Experimental investigation and optimization of process parameters on digital light processing (DLP) 3D printing process based on Taguchi-grey relational analysis
R Prabhakaran, P Pitchipoo, S Rajakarunakaran, R Venkatesh- Industrial and Manufacturing Engineering
- Mechanical Engineering
Additive manufacturing technology, specifically Digital Light Processing (DLP), offers a unique approach to fabricating intricate objects without the need for traditional molds or machining. The technique described entails the incremental construction of layers through the curing process polymer, facilitated by a projection light source. Digital Light Processing 3D printing is particularly suited for creating complex structures with small cross-sections, requiring high surface polish and durability. In this study, DLP-printed test coupons were produced using an Ultraviolet-sensitive resin, and the investigation focused on examining the impact of surface quality and toughness, adhering to guidelines outlined by the ASTM specifications. To optimize the DLP printing process, Taguchi and Grey relational optimization techniques were employed to find the best values for multiple factors. The parameters considered in this study were speed, layer thickness, orientation, and pattern type. By employing these techniques, the optimal parameters were identified as follows: The printing process is conducted at a speed of 3 mm per second, with each layer being 0.05 mm thick and the orientation of the printed object is set at 90 degrees, and the pattern employed is of the square type. Experimental results revealed that utilizing these parameters yielded a surface roughness of 2.58 µm and an impact strength of 9.1J. The results showed that the Grey relational grade improved from 0.6244 to 0.7974, with a deviation of 0.173 and 27.71%, indicating a significant enhancement in the optimization process. Additionally, contour plots depicting the relationships between speed and orientation, speed and layer thickness, and orientation and layer thickness were generated for surface roughness, impact strength, and the Grey relational grade. This research offers valuable insights into optimizing DLP 3D printing processes to achieve enhanced surface quality, toughness, and material efficiency while reducing costs and production time.