Model-based Engineering for the Integration of Manufacturing Systems with Advanced Analytics

Abstract : To employ data analytics effectively and efficiently on manufacturing systems, engineers and data scientists need to collaborate closely to bring their domain knowledge together. In this paper, we introduce a domain-specific modeling approach to integrate a manufacturing system model with advanced analytics, in particular neural networks, to model predictions. Our approach combines a set of meta-models and transformation rules based on the domain knowledge of manufacturing engineers and data scientists. Our approach uses a model of a manufacturing process and its associated data as inputs, and generates a trained neural network model as an output to predict a quantity of interest. This paper presents the domain-specific knowledge that the approach should employ, the formal workflow of the approach, and a milling process use case to illustrate the proposed approach. We also discuss potential extensions of the approach.
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Communication dans un congrès
13th IFIP International Conference on Product Lifecycle Management (PLM16), Jul 2016, Columbia, SC, United States
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Contributeur : David Lechevalier <>
Soumis le : mardi 6 décembre 2016 - 23:31:48
Dernière modification le : jeudi 8 décembre 2016 - 01:03:56
Document(s) archivé(s) le : mardi 21 mars 2017 - 00:21:52

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David Lechevalier, Anantha Narayanan, Sudarsan Rachuri, Sebti Foufou, Y Tina Lee. Model-based Engineering for the Integration of Manufacturing Systems with Advanced Analytics. 13th IFIP International Conference on Product Lifecycle Management (PLM16), Jul 2016, Columbia, SC, United States. <hal-01411066>

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