Journal of Computer Aided Manufacturing and Automation ISSN: 3107-7404 (Online)

Authors : Priya Patel, Devesh Bhandari

Abstract : Additive manufacturing (AM), also known as 3D printing, is revolutionizing the manufacturing industry by enabling the creation of complex geometries and personalized products with reduced material waste. However, challenges persist in optimizing design and production processes for efficiency, quality, and speed. This paper explores the integration of machine learning (ML) techniques in AM, highlighting their potential in optimizing design workflows, predicting material properties, improving production speeds, and ensuring quality control. By analyzing various machine learning models such as supervised learning, reinforcement learning, and deep learning, the paper demonstrates their application in AM settings and provides insights into overcoming existing manufacturing limitations. The findings suggest that machine learning can play a pivotal role in transforming AM into a more efficient and scalable process.

Keywords : Machine learning, additive manufacturing, design optimization, production efficiency, quality control, deep learning, reinforcement learning, material prediction, 3D printing.