Machine Learning in Additive Manufacturing

Le résumé fourni par la source

Machine learning (ML), an area of artificial intelligence (AI), facilitates machines in acquiring knowledge and advancing their capabilities through experience rather than explicit coding. Owing to its remarkable capabilities in data tasks, including classification, clustering, and regression, it has gathered huge attention in additive manufacturing (AM) over the past few years. Commonly referred to as three-dimensional (3D) printing or rapid prototyping, AM is a type of manufacturing process that involves the sequential addition of layers of raw materials to create a printed part under the guidance of a computer-aided design model. This chapter explores the confluence of these two promising fields, providing a comprehensive analysis of how the ML algorithms can enhance the efficiency of AM processes. Further, among various essential techniques such as supervised and unsupervised learning and reinforcement learning, supervised learning has been seen as the most utilized ML method in the realms of AM. This is because of its ability to handle large datasets, powerful computational capabilities, and advanced algorithmic architecture. This chapter provides valuable insight for researchers and industry stakeholders to understand the importance of ML in the AM processes across various stages of fabricating printed parts and beyond.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.