Resumen
In order to improve the fatigue performance of a welded A-type frame in a heavy off-road mining truck, a novel method was presented to implement lifetime and weight collaborative optimization while considering uncertainties in geometry dimension, material properties, and bearing load. The mechanical and cyclic material parameters were obtained from experimental work to characterize the base metal and the weldment. The finite element model of a welded A-type frame was constructed to analyze stress distribution and predict fatigue life, the force time histories of which were acquired from multi-body dynamics simulation. The simulated failure position and fatigue life had a good agreement with the actual results. Then, both structural lifetime and weight were considered as optimization objectives. The thickness of main steel plates and elastic and cyclic material parameters were chosen as uncertain design variables as well as main loads at connection locations. The fifty sample points in the light of Latin hypercube sampling method and its responses calculated by finite element analysis were supposed to build the approximation model based on the Kriging approximation method. After its fitting precision was guaranteed, the non-dominated sorting genetic algorithm II (NSGA-II) was utilized to find the optimal solution. Finally, the fatigue life of a welded A-type frame was increased to 2.40 × 105 cycles and its mass was lessened by 8.2%. The optimized results implied that good fatigue performance of this welded A-type frame needs better welding quality, lower running speed for downhill and turning road surface, and thicker front plates.