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To meet the requirements of equipment performance optimization in the intelligent environment of mining machinery, a solution model of excavator resistance with wide applicability is established, which made the solution model of excavation resistance have high efficiency and accuracy. The theoretical equation of excavation force is established and solved based on d′Alembert′s principle, and its accuracy is verified by dynamic simulation. The precise soil parameters are calibrated with theoretical results, and a simulation model is constructed by coupling drive of Adams-EDEM to obtain the precise solution of excavation resistance. The accuracy of the method is verified by the measured stress. In addition, the influence of different soil parameters on simulated excavation resistance is analyzed. The results show that compared with the traditional theoretical equation, the proposed model has advantages in precision and computational efficiency. The excavation resistance output from the theoretical model is used as the standard to modify the simulated soil model. The stress output from the modified simulation model is consistent with the measured stress, which verified the accuracy of the proposed method. This method provides an important basis for the calibration of complex soil parameters and the establishment of accurate soil model.

The uncertainty analysis of test results of nonlinear system shows the dispersion of test results. In this paper, an evaluation method of test uncertainty of nonlinear system based on polynomial chaos expansion is suggested. The evaluation method is compared with the method specified in the national standard Monte Carlo Method for Evaluation of Measurement Uncertainty through Ishigami test model. The results show that the suggested method has higher accuracy and efficiency. Taking the seat belt anchorages test as an example, the surrogate model of the test is established by polynomial chaos expansion method, and the uncertainty evaluation of the test is realized. To solve the problem of insufficient sample size, the improved hyperbolic truncation technique and Sobol sensitivity analysis based on polynomial chaos expansion model are used to simplify the model. The results show that the two simplified methods can significantly reduce the demand for samples size. The results show that polynomial chaos expansion and its simplification can be widely applied to the uncertainty analysis for various product tests.

In engineering problems,there is interaction between the layout of supporting locations and the distribution of structural material. Thus,simultaneously optimizing the topology of the structure and its supporting locations under a certain limit of material and supports is an effective approach to improve the mechanical properties of the structure. In this paper,supporting locations are described by continuous variables with the introduction of the independent supporting elements,then a hybrid optimization method of continuum structure and its supporting locations is proposed. The mathematical models of multi-objective optimization are established considering both the structural stiffness and the mean eigenvalue of the structure. The optimal solution of multi-objective optimization is determined by a certain decision factor. A simply supported plate under bending and bending-torsion loading conditions are optimized by the proposed hybrid optimization method. The results show that the distribution of material and supporting locations is clear,and the layout of supporting locations and the topology of structure can be effectively optimized at the same time. Compared with the design results from the standard topology optimization with given supporting conditions,the static and dynamic performances of the two examples are improved more than 30% and 88%,respectively. It is found that the proposed design method can satisfy the constraints,and remarkably improve the static and dynamic performance of the structure with a smaller number of supporters.

In view of the fact that the gearbox body has excess strength and the mass is too heavy,it needs to be optimized.In the current research on optimization constraints,most scholars use static strength as the strength constraint but lack the consideration of fatigue strength. Therefore,based on the fatigue theory,combined with the rain flow cycle count,according to the transmission box specifications to linearly extrapolate the number of cycles to obtain the number of cycles and then as its life,compare the number of extrapolation cycles with the number of test load spectrum cycles,combined with S-N curve of 90%reliability,to obtain the theoretical stress amplitude. According to the dynamic load coefficient,the maximum stress is calculated.Compare it with tensile strength to get individual component strength constraints. It can provide more accurate constraints for optimization,save the subsequent verification process,and improve the optimization efficiency.

The existing adaptive growth technique deducted the optimization iteration formula according to the condition named Karush-Kuhn-Tucker condition. It is high efficiency and good, which is aimed to strain energy and bound to the volume against optimization problem of stiffener distribution for plate and shell structures. However, different optimization iteration formula, which deducted by different kinds of problem, is poor and difficult to generalize, especially for the optimization problems with more than one constraint. Against with the lack of adaptive growth technique, a new method that is moving asymptotes（MMA） is suggested by this article and the adaptive growth technique is applied to truss structure topology optimization design. The design of typical truss structures with single constraint and multiple constraints is discussed, and the multi load cases are studied. The results of numerical examples show that the proposed method can obtain clear information of rod distribution and dimension, and has high design efficiency and good applicability. It is convenient for practical machining and has a good application prospect.