In modern industry, the development of complex products involves engineering changes that frequently require redesigning or altering the products or their components. In an Engineering Change process, Engineering Change Requests (ECRs) are natural language written texts exchanged among process operators. ECRs describe the required change on a product or a component and the solution. After the change implementation, ECRs are stored but never consulted, missing opportunities to learn from previous projects. This paper explores the application of text clustering to natural language texts written during the Engineering Change process in industry. In detail, the use of Self Organizing Map (SOM) to the problem of unsupervised clustering of ECR texts is explored. A case study is presented in which ECRs collected during the Engineering Change process of a railways industry are analysed. The results show that SOM text clustering has a good potential to improve overall knowledge reuse and exploitation.

On the Application of Text Clustering in Engineering Change Process

GRIECO, Antonio Domenico;PACELLA, Massimo;BLACO, MARZIA
2017

Abstract

In modern industry, the development of complex products involves engineering changes that frequently require redesigning or altering the products or their components. In an Engineering Change process, Engineering Change Requests (ECRs) are natural language written texts exchanged among process operators. ECRs describe the required change on a product or a component and the solution. After the change implementation, ECRs are stored but never consulted, missing opportunities to learn from previous projects. This paper explores the application of text clustering to natural language texts written during the Engineering Change process in industry. In detail, the use of Self Organizing Map (SOM) to the problem of unsupervised clustering of ECR texts is explored. A case study is presented in which ECRs collected during the Engineering Change process of a railways industry are analysed. The results show that SOM text clustering has a good potential to improve overall knowledge reuse and exploitation.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11587/415220
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