Development of Semi-liquid Die Casting Process.

Die-Casting Process Modeling Using Neural Networks

This chapter presents the application of a neural network to the industrial process modeling of high-pressure die casting (HPDC). The large number of interand intradependent process parameters makes it difficult to obtain an accurate physical model of the HPDC process that is paramount to understanding the effects of process parameters on casting defects such as porosity. The first stage of the...

Study of Process Parameters of Gravity Die Casting Defects

In today’s world a casting production without any defect is almost impossible. After the application of various optimization techniques and process we still are not able to get the defect free castings. So it is critically important to analyze the root cause of casting defect with in the process itself, so that any changes which can hamper the defect intensity can be invoked at the time of manu...

Optimization of Aluminium Die Casting Process Using Artificial Neural Network

In the present paper, optimization of process parameters of an aluminium die casting operation is discussed. The quality problem encountered during the manufacturing of a die casted component was porosity and the potential factors causing it are identified through causeeffect analysis. An analysis of variance (ANOVA) is conducted to find the factors with significant effects on porosity. The pre...

Die Steels and Improved Productivity in Die Casting

Computer-Aided System for Multi-Cavity Die-Casting Die-Design

Die-design for the die-casting process is a time consuming and complex activity that requires domain knowledge and vast experience of the die-designer besides information about manufacturing resources, part geometry, delivery time and cost preferences, etc. Complexity of the die-design activity further increases in case of a multi-cavity die. Currently available die-design systems lack in the l...