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Related Experiment Video

Updated: Aug 9, 2025

A Soft Tooling Process Chain for Injection Molding of a 3D Component with Micro Pillars

A Soft Tooling Process Chain for Injection Molding of a 3D Component with Micro Pillars

Published on: August 4, 2018

Generative machine learning-based multi-objective process parameter optimization towards energy and quality of

Yirun Wu¹, Yiqing Feng¹, Shitong Peng²

¹Department of Mechanical Engineering, Shantou University, Shantou, 515063, China.

Environmental Science and Pollution Research International

|February 22, 2023

Summary

This study optimized injection molding using machine learning to reduce energy use and part weight variation. The developed model achieved significant improvements, enhancing sustainable plastic manufacturing.

Keywords:

Energy consumption Injection molding Multi-objective optimization Product quality Random forest Sustainable manufacturing

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Area of Science:

Manufacturing Engineering
Materials Science
Artificial Intelligence

Background:

The global plastic industry faces high energy demands and stringent quality requirements in injection molding.
Part weight variation in multi-cavity molds is a key indicator of quality performance.
Minimizing energy consumption and ensuring part consistency are critical for sustainable manufacturing.

Purpose of the Study:

To develop a generative machine learning-based multi-objective optimization model for injection molding.
To predict part quality based on processing variables and optimize parameters for minimal energy consumption and weight difference.
To identify key processing parameters influencing energy efficiency and part quality.

Main Methods:

Development of a generative machine learning model for multi-objective optimization.
Statistical validation using F1-score and R2 for algorithm performance assessment.
Physical experiments to measure energy consumption and part weight differences under varying parameters.
Permutation-based mean square error reduction to determine parameter importance.

Main Results:

The optimized processing parameters reduced energy consumption by approximately 8% and weight difference by approximately 2%.
Maximum speed and first-stage speed were identified as critical factors for quality performance and energy consumption, respectively.
The model effectively predicted part qualification and optimized processing variables.

Conclusions:

Machine learning-driven optimization can significantly improve energy efficiency and part consistency in injection molding.
The developed model offers a pathway for quality assurance and sustainable plastic manufacturing.
Identifying critical parameters like maximum and first-stage speed is crucial for process control.