Fuzzy assessment of process parameter interactions on warpage defect modeling in plastic injection molding

Studying the interactions among major plastic injection molding process parameters is necessary to understand how they collectively influence major defects such as warpage hence enabling optimization of the process for improved product quality. Existing process parameter interaction studies have used statistical approximations, which have limitations such as reduced predictive power and limited accuracy. To overcome these limitations, this study presents an alternative method of analysis of the interactions among process parameters based on fuzzy logic intelligent algorithm. Through computer aided engineering (CAE), factorial design of experiment and fuzzy logic modelling, the study evaluated the effects of major injection molding process parameter interactions on warpage. The results obtained indicated a general increase in warpage with increase in parameters such as melt temperature, mold temperature, injection pressure and cool time whereas an increase in parameters such as ambient temperature and packing pressure decreased warpage. Parameter interactions were obtained both statistically and based on fuzzy logic model and their significance tested through ANOVA. Ambient temperature (30.6%) and melt temperatures (18.7%) had the greatest effect on warpage all with P-values of 0.000 whereas cooling time (0.1%) had the least effect with P-value of 0.250. The largest two way interaction affecting warpage involved melt temperature and cooling time with a contribution of 12.2% whereas the largest three way interaction involves ambient temperature, packing pressure and injection pressure with a contribution of 2.7%. Also, despite cooling time having the least mains effect, most interaction terms with greater effect on warpage involved cooling time and melt temperature. The results from this study provides an insight on targeted injection molding process parameter control for defect minimization.