Identifying key interactions between process variables of different material categories using mutual information-based network inference method

Shailesh Tripathi, Herbert Jodlbauer, Christian Mittermayr, Frank Emmert-Streib

Research output: Contribution to journalConference articlepeer-review

Abstract

This paper analyzes production data from injection molding processes to identify key interactions between the process variables from different material categories using the network inference method called "bagging conservative causal core network" (BC3net). This approach is an ensemble method with mutual information that is measured between process variables to select pairs that show significant shared information. We construct networks for different time intervals and aggregate them by calculating the proportion of significant pairs of process variables (weighted edges) for each production process over time. The weighted edges of the aggregated network for each product are used in a machine learning model to optimize the network interval size (interval split) and feature selection, where edge weights are the input features and material categories are the output classification labels. The time intervals are optimized based on the classification accuracy of the machine learning model. Our analysis shows that the aggregated edge features of inferred networks can classify different material categories and identify critical features that represent interdependence in the associated process variables. We further used the "one vs. other" labels for the machine learning models to identify material-specific interactions for each material category. Additionally, we constructed an aggregated network over all samples in which the process variable interactions were steady over time. The resulting network showed modular characteristics where process variables of similar categories were grouped in the same community.

Original languageEnglish
Pages (from-to)1550-1564
Number of pages15
JournalProcedia Computer Science
Volume200
DOIs
Publication statusPublished - 2022
Event3rd International Conference on Industry 4.0 and Smart Manufacturing, ISM 2021 - Linz, Austria
Duration: 19 Nov 202121 Nov 2021

Keywords

  • injection molding
  • machine learning models
  • network inference
  • process variable interactions
  • process variable network

Fingerprint

Dive into the research topics of 'Identifying key interactions between process variables of different material categories using mutual information-based network inference method'. Together they form a unique fingerprint.

Cite this