Multivariate chance-constrained method applied in multi-objective optimization problems of manufacturing processes

Agência 1In the multi-objective optimization problems of manufacturing processes, the responses of interest are often significantly correlated. In addition to the multivariate nature of the problems, product demands, productive capacities, cycle times, the costs of labor, machines, and tools are just some of the many random variables involved in the optimization model. In particular, when using Design of Experiments (DoE) techniques and regression methods, the estimated coefficients for the empirical models - such as response surface models - are also stochastic. However, it has been observed that most of the articles published in this research area are limited to represent the stochastic variables in a deterministic way. Within this context, the present study aimed to propose the use of stochastic programming techniques combined with multivariate statistical methods including some process capability indices widely used in the industry, such as the capacity index and the Parts Per Million () index. The use of the methods combined used resulted in the proposal of the Multivariate Chance-Constrained Programming (MCCP). To test the applicability of the MCCP method, a multi-objective optimization problem of the AISI 52100 hardened steel turning process was selected as a case study given its widespread use and relevance to the industry nowadays. As a starting point for this study, a set of experimental results obtained from a central composite design was used. The decision variables were the cutting speed (), the feed rate () and the depth of cut (). The responses of interest selected for this work were the total machining cost per part (), the material removal rate (), the tool life (), the average roughness () and the total roughness (). After analyzing the data and building the mathematical models for the responses of interest, three approaches were carried out. In the first approach, the index included the calculation of the variance of the response surface model of . In the second approach, the probability that is less than or equal to a predefined value was modelled as a stochastic objective function. Finally, the third approach described the application of the proposed MCCP method. In this approach, the index was calculated using a normal bivariate distribution for both and . The main results of this research were: a) the demonstration and validation of an equation used to calculate the variance of a continuous, derivable and dependent function of stochastic variables; b) the analysis of the impact of seven stochastic industrial variables (setup time, lot size, machine and labor costs, insert changing time, tool holder price, tool holder life and insert price) on the cost of the process; c) finding that maximizing tool life may reduce cost in some cases – for example when using Wiper tools – but the change of the cutting conditions alone does not necessarily reduce the cost of the process, as in what occurred in the case study analyzed

Perovskite Solar Cells: Developing a simple, fast and low-cost Fabrication Technology

Solar energy is the most abundant renewable resource and is regarded as the most promising for the sustainability of our society. Perovskites are a class of semiconductor materials with unique properties since they allow films fabrication with high electronic quality using non-vacuum solution techniques. Therefore, such materials are interesting for a wide range of opto-electronic applications. Perovskites allow rapid, simple and low-cost solar cell manufacturing, being nowadays considered the most promising material to compete with silicon in photovoltaics technology. However, the production of homogeneous MAPbI3 films by Spin Coating is challenging, as it requires precise control of several factors that influence the films’ properties. In this work, the influence of the main deposition parameters on the MAPbI3 thin films manufacture was studied to find the best processing conditions that enable obtaining films as homogeneous and uniform as possible. This allowed attaining MAPbI3 polycrystalline films with state-of-art quality, having grain sizes between 3 and 13 μm and UV-Visible absorption of 85-90 %. The remaining layers (i.e. selective contacts) of the Perovskite cell structure were investigated as well, allowing the fabrication of sets of full solar cells with a maximum VOC of 0.77 V and JSC of 7.65 mA.cm-2