| description abstract | There is a growing interest in using deep learning technologies within the manufacturing industry to improve quality, productivity, safety, and efficiency, while also reducing costs and cycle time. This position paper discusses the applications of deep learning currently being employed in manufacturing, including identifying defects, optimizing processes, streamlining the supply chain, predicting maintenance needs, and recognizing human activity. This paper aims to provide a description of the challenges and opportunities in this area to beginning researchers. The paper offers a brief summary of the various components of deep learning technology and their roles. Additionally, the paper draws attention to the current challenges and limitations that need to be addressed to fully realize the potential of deep learning technology in manufacturing. Lastly, several future directions for research within the field are proposed to further improve the use of deep learning in manufacturing. | |
