Additive Manufacturing Modification by Artificial Intelligence, Machine Learning, and Deep Learning: A Review
Mohsen Soori, Fooad Karımı Ghaleh Jough, Roza Dastres, Behrooz Arezoo
Abstract
The manufacturing sector has been transformed owing to additive manufacturing (AM), which has made it possible to create intricate, personalized items with little material waste. However, optimizing and enhancing AM processes remain challenging owing to the intricacies involved in design, material selection, and process parameters. This review explores the integration of artificial intelligence (AI), machine learning (ML), and deep learning (DL) techniques to improve and innovate in the field of AM. AI-driven design optimization procedures offer innovative solutions for the 3D printing of complex geometries and lightweight structures. By leveraging machine learning (ML) algorithms, these procedures analyze extensive data from previous manufacturing processes to enhance efficiency and productivity. ML models facilitate design and production automation by learning from historical data and identifying intricate patterns that human operators may miss. Deep learning (DL) further augments this capacity by utilizing sophisticated neural networks to manage and interpret complex information and provide deeper insights into the manufacturing process. Integrating AI, ML, and DL into AM enables the creation of optimized, lightweight components that are crucial for reducing fuel consumption in the automotive and aviation industries. These advanced AI techniques optimize the design and production processes and enhance predictive modeling for process optimization and defect detection, leading to improved performance and reduced manufacturing costs. Therefore, integrating AI, ML, and DL into AM improves precision in component fabrication, enabling advanced material design innovations and opening new possibilities for innovation in product design and material science. This review discusses and highlights significant advancements and identifies future directions for applying AI, ML, and DL in AM. By leveraging these technologies, AM processes can achieve unprecedented levels of precision, customization, and productivity for analysis and modification.