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

Tutkimustuotos: KonferenssiartikkeliScientificvertaisarvioitu

1 Lataukset (Pure)

Abstrakti

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.

AlkuperäiskieliEnglanti
Otsikko3rd International Conference on Industry 4.0 and Smart Manufacturing
KustantajaElsevier
Sivut1550-1564
Sivumäärä15
DOI - pysyväislinkit
TilaJulkaistu - 2022
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaInternational Conference on Industry 4.0 and Smart Manufacturing - Linz, Itävalta
Kesto: 19 marrask. 202121 marrask. 2021

Julkaisusarja

NimiProcedia Computer Science
KustantajaElsevier
Vuosikerta200
ISSN (painettu)1877-0509

Conference

ConferenceInternational Conference on Industry 4.0 and Smart Manufacturing
Maa/AlueItävalta
KaupunkiLinz
Ajanjakso19/11/2121/11/21

Julkaisufoorumi-taso

  • Jufo-taso 1

!!ASJC Scopus subject areas

  • Computer Science(all)

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