Role of artificial intelligence in data-centric additive manufacturing processes for biomedical applications.

Journal: Journal of the mechanical behavior of biomedical materials
PMID: 40036906

Abstract

The role of additive manufacturing (AM) for healthcare applications is growing, particularly in the aspiration to meet subject-specific requirements. This article reviews the application of artificial intelligence (AI) to enhance pre-, during-, and post-AM processes to meet a wider range of subject-specific requirements of healthcare interventions. This article introduces common AM processes and AI tools, such as supervised learning, unsupervised learning, deep learning, and reinforcement learning. The role of AI in pre-processing is described in the core dimensions like structural design and image reconstruction, material design and formulations, and processing parameters. The role of AI in a printing process is described based on hardware specifications, printing configurations, and core operational parameters such as temperature. Likewise, the post-processing describes the role of AI for surface finishing, dimensional accuracy, curing processes, and a relationship between AM processes and bioactivity. The later sections provide detailed scientometric studies, thematic evaluation of the subject topic, and also reflect on AI ethics in AM for biomedical applications. This review article perceives AI as a robust and powerful tool for AM of biomedical products. From tissue engineering (TE) to prosthesis, lab-on-chip to organs-on-a-chip, and additive biofabrication for range of products; AI holds a high potential to screen desired process-property-performance relationships for resource-efficient pre- to post-AM cycle to develop high-quality healthcare products with enhanced subject-specific compliance specification.

Authors

  • Saman Mohammadnabi
    Energy and Mechanical Engineering Department, Shahid Beheshti University, Tehran 1983969411, Iran.
  • Nima Moslemy
    Institute of Mechanical, Process and Energy Engineering, School of Engineering and Physical Sciences, Heriot-Watt University, Scotland, UK.
  • Hadi Taghvaei
    Energy and Mechanical Engineering Department, Shahid Beheshti University, Tehran 1983969411, Iran.
  • Abdul Wasy Zia
    Institute of Mechanical, Process and Energy Engineering, School of Engineering and Physical Sciences, Heriot-Watt University, Scotland, UK.
  • Sina Askarinejad
    School of Science and Engineering, University of Dundee, Dundee, UK.
  • Faezeh Shalchy
    Institute of Mechanical, Process and Energy Engineering, School of Engineering and Physical Sciences, Heriot-Watt University, Scotland, UK. Electronic address: f.shalchy@hw.ac.uk.

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