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Product Quality Control in Assembly Machine under Data Restricted Settings

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

Evaluating the product quality in an assembly machine is critical yet time-consuming since, in product assessment in batch manufacturing, a certain amount of products should be investigated in an invasive manner. However, continuous manufacturing ensures product quality assessment during assembly with high efficiency and traceability. This paper proposes a quality assessment method for an industrial use case. First, the data is prepared based on two indicators and expert knowledge. Then two data classification approaches (one-class classification and binary classification) are applied to evaluate the products' quality by analysing the related data. Finally, the most efficient model is selected to predict the product labels and deviate anomalies from normal products. For the studied use case and the limited number of products, the binary classifier guarantees to detect 100% of defective products. The proposed approach can provide the engineers and operators with understandable extracted process knowledge, and can therefore be adapted to a high-speed manufacturing line where large data volume and process complexity can be problematic.

Original languageEnglish
Title of host publication2022 IEEE 20th International Conference on Industrial Informatics, INDIN 2022
Number of pages7
Publication year15 Dec 2022
ISBN (Electronic)978-1-7281-7568-3
Publication statusPublished - 15 Dec 2022
Event2022 IEEE 20th International Conference on Industrial Informatics - Online, Perth, Australia
Duration: 25 Jul 202228 Jul 2022


Conference2022 IEEE 20th International Conference on Industrial Informatics

    Research areas

  • Anomaly Detection, Binary Classifier, Medical Device Assembly, One Class Support Vector Machine, Product Quality Assessment

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