Abstract
One of the main goals in flux-cored arc welding processes is the optimization of bead geometry, in which multiple geometric characteristics of the welding bead are important; therefore, multiobjective optimization programming is often applied. However, several optimization problems that use stochastic programming do not consider the impact of the correlation between the output variables on their probabilistic constraints. In this context, this paper aims to present a multiobjective optimization method based on multivariate stochastic programming. To demonstrate the applicability of the proposal, we conducted a design of experiments to optimize a flux-cored arc welding process for stainless-steel claddings. The weighting-sums method was applied to formulate the multiobjective optimization problem. It was possible to formulate a multivariate probability distribution for the penetration and dilution. In addition, a 95% probability to meet the predefined specification limits of the geometric characteristics was achieved.
Highlights
In practical optimization problems of industrial processes, the assumption that the input data are deterministic is rarely sustained
We aimed to optimize a flux-cored arc welding (FCAW) process used in the manufacturing of stainless-steel claddings
The authors proposed the use of stochastic programming and multivariate statistics to include the variability of the geometry characteristics to the optimization problem
Summary
In practical optimization problems of industrial processes, the assumption that the input data are deterministic is rarely sustained. Certain key inputs that are clearly random are instead represented by their expected values. Such an approach may be justified under special conditions; in several applications, it is possible to demonstrate that such a formulation is inadequate [1]. In most approaches reported in the literature, matrices C and A and vector b are composed of deterministic values. These vectors and matrices may have random inputs in actual problems
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