Meta-process modeling methodology for process model generation in intelligent manufacturing

• Type: Article in International Conference Proceedings Book
• Date: 2017-11-01

Authors

João Reis (Author) FEUP; Gil Manuel Gonçalves (Author) FEUP; Norbert Link (Author) Other

Conference proceedings International

Title: IECON 2017 - 43RD ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY

Pages: 3396-3402

43rd Annual Conference of the IEEE-Industrial-Electronics-Society (IECON)

Beijing, PEOPLES R CHINA, OCT 29-NOV 01, 2017

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Other information

Authenticus ID: P-00N-J5X

DOI: 10.1109/iecon.2017.8216575

Abstract (EN): The present paper details a novel methodology called Meta-Process Model that is able to generate new data-based models for manufacturing processes when no experimental data is available. For that purpose, the concept of Hyper-Models was used to create a higher level of abstraction of these manufacturing processes, along with a Statistical Shape Model (SSM) that is able to capture the modes of shape variations and build up a deformable model to generate new shapes. The main premise of the present work is to interpret a process model as a n-dimensional shape and use SSM to capture the variations among a set of different process models. This methodology is evaluated by using two already existing process models for a model generalization, from which a new process model is derived just with new, given process conditions. This new process model is then compared with a process model, which was independently estimated using real experimental data acquired under the same process conditions. The results show that a previously nonexistent process model that captures the dynamics of the real process can be generated, even when there's no experimental data and only the new process conditions are available.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 7