Optimization of Low-Pressure Die Casting Process with Soft Computing

Abstract

The paper presents a hybrid strategy in a soft computing paradigm for the optimization of the low-pressure die casting process. Casting process parameters, such as various parts temperatures of die, pouring temperature are considered. The hybrid strategy combines numerical simulation software, a genetic algorithm and a multilayer neural network to optimize the process parameters. An approximate analysis model is developed using a BP neural network in order to avoid the expensive computation resulting from the numerical simulation software. According to the characteristic of the optimization problem, a real-code genetic algorithm is applied to solve the optimization model. The effectiveness of the improved strategy is shown by an A356 automotive wheel.

Modeling and Optimization of Die Casting Process for ZAMAK Alloy

“…Moreover, by selection of optimum process parameters the die casting defects such as porosity, insufficient spread of molten material, flash etc., were also minimized. Zhang et al [2] present a hybrid strategy in a soft computing paradigm for the optimization of the low-pressure die casting process. Casting process parameters, such as temperatures of die, pouring temperature were considered.…”

Modelling in Squeeze Casting Process-Present State and Future Perspectives

“…In recent years neural networks has been applied for the squeeze casting process to forecast the solidification time, temperature difference and secondary dendrite arm spacing [74] of the squeeze cast components. To avoid the rule of thumb, expert advice, try-error method used in shop floor practice, neural networks has been successfully implemented to predict filling time, solidification time and casting defects ,surface defects [75,76], solidification time [77,78], filling time and porosity , injection time [79,80], of pressure die casting process. To predict interfacial heat transfer coefficients at metal-mould interface [81], compressive strength, secondary dendrite arm spacing [82], mechanical properties [83], permeability [84] of different casting processes the soft computing based neural networks were used.…”