Geometric Dimensional Errors in Injection Molding of Conformal Cooling Channels Using Qualitative and Quantitative Analysis

The geometric dimension error of plastic parts is one of the common defects in plastic injection molding (PIM). The main reason for this defect is the uniformity of the cooling of the molten plastic. The conformal cooling channel is widely used in injection mold processing due to its excellent cooling effect. However, complex injection molding processes can affect the quality of plastic parts. An optimization model for injection molding process parameters was proposed based on Grey Relational Analysis (GRA) and Response Surface Methodology (RSM). Seven important molding parameters were determined, including injection time, holding pressure, mold temperature, injection pressure, holding time, melt temperature, and mold opening time. The X, Y, and Z directions of plastic part warping deformation were selected as quality indicators. An L18 orthogonal design experiment was established based on the signal-to-noise ratio, calculated the grey relational coefficient and relational degree, and conducted the qualitative analysis to screen and evaluate the influencing factors with high relational degree of holding pressure, holding time, and melt temperature. A second-order polynomial regression model was established using RSM, and a quantitative analysis of warping deformation was conducted. The results showed that when the holding pressure was 110 MPa, the melt temperature was 250 °C, the mold opening time was 6 seconds, the X, Y, and Z direction warping deformations of the optimized product were 0.4354 mm, 0.1411 mm, and 0.2951 mm, respectively, which were reduced by 51.60%, 43.67%, and 45.02% compared to before optimization. The research results have verified the accuracy and reliability of the optimization of injection mold process parameters derived from qualitative and quantitative analysis. It is worth noting that this method has significant advantages in qualitatively identifying the primary and secondary relationships between different conditional parameters and quantitatively determining the optimal combination level for each parameter. This method will provide a framework for optimizing the design parameters of injection molding processes and improve the efficiency of identifying the optimal target combination.