| 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],53(24):154-164 |
| Optimization of Surface Roughness Resulting from Surface Mechanical Rolling of 1060 Aluminum by Coupling GA-BP-ANN with RSM |
| Received:March 07, 2024 Revised:June 23, 2024 |
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| DOI：10.16490/j.cnki.issn.1001-3660.2024.24.014 |
| KeyWord:surface roughness surface mechanical rolling treatment 1060 aluminum response surface methodology (RSM) GA-BP-ANN |
| Author | Institution |
| LIU Xiang |
School of Mechanical and Electrical Engineering, Anhui University of Science and Technology, Anhui Huainan , China |
| WANG Cheng |
School of Mechanical and Electrical Engineering, Anhui University of Science and Technology, Anhui Huainan , China |
| WANG Senhui |
School of Mechanical and Electrical Engineering, Anhui University of Science and Technology, Anhui Huainan , China |
| SUN Kun |
School of Mechanical and Electrical Engineering, Anhui University of Science and Technology, Anhui Huainan , China |
| FEI Shuhui |
School of Mechanical and Electrical Engineering, Anhui University of Science and Technology, Anhui Huainan , China |
| LI Baokun |
School of Mechanical and Electrical Engineering, Anhui University of Science and Technology, Anhui Huainan , China |
| DENG Haishun |
School of Mechanical and Electrical Engineering, Anhui University of Science and Technology, Anhui Huainan , China |
| SHEN Gang |
School of Mechanical and Electrical Engineering, Anhui University of Science and Technology, Anhui Huainan , China |
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| Abstract: |
| 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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