Knowledge Extraction in Manufacturing using Data Mining Techniques
2008 (English)In: Proceedings of the Swedish Production Symposium 2008, Stockholm, Sweden, November 18-20, 2008, 2008, 8 sidor- p.Conference paper (Refereed)
Nowadays many production companies collect and store production and process data in large databases. Unfortunately the data is rarely used in the most value generating way, i.e., finding patterns of inconsistencies and relationships between process settings and quality outcome. This paper addresses the benefits of using data mining techniques in manufacturing applications. Two different applications are being laid out but the used technique and software is the same in both cases. The first case deals with how data mining can be used to discover the affect of process timing and settings on the quality outcome in the casting industry. The result of a multi objective optimization of a camshaft process is being used as the second case. This study focuses on finding the most appropriate dispatching rule settings in the buffers on the line. The use of data mining techniques in these two cases generated previously unknown knowledge. For example, in order to maximize throughput in the camshaft production, let the dispatching rule for the most severe bottleneck be of type Shortest Processing Time (SPT) and for the second bottleneck use any but Most Work Remaining (MWKR).
Place, publisher, year, edition, pages
2008. 8 sidor- p.
Data mining, Quality engineering, Knowledge extraction
IdentifiersURN: urn:nbn:se:his:diva-7129OAI: oai:DiVA.org:his-7129DiVA: diva2:603649
Swedish Production Symposium 2008, Stockholm, Sweden, November 18-20, 2008