Design and optimization of welded products using genetic algorithms, model trees and the finite element method

Zusammenfassung

One of the fundamental requirements in the phases of design and manufacture of any welded product is the reduction of residual stresses and strains. These stresses and strains can cause substantial changes in the geometry of the finished products which often require subsequent machining in order to fit to the dimensions specified by the customer, and are usually caused by the contribution of an external heat flux in a small area. All welded joints contain welding seams with more or less regular geometry. This geometry gives the welded product the strength and quality required to support the mechanical demands of the design, and is affected by the parameters controlling the welding process (speed, voltage and current). Some researchers have developed mathematical models for predicting geometry based on the height, width and cord penetration, but is a difficult task as many of the parameters affecting the quality and geometry of the cord are unknown. As the welded product becomes more and more complex, residual stresses and strains are more difficult to obtain and predict as they depend greatly on the sequence followed to manufacture the product. Over several decades, the Finite Element Method (FEM) has been used as a tool for the design and optimization of mechanical components despite requiring validation with experimental data and high computational cost, and for this reason, the models based on FEM are currently not efficient. One of the potential methodologies used for adjusting the Finite Element models (FE models) is Genetic Algorithms (GA). Likewise, Data Mining techniques have the potential to provide more accurate and more efficient models than those obtained by FEM alone. One of the more common Data Mining techniques is Model Trees (MT). This paper shows the combination of FEM, GA and MT for the design and optimization of complex welded products. © Springer International Publishing Switzerland 2014.