Assessing an Effective Approach for Adjusting Casting Process Parameters through Optimization

Foundries in India are facing lower productivity issues due to low quality of castings produced as well as wrong practices followed, therein. Minimizing rejection as well as production costs is the key area of concern in these foundries. Casting process parameters are important in the optimization of casting process. Design optimization using reliability has also emerged as a promising tool for optimization, using the probabilistic approach. This approach works on probability of failures through various parameters. Markov process is a predictive technique for generation of a stochastic model with sequence of possible events. This research study has focused on the development of a Markov chain model for investigating the casting failure. In this paper, several casting defects and parameters was carried out. Cold shut, Inclusion, Mould shift, Porosity, Shrinkage, Blow holes were the casting defects and Pouring temperature, Mould hardness, Moisture content, Improper handling, Permeability and Green compressive strength were the process parameters considered in the study. This study has proposed a hierarchical model that focuses on the casting failure in industries and accordingly the model was tested for various process parameters. The outcome of the study provides the priority ranking of the process parameters for various casting failure in real time through which the critical process parameters were identified in the order. The Pouring temperature came out as the most critical process parameter for casting failure. Also, the priority ranking of the process parameters for various casting failure in real time were identified and found to be identical.