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In the past, plastic injection molding (PIM) product quality was usually measured by one single quality characteristic or by multiple quality characteristic with independent parameters one another. In this study, optimization of process parameters using design of experiment (DOE), response surface methodology (RSM), and genetic algorithm (GA) were proposed to generate the optimal process parameters settings of multiple-quality characteristics. In the first stage, significant PIM process parameters can be determined by DOE screening experiments. Then the optimal process parameter settings are obtained via computer aided engineering (CAE) simulation integrated with RSM and GA, which are taken as practically initial settings of process-related parameters. The experimental results show that the propose optimization model is very successful and can be used in industrial applications.

This study proposes an optimization system to find out the optimal process parameters of plastic injection molding (PIM). The system is divided into two phases. In the first phase, the Taguchi method and analysis of variance (ANOVA) are employed to perform the experimental work, calculate the signal-to-noise (S/N) ratio, and determine the initial process parameters. In the second phase, the back-propagation neural network (BPNN) is employed to construct an S/N ratio predictor. The S/N ratio predictor and genetic algorithms (GA) are integrated to search for the optimal parameter combination. The purpose of this stage is to reduce the process variance and promote product quality. Experimental results show that the proposed optimization system can not only satisfy the quality specification, but also improve stability of the PIM process.

Due to the increasing advancement in technology and limited resources as well as environment being polluted at the same time, the natural solar energy, which has the advantage of environmental protection and being the newly developed approach, will become the rushing field of study around the world in order to implement energy efficiency and reduce carbon pollution. In the meantime, the solar industry technology and new product development have become the considerations to the survival and competitiveness of enterprises. In this study, literature review and expert interviews are utilized to obtain five major key dimensions and 19 subordinate criteria regarding new product development. The Interpretive Structural Model (ISM) is employed to obtain the dimension-dimension and criterion-criterion dependence relationship, and used the Fuzzy Analytic Network Process (Fuzzy ANP) to determine the top priority weight for assessment improvement in the new product development solutions of the enterprises.

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