张兰,陈敏,高洁,李文东,吴士宾.激光抛光3D打印景观设计316L构件的表面质量及性能研究[J].表面技术,2025,54(3):162-170, 181.
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].Surface Technology,2025,54(3):162-170, 181 |
| 激光抛光3D打印景观设计316L构件的表面质量及性能研究 |
| Surface Quality and Performance of 3D Printed 316L Landscape Design Components via Laser Polishing |
| 投稿时间：2024-03-16 修订日期：2024-09-26 |
| DOI：10.16490/j.cnki.issn.1001-3660.2025.03.014 |
| 中文关键词: 3D打印 景观设计 激光抛光 增材制造 表面工程 组织性能 |
| 英文关键词:3D printing landscape design laser polishing additive manufacture surface engineering microstructure and property |
| 基金项目:广东省教育科学规划项目(2024GXJK084);广州市哲学社科规划项目(2024GZGJ184) |
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| Author | Institution |
| ZHANG Lan | Department of Art and Design, Guangdong Polytechnic, Guangdong Foshan 528041, China |
| CHEN Min | Department of Art and Design, Guangdong Polytechnic, Guangdong Foshan 528041, China |
| GAO Jie | Department of Art and Design, Guangdong Polytechnic, Guangdong Foshan 528041, China |
| LI Wendong | Department of Art and Design, Guangdong Polytechnic, Guangdong Foshan 528041, China |
| WU Shibing | Three Gorges Jinji Tianjin New Energy Power Generation Co., Ltd., Tianjin 301900, China |
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| 中文摘要: |
| 目的 本研究旨在通过激光表面抛光技术，探索提升3D打印景观设计构件的表面质量和综合性能的可行方法。通过优化激光抛光工艺参数，寻找最佳参数以显著降低构件的表面粗糙度，并提升其力学性能和耐蚀性，从而为增材制造景观构件的高效后处理提供技术支持。方法 首先，采用人工神经网络模型对激光抛光工艺参数进行预测和优化，以获得最佳表面粗糙度所需的参数。随后，用该优化参数对激光选区熔化成形的316L不锈钢景观设计构件进行激光抛光，并对抛光后的表面形貌、显微组织、力学性能及耐蚀性能进行系统表征和分析。结果 激光抛光技术有效地将SLM构件表面粗糙度从原始的4 μm降低至0.15 μm以下，并在表面形成约37.6 μm厚的致密抛光层。抛光层由晶粒尺寸从1.37 μm细化至0.88 μm的等轴晶组成，显微硬度提升了26.7%，残余拉应力从55.7 MPa降至4.17 MPa。此外，抛光层使腐蚀电流密度降低了1个数量级，显著提高了构件的耐蚀性能。结论 激光抛光通过快速凝固机制，细化了晶粒尺寸，提升了构件的硬度和耐蚀性，显著改善了SLM构件的表面质量和综合性能。 |
| 英文摘要: |
| 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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