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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.
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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