Deep learning for smart manufacturing: Methods and applications
By Jinjiang Wang, Yulin Ma, Laibin Zhang, Robert X. Gao, Dazhong Wu. Journal of manufacturing systems, 2018
1,515 citations recorded by the source corpus.
Abstract Smart manufacturing refers to using advanced data analytics to complement physical science for improving system performance and decision making. With the widespread deployment of sensors and Internet of Things, there is an increasing need of handling big manufacturing data characterized by high volume, high velocity, and high variety. Deep learning provides advanced analytics tools for processing and analysing big manufacturing data. This paper presents a comprehensive survey of commonly used deep learning algorithms and discusses their applications toward making manufacturing “smart”. The evolvement of deep learning technologies and their advantages over traditional machine learning are firstly discussed. Subsequently, computational methods based on deep learning are presented specially aim to improve system performance in manufacturing. Several representative deep learning models are comparably discussed. Finally, emerging topics of research on deep learning are highlighted, and future trends and challenges associated with deep learning for smart manufacturing are summarized.
Subfields: Industrial and Manufacturing Engineering
View Paper: https://www.semanticscholar.org/paper/354923bc917ac774438a048b87db9fabed4d757e
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In Industrial and Manufacturing Engineering
Citations in the corpus
- Human–Cyber–Physical Systems (HCPSs) in the Context of New-Generation Intelligent Manufacturing (2019, background): “0; however, such a method presents the following major challenges: (1) In big data intelligent modeling, how can industrial big data be acquired and managed with greater efficiency? How can the effective learning of knowledge in big data be enabled? How can the ability to solve uncertain and complex”
- Digital Twin for rotating machinery fault diagnosis in smart manufacturing (2018, background): “They aim at flexible and adaptive manufacturing operations, more visibility into the physical processes, improved condition-awareness of machine equipment, and better control of production processes (Chavarríabarrientos et al. 2017; Wang et al. 2018).”
- Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook (2020, neutral): “[9,10], respectively.”
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- Big data and stream processing platforms for Industry 4.0 requirements mapping for a predictive maintenance use case (2020, background): “…machine learning, data mining, and deep learning techniques have extensively been used in many industry 4.0 applications (e.g., pattern recognition, product identification, product steering, predictive maintenance, scheduling, material flow control, and predictive analytics in supply chains) [19].”
- A transfer convolutional neural network for fault diagnosis based on ResNet-50 (2019, background): “However, it is difficult to know that what features should be extracted, and these features have great impacts on the final results of machine learning methods [12].”
- Small business awareness and adoption of state-of-the-art technologies in emerging and developing markets, and lessons from the COVID-19 pandemic (2020, neutral): “0 (Wang et al. 2018).”
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- Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY (2020, background): “In 2018, Wang et al. [209] summarized the capabilities of deep learning for smart manufacturing and highlighted how deep learning changes future trends in industry.”