A Comprehensive Review of the Use of Integrated Approaches for Parameter Optimization and Defect Reduction in Foundries

摘要: Casting is a versatile, cost-efficient manufacturing process for parts with no shape and size constraints. Although a range of metals and their alloys may be easy to work with, process design and control are complex. Foundries struggle a lot with defects and yield of castings, losing crucial time in trial-and-error experimentation or following their own thumb rules of development despite the product complexities. This review will shed light on the use of combined techniques like integration of the Taguchi method with numerical simulation, grey relational analysis (GRA), artificial neural network (ANN), and genetic algorithm (GA). This paper also includes combined methods that include Response Surface Methodology (RSM), Numerical Simulation, Artificial Fish-Swarm Algorithm (AFSA), Particle Swarm Optimization (PSO), Weighted Aggregated Sum Product Assessment (WASPAS), Material Generation Algorithm (MGA), Sunflower Optimization, Ant Lion Algorithm for multiobjective optimization, and defect reduction in majorly used casting processes.