Deep Learning-Driven Data Curation and Model Interpretation for Smart Manufacturing
Abstract Characterized by self-monitoring and agile adaptation to fast changing dynamics in complex production environments, smart manufacturing as envisioned under Industry 4.0 aims to improve the throughput and reliability of production beyond the state-of-the-art. While the widespread application...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2021-07-01
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| Series: | Chinese Journal of Mechanical Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s10033-021-00587-y |
| _version_ | 1818576527399321600 |
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| author | Jianjing Zhang Robert X. Gao |
| author_facet | Jianjing Zhang Robert X. Gao |
| author_sort | Jianjing Zhang |
| collection | DOAJ |
| description | Abstract Characterized by self-monitoring and agile adaptation to fast changing dynamics in complex production environments, smart manufacturing as envisioned under Industry 4.0 aims to improve the throughput and reliability of production beyond the state-of-the-art. While the widespread application of deep learning (DL) has opened up new opportunities to accomplish the goal, data quality and model interpretability have continued to present a roadblock for the widespread acceptance of DL for real-world applications. This has motivated research on two fronts: data curation, which aims to provide quality data as input for meaningful DL-based analysis, and model interpretation, which intends to reveal the physical reasoning underlying DL model outputs and promote trust from the users. This paper summarizes several key techniques in data curation where breakthroughs in data denoising, outlier detection, imputation, balancing, and semantic annotation have demonstrated the effectiveness in information extraction from noisy, incomplete, insufficient, and/or unannotated data. Also highlighted are model interpretation methods that address the “black-box” nature of DL towards model transparency. |
| first_indexed | 2024-12-16T06:15:26Z |
| format | Article |
| id | doaj.art-006c595c09b044db867998efaab74dde |
| institution | Directory Open Access Journal |
| issn | 1000-9345 2192-8258 |
| language | English |
| last_indexed | 2024-12-16T06:15:26Z |
| publishDate | 2021-07-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Chinese Journal of Mechanical Engineering |
| spelling | doaj.art-006c595c09b044db867998efaab74dde2022-12-21T22:41:16ZengSpringerOpenChinese Journal of Mechanical Engineering1000-93452192-82582021-07-0134112110.1186/s10033-021-00587-yDeep Learning-Driven Data Curation and Model Interpretation for Smart ManufacturingJianjing Zhang0Robert X. Gao1Department of Mechanical and Aerospace Engineering, Case Wester Reserve UniversityDepartment of Mechanical and Aerospace Engineering, Case Wester Reserve UniversityAbstract Characterized by self-monitoring and agile adaptation to fast changing dynamics in complex production environments, smart manufacturing as envisioned under Industry 4.0 aims to improve the throughput and reliability of production beyond the state-of-the-art. While the widespread application of deep learning (DL) has opened up new opportunities to accomplish the goal, data quality and model interpretability have continued to present a roadblock for the widespread acceptance of DL for real-world applications. This has motivated research on two fronts: data curation, which aims to provide quality data as input for meaningful DL-based analysis, and model interpretation, which intends to reveal the physical reasoning underlying DL model outputs and promote trust from the users. This paper summarizes several key techniques in data curation where breakthroughs in data denoising, outlier detection, imputation, balancing, and semantic annotation have demonstrated the effectiveness in information extraction from noisy, incomplete, insufficient, and/or unannotated data. Also highlighted are model interpretation methods that address the “black-box” nature of DL towards model transparency.https://doi.org/10.1186/s10033-021-00587-yDeep learningData curationModel interpretation |
| spellingShingle | Jianjing Zhang Robert X. Gao Deep Learning-Driven Data Curation and Model Interpretation for Smart Manufacturing Chinese Journal of Mechanical Engineering Deep learning Data curation Model interpretation |
| title | Deep Learning-Driven Data Curation and Model Interpretation for Smart Manufacturing |
| title_full | Deep Learning-Driven Data Curation and Model Interpretation for Smart Manufacturing |
| title_fullStr | Deep Learning-Driven Data Curation and Model Interpretation for Smart Manufacturing |
| title_full_unstemmed | Deep Learning-Driven Data Curation and Model Interpretation for Smart Manufacturing |
| title_short | Deep Learning-Driven Data Curation and Model Interpretation for Smart Manufacturing |
| title_sort | deep learning driven data curation and model interpretation for smart manufacturing |
| topic | Deep learning Data curation Model interpretation |
| url | https://doi.org/10.1186/s10033-021-00587-y |
| work_keys_str_mv | AT jianjingzhang deeplearningdrivendatacurationandmodelinterpretationforsmartmanufacturing AT robertxgao deeplearningdrivendatacurationandmodelinterpretationforsmartmanufacturing |