Sustainable AI-Driven Hybrid Manufacturing Using Additive and Subtractive Processes

Abstract

Combining Additive Manufacturing (AM) and Subtractive Manufacturing (SM) technologies holds significant potential for transforming industrial production. However, integrating these two approaches remains challenging due to factors such as process compatibility, material loss, and issues in the design and fabrication processes. This research addresses these challenges using deep learning-based Artificial Intelligence (AI) frameworks to enhance hybrid manufacturing systems. Utilizing Convolutional Neural Networks (CNNs) and Reinforcement Learning (RL) models, the study proposes a feedforward intelligent control model that adapts tool path generation, material usage, and defect recognition in real time. Experimental results on benchmark manufacturing datasets demonstrate that the proposed method achieves a 23% reduction in material wastage and a 15% improvement in efficiency compared with existing hybrid methods. Moreover, defect detection accuracy increased to 98.7%, validating the effectiveness of AI-generated quality assurance tools. Production schedules were also reduced by 12% through efficient design-for-manufacturing integration. These observations highlight that deep learning is pivotal in reconciling additive and subtractive techniques, opening new possibilities for modern, sustainable, accurate, and efficient manufacturing processes.