Erica Ximenes Dias’s research while affiliated with São Paulo State University and other places

Complex phase steels, known for their high levels of conformability, energy absorption, and deformation capacity, are among the more advanced high-strength steels. The objective of this study was to compare the mechanical properties of CPW 800-class complex phase steels, with and without laser welding. The analysis involved determining tensile strength, yield strength, elongation, and area reduction through tensile tests, in scenarios both with and without laser welding. Additionally, the number of cycles was assessed via fatigue tests, and absorbed energy was measured using impact tests. The non-parametric Kruskal–Wallis test, at a 5% significance level, revealed that tensile strength, yield strength, area reduction, and absorbed energy were statistically similar regardless of laser welding. However, elongation and the number of cycles showed significant differences. The fractured surface from axial fatigue tests exhibited ductile characteristics, with the additional presence of dimples or alveoli.

In many practical situations, it is important to evaluate the relationships between the factors that compose an industrial process and their effects on one or more response variables that are of interest to an enterprise. The main contribution of this present study is to propose a new conceptual hybrid framework based on the DMAIC (Define, Measure, Analyze, Improve, and Control) methodological structure, to optimize complex experimental problems with multiple responses. This procedure combines Response Surface Methodology, with the Desirability (D), Modified Desirability (MD), Compromise Programming (CP) functions, with Generalized Reduced Gradient (GRG) and Evolutionary Algorithms (EA). We made real application to a glass lamination process case study to describe how to use the proposed framework. The procedure allowed several configurations to be tested involving the D, MD and CP functions, adopting the GRD and EV, to optimize the studied industrial process. The best configuration was defined by a practical confirmation experiment and validated by company engineers and experts. As examples of the advantages of adopting the proposed framework in the glass lamination problems, the best solutions resulted in a 49.86% increase in grinding wheel shelf life, corresponding to a 927kg reduction of steel-use per year, and a 41.7% reduction in dressing stone consumption, saving 17,200 stones per year.

In many practical situations, it is important to evaluate the relationships between the factors that compose an industrial process and their effects on one or more response variables that are of interest to an enterprise. The main contribution of this present study is to propose a new conceptual hybrid framework based on the DMAIC (Define, Measure, Analyze, Im- prove, and Control) methodological structure, to optimize complex experimental problems with multiple responses. This procedure combines Response Surface Methodology, with the Desirability (D), Modified Desirability (MD), Compromise Programming (CP) functions, with Generalized Reduced Gradient (GRG) and Evolutionary Algorithms (EA). We made real application to a glass lamination process case study to describe how to use the proposed framework. The procedure allowed several configurations to be tested involving the D, MD and CP functions, adopting the GRD and EV, to optimize the studied industrial process. The best configuration was defined by a practical confirmation experiment and validated by company engineers and experts. As examples of the advantages of adopting the proposed framework in the glass lamination problems, the best solutions resulted in a 49,86% increase in grinding wheel shelf life, corresponding to a 927[kg] reduction of steel-use per year, and a 41.7% reduction in dressing stone consumption, saving 17,200 stones per year.

This work has been developed in a large steel industry in Brazil, which produces railway and industrial components, and whose aim was to reduce casting defects. Usually, in industrial processes, identifying the causes of defects and their control are relatively complex activities, due to the many variables involved. In this context, the production processes of seven products, involving 38 process variables (inputs and outputs), have been evaluated adopting a new and innovative procedure. Initially, using a Weighted Goal Programming ‐ Multiple Criteria Data Envelopment Analysis (WGP‐MCDEA) model, we identified the most relevant input and output variables, and the studied company validated the results. Next, using the multiple regression technique, empirical functions were constructed for two response variables chosen by the company – number of external cracks and number of internal cracks. Then, to model the real processes adequately, we introduced the occurrence of uncertainty on the coefficients of these functions, considering them as random variables, according to triangular probability functions. Finally, applying the optimizer Optquest, optimization via Monte Carlo simulation (OvMCS) was performed, and with the Ordinary Least Square technique, we obtained the best fit for the two response variables. Specialists from the company validated the proposed procedure. They found that the values of input and output variables obtained by OvMSC, as well as the values of the response variables, belonged to the database available in the ERP system of the company. These results showed that the procedure proposed herein provided feasible and useful solutions to improve the industrial processes under study.

The response surface methodology (RSM), which uses a quadratic empirical function as an approximation to the original function and allows the identification of relationships between independent variables xi and dependent variables ys associated with multiple responses, stands out. The main contribution of the present study is to propose an innovative procedure for the optimization of experimental problems with multiple responses, which considers the insertion of uncertainties in the coefficients of the obtained empirical functions in order to adequately represent real situations. This new procedure, which combines RSM with the finite element (FE) method and the Monte Carlo simulation optimization (OvMCS), was applied to a real stamping process of a Brazilian multinational automotive company. For RSM with multiple responses, were compared the results obtained using the agglutination methods: compromise programming, desirability function (DF), and the modified desirability function (MDF). The functions were optimized by applying the generalized reduced gradient (GRG) algorithm, which is a classic procedure widely adopted in this type of experimental problem, without the uncertainty in the coefficients of independent factors. The advantages offered by this innovative procedure are presented and discussed, as well as the statistical validation of its results. It can be highlighted, for example, that the proposed procedure reduces, and sometimes eliminates, the need for additional confirmation experiments, as well as a better adjustment of factor values and response variable values when comparing to the results of RSM with classic multiple responses. The new proposed procedure added relevant and useful information to the managers responsible for the studied stamping process. Moreover, the proposed procedure facilitates the improvement of the process, with lower associated costs.

The Response Surface Methodology (RSM), which uses a quadratic empirical function as an approximation to the original function and allows the identification of relationships between independent variables xi and dependent variables ys associated with multiple responses, stands out. The main contribution of the present study is to propose an innovative procedure for the optimization of experimental problems with multiple responses, which considers the insertion of uncertainties in the coefficients of the obtained empirical functions in order to adequately represent real situations. This new procedure, which combines RSM with the Finite Elements (FE) method and the Monte Carlo Simulation Optimization (OvMCS), was applied to a real stamping process of a Brazilian multinational automotive company. For RSM with multiple responses, were compared the results obtained using the agglutination methods: Compromise Programming, Desirability Function (DF), and the Modified Desirability Function (MDF). The functions were optimized by applying the Generalized Reduced Gradient (GRG) algorithm, which is a classic procedure widely adopted in this type of experimental problem, without the uncertainty in the coefficients of independent factors. The advantages offered by this innovative procedure are presented and discussed, as well as the statistical validation of its results. It can be highlighted, for example, that the proposed procedure reduces, and sometimes eliminates, the need for additional confirmation experiments, as well as a better adjustment of factor values and response variable values when comparing to the results of RSM with classic multiple responses. The new proposed procedure added relevant and useful information to the managers responsible for the studied stamping process. Moreover, the proposed procedure facilitates the improvement of the process, with lower associated costs.

Data envelopment analysis (DEA) allows evaluation of the relative efficiencies of similar entities, known as decision making units (DMUs), which consume the same types of resources and offer similar types of products. It is known that under certain circumstances, when the number of DMUs does not meet the DEA Golden Rule, that is, this number is not sufficiently large compared to the total number of inputs and outputs, traditional DEA models often yield solutions that identify too many DMUs as efficient. In fact, this weak discrimination power and unrealistic weight distribution presented by DEA models remain a major challenge, leading to the development of models to improve this performance, such as: multiple criteria data envelopment analysis (MCDEA), bi-objective multiple criteria data envelopment analysis, goal programming approaches to solve weighted goal programming (WGP–MCDEA) and extended–MCDEA. This paper proposes a new MCDEA model which is based on goal programming, with and without super efficiency concepts, and presents test results that show its advantages over the above cited models. A set of problems from the literature and real-world applications are used in these tests. The results show that the new MCDEA model provides better discrimination of DMUs in all tested problems, and provides a weight dispersion that is statistically equal to that obtained by other MCDEA models. An additional feature of the proposed model is that it allows the identification of the input and output variables that are most important to the problem, to make it easier for the decision maker to improve the efficiency of the DMUs involved. This is very useful in practice, because in general, the available resources are scarce, so it is a further advantage of the proposed MCDEA model over the others tested.