Abstract
In order to improve fuzzy fatigue reliability and make weight reduction of A-type frame in an electric mining dump truck considering multi-source uncertainties from design, manufacturing and use stage, a multi-objectives optimization method based on the interval theory was proposed in this paper. The thickness of four steel plates in welded A-type frame was regarded as certain design variables, while the elastic modulus and density of material was considered as uncertain variables as well as the load at front traction joint. The relationship between optimization objectives and variables was constructed by the response surface method, and was transformed by the interval method. Then, the optimization problem was solved by the non-dominated sorting genetic algorithm, which was found that the fuzzy fatigue reliability reached up to 98.45 % at the expense of some weight.
Highlights
The A-type frame that pertains to the truck’s steering system is a pivotal load-caring part, especially under downhill turning braking condition
Based on the stress-strength interference model, the fatigue reliability of the design point was analyzed by the joint probability integral method, and a reliability-based optimization design process was accomplished through the Kriging model considering both certain and uncertain variables [8,9,10,11]
It was clearly seen that the estimated data located at around the 45° equal line all agreed well with the simulated results, which indicated that the constructed response surface models were precise, and could be utilized to conduct on the multi-objectives optimization design in the following part
Summary
The A-type frame that pertains to the truck’s steering system is a pivotal load-caring part, especially under downhill turning braking condition This welded structure has to endure the cyclic loads caused by the random mine road surface, and its fatigue reliability needs to be guaranteed. Numerous researches have been conducted on optimization design of engineering structure with uncertain parameters Those approaches could be categorized into three major groups: probability [3,4], fuzzy [5]and interval optimization models [6]. The interval multi-objectives optimization function for improving fuzzy fatigue reliability and reducing weight of A-type frame in an electric mining dump truck considering multi-source uncertainties from design, manufacturing and use stage was firstly defined. The optimal solution was found through the nondominated sorting genetic algorithm
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