Parameter Optimisation of Squeeze Casting Process using LM 20 Alloy: Numeral Analysis by Neural Network and Modified Coefficient-based Deer Hunting Optimization

Abstract

ABSTRACT The squeeze casting process blends the features of casting, forging, and also helps in achieving better manufacturing abilities; attain smooth uniform surface, high productivity, superior mechanical properties, and refined microstructure. The precise control of process variables is the most feasible solution to obtain a defect-free casting. In the squeeze casting process, surface roughness, hardness, ultimate tensile strength, and yield strength are influenced by process variables. Hence, this paper deals with the new intelligent-based squeeze casting process using LM-20 alloy. Aluminium is commonly employed in automobile and aerospace applications. The proposed casting process considers the input parameters such as squeeze pressure, pressure duration, pouring temperature, and Die preheats temperature. The output variables such as Yield strength, hardness, Ultimate tensile strength, and Surface roughness is artificially computed using the regression equations obtained for different responses using the non-linear regression models based on Central Composite Design (CCD). Initially, the considered process variables are trained in a Neural Network (NN). Further, the employed parameters are optimised to attain the best solution within the concerned limit using an improved meta-heuristic algorithm called Modified Coefficient based DHOA called MC-DHOA. The fitness evaluation of parameter optimisation depends on NN for prediction, and the objective function intends to minimise the error between optimised and target value. The analysis proves that the implemental model helps to select the most influential process parameters in the squeeze casting process within less duration. The overall performance of the proposed MC-DHOA is 16.5% better than PSO, 1% better than FF, 11% better than GWO, 4.6% better than WOA, and 2.15% better than DHOA.