Reinforcement learning-based defect mitigation for quality assurance of additive manufacturing

Jihoon Chung, Bo Shen, Andrew Chung Chee Law, Zhenyu (James) Kong

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Additive Manufacturing (AM) is a powerful technology that produces complex 3D geometries using various materials in a layer-by-layer fashion. However, quality assurance is the main challenge in AM industry due to the possible time-varying processing conditions during AM process. Notably, new defects may occur during printing, which cannot be mitigated by offline analysis tools that focus on existing defects. This challenge motivates this work to develop online learning-based methods to deal with the new defects during printing. Since AM typically fabricates a small number of customized products, this paper aims to create an online learning-based strategy to mitigate the new defects in AM process while minimizing the number of samples needed. The proposed method is based on model-free Reinforcement Learning (RL). It is called Continual G-learning since it transfers several sources of prior knowledge to reduce the needed training samples in the AM process. Offline knowledge is obtained from literature, while online knowledge is learned during printing. The proposed method develops a new algorithm for learning the optimal defect mitigation strategies proven the best performance when utilizing both knowledge sources. Numerical and real-world case studies in a fused filament fabrication (FFF) platform are performed and demonstrate the effectiveness of the proposed method.

Original languageEnglish (US)
Pages (from-to)822-835
Number of pages14
JournalJournal of Manufacturing Systems
Volume65
DOIs
StatePublished - Oct 2022

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Software
  • Hardware and Architecture
  • Industrial and Manufacturing Engineering

Keywords

  • Additive Manufacturing (AM)
  • Defect Mitigation
  • Fused Filament Fabrication (FFF)
  • G-Learning
  • Knowledge Transfer
  • Model-free Reinforcement Learning (RL)

Fingerprint

Dive into the research topics of 'Reinforcement learning-based defect mitigation for quality assurance of additive manufacturing'. Together they form a unique fingerprint.

Cite this