The 22MnB5-galvannealed steel is extensively used in the hot stamping process to produce car anti-collision structure parts. Furthermore, the resistance spot welding (RSW) is an important process in the automobile industry, especially in body construction, and the 22MnB5-galvannealed steels are a big challenge for the joining methods because their microstructure and mechanical properties are different from those of the conventional steels. In view of this, the present paper aims to optimize the parameters of the RSW process of the 22MnB5-galvannealed steel. Initially, the goal was to remove the galvannealed coating and in the next stage, the following responses were maximized: the nugget width, the nugget cross-sectional area, the penetration, the strength, the joint efficiency, and the energy absorption, whereas the indentation, heat affected zone and separation were used as constraints. The process parameters selected were the effective welding time, the effective welding current, the quenching time, and the upslope time. Response surface methodology (RSM) was applied jointly with the global criterion method based on principal components. The results of the multiobjective optimization are close to the individual targets for each response, highlighting the importance of considering the correlation structure presented in the responses.

Cluster analysis is a multivariate data mining technique that is widely used in several areas. It aims to group automatically the n elements of a database into k clusters, using only the information of the variables of each case. However, the accuracy of the final clusters depends on the clustering method used. In this paper, we present an evaluation of the performance of main methods for cluster analysis as Ward, K-means, and Self-Organizing Maps. Differently from many studies published in the area, we generated the datasets using the Design of Experiment (DOE) technique, in order to achieve reliable conclusions about the methods through the generalization of the different possible data structures. We considered the number of variables and clusters, dataset size, sample size, cluster overlapping, and the presence of outliers, as the DOE factors. The datasets were analyzed by each clustering method and the clustering partitions were compared by the Attribute Agreement Analysis, providing invaluable information about the effects of the considered factors individually and about their interactions. The results showed that, the number of clusters, overlapping, and the interaction between sample size and number of variable significantly affect all the studied methods. Moreover, it is possible to state that the methods have similar performances, with a significance level of 5%, and it is not possible to affirm that one outperforms the others.

In the light of Brazilian energy regulatory context, cluster strategies are required to classify groups of substations for voltage sag purposes. Tuning cluster algorithms is not a trivial task, due to the fact that these methods are sensitive to small errors. Therefore, this study proposes a new methodology based on principal components analysis (PCA), attribute agreement and analysis of covariance to verify the level of consistency and sensitivity of the linkage methods in the cluster formation for voltage sag studies. In order to prove this methodology, real data from power quality indices of distribution substations are used. Four distinct scenarios with disturbances are evaluated. PCA is applied for dimensionality reduction of the data. Then, grouping is performed for eight different linkage methods and agreement analysis is applied. Ward method was the only one that presented 100% consistency in all scenarios, considered as the most robust method whereas k-means showed consistency of 94.11%, with inversion of the clusters. However, when evaluating their groupings, it was found that k-means was unable to adequately separate the groups for this dataset. Finally, the proposed methodology is adequate for choose cluster methods for extensive data and it can be extended to applications in different areas.

Resumo Neste estudo, buscou-se analisar a variabilidade de instrumentos utilizados na indústria para avaliar as características geométricas de um ponto de solda. Para tal, inicialmente, utilizou-se da técnica de planejamento de experimentos para gerar um arranjo fatorial fracionado para as configurações dos parâmetros de soldagem em corpos de prova de aços galvanizados por imersão a quente, a fim de representar a amplitude real do processo de soldagem por pontos. Para as análises, utilizou-se o estudo de repetitividade e reprodutibilidade (GR&R), pelo método de análise de variância, para identificar os componentes de variação do sistema de medição avaliando três instrumentos distintos, comparando as medições realizadas por um analisador de imagens, a partir do ensaio metalográfico, e métricas de instrumentos convencionais de metrologia como o relógio apalpador e o paquímetro manual. Foram avaliadas duas características da qualidade, sendo elas: a profundidade de indentação e o diâmetro do ponto. A partir dos resultados foi possível verificar que o analisador de imagem apresentou uma menor variabilidade nas medições, caracterizando-se como a melhor escolha para as medições das respostas de qualidade do processo de solda por pontos apresentando um GR&R classificado como aceitável.