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.

Autore Pugliese

• Grieco Antonio Domenico , Pacella Massimo

Tutti gli autori

• Grieco A.D. , Pacella M. , Blaco M.

Titolo volume/Rivista

PROCEDIA CIRP

Anno di pubblicazione

2017

ISSN

2212-8271

ISBN

Non Disponibile

Numero di citazioni Wos

1

Ultimo Aggiornamento Citazioni

27/04/2018

Numero di citazioni Scopus

1

Ultimo Aggiornamento Citazioni

22/04/2018

Settori ERC

Non Disponibile

Codici ASJC

Non Disponibile