Dynamic Industrial Optimization: A Framework Integrates Online Machine Learning for Processing Parameters Design
We develop the online process parameter design (OPPD) framework for efficiently handling streaming data collected from industrial automation equipment. This framework integrates online machine learning, concept drift detection and Bayesian optimization techniques. Initially, concept drift detection...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/16/3/94 |
| _version_ | 1797240983203610624 |
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| author | Yu Yao Quan Qian |
| author_facet | Yu Yao Quan Qian |
| author_sort | Yu Yao |
| collection | DOAJ |
| description | We develop the online process parameter design (OPPD) framework for efficiently handling streaming data collected from industrial automation equipment. This framework integrates online machine learning, concept drift detection and Bayesian optimization techniques. Initially, concept drift detection mitigates the impact of anomalous data on model updates. Data without concept drift are used for online model training and updating, enabling accurate predictions for the next processing cycle. Bayesian optimization is then employed for inverse optimization and process parameter design. Within OPPD, we introduce the online accelerated support vector regression (OASVR) algorithm for enhanced computational efficiency and model accuracy. OASVR simplifies support vector regression, boosting both speed and durability. Furthermore, we incorporate a dynamic window mechanism to regulate the training data volume for adapting to real-time demands posed by diverse online scenarios. Concept drift detection uses the EI-kMeans algorithm, and the Bayesian inverse design employs an upper confidence bound approach with an adaptive learning rate. Applied to single-crystal fabrication, the OPPD framework outperforms other models, with an RMSE of 0.12, meeting precision demands in production. |
| first_indexed | 2024-04-24T18:16:06Z |
| format | Article |
| id | doaj.art-ac9316bf1b97438a9fd05763c93b9fbd |
| institution | Directory Open Access Journal |
| issn | 1999-5903 |
| language | English |
| last_indexed | 2024-04-24T18:16:06Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj.art-ac9316bf1b97438a9fd05763c93b9fbd2024-03-27T13:42:16ZengMDPI AGFuture Internet1999-59032024-03-011639410.3390/fi16030094Dynamic Industrial Optimization: A Framework Integrates Online Machine Learning for Processing Parameters DesignYu Yao0Quan Qian1School of Computer Engineering & Science, Shanghai University, Shanghai 200444, ChinaSchool of Computer Engineering & Science, Shanghai University, Shanghai 200444, ChinaWe develop the online process parameter design (OPPD) framework for efficiently handling streaming data collected from industrial automation equipment. This framework integrates online machine learning, concept drift detection and Bayesian optimization techniques. Initially, concept drift detection mitigates the impact of anomalous data on model updates. Data without concept drift are used for online model training and updating, enabling accurate predictions for the next processing cycle. Bayesian optimization is then employed for inverse optimization and process parameter design. Within OPPD, we introduce the online accelerated support vector regression (OASVR) algorithm for enhanced computational efficiency and model accuracy. OASVR simplifies support vector regression, boosting both speed and durability. Furthermore, we incorporate a dynamic window mechanism to regulate the training data volume for adapting to real-time demands posed by diverse online scenarios. Concept drift detection uses the EI-kMeans algorithm, and the Bayesian inverse design employs an upper confidence bound approach with an adaptive learning rate. Applied to single-crystal fabrication, the OPPD framework outperforms other models, with an RMSE of 0.12, meeting precision demands in production.https://www.mdpi.com/1999-5903/16/3/94online processing parameters designonline machine learningconcept drift detectionBayesian optimization |
| spellingShingle | Yu Yao Quan Qian Dynamic Industrial Optimization: A Framework Integrates Online Machine Learning for Processing Parameters Design Future Internet online processing parameters design online machine learning concept drift detection Bayesian optimization |
| title | Dynamic Industrial Optimization: A Framework Integrates Online Machine Learning for Processing Parameters Design |
| title_full | Dynamic Industrial Optimization: A Framework Integrates Online Machine Learning for Processing Parameters Design |
| title_fullStr | Dynamic Industrial Optimization: A Framework Integrates Online Machine Learning for Processing Parameters Design |
| title_full_unstemmed | Dynamic Industrial Optimization: A Framework Integrates Online Machine Learning for Processing Parameters Design |
| title_short | Dynamic Industrial Optimization: A Framework Integrates Online Machine Learning for Processing Parameters Design |
| title_sort | dynamic industrial optimization a framework integrates online machine learning for processing parameters design |
| topic | online processing parameters design online machine learning concept drift detection Bayesian optimization |
| url | https://www.mdpi.com/1999-5903/16/3/94 |
| work_keys_str_mv | AT yuyao dynamicindustrialoptimizationaframeworkintegratesonlinemachinelearningforprocessingparametersdesign AT quanqian dynamicindustrialoptimizationaframeworkintegratesonlinemachinelearningforprocessingparametersdesign |