| 摘要: | 增材製造技術已成為許多行業的熱門選擇，其中選擇性雷射熔融技術(Selective laser melting，SLM)是金屬增材製造的一種方式，雷射將金屬粉末融化並層層堆疊構建物體，此技術被廣泛應用於客製化和高精度的製造領域，如生物醫療、汽車製造及航空航天，但SLM技術在不同製程條件下仍存在許多挑戰，本論文旨在針對SLM製程參數進行系統性的優化，透過田口品質分析法、變異數分析及灰色關聯法分析雷射功率、掃描間距、雷射掃描速度及掃描方式對列印品質的影響，針對表面粗糙度、孔隙率及硬度提出優化製程的建議，此外本論文探討了使用五軸加工的銑削作為SLM的後處理方法，同時可以改善五軸加工與SLM的缺點，增材與減材製造的相互使用下縮短元件製作的時間，並且提高SLM生產的316L不鏽鋼零件的表面粗糙度及加工精度，提出了Z軸校準方式，透過三個研究案例的實驗，驗證了從SLM到五軸加工的整個工作流程，這種方法具有減少材料的消耗及減少刀具磨損的優點，透過系統性的優化及後處理銑削可以改善SLM製程的缺點，透過灰關聯分析能夠找出較適參數以達到各品質的最適化，藉由本研究的技術，未來可以增強SLM的實用性和潛在應用。
Additive manufacturing technology has become a popular choice in many industries. Selective Laser Melting (SLM) is one technology of metal additive manufacturing. In the SLM process, the metal powder was melted by the laser, and the object was built layer by layer. This technology is widely used in customized and high-precision manufacturing fields, such as biomedical, automotive, and aerospace industries. However, the SLM technique still faces various challenges under different processing conditions. This study aims to systematically optimize the parameters of the SLM process. Using the Taguchi method, analysis of variance (ANOVA), and grey relational analysis, the effects of laser power, hatch distance, scanning speed, and scanning strategy on printing quality are analyzed. The study provides optimization recommendations for surface roughness, porosity, and hardness. Furthermore, this paper investigates the use of a five-axis computer numerical control (CNC) milling machine as a post-processing method for SLM. This approach improves the shortcomings of both SLM and traditional machining. It shortens component manufacturing time through the combined use of additive and subtractive manufacturing methods and also enhances the surface roughness and machining accuracy of SLM-produced 316L stainless steel parts. A Z-axis alignment method was proposed, and the entire workflow from SLM to five-axis milling was validated through experiments in three case studies. It offers advantages for reduced material waste and tool wear. By implementing systematic optimization and post-processing milling, the limitations of the SLM process can be effectively addressed and enhanced. Through grey relational analysis, optimal parameters can be identified to achieve the best optimization for each quality factor. The techniques developed in this study can enhance the practicality and potential applications of SLM in the future. |
