Development of physical property prediction models for polypropylene composites with optimizing random forest hyperparameters

Chonghyo Joo, Hyundo Park, Jongkoo Lim, Hyungtae Cho, Junghwan Kim

Research output: Contribution to journalArticlepeer-review


The physical properties required in polypropylene composites (PPCs) vary depending on the purpose of use. In the manufacturing of PPCs, it is crucial to determine the types and quantities of numerous reinforcements to meet the required physical properties. Owing to industrial complexity, most PPC manufacturers produce the composites repeatedly until the desired physical properties are obtained. Hence, to reduce trial and error, we developed prediction models for the physical properties of PPCs based on commercial recipe data. The recipe data included information about five physical properties of composites manufactured using 90 materials. In complex industrial environments, because one recipe is usually composed of 2–12 materials, numerous combinations of data sets are created. It causes the lack of the same material combination data sets and thus makes it difficult to develop a good performance model. Therefore, a novel categorization process is suggested as data preprocessing to overcome the data imbalance problem. The models for predicting the five physical properties (flexural strength, melting index, tensile strength, specific gravity, and flexural modulus) were developed using random forest, and the performance of the prediction models was improved via hyperparameter optimization. Furthermore, the effects of the materials on the performance of the models were numerically described through variable importance analysis. Finally, a software was developed to implement the prediction models in the industry. The software was applied to a commercial composite and achieved high accuracy, demonstrating the effectiveness of this study. Thus, the software suggests decision-making solutions to save cost and time by reducing the trial and error in the industrial environment with high complexity.

Original languageEnglish
Pages (from-to)3625-3653
Number of pages29
JournalInternational Journal of Intelligent Systems
Issue number6
Publication statusPublished - 2022 Jun

Bibliographical note

Funding Information:
This study has been conducted with the support of the Korea Institute of Industrial Technology as “Development of AI Platform Technology for Smart Chemical Process (kitech JH-21-0005).”

Publisher Copyright:
© 2021 The Authors. International Journal of Intelligent Systems Published by Wiley Periodicals LLC.

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Human-Computer Interaction
  • Artificial Intelligence


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