AI For Defect Detection in Additive Manufacturing: Applications In Renewable Energy And Biomedical Engineering

Abstract

Defect detection in Additive Manufacturing (AM) is a critical aspect of ensuring product quality, particularly in industries such as renewable energy and biomedical engineering, where reliability and precision are paramount. This study conducted a systematic review of 152 peer-reviewed articles, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, to analyze the adoption of Artificial Intelligence (AI) techniques in defect detection within AM processes. The review revealed that machine learning (ML) and deep learning (DL) techniques, such as Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs), are widely employed for identifying common defects like porosity, delamination, and dimensional inaccuracies. Hybrid AI models, integrating ML and DL, demonstrated superior performance in detecting complex, multi-dimensional defects across various AM applications. Additionally, the integration of multimodal data, including thermal imaging, acoustic signals, and optical measurements, was found to improve defect detection rates by an average of 22%, enhancing the robustness and accuracy of AI models. The study also identified significant challenges, including dataset scarcity and annotation inconsistencies, which limit the generalizability and scalability of AI solutions. Comparative analyses further highlighted the distinct advantages of tailored AI approaches for specific applications, with renewable energy and biomedical engineering being key focus areas. This review underscores the transformative potential of AI in advancing defect detection in AM, providing a comprehensive understanding of its capabilities, challenges, and implications for high-stakes manufacturing industries.