Resumo:
To assist in solving the problem of process improvement, restrictions and better welding
operation conditions, this work applies the Design of Experiments (DoE), Multiobjective
Optimization and Multivariate Statistics methodologies together to provide the necessary
support in the management of the production process of MIG welding (Metal Inert Gas), of
anti-corona protection rings, manufactured with tubes aluminum alloy 6063 (Aluminum Alloy
6063 - AA6063), T4, 100 mm in diameter and 2 mm thick. This type of process can be
controlled by a relatively small number of input variables, that is, the wire feed rate (WF),
voltage (V), welding speed (Fr) and the distance from the contact tip to the part of work (Cf).
In addition, many outputs can be evaluated and optimized simultaneously. In the present work,
the variables of yield (Y), dilution (D), reinforcement index (IR) and penetration index (PI)
were investigated. To consider the multivariate nature of the problem, techniques such as Factor
Analysis and Bonferroni's simultaneous confidence intervals were applied combined with
elliptical constraints. The response variables were modeled mathematically using Poisson
regression and the results obtained were satisfactory, since accurate models were achieved. The
normal bound intersection method (NBI) produced a set of viable configurations for the input
variables that allows the experimenter to find the best configuration of the system in relation to
the level of importance of each response. The application demonstrated the optimal parameter
solution for the welding process in AA6063 and presented characteristics of minimizing the
weld bead geometry to contribute to the better efficiency and effectiveness of the productive
management of the welding process. An experimental confirmation procedure was successfully
performed to validate the theoretical results obtained in the prediction model.
