Model-based engineering for the integration of manufacturing systems with advanced analytics
Author | Lechevalier, David |
Author | Narayanan, Anantha |
Author | Rachuri, Sudarsan |
Author | Foufou, Sebti |
Author | Lee, Y. Tina |
Available date | 2021-09-08T06:49:44Z |
Publication Date | 2016 |
Publication Name | IFIP Advances in Information and Communication Technology |
Resource | Scopus |
ISSN | 18684238 |
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. IFIP International Federation for Information Processing 2016. |
Sponsor | The research in this paper was supported by National Institute of Standards and Technology?s Foreign Guest Researcher Program, and Cooperative Agreement No. 70NANB14H250. |
Language | en |
Publisher | Springer New York LLC |
Subject | Data analytics Manufacturing process Meta-model Neural network Predictive modeling |
Type | Conference Paper |
Pagination | 146-157 |
Volume Number | 492 |
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Computer Science & Engineering [2211 items ]