Semantic data mining-based decision support for quality assessment in steel industry

This study evaluates quality management practices in Industry 4.0 in a specific case of steel manufacturing. We formulate a novel proposal based on Semantic Data Mining techniques a step towards knowledge-driven decision support based on the industrial Six Sigma approach. In our research we combine machine learning classifiers, and explanation generation algorithms with the Six Sigma practice to automate the evaluation of quality of steel products and determine origins of their defects. We describe our original method, and provide evaluation of the results with real-life data from our industrial partner.