| ZHANG Lan,CHEN Min,GAO Jie,LI Wendong,WU Shibing.Surface Quality and Performance of 3D Printed 316L Landscape Design Components via Laser Polishing[J],54(3):162-170, 181 |
| Surface Quality and Performance of 3D Printed 316L Landscape Design Components via Laser Polishing |
| Received:March 16, 2024 Revised:September 26, 2024 |
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| DOI：10.16490/j.cnki.issn.1001-3660.2025.03.014 |
| KeyWord:3D printing landscape design laser polishing additive manufacture surface engineering microstructure and property |
| Author | Institution |
| ZHANG Lan |
Department of Art and Design, Guangdong Polytechnic, Guangdong Foshan , China |
| CHEN Min |
Department of Art and Design, Guangdong Polytechnic, Guangdong Foshan , China |
| GAO Jie |
Department of Art and Design, Guangdong Polytechnic, Guangdong Foshan , China |
| LI Wendong |
Department of Art and Design, Guangdong Polytechnic, Guangdong Foshan , China |
| WU Shibing |
Three Gorges Jinji Tianjin New Energy Power Generation Co., Ltd., Tianjin , China |
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| Abstract: |
| This study presents an innovative approach to enhancing the surface quality and overall performance of 3D-printed landscape design components made from selective laser melted (SLM) 316L stainless steel via laser surface polishing. Unlike traditional post-processing methods, laser polishing offers a more efficient and precise technique for achieving smoother surfaces and improving mechanical and corrosion resistance properties. To optimize the laser polishing parameters, an artificial neural network (ANN) model is applied to predict the optimal settings for reducing surface roughness. The optimized parameters are then applied to the laser polishing of SLM-fabricated components, and the polished surface's morphology, microstructure, mechanical properties, and corrosion resistance are systematically characterized. The artificial neural network model used in this study is designed to predict the ideal laser polishing parameters for minimizing surface roughness. The inputs to the ANN include laser power, scanning speed, and overlapping ratio, while the output is the surface roughness of the polished component. After training and validation using 729 data sets, the model identifies optimal polishing conditions:a laser power of 90 W, a scanning speed of 450 mm/s, and an overlap ratio of 21%. These parameters are used to perform laser polishing on SLM-produced 316L stainless steel components. Surface characterization is performed by 3D optical profiler and scanning electron microscopy (SEM), while the microhardness, residual stress, and corrosion resistance are assessed by Vickers hardness tests, X-ray diffraction (XRD), and electrochemical polarization tests, respectively. The laser polishing process successfully reduces the surface roughness of the SLM components from an initial Ra of over 4 μm to less than 0.15 μm. The polished layer, approximately 37.6 μm thick, consists of fine, equiaxed grains with the average grain size reduced from 1.37 μm to 0.88 μm. This significant grain refinement contributes to a 26.7% increase in microhardness, raising the hardness from 520HV to 660HV. Additionally, the residual tensile stress is drastically reduced from 55.7 MPa to just 4.17 MPa. These improvements are attributed to the rapid thermal cycling during laser polishing, which promotes the formation of a fine-grained, dense polished layer. In terms of corrosion resistance, the laser-polished components exhibit a remarkable reduction in corrosion current, from 2.91×10–3 A to 5.52×10–4 A, a decrease by an order of magnitude. This improvement is primarily due to the formation of a dense polished layer, which acts as a barrier against corrosive agent. The polished surface also shows fewer and shallower corrosion pits, indicating enhanced corrosion protection compared with the untreated SLM surface. This research makes several novel contributions to the field of laser polishing and additive manufacturing. First, the successful integration of an artificial neural network for process optimization represents a significant advancement in achieving precise control over the laser polishing parameters, leading to a dramatic improvement in surface quality. Second, the study elucidates the mechanisms behind the grain refinement and its impact on mechanical properties, demonstrating that the rapid solidification process inherent in laser polishing not only enhances hardness but also significantly reduces residual tensile stress. Third, the findings highlight the role of fine equiaxed grains and dense microstructure in boosting the corrosion resistance of SLM-fabricated components, providing new insights into the microstructural evolution during laser polishing. Laser surface polishing, combined with advanced machine learning techniques for parameter optimization, offers a highly effective method for enhancing the surface quality, hardness, and corrosion resistance of SLM 316L stainless steel components. The grain refinement and reduction in residual stress achieved through rapid solidification processes play a critical role in these performance enhancements. This study opens new pathways for the application of laser polishing in the manufacturing of high-performance 3D-printed components, particularly in fields requiring precise surface control and improved durability. |
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