A Real-Time Quality Control System Based on Manufacturing Process Data

Duan, Guijiang; Yan, Xin

doi:10.1109/access.2020.3038394

Cited by 6 publications

(2 citation statements)

References 34 publications

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“…It provides a method for learning the structure of the P-E model, and the quality characteristic (QC) relationship is determined by empirical data 32 , 33 . By analyzing the relationship between manufacturing resources and product quality status 34 , proposed a real-time quality control system (RTQCS) based on manufacturing process data, establishing the relationship between real-time product quality status and machining task processes 35 . A single-board computer and sensors were used to construct an edge device that can collect, process, store, and analyze data.…”

Section: Related Workmentioning

confidence: 99%

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Edge computing-based proactive control method for industrial product manufacturing quality prediction

Chen,

Wei,

et al. 2024

Sci Rep

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With the emergence of intelligent manufacturing, new-generation information technologies such as big data and artificial intelligence are rapidly integrating with the manufacturing industry. One of the primary applications is to assist manufacturing plants in predicting product quality. Traditional predictive models primarily focus on establishing high-precision classification or regression models, with less emphasis on imbalanced data. This is a specific but common scenario in practical industrial environments concerning quality prediction. A SMOTE-XGboost quality prediction active control method based on joint optimization hyperparameters is proposed to address the problem of imbalanced data classification in product quality prediction. In addition, edge computing technology is introduced to address issues in industrial manufacturing, such as the large bandwidth load and resource limitations associated with traditional cloud computing models. Finally, the practicality and effectiveness of the proposed method are validated through a case study of the brake disc production line. Experimental results indicate that the proposed method outperforms other classification methods in brake disc quality prediction.

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Section: Related Workmentioning

confidence: 99%

“…Therefore, the quality diagnosis and prediction model based on historical data is difficult to adapt to current production requirements. Some articles have also studied the update mechanism of predictive models 34 , 38 .…”

Section: Industrial Product Manufacturing Quality Prediction Frame Workmentioning

confidence: 99%