Progress, challenges and opportunities of using artificial intelligence in additive manufacturing

Abstract

Additive Manufacturing (AM) has transformed modern manufacturing by enabling the creation of complex, customized structures with specific geometrical configurations. Despite its revolutionary impact, AM faces significant challenges such as optimizing process parameters, material customization, quality assurance, and reproducibility. Each AM process involves over 250 parameters that must be tailored for different materials, making the process labor-intensive and reliant on highly skilled professionals. To address these issues, Artificial Intelligence (AI) and Machine Learning (ML) are being explored as transformative tools. AI, particularly through deep neural networks (DNN) and artificial neural networks (ANN), has shown potential to simplify AM processes, automate decision-making, and enhance manufacturing efficiency. These models can learn from large datasets to predict optimal process parameters, thus reducing human dependency and improving product quality. The synergy between AI and AM is promising in areas such as process optimization, defect detection, material development, quality control, topology optimization, and predictive maintenance. By integrating AI-driven systems, AM can evolve into a more intelligent, data-driven production environment where parameters are optimized in real-time, improving reproducibility and reducing defects. Combining AI with AM paves the way for more efficient, innovative, and scalable manufacturing, particularly in the context of Industry 4.0.