Optimization of Dimensional Deviations in Wax Patterns for Investment Casting

Abstract

Investment casting is a versatile manufacturing process to produce high quality parts with high dimensional accuracy. The process begins with the manufacture of wax patterns. The dimensional accuracy of the model affects the quality of the finished part. The present study investigated the control and optimization of dimensional deviations in wax patterns. A mold for an H-shaped wax pattern was designed and fabricated and the two most important dimensional deviations (sink marks and warpage), are investigated. Four process parameters (injection temperature, injection pressure, hold time, cooling time) affecting dimensional deviations of the wax pattern were measured. Using a 2k factorial DOE technique, 32 experiments were designed to investigate the effect of these parameters on the two main defects in wax patterns. The results show the effect of the parameters on warpage and sink marks (output variables). The relationships between these inputs and the output variables were identified using an artificial neural network. The optimal level of each factor to minimize warpage and sink marks was determined using a multi-objective genetic algorithm. The results of this research can help decrease the time and cost of the process, dimensional deviations, and waste.