Litcius/Paper detail

The Role of Deep Learning in Manufacturing Applications: Challenges and Opportunities

Rishi K. Malhan, Satyandra K. Gupta

2023Journal of Computing and Information Science in Engineering28 citationsDOI

Abstract

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.

Topics & Concepts

ProductivityDeep learningSupply chainComputer scienceQuality (philosophy)Field (mathematics)Advanced manufacturingEngineeringManufacturing engineeringArtificial intelligenceRisk analysis (engineering)BusinessMarketingMathematicsPure mathematicsEpistemologyPhilosophyMacroeconomicsEconomicsIndustrial Vision Systems and Defect DetectionFault Detection and Control SystemsAdvanced Statistical Process Monitoring