces. Finally, the aggregation degree of these small-scale response surface optimization parameters is counted, and the surface mechanical rolling parameters within the given parameter range are predicted in order to obtain the minimum surface roughness, and the experimental verification is carried out. The optimization method of coupling GA-BP-ANN with RSM can provide a new solution for optimization problems in other fields. |
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