With respect to heavy carrying tasks and uneven road surface in open pit mine, the mechanical performances of electric wheel dump truck frame regarded as the main bearing component are extremely important for its safe operation. For the problem of insufficient strength and stiffness, an interval multi-objectives optimization design method based on blind number theory is presented in this paper, taking into account uncertain factors caused by manufacturing process. The allowable interval strength of welding material and permissible interval bending and torsional stiffness are obtained by means of experiments and simulations, using the blind number theory. Based on Latin hypercube sampling technique, the strength and stiffness of frame under different conditions is estimated. It shows that in the case of braking interference between interval strength and interval dynamic stress appears. Strength and stiffness are considered as optimization objectives, while the thicknesses of top longitudinal beam, stiffener, lateral longitudinal beam and hanging ear in the frame are deeded as design variables, and Young’s modulus and Poisson’s ratio are taken as uncertain variables. The optimized results show that the stress and deformation of the frame due to dynamic loading decreased. This result enables to formulate the following sentence: the mechanical performance of frame is improved.

A large number of sample data is needed to ascertain the characteristic parameters of traditional membership function, so that the calculated fuzzy fatigue reliability based on this method has certain errors for engineering structures without enough samples. A fuzzy fatigue reliability analysis method based on self-configuring membership function is proposed, while considering its multi-source uncertainties in the design, manufacture, and use stage in order to accurately evaluate fatigue reliability of welded A-type frame. In this paper, a novel membership function was presented on account of a small amount of sample data, which some experimental results verified. The mathematical expression for failure probability was deduced from the suggested model, as well as fatigue reliability. Subsequently, the thickness of steel plate defined in design stage, the material properties of weld metal that is produced in manufacture stage, and the loads at different connection sites determined in use stage were all considered as the random variables, which were obtained from Latin hypercube sampling, and the fatigue limit of weld metal was deemed as the fuzzy variable. Based on the response surface method, the fuzzy fatigue reliability performance function was constructed to assess failure probability of welded A-type frame under the condition of downhill and turning braking with full load, while its fatigue reliability was found to be far less than 90%. The fuzzy fatigue reliability optimization that was based on genetic algorithm was implemented, which showed that its reliability varied from 69.47% to 95.12%.