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A Neural Network Meta-Model and its Application for Manufacturing

Published

Author(s)

David J. Lechevalier, Ronay Ak, Steven Hudak, Yung-Tsun T. Lee, Sebti Foufou

Abstract

Manufacturing generates a vast amount of data both from operations and simulation. Extracting appropriate information from this data can provide insights to increase a manufacturer's competitive advantage through improved sustainability, productivity, and flexibility of their operations. Manufacturers, as well as other industries, have successfully applied a promising statistical learning technique, called neural networks (NNs), to extract meaningful information from large data sets, so called big data. However, the application of NN to manufacturing problems remains limited because it involves the specialized skills of a data scientist. This paper introduces an approach to automate the application of analytical models to manufacturing problems. We present a NN meta-model (MM), which defines a set of concepts, rules, and constraints to represent NNs. A NN model can be automatically generated and manipulated based on the specifications of the NN MM. The NN MM is implemented in a tool called Generic Modeling Environment (GME). In addition, we present an algorithm to generate a predictive model from a NN and available data. The predictive model is represented in either Predictive Model Markup Language (PMML) or Portable Format for Analytics (PFA). Then we illustrate the approach in the context of a specific manufacturing system. Finally, we identify future steps planned towards later implementation of the proposed approach.
Proceedings Title
2015 IEEE International Conference on Big Data
Conference Dates
October 29-November 1, 2015
Conference Location
Santa Clara, CA

Keywords

neural network, meta-model, data analytics, PMML, manufacturing

Citation

Lechevalier, D. , Ak, R. , Hudak, S. , Lee, Y. and Foufou, S. (2015), A Neural Network Meta-Model and its Application for Manufacturing, 2015 IEEE International Conference on Big Data, Santa Clara, CA, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=919287 (Accessed June 13, 2024)

Issues

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Created October 29, 2015, Updated February 19, 2017