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Updated: May 14, 2025

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Machine Learning-Based Process Control for Injection Molding of Recycled Polypropylene.

Joshua Krantz1, Juliana Licata1, Muntaqim Ahmed Raju2

  • 1Department of Plastics Engineering, University of Massachusetts Lowell, Lowell, MA 01854, USA.

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Summary

Machine learning enhances injection molding with recycled materials by predicting quality outcomes. This closed-loop control improves process stability and sustainable material use.

Keywords:
injection moldingmachine learningprocess controlrecycled polypropylene

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Area of Science:

  • Materials Science and Engineering
  • Artificial Intelligence and Machine Learning
  • Manufacturing Processes

Background:

  • The manufacturing sector increasingly uses artificial intelligence (AI) and machine learning (ML) for process optimization and efficiency gains.
  • Injection molding faces challenges with recycled material variability, impacting part quality and processing stability.
  • Sustainable material adoption is hindered by difficulties in controlling recycled material properties.

Purpose of the Study:

  • To develop and evaluate a novel closed-loop process control approach for injection molding using machine learning.
  • To predict processing inputs and quality outcomes adaptively for recycled materials.
  • To assess the performance of different machine learning models in predicting material properties and process parameters.

Main Methods:

  • Utilized artificial neural networks (ANNs), linear regression, and polynomial regression to model relationships between recycled polypropylene (rPP) properties and injection molding parameters.
  • Implemented an ANN model using TensorFlow and Keras with specific architectural choices (six hidden layers, 32 neurons/layer, ReLU activation, Adam optimizer).
  • Employed empirical tuning and early stopping for model optimization and employed mean absolute error (MAE), mean squared error (MSE), and percentage error for prediction evaluation.

Main Results:

  • Yield stress, ultimate elongation, and part weight were predicted with high accuracy (within 5-10% error) across models.
  • Modulus predictions showed higher variability and less reliability (up to 40% error), particularly with polynomial regression.
  • Processing input predictions had errors ranging from 3% to 25%, varying by model and response.

Conclusions:

  • Closed-loop process control powered by machine learning effectively predicts quality parameters in injection molding of recycled materials.
  • The study highlights the complexity of modeling recycled material behavior, with no single approach consistently outperforming others.
  • The proposed approach can enhance process stability, improve material utilization, and promote the use of sustainable materials in manufacturing.