Please use this identifier to cite or link to this item: https://hdl.handle.net/10216/109863
Author(s): João Reis
Gil Manuel Gonçalves
Norbert Link
Title: Meta-process modeling methodology for process model generation in intelligent manufacturing
Issue Date: 2017-11-01
Abstract: 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.
URI: https://repositorio-aberto.up.pt/handle/10216/109863
Source: IECON 2017 - 43RD ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY
Document Type: Artigo em Livro de Atas de Conferência Internacional
Rights: restrictedAccess
Appears in Collections:FEUP - Artigo em Livro de Atas de Conferência Internacional

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