Predicting and optimising the surface roughness of additive manufactured parts using an artificial neural network model and genetic algorithm - AKGÜN, GAZİ (My University, 2023)
The selection of parameters affects the surface roughness in the additive manufacturing process. This study aims to determine the optimal combination of input parameters for predicting and minimising the surface roughness of samples produced by Fused Deposition Modelling on a 3D printer using a cascade-forward neural network (CFNN) and genetic algorithm. Box–Behnken Design with four independent printing parameters at three levels is used, and 25 parts are fabricated with a 3D printer. Roughness tests are performed on the fabricated parts. Models generated by the hybrid algorithm achieve the best results for predicting and optimising surface roughness in 3D-printed parts. The surface roughness prediction accuracy of the trained CFNN with optimised parameters is more accurate compared to previous random test results.
Keywords: Fizik; Yoğun Madde 1:Yapısal, Mekanik ve Termal Özellikler; Temel Bilimler; Mühendislik ve Teknoloji; Physics; Condensed Matter 1: Structural, Mechanical and Thermal Properties; Natural Sciences; Engineering and Technology; Mühendislik, Bilişim ve Teknoloji (ENG); Temel Bilimler (SCI); Malzeme Bilimi; FİZİK, YOĞUN MADDE; Engineering, Computing & Technology (ENG); Natural Sciences (SCI); MATERIALS SCIENCE; PHYSICS; PHYSICS, CONDENSED MATTER; Genel Malzeme Bilimi; Fizik Bilimleri; Yoğun Madde Fiziği; General Materials Science; Physical Sciences; Condensed Matter Physics; Additive manufacturing; box-benken design; cascade forward artificial neural network; fused deposition modelling; genetic algorithm; surface roughness; Additive manufacturing; surface roughness; fused deposition modelling; box-benken design; cascade forward artificial neural network; genetic algorithm