Porosity Classification in High Pressure Die Casting using Thermal Images and Sensor Data Fusion via Fuzzy Cognitive Maps

Accurate process monitoring and fast defect detection is very crucial nowadays in the industrial manufacturing, as many of the products must meet the safety and performance requirements. In the High Pressure Die Casting (HPDC) process one of the main and most severe defect is a porosity, which can be caused by many factors. In order to detect its occurrence, most often destructive tests or time-consuming methods like cuts, leakage tests, Computed Tomography, or X-Ray have to be made. Due to that fact, there is growing demand on methods which can be used inline and without waste production, even as only a preliminary check which reduces the number of parts for the more throughout examination. This paper presents a novel Fuzzy Cognitive Map- based fused sensor classifier for porosity prediction in HPDC parts. The main contributions are: fusion of HPDC machine sensor readouts and thermal images (before and after spraying); feature extraction methods tailored to the HPDC dataset; and a feature selection study analyzing their impact on model performance. To our knowledge, this is the first application of Fuzzy Cognitive Maps for porosity classification in die casting using fused thermal and sensor data. This solution supports sustainability, waste reduction, and inline, non- destructive visual quality control in the metallurgic industry.