Optimization of Injection Moulding Process Parameters for Reducing Shrinkage by Using Genetic Algorithm Technique

Mechanical Engineering

Injection molding is an important polymer processing operation in the plastic industry. In this process, polymer is injected into a mold cavity, and solidifies to the shape of the mold. Optimizing the parameters of the injection molding process is critical to enhance productivity. Productivity and quality are two important aspects have become great concerns in today’s competitive global market. Every production/manufacturing unit mainly focuses on these areas in relation to the process as well as product developed. Injection moulding process, even now it is an experience process, wherein still the selected parameters are often far from the maximum, and at the same time selecting optimization parameters is costly and time consuming. In this work the sink mark or shrinkage during the process has been considered as productivity estimate with the aim to minimize it. This response requirements have been satisfied by selecting an optimal process environment (optimal parameter setting). The setting data and objective function is obtained by artificial neural network and regression analysis. Then objective function is optimized using Genetic Algorithm technique. The model is shown to be effective percentage shrinkage mark improved using optimized injection moulding process parameters as compared to previous literature.

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