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

被引:16
作者
Malhan, Rishi [1 ]
Gupta, Satyandra K. [1 ]
机构
[1] Univ Southern Calif, Dept Aerosp & Mech Engn, Ctr Adv Mfg, Los Angeles, CA 90089 USA
关键词
artificial intelligence; machine learning; deep learning; manufacturing applications; DEFECT DETECTION; MODEL;
D O I
10.1115/1.4062939
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
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.
引用
收藏
页数:8
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[1]   A multimodal and hybrid deep neural network model for Remaining Useful Life estimation [J].
Al-Dulaimi, Ali ;
Zabihi, Soheil ;
Asif, Amir ;
Mohammadi, Arash .
COMPUTERS IN INDUSTRY, 2019, 108 :186-196
[2]   Image-Based Surface Defect Detection Using Deep Learning: A Review [J].
Bhatt, Prahar M. ;
Malhan, Rishi K. ;
Rajendran, Pradeep ;
Shah, Brual C. ;
Thakar, Shantanu ;
Yoon, Yeo Jung ;
Gupta, Satyandra K. .
JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING, 2021, 21 (04)
[3]  
Cem D., 2022, Top 7 Deep Learning Applications in Manufacturing in 2023
[4]   Solar cell surface defect inspection based on multispectral convolutional neural network [J].
Chen, Haiyong ;
Pang, Yue ;
Hu, Qidi ;
Liu, Kun .
JOURNAL OF INTELLIGENT MANUFACTURING, 2020, 31 (02) :453-468
[5]   Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges, and Opportunities [J].
Chen, Kaixuan ;
Zhang, Dalin ;
Yao, Lina ;
Guo, Bin ;
Yu, Zhiwen ;
Liu, Yunhao .
ACM COMPUTING SURVEYS, 2021, 54 (04)
[6]   Generative Adversarial Networks An overview [J].
Creswell, Antonia ;
White, Tom ;
Dumoulin, Vincent ;
Arulkumaran, Kai ;
Sengupta, Biswa ;
Bharath, Anil A. .
IEEE SIGNAL PROCESSING MAGAZINE, 2018, 35 (01) :53-65
[7]   Visual-Based Defect Detection and Classification Approaches for Industrial Applications-A SURVEY [J].
Czimmermann, Tamas ;
Ciuti, Gastone ;
Milazzo, Mario ;
Chiurazzi, Marcello ;
Roccella, Stefano ;
Oddo, Calogero Maria ;
Dario, Paolo .
SENSORS, 2020, 20 (05)
[8]   From Model, Signal to Knowledge: A Data-Driven Perspective of Fault Detection and Diagnosis [J].
Dai, Xuewu ;
Gao, Zhiwei .
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2013, 9 (04) :2226-2238
[9]  
2019, Journal of Strategic Innovation and Sustainability, V14, DOI [10.33423/jsis.v14i3.2105, 10.33423/jsis.v14i3.2105, DOI 10.33423/JSIS.V14I3.2105]
[10]   DeepLog: Anomaly Detection and Diagnosis from System Logs through Deep Learning [J].
Du, Min ;
Li, Feifei ;
Zheng, Guineng ;
Srikumar, Vivek .
CCS'17: PROCEEDINGS OF THE 2017 ACM SIGSAC CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY, 2017, :1285-1298