| HUI Jizhuang,LUO Wei,YAN Zhiqiang,WANG Junjie,LYU Jingxiang,GUO Xu,ZHANG Hao.Surface Roughness Prediction, Optimization and Surface Morphology Analysis of AlSi10Mg by Selective Laser Melting[J],53(15):129-140, 151 |
| Surface Roughness Prediction, Optimization and Surface Morphology Analysis of AlSi10Mg by Selective Laser Melting |
| Received:October 20, 2023 Revised:December 21, 2023 |
| View Full Text View/Add Comment Download reader |
| DOI：10.16490/j.cnki.issn.1001-3660.2024.15.012 |
| KeyWord:surface roughness selective laser melting AlSi10Mg process parameter optimization surface morphology prediction model |
| Author | Institution |
| HUI Jizhuang |
School of Construction Machinery, Chang'an University, Xi'an , China |
| LUO Wei |
School of Construction Machinery, Chang'an University, Xi'an , China |
| YAN Zhiqiang |
School of Construction Machinery, Chang'an University, Xi'an , China |
| WANG Junjie |
School of Construction Machinery, Chang'an University, Xi'an , China |
| LYU Jingxiang |
School of Construction Machinery, Chang'an University, Xi'an , China |
| GUO Xu |
School of Construction Machinery, Chang'an University, Xi'an , China |
| ZHANG Hao |
School of Construction Machinery, Chang'an University, Xi'an , China |
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| Abstract: |
| The surface roughness of the part manufactured by selective laser melting (SLM) is a macroscopic manifestation of the surface morphology, and this indicator significantly affects the fatigue life of the part. Due to the complex and multi-physical intersection of in the SLM process, it is challenging and obstructive to establish the theoretical model between surface roughness and the process parameters. Therefore, the data-driven approach can be a better choice to study this phenomenon. In this work, the Bi-directional Long Short-Term Memory network combined with the Sparrow Search Algorithm was used to predict the surface roughness. Meantime, the applicability of the proposed method was demonstrated through the comparison with other models. Firstly, a three-factor and four-level full-factor test was carried out to AlSi10Mg materials by SLM technology, followed by the measurement of the surface roughness via a surface roughness meter. Then, the data-driven model was established based on the Bi-directional Long Short-Term Memory network and the test data were obtained, during which the laser power, scanning speed, and hatch spacing were input variable, and the surface roughness value was output variable. Next, the Genetic Algorithm was applied to obtain the best combination of process parameters, and the reliability of the prediction was verified experimentally. Finally, the surface morphology of the formed parts under different scanning speed, hatch spacing, and laser power was analyzed to investigate the effect of each process parameter and their coupling effect on the surface quality. The constructed model had Coefficient of Determination of 0.920 3, which indicated that the model had higher goodness of fit. The Root Mean Squared Error of 0.872 3 and the Mean Absolute Error of 0.785 7 meant that model had better applicability, and Residual Predictive Deviation of 2.429 6 meant higher reliability and stability. This model reduced the training time for a large number of model parameters, enhanced the global search ability, and effectively improved the training efficiency and generalization ability. The optimal configuration of hatch spacing, scanning speed and laser power was 0.12 mm, 1 800 mm/s and 280 W, respectively. By adjusting the process parameter combination for the experiment, the average surface roughness of the sample obtained was 10.897 μm, and the error was only 4.5% compared with the predicted value. The effect of process parameters on the surface morphology was in such an order:scanning speed>laser power>hatch spacing. In addition, each factor had a great effect on the surface morphology of the molded parts and the coupling of the three factors together determined the laser energy density. Too low laser energy density led to insufficient melting of the powder, resulting in powder adhesion, the molten pool was difficult to tightly overlap and the porosity also increased dramatically. Too high energy density made the molten pool state extremely unstable, prone to molten pool splash, porosity, overburning and other effects of the molten pool. Therefore, the data-driven prediction model constructed based on the optimized Bidirectional Long Short-Term Memory network of the Sparrow Search Algorithm is suitable for surface roughness prediction and optimization, which can achieve accurate prediction of surface roughness of the sample parts and guide the practice to ensure the machining quality. |
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