Recommendation System for DFMEA Analysis Using Neural Network Embeddings
DOI:
https://doi.org/10.24425/mper.2025.154937Abstract
This paper focuses on the application of machine learning in the Failure Mode and Effects Analysis (FMEA) process for analyzing failure modes and effects using data modeling. FMEA is a recognized methodology used to detect and assess potential problems in products and processes before they occur. The main objective was to develop a neural network model that could predict potential failure modes and their effects, using a specially prepared anonymised table derived from industrial DFMEA records. Utilizing machine learning in the context of FMEA opens new perspectives in terms of accuracy, objectivity, and efficiency of analysis, while reducing subjectivity and the time required for the traditional FMEA analysis approach. The proposed neural network model performs calculations and analyses, enabling a deeper understanding of the patterns in the data and their potential applications in the industry.References
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