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Quality Prediction for Injection Molding by Using a Multilayer Perceptron Neural Network.

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Quality Classification of Injection-Molded Components by Using Quality Indices, Grading, and Machine Learning.

Kun-Cheng Ke1, Ming-Shyan Huang1

  • 1Department of Mechatronics Engineering, National Kaohsiung University of Science and Technology, Kaohsiung City 824, Taiwan.

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|January 27, 2021
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Summary

This study introduces a machine learning approach using a multilayer perceptron (MLP) neural network to classify injection molded part quality. The method accurately predicts part quality and reduces inspection costs by analyzing quality indices from pressure curves.

Keywords:
cavity pressure curveinjection moldingmachine learningmultilayer perceptron neural networkquality controlquality indices

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Area of Science:

  • Manufacturing Engineering
  • Artificial Intelligence
  • Quality Control

Background:

  • Conventional quality assessment for injection molded components is costly, time-consuming, and relies on imprecise statistical process control.
  • There is a need for efficient and accurate methods to assess part quality in mass production.
  • Machine learning offers a potential alternative for automated quality classification.

Purpose of the Study:

  • To develop and validate a machine learning model for classifying the quality of injection molded parts.
  • To utilize quality indices derived from pressure curves for accurate quality prediction.
  • To reduce quality control costs by optimizing the inspection process.

Main Methods:

  • A multilayer perceptron (MLP) neural network was employed for quality prediction.
  • Quality indices were extracted from pressure curves and used as input features.
  • Data preprocessing included outlier filtering, and output data was converted into quality grades.

Main Results:

  • The MLP model accurately predicted the quality of 'qualified' and 'unqualified' parts.
  • The prediction accuracy was enhanced by data filtering and quality grading.
  • A 'to-be-confirmed' area was identified and classified, enabling targeted further evaluation and significant cost reduction.

Conclusions:

  • Machine learning, specifically an MLP neural network, provides an effective alternative to conventional quality assessment methods in injection molding.
  • The proposed method accurately classifies part quality and optimizes the quality control process.
  • Experimental validation using an integrated circuit tray demonstrated the feasibility and cost-effectiveness of the approach.