Abstract
This study focuses on applying intelligent modeling methods to different injection molding process parameters, to analyze the influence of temperature distribution and warpage on the actual development of auto locks. It explores the auto locks using computer-aided engineering (CAE) simulation performance analysis and the optimization of process parameters by combining multiple quality characteristics (warpage and average temperature). In this experimental design, combinations were explored for each single objective optimization process parameter, using the Taguchi robust design process, with the L18 (21 × 37) orthogonal table. The control factors were injection time, material temperature, mold temperature, injection pressure, packing pressure, packing time, cooling liquid, and cooling temperature. The warpage and temperature distribution were analysed as performance indices. Then, signal-to-noise ratios (S/N ratios) were calculated. Gray correlation analysis, with normalization of the S/N ratio, was used to obtain the gray correlation coefficient, which was substituted into the fuzzy theory to obtain the multiple performance characteristic index. The maximum multiple performance characteristic index was used to find multiple quality characteristic-optimized process parameters. The optimal injection molding process parameters with single objective are a warpage of 0.783 mm and an average temperature of 235.23 °C. The optimal parameters with multi-objective are a warpage of 0.753 mm and an average temperature of 238.71 °C. The optimal parameters were then used to explore the different cooling designs (original cooling, square cooling, and conformal cooling), considering the effect of the plastics temperature distribution and warpage. The results showed that, based on the design of the different cooling systems, conformal cooling obtained an optimal warpage of 0.661 mm and a temperature of 237.62 °C. Furthermore, the conformal cooling system is smaller than the original cooling system; it reduces the warpage by 12.2%, and the average temperature by 0.46%.
Highlights
With the development of the plastics industry, the injection molding process has become the most widely used technology for molding plastics, and the majority of plastic products are manufactured in this way
The combinations of parameters for the experimental injection molded sample were assessed with the Taguchi L18 (21 × 37) Orthogonal array (OA), using the warpage and average temperature as single quality characteristics
The degree of impact of each factor on the warpage deflection can be known from the factor response table and graph, which are in the following order: cooling liquid, holding pressure, cooling temperature, holding time, mold temperature, filling time, plastic temperature, and injection pressure
Summary
With the development of the plastics industry, the injection molding process has become the most widely used technology for molding plastics, and the majority of plastic products are manufactured in this way. The CAE mold flow analysis software is used with the Taguchi robust process design method, to find the combination of each single quality optimization parameter, and the warpage deformation and average temperature are discussed separately. Using OA to collect appropriate data, and applying intelligent modeling methods to optimize the multiple quality characteristics of the automotive lock injection molding process, can reduce development and manufacturing costs In comparison with this experimental method, Wang et al [15] studied automotive parts, similar to the subject of this study, and the parts were three-dimensional complex-shaped automotive interior parts.
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