World steel trade becomes more competitive every day and new high international quality standards and productivity levels can only be achieved by applying the latest computational technologies. Data driven analysis of complex processes is necessary in many industrial applications where analytical modeling is not possible. This paper presents the deployment of KDD technology in one real industrial problem: the development of new tinplate quality diagnostic models.The electrodeposition of tin on steel strips is the most critical stage of a complex process that involves a great amount of variables and operating conditions. Its optimization is not only a great commercial and economic challenge but also a compulsion due to the social impact of the tinplate product-more than 90% of the production is used for food packaging. The necessary certification with standards, like ISO 9000, requires the use of diagnostic models to minimize the costs and the environmental impact. This aim has been achieved following the multi-stage DM methodology CRISP-DM and a novel application of pro-active maintenance methods, as FMEA, for the identification of the specific process anomalies. Three DM tools have been used for the development of the models. The final results include two ANN tinplate quality diagnostic models, that provide the estimated quality of the final product just seconds after its production and only based on the process data. The results have much better performance than the classical Faraday's models widely used for the estimation.

Models are descriptions of real facts that serve us to think and reason. In building models, a compromise always exists between accuracy, that is, how precisely the model describes reality, and simplicity, without which the model would be useless. So, a good model must be simple and intuitive while being accurate enough. In this paper we propose a novel approach based on visual techniques aiming to help the human in fine-tuning fuzzy decision trees to enhance its interpretability and insightfulness with a minimal loss of accuracy. By involving the human in the design process, these techniques allow to include prior knowledge in the selection of membership functions as well as to assess the significance of rules in the model to help in the pruning stage.