Skip to Main content Skip to Navigation
Conference papers

A Neural Network Meta-Model and its Application for Manufacturing

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 an NN meta-model (MM), which defines a set of concepts, rules, and constraints to represent NNs. An NN model can be automatically generated and manipulated based on the specifications of the NN MM. In addition, we present an algorithm to generate a predictive model from an 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.
Complete list of metadatas

Cited literature [25 references]  Display  Hide  Download
Contributor : David Lechevalier <>
Submitted on : Tuesday, December 6, 2016 - 10:30:40 PM
Last modification on : Tuesday, May 12, 2020 - 10:49:12 AM
Document(s) archivé(s) le : Tuesday, March 21, 2017 - 4:59:25 AM


Files produced by the author(s)


  • HAL Id : hal-01411031, version 1


David Lechevalier, Ronay Ak, Y Lee, Steven Hudak, Sebti Foufou. A Neural Network Meta-Model and its Application for Manufacturing. 2015 IEEE International Conference on Big Data, Oct 2015, Santa Clara, United States. ⟨hal-01411031⟩



Record views


Files downloads