·Table of Contents ·Methods and Instrumentation | A Computer Based Methodology for Better Welding QualityFairouz Bettayeb, Amar BenchaalaScientific Research Center on Welding and Non Destructive testing. C.S.C, Route de Dely Brahim, BP:64, Chéraga, Algiers. Algeria. Tel/Fax: (213-2) 361850. Email: bettayeb@excite.com, f_bettayeb@email.com Rabah Benraghda University of Bab Ezzouar.USTHB.BP:32 El Alia.Algiers Contact |
3.1 The welding parameters
Parameters for the welding of structural steels are chosen primarily to match the strength and toughness properties of the base material. Typical selection criteria may be: type and thickness of base material, chemical composition, required mechanical properties of filler metal, type of joint, welding position, welding technique, electrode type, diameter and position, hardness, preheating, and post weld heat treatment [4].
'Optima' has been developed on the study of the properties of the welding information system. This study has shown that the system can be represented by a decision graph, in which each welding parameter is a graph node connected to the other parameter by links that have influences on the optimum value[5]. These influences have been implemented by weighting factors.
3.2 Classification of the optimizing parameters
The relevant parameters have been organized in 3 sets as follow:
The complexity of the combinatorial optimization trials, makes the necessity to link the artificial intelligence and the operational analysis tools, in order to find solutions for complex and imprecise problems that cannot be resolved by exact solutions [6][7]. In reality, the most manufactured processes are based on complex physical phenomena for which it is difficult to lay down realistic analytical models.
However, many approaches as the neural networks, the genetic algorithms, the simulated annealing and others, will determine optimal manufactured conditions, but no one of these techniques has conducted yet to a universal methodology. In this paper, the simulated annealing solution (S.A) has been chosen as a resolving strategy, thanks to the convergence of the annealing and the welding principles, as being described ensuing.
4.1 Convergence of the annealing and the welding fundamentals.
The annealing is a thermal treatment that consists in the heating of the metal at a highest temperature, and the cooling of this metal gradually in order to obtain a homogeneous structure. The physical principle of the arc welding is based on the calorific energy produced by the electrical arc flashing between the electrode and the pieces, which creates a fusion bath that solidify after cooling. This fundamental similitude between the physical states, has guided to the choice of the simulated annealing algorithm for the optimization scheme.
4.2 The simulated annealing approach
The 'Metropolis' algorithm [8], which implements the S.A approach brings the analogy of a combinatorial optimization problem, with a physical system composed of a great number of particles in iterations. In such system, the objective function is simulated by the free energy of a physical system, the parameters are simulated by the particles coordinates, and the exploration of a good configuration by the study of the low energy structures. The simulated annealing variables are: the initial temperature, the initial solution, the choice of an objective function, the time of the relaxation, the decrease law, and the resolving space.
The algorithm
4.3 Mathematical formulation of the cost function
Optimum design is a key synthesis which collects all-important engineering aspect to develop modern structural constructions, not only safe but also economic. The economy is achieved by minimizing a cost function and the safety is guaranteed by implementing the design constraints. Therefore the problem can be defined mathematically as a constrained function minimization task, which may be solved by mathematical programming methods [6].
The complexity of the system 'Optima' is defined by a highest cardinal space and several constraints. Thus, the objective function has been defined by the sum of the relative errors near the neighboring of the required criteria of the system [9]. In the following definition, an estimation is assigned to each welding parameter, with consideration of the constraints features of the requested quality. The mathematical optimizing formulation of the objective function is:
(1) |
Diagram 1 |
2 - Since the dominant character of the ameliorative heuristics is the random selection, and because the indiscriminate property of the S.A method, we have worked by simulations on 5 examples dealing with the influence of the algorithm initializations on the finale solution. The initialized parameters are the initial temperature (ti°.), the iteration count (nt.) for each temperature scale, and the decreasing temperature ratio (rt.).
The diagram 2 indicates that:
Diagram 2 | Diagram 3 | Diagram 4 |
On the entrance of the 21^{st} century, the power of information technology and materials technology, including its transportation and recycling, determine precisely the expectations and perspectives of the modeling tools for better fabrication quality. An optimum design procedure can be devised into 3 main phases as ensue:
© AIPnD , created by NDT.net | |Home| |Top| |