H.Y. Lau, X. Li, and R. Du
plastic injection molding, monitoring, optimization, Artificial Neural Network (ANN), Support Vector Machine (SVM), Virtual Search Method (VSM)
This paper presents a new method for monitoring and tuning plastic injection molding machines. It consists of two parts: quality monitoring using the Radial Basis Function Neural Network (RBFNN), and operation parameter tuning using Support Vector Machine (SVM) and Virtual Search Method (VSM). The quality is measured by the part weight, which is estimated using hydraulic pressure signal and ram position signals through a RBFNN. The tuning is aimed at finding the optimal setting of the machine operation condition. It is done in two steps: first, a SVM model is established as the virtual model to track the part quality variations, and then the quality variation is minimized by tuning the machine operating conditions using VSM. The new method is validated by two sets of practical experiments, and the results are very promising.
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