Incorporation of machine learning in additive manufacturing: a review

Ali Raza, Kashif Mairaj Deen, Russlan Jaafreh, Kotiba Hamad, Ali Haider, Waseem Haider

Research output: Contribution to journalReview articlepeer-review

Abstract

Machine learning (ML) has undeniably turned into a mainstream idea by enhancing any system’s throughput by allowing a more intelligent usage of materials and processes and managing their resultant properties. In industrial applications, usage of ML not only decreases the lead time of the manufacturing process involved but because of iterative steps of process parameters optimization, it also increases the quality and properties of the parts produced. Furthermore, ML provides an opportunity for creating completely or partially autonomous frameworks. A subset of ML, i.e., deep learning (DL), has capabilities of interpreting data in a layered pattern with little or no requirement of the labeled data for training. On the other hand, additive manufacturing (AM) offers benefits in designing intricated 3D shapes and gaining well-defined control over processing parameters, which eventually control the quality of a final product. This review discusses the utilization of ML techniques in various areas of AM ranging from the selection of material and alloy development to AM process parameter optimization. ML data training also helps in establishing the relation between AM process-structure–property relationship and defect detection in the printed objects. Consecutive steps of the process, i.e., data gathering, population establishment, model selection, training, and application, have been discussed. Also, certain challenges associated with the long-term incorporation of ML in the AM have been identified and their probable solutions have been provided.

Original languageEnglish
Pages (from-to)1143-1166
Number of pages24
JournalInternational Journal of Advanced Manufacturing Technology
Volume122
Issue number3-4
DOIs
StatePublished - Sep 2022

Keywords

  • Additive manufacturing
  • Defect detection
  • Machine learning
  • Parameter optimization
  • Process-structure–property relationship

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