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Product Quality Control in Assembly Machine under Data Restricted Settings

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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.

OriginalsprogEngelsk
Titel2022 IEEE 20th International Conference on Industrial Informatics, INDIN 2022
Antal sider7
ForlagIEEE
Udgivelsesår15 dec. 2022
Sider735-741
ISBN (Elektronisk)978-1-7281-7568-3
DOI
StatusUdgivet - 15 dec. 2022
Begivenhed2022 IEEE 20th International Conference on Industrial Informatics - Online, Perth, Australien
Varighed: 25 jul. 202228 jul. 2022
https://2022.ieee-indin.org/

Konference

Konference2022 IEEE 20th International Conference on Industrial Informatics
LokationOnline
LandAustralien
ByPerth
Periode25/07/202228/07/2022
Internetadresse

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