Surface quality prediction and quantitative evaluation of process parameter effects for 3D printing with transfer learning-enhanced gradient-boosting decision trees

Jianjian Zhu, Zhongqing Su, Qingqing Wang, Zifeng Lan, Frankie Siu-fai Chan, Zhibin Han, Zhaokun Wang, Sidney Wing-fai Wong, Andy Chi-fung Ngan

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

3D printing has the potential to revolutionize industrial manufacturing through efficient and sustainable techniques. Fused Deposition Modeling (FDM) is a broadly deployed technique among various 3D printing methods. However, the surface quality of FDM is greatly influenced by multiple factors, making it challenging to unravel the relationship between printing quality and parameter settings. To break through this bottleneck, this study proposes an intelligent approach that combines Transfer Learning (TL)-based Feature Extractor (FE) and Gradient-Boosting Decision Trees (GBDT) to investigate the effects of FDM printing parameters on surface quality. Experiments are conducted in the laboratory to validate the effectiveness of the FE-GBDT, which is then compared with the exemplary Machine Learning (ML) algorithms. The results show that our proposed TL model can achieve high precision and accuracy over 0.9900, demonstrating the efficacy of FE-GBDT in deciphering the impact of FDM printing parameters on surface quality. The contribution of each parameter is evaluated and indicates that layer height could dramatically affect the surface quality with an importance score of 0.626. The results provide valuable insights for the 3D printing community, proving that the FE-GBDT approach offers improved generalization, faster training, enhanced feature extraction, addressing data scarcity, and the ability to leverage the strengths of both approaches for superior performance across various tasks.

Original languageEnglish
Article number121478
JournalExpert Systems with Applications
Volume237
DOIs
Publication statusPublished - 1 Mar 2024

Keywords

  • 3D printing
  • Fused deposition modeling
  • Gradient-boosting decision trees
  • Process parameters
  • Surface quality
  • Transfer learning

ASJC Scopus subject areas

  • General Engineering
  • Computer Science Applications
  • Artificial Intelligence

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