###### Abstract
If the model structure does not explain the reasoning behind the results, the transparency should be increased, and the concept of XAI takes this into account with other four important aspects: causality, bias, fairness, and safety [14]. The goal of XAI is that a human can easily understand and analyze the AI system and make decisions based on the explanation. The surface roughness is one of the major quality issues in stainless steel strip processing, but it can be visually detected only after the surface polishing after the whole rolling process. The strip runs through two process lines: first, a hot rolling line, and then an integrated rolling, annealing and pickling line, called a RAP-line. The improvement of the yield is one of the most important goals in production, and the quality risks should be detected as early as possible in order to save the product or avoid futile work in the case of rejection. In addition, it would be essential to find the root causes behind the increased roughness, because then the failure could be prevented, the good quality of the products could be ensured, and the competitiveness of the company could be improved. In this article, we demonstrate how data mining methods can help when a new measurement device is introduced to the process, and with this, novel information can be collected. Because the quality is measured at the end of the process, data mining and machine learning provide tools to inspect the property already during the production when process parameters can still be adjusted. However, it is not a simple task to utilize data analysis methods, explore the vast amount of collected process data, and identify all the affecting process variables that need to be controlled in order to improve the quality. Our solution is based on statistical quality predictions made with generalized boosted regression models [15], and by using XAI methods, it finds the most probable candidatesfor quality deterioration and recommends the actions that could improve the quality. In addition, the process engineers and operators will learn more about the steel making process and how the process settings affect the quality property. Thus, it is possible to pinpoint the root causes behind the failure more closely and to find the process steps in which they form. Usually, project workers have some kind of idea how certain process parameters are affected during the process, but the assumption is not evidence-based. The process should be optimized with a new quality parameter, because there is a considerable risk of using harmful settings accidentally when lacking knowledge about this quality property. With data mining methods, it is possible to explore this already before optimization and to learn the real effects of the process parameters on this particular quality property. The current data may contain useful information also from the extreme situations, which may not actually appear in the data after optimization. Thus, it would be more reliable to set good process parameter settings and find the boundaries for safe operation. In this work, an intelligent decision support system for the manufacturing industry is developed, and the data mining methods are applied to the surface roughness of the steel strip. The article is organized as follows: Section 2 describes the practical issues related to smart decision support. Section 3 introduces the used data collection technique. Machine learning and XAI methods are explained in Section 4. Training models for roughness prediction and modeling results are shown in Section 5. The XAI empowered decision support is then presented in Section 6. Finally, the discussion and conclusions are in Section 7.