Frame weight and anti-fatigue co-optimization of a mining dump truck based on Kriging approximation model

Mi, Chengji; Gu, Zhengqi; Zhang, Yong; Liu, Shuichang; Sha, Zhihua; Nie, Duzhong

doi:10.1016/j.engfailanal.2016.03.021

Cited by 22 publications

(23 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, the Kriging approximation model was chosen to describe their mathematical links, while the NSGA-II was used to search for the optimal solution in this optimization problem. These two methods are described in my previous works [22,23]. The whole optimization process is shown in Figure 24.…”

Section: Multi-objective Optimization Functionmentioning

confidence: 99%

“…The Kriging approximation model could take the place of the finite element model to describe the relationship between design variables and optimization objectives, which was explained in my previous publications [22,23]. In this paper, the other 10 sample points obtained from Latin hypercube sampling method were used to testify the accuracy of the approximation model, shown in Figure 25.…”

Section: Approximation Modelmentioning

confidence: 99%

See 1 more Smart Citation

Lifetime Assessment and Optimization of a Welded A-Type Frame in a Mining Truck Considering Uncertainties of Material Properties and Structural Geometry and Load

Xiao

et al. 2019

Applied Sciences

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Multi-objective Optimization Functionmentioning

confidence: 99%

Section: Approximation Modelmentioning

confidence: 99%

Lifetime Assessment and Optimization of a Welded A-Type Frame in a Mining Truck Considering Uncertainties of Material Properties and Structural Geometry and Load

Xiao

et al. 2019

Applied Sciences

Self Cite

View full text Add to dashboard Cite

show abstract

“…The results are listed in Table 1. The flat dog-bone shaped specimen was designed to characterize the mechanical properties of butt joint [1,2,4,19]. Then, the ultimate tensile strength in each test was calculated by Equation (6), Table 1 [19].…”

Section: The Strength Of Welded Joints At Intervalsmentioning

confidence: 99%

“…It has 300 tons of load capacity, and runs on uneven road surface for 24 working hours every day [1][2][3]. The frame is considered as one of pivotal bearing components, and thence its mechanical properties connected with strength and stiffness have significant influence on vehicle's safe operation [4]. In designing of frame structure, the mechanical parameters and geometrical features are considered as deterministic values.…”

Section: Introductionmentioning

confidence: 99%

Interval Multi-Objectives Optimization of Electric Wheel Dump Truck Frame Based on Blind Number Theory

Liu

Xiao

et al. 2019

Applied Sciences

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…Particularly, the Non-Dominated Sorting Genetic Algorithm (NSGA-II), an extended form of GA, has become one of the most efficient algorithms for the multiobjective optimization in recent years (Chen et al, 2015). Compared with traditional algorithms, NSGA-II could avoid the dependence on selecting proper weight values, which is still a challenging problem (Gholami and Azizi, 2014;Mi et al, 2016). As NSGA-II presents an extremely outstanding performance in the aspect of solving multiobjective optimization problems, this research harnesses NSGA-II to achieve a most appropriate variation coefficient of the stress (Murugan et al, 2009).…”

Section: Determination Of Variation Coefficient With Nsga-iimentioning

confidence: 99%

Fatigue Life Prediction of Screw Blade in Screw Sand Washing Machine under Random Load with Gauss Distribution

Gong

Xia

et al. 2016

American Journal of Engineering and Applied Sciences

View full text Add to dashboard Cite

The purpose of this study is to present a new method for estimating the fatigue life of the screw blade in the screw sand washing machine. To ensure the accuracy of numerical simulation, the loading area and the value of load are determined by means of the theoretical analysis. To ascertain the location of the stress peak and stress range, the static analysis of the screw blade is executed via the finite element method. To reduce the research cost and ensure the feasibility of the research method, Markov chain Monte Carlo (MCMC) is employed to simulate the random load with the Gauss distribution on the screw blade. In addition, the Nondominated Sorting Genetic Algorithm (NSGA-II) is utilized to find out an optimum variation coefficient of the stress, aiming at guaranteeing the precision of the random load. The rainflow cycle extrapolation is adopted to generate the fatigue load spectrum closer to the real condition, taking account of the possibility of the extreme loads caused by overload occurrence. Subsequently, the rainflow matrix after extrapolation, the estimated P-S-N curve, Goodman stress correction method and Miner's rules are made use of assessing the service life of the screw blade. In particular, the effects of the surface roughness, residual stresses and fatigue notch factors on the fatigue life are taken into consideration. Ultimately, the non-linear surface fitting technique is used to obtain the equation concerning the fatigue life of the screw blade versus residual stresses and fatigue notch factors. The numerical results show that the stress peak is in the root of the screw blade and the service life of the screw blade declines exponentially with growing residual stresses and fatigue notch factors.

show abstract

Frame weight and anti-fatigue co-optimization of a mining dump truck based on Kriging approximation model

Cited by 22 publications

References 10 publications

Lifetime Assessment and Optimization of a Welded A-Type Frame in a Mining Truck Considering Uncertainties of Material Properties and Structural Geometry and Load

Lifetime Assessment and Optimization of a Welded A-Type Frame in a Mining Truck Considering Uncertainties of Material Properties and Structural Geometry and Load

Interval Multi-Objectives Optimization of Electric Wheel Dump Truck Frame Based on Blind Number Theory

Fatigue Life Prediction of Screw Blade in Screw Sand Washing Machine under Random Load with Gauss Distribution

Contact Info

Product

Resources

About