Scholarlink

<p>Plastic Injection Molding (PIM) is key for making precise polymer parts. Shrinkage and warpage continue to be major issues. They lead to dimensional inaccuracies and part distortion, which reduce quality consistency. This affects the reliability of molded components. This study aims to develop a regression model to predict and enhance defects in polypropylene mobile covers. The study focused on important process parameters. These were melt and mold temperatures, packing and injection pressures, and cooling and packing times. It assessed how these factors influenced shrinkage and warpage. Scatter plots, histograms, and Q–Q plot analyses showed little bias and a nearly normal distribution of residuals. Feature importance analysis showed that cooling time (0.0834&#xa0;s) and packing time (0.0724&#xa0;s) are the main factors affecting shrinkage. Regression equations showed how parameters relate to defects. The Genetic Algorithm (GA) optimization reduced shrinkage to 1.42% and warpage to 0.0133&#xa0;mm. This shows a big improvement in reducing defects. The model’s prediction error is between 5% and 11%. This is due to the complex and nonlinear interactions in the injection molding process. These findings offer a useful method for manufacturers. They can adjust molding conditions, cut down on part rejection, and boost product quality. This can be done without needing heavy computational models like neural networks.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Regression-Based Prediction of Shrinkage and Warpage in Polypropylene Mobile Covers Produced by Injection Molding

  • 期刊论文

  • 发表日期：

    2026年5月2日

查看全文
  • Deepak Kumar,
  • Chandni Kirpalani,
  • Dheeraj Joshi,
  • Chandan Kumar,
  • Sumit Sharma,
  • Abhishek Kumar Tripathi

摘要

Plastic Injection Molding (PIM) is key for making precise polymer parts. Shrinkage and warpage continue to be major issues. They lead to dimensional inaccuracies and part distortion, which reduce quality consistency. This affects the reliability of molded components. This study aims to develop a regression model to predict and enhance defects in polypropylene mobile covers. The study focused on important process parameters. These were melt and mold temperatures, packing and injection pressures, and cooling and packing times. It assessed how these factors influenced shrinkage and warpage. Scatter plots, histograms, and Q–Q plot analyses showed little bias and a nearly normal distribution of residuals. Feature importance analysis showed that cooling time (0.0834 s) and packing time (0.0724 s) are the main factors affecting shrinkage. Regression equations showed how parameters relate to defects. The Genetic Algorithm (GA) optimization reduced shrinkage to 1.42% and warpage to 0.0133 mm. This shows a big improvement in reducing defects. The model’s prediction error is between 5% and 11%. This is due to the complex and nonlinear interactions in the injection molding process. These findings offer a useful method for manufacturers. They can adjust molding conditions, cut down on part rejection, and boost product quality. This can be done without needing heavy computational models like neural networks.