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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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Contributor : David Lechevalier <>
Submitted on : Tuesday, December 6, 2016 - 11:31:48 PM
Last modification on : Tuesday, May 12, 2020 - 10:49:12 AM
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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 (PLM), Jul 2016, Columbia, SC, United States. pp.146-157, ⟨10.1007/978-3-319-54660-5_14⟩. ⟨hal-01411066⟩



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