Experimental comparison to optimize defects in aluminium alloy (A356) casting using Taguchi DOE and MCDM

Casting is a process where complicated parts are made by pouring molten metal in the mold to produce near net parts. A substantial progress has been made in the field of casting over the years, as evidenced by the progression seen at the meetings for Modelling of Casting, Welding, and Advanced Solidification Processes. Choosing the best casting process is still a crucial decision. To generate various rankings and indices, this study uses a variety of Multi-Criteria Decision-Making (MCDM) approaches, including TOPSIS, SAW, ARAS, WASPAS, and MABAC. The ability to anticipate physical events that are challenging to witness firsthand has increased our knowledge of solidification processes. Predicting casting performance and fault incidence with accuracy, however, is still difficult. This study examines the root causes of defect growth in shape and GDC casting processes and investigates how to make casting models more predictable and quantifiable. In order to choose the best aluminium alloy, this study uses the Analytical Hierarchy Process (AHP), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and other MCDM methods. These strategies' outcomes and consequences are examined. This research presents a comparative analysis between the Multi-Criteria Decision-Making (MCDM) approach and the Taguchi Method. These findings underscore Taguchi's superior analytical performance and efficiency over MCDM in the context of optimization processes. The study highlights that Taguchi's two-element vector optimization without data augmentation yields a 98% efficient model, surpassing MCDM's 95% efficiency with five-element vectors and data augmentation.