A Novel Approach to Improve Quality Control by Comparing the Tagged Sequences of Product Traceability
| Autor: | Jing-Doo Wang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
020203 distributed computing
Product traceability Computer science lcsh:TA1-2040 media_common.quotation_subject Control (management) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Quality (business) 02 engineering and technology lcsh:Engineering (General). Civil engineering (General) Reliability engineering media_common |
| Zdroj: | MATEC Web of Conferences, Vol 201, p 05002 (2018) |
| Popis: | Quality control is an essential issue for manufacture, especially when the manufacture is towards intelligent manufacturing that is associated with “Internet of thing”(IOT) and “Artificial Intelligence”(AI) to speed up the rate of product line automatically nowadays. To monitor product quality automatically, it is necessary to collect and monitor the data generated by sensors, or to record parameters by machine operators, or to save the types (brands) of materials used when producing products. In this study, it is assumed that the sequences of the traceability of unqualified products are different from that of qualified ones, and these different values (or points) within the sequences result in these products qualified or unqualified. This approach extracts maximal repeats from the tagged sequences of product traceability, and meanwhile computes the class frequency distribution of these repeats, where the classes, e.g. “qualified” or “unqualified”, are derived from the tags. Instead of inspecting all of the sequences of product traceability aimlessly, quality control engineers can filter out those maximal repeats whose frequency distributions are unique to specific classes and then just check the corresponding processes of these repeats. However, from the practical point of view, it should be estimated as a big-data problem to extract these maximal repeats and meanwhile compute their corresponding class frequency distribution from a huge amount of tagged sequential data. To have this work practical, this study uses one previous work that is based on Hadoop MapReduce programming model. and has been applied for an U.S.A patent (US Patent App. 15/208,994). Therefore, it is expected to be able to handle a huge amount of sequences of product traceability. With this approach that can narrow down the range for identifying false points (processes) within product line, it is expected to improve quality control by comparing tagged sequences of product traceability in the future. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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