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Learning to predict characteristics for engineering service projects

Published online by Cambridge University Press:  01 December 2016

Lei Shi ,
Linda Newnes ,
Steve Culley  and
Bruce Allen
Lei Shi*
Affiliation:
Department of Mechanical Engineering, University of Bath, Bath, United Kingdom
Linda Newnes
Affiliation:
Department of Mechanical Engineering, University of Bath, Bath, United Kingdom
Steve Culley
Affiliation:
Department of Mechanical Engineering, University of Bath, Bath, United Kingdom
Bruce Allen
Affiliation:
AIRBUS Operations Ltd, Filton, United Kingdom
*
Reprint requests to: Lei Shi, Department of Mechanical Engineering, University of Bath, Bath BA2 7AY, UK. E-mail: l.shi@bath.ac.uk
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Abstract

An engineering service project can be highly interactive, collaborative, and distributed. The implementation of such projects needs to generate, utilize, and share large amounts of data and heterogeneous digital objects. The information overload prevents the effective reuse of project data and knowledge, and makes the understanding of project characteristics difficult. Toward solving these issues, this paper emphasized the using of data mining and machine learning techniques to improve the project characteristic understanding process. The work presented in this paper proposed an automatic model and some analytical approaches for learning and predicting the characteristics of engineering service projects. To evaluate the model and demonstrate its functionalities, an industrial data set from the aerospace sector is considered as a the case study. This work shows that the proposed model could enable the project members to gain comprehensive understanding of project characteristics from a multidimensional perspective, and it has the potential to support them in implementing evidence-based design and decision making.

Keywords

Characteristics LearningCharacteristics PredictionEngineering Service ProjectKnowledge Discovery
Type
Regular Articles
Information
AI EDAM , Volume 31 , Special Issue 3: Uncertainty Quantification for Engineering Design , August 2017 , pp. 313 - 326
Copyright
Copyright © Cambridge University Press 2016 

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