Investigation of Optimum Quality Parameters of Low-cost Metal Material Extrusion using Machine Learning and Response Surface Methodology

Abstract

Additive manufacturing (AM) is a widely used layer-by-layer manufacturing process. However, the good quality production of additively manufactured parts is limited by the use of different materials, machines and process parameters. Material Extrusion (ME) is one of the most widely used AM technologies. Thus, it is adopted in this research. Low-cost Metal Material Extrusion (LCMME) is a new AM technology used to fabricate metal parts using sintering metal infused filament material. Since the filaments used in LCMME are relatively new, there is a need to investigate the most suitable process parameters of the LCMME process for real-world applications. Each step of the process such as 3D printing of the samples and the sintering will affect the quality of final part significantly. By using Machine Learning and Response Surface Methodology (RSM), a comprehensive quality analysis of the Bronze samples fabricated by the LCMME process is developed in this research. RSM can assist researchers in sophisticated pre-manufacturing planning and product quality assessment and control. The findings of this study can help researchers and engineers to optimize the process parameters to obtain high quality metal parts fabricated by LCMME process.