Case Studies Based on the Latest Developments and Requirements in ML/AI-Based AM

Abstract

Additive manufacturing (AM) has grown extensively due to the process of layer-by-layer deposition that offers flexibility and mass customisation of three-dimensional parts having a widespread range of applications in various industries, from aerospace and healthcare to consumer goods and beyond. Implementation of Artificial Intelligence (AI) is transforming AM through various innovative applications. Generative AI (GAI) enhances AM processes by optimising design, improving material development, and ensuring quality control. Additionally, AI techniques like Deep Learning and Machine Learning (ML) are employed for real-time defect detection. Notably, ML techniques are employed for optimising the fabrication of Functionally Graded Materials (FGMs), improving surface roughness in Wire Arc Additive Manufacturing (WAAM). Overall, AI’s integration into AM not only streamlines processes but also fosters innovation and quality assurance, making it a pivotal element in the future of manufacturing. The present chapter focuses on AI and ML implementation in different AM domains. One of the primary applications of AI in additive manufacturing is the optimisation of the design and production process. AI algorithms can be used to analyse vast amounts of data, identify patterns, and make predictions to improve the efficiency of the manufacturing process. For example, AI-powered predictive maintenance can help anticipate and prevent equipment failures, reducing downtime and improving overall productivity. Additionally, AI can be used to monitor and control the manufacturing process in real time, ensuring consistent quality and identifying potential issues before they occur.