A Novel FDM Based Additive Manufacturing of PLA Components Using Optimized Deep Learning Strategy

• Published in: International journal for research in applied science and engineering technology 2022-04, Vol.10 (4), p.1042-1049
• Main Authors: Gotkhindikar, Nitin, Mehta, Parshwa, Londhe, Shrutika, Kulkarni, Anushka, Rekhe, Maithili
• Format: Article
• Language: English
• ISSN: 2321-9653; 2321-9653
• DOI: 10.22214/ijraset.2022.41436

Fused Deposit modeling (FDM) is an additive manufacturing (AM) process that's frequently used to fabricate geometrically complex shaped prototypes and complex parts. It's gaining market as it reduces cycle time for product development without the need for high priced tools. Still, the commercialization of FDM technology in other artificial operations is presently limited due to several failings, alike as inadequate mechanical properties, poor surface quality, and low dimensional accuracy. The rates of FDM- produced products are affected by other process parameters, for illustration, layer thickness, build angle, raster width, or print speed. The process parameters and their range depends on the section of FDM machines. Filament materials, nozzle dimensions, and the type of machine determine the range of other parameters. The optimum setting of parameters is supposed to ameliorate the rates of three-dimensional (3D) printed specimens and may reducepost-production work. This paper intensely reviews state-of-the- art literature on the influence of parameters on part qualities and the being work on process parameter optimization. Also, the failings of being workshop are linked, challenges and openings to work in this field are estimated, and directions for future research and development in this field are suggested Keywords: Fused deposition modelling; process parameters; part characteristics; optimization