Research On Influence Of Cooling Processing Parameters And Multi-objective Comprehensive Evaluation Of Cooling Quality In Injection Molding

The traditional design and adjustment of processing parameters has been lack of theoretical basis and scientific computing, mainly depends on designer's experience and intuition in injection molding, and there are interactions among process parameters, it is difficult to get the best combination of processing parameters relying on experience. In addition, the most difficulty of processing control lies in the lack of understanding the relationship between processing parameters and the cooling quality indicators, which cause blindness of the adjustment processing parameters, increase difficulty of cooling quality control. The cooling stage accounted for 80% of the entire cycle time, and the cavity temperature distribution uniformity directly affect the quality of the plastic parts, it is necessary to study the relationship between the cooling process parameters and cooling quality, and base on modern optimization algorithm to optimize the cooling process parameters designed to improve the quality of plastic parts.In this paper, ABS (semi-amorphous) and PP (crystalline) were studied. Firstly, two-way interactions of cooling process parameters (melt temperature, mold temperature, ejection temperature, ambient temperature, coolant inlet temperature, coolant Reynolds number, and mold open time) to the cycle time and the cavity temperature distribution uniformity index were studied by fractional factorial design method. It was fond the cooling quality in injection molding is affected by factors in mutual interactions, some interactions are more important than separate factor. What'more, the important process parameters and the interactions were screened out by fractional factorial design method, provide a scientific basis of selecting factors for the follow-up orthogonal experimental design.Based on the results of fractional factorial experimental design, orthogonal experimental design was employed to study the effect of the processing parameters and their main interactions on the cycle time and the cavity temperature distribution uniformity index. The results show that the interactions and the special thermal properties of the crystalline material PP actually affected cooling quality indicators curve with the changes of a separate factor.The two cooling quality targets is the minimum cycle time and the minimum cavity temperature distribution uniformity index, their importance is different depending on the situations, and it is difficult to achieve the optimum simultaneously. Therefore, it is necessary to establish a comprehensive evaluation method to realize the two targets as good as possible. Then, based on fractional factorial experimental results selected the appropriate factors, orthogonal experimental design method was proposed to get the best combination of process parameters. Application of BP neural network technology, the relationship models between the cooling process parameters and the composite indicator was used to predict cooling composite quality online, the design and adjustment for the injection molding process parameters would be more convenient.