Comparison of Different ANFIS Models for the Condition  Monitoring of a Rack and Pinion Contact Using Methods  of Explainable Artificial Intelligence

Tobias Biermann; Jonathan Millitzer; Karsten Schmidt

doi:10.37965/jdmd.2025.787

Authors

Tobias Biermann Department of Computer Science and Engineering, Frankfurt University of Applied Sciences, Frankfurt am Main, Germany https://orcid.org/0009-0009-1202-8944
Jonathan Millitzer Fraunhofer Institute for Structural Durability and System Reliability LBF, Darmstadt, Germany https://orcid.org/0009-0000-5883-8074
Karsten Schmidt Department of Computer Science and Engineering, Frankfurt University of Applied Sciences, Frankfurt am Main, Germany https://orcid.org/0000-0001-5544-5136

DOI:

https://doi.org/10.37965/jdmd.2025.787

Abstract

This paper investigates the use of explainable artificial intelligence (XAI) and trustworthy artificial intelligence (TAI) methods for condition monitoring on a laser cutting machine. The focus is on the analysis of the rack and pinion contact with wear being predicted by four differently derived adaptive-network-based fuzzy inference system(s) (ANFIS) models. Using both model-agnostic and model-specific parameters integrated in a weighted evaluation framework, the models are evaluated with respect to the effectiveness of explanations. This frameworkisbasedontheobservation oftheoutputs oftheindividual layers of ANFIS, also focusing onaspects of two multivalued logics, namely fuzzy logic and support logic. The results show that the introduced weighted evaluation framework makes it possible to quantify theexplainability of the individual models in terms of XAI and TAI. Finally, a preselection of a model for predicting the wear of the rack and pinion contact can be made.

Conflict of Interest Statement

The authors declare no conflicts of interest.