Model-based Engineering for the Integration of Manufacturing Systems with Advanced Analytics - Product Lifecycle Management for Digital Transformation of Industries Access content directly
Conference Papers Year : 2016

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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hal-01411066 , version 1 (06-12-2016)

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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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