科创中国

Due to the insufficiency of utilizing knowledge to guide the complex optimal searching, existing genetic algorithms fail to effectively solve excavator boom structural optimization problem. To improve the optimization efficiency and quality, a new knowledge-based real-coded genetic algorithm is proposed. A dual evolution mechanism combining knowledge evolution with genetic algorithm is established to extract, handle and utilize the shallow and deep implicit constraint knowledge to guide the optimal searching of genetic algorithm circularly. Based on this dual evolution mechanism, knowledge evolution and population evolution can be connected by knowledge influence operators to improve the configurability of knowledge and genetic operators. Then, the new knowledge-based selection operator, crossover operator and mutation operator are proposed to integrate the optimal process knowledge and domain culture to guide the excavator boom structural optimization. Eight kinds of testing algorithms, which include different genetic operators, are taken as examples to solve the structural optimization of a medium-sized excavator boom. By comparing the results of optimization, it is shown that the algorithm including all the new knowledge-based genetic operators can more remarkably improve the evolutionary rate and searching ability than other testing algorithms, which demonstrates the effectiveness of knowledge for guiding optimal searching. The proposed knowledge-based genetic algorithm by combining multi-level knowledge evolution with numerical optimization provides a new effective method for solving the complex engineering optimization problem.

The current research of the decomposition methods of complex optimization model is mostly based on the principle of disciplines, problems or components. However, numerous coupling variables will appear among the sub-models decomposed, thereby make the efficiency of decomposed optimization low and the effect poor. Though some collaborative optimization methods are proposed to process the coupling variables, there lacks the original strategy planning to reduce the coupling degree among the decomposed sub-models when we start decomposing a complex optimization model. Therefore, this paper proposes a decomposition method based on the global sensitivity information. In this method, the complex optimization model is decomposed based on the principle of minimizing the sensitivity sum between the design functions and design variables among different sub-models. The design functions and design variables, which are sensitive to each other, will be assigned to the same sub-models as much as possible to reduce the impacts to other sub-models caused by the changing of coupling variables in one sub-model. Two different collaborative optimization models of a gear reducer are built up separately in the multidisciplinary design optimization software iSIGHT, the optimized results turned out that the decomposition method proposed in this paper has less analysis times and increases the computational efficiency by 29.6%. This new decomposition method is also successfully applied in the complex optimization problem of hydraulic excavator working devices, which shows the proposed research can reduce the mutual coupling degree between sub-models. This research proposes a decomposition method based on the global sensitivity information, which makes the linkages least among sub-models after decomposition, and provides reference for decomposing complex optimization models and has practical engineering significance.

Most of recent research on carbody lightweighting has focused on substitute material and new processing technologies rather than structures.However,new materials and processing techniques inevitably lead to higher costs.Also,material substitution and processing lightweighting have to be realized through body structural profiles and locations.In the huge conventional workload of lightweight optimization,model modifications involve heavy manual work,and it always leads to a large number of iteration calculations.As a new technique in carbody lightweighting,the implicit parameterization is used to optimize the carbody structure to improve the materials utilization rate in this paper.The implicit parameterized structural modeling enables the use of automatic modification and rapid multidisciplinary design optimization(MDO)in carbody structure,which is impossible in the traditional structure finite element method(FEM)without parameterization.The structural SFE parameterized model is built in accordance with the car structural FE model in concept development stage,and it is validated by some structural performance data.The validated SFE structural parameterized model can be used to generate rapidly and automatically FE model and evaluate different design variables group in the integrated MDO loop.The lightweighting result of body-in-white(BIW)after the optimization rounds reveals that the implicit parameterized model makes automatic MDO feasible and can significantly improve the computational efficiency of carbody structural lightweighting.This paper proposes the integrated method of implicit parameterized model and MDO,which has the obvious practical advantage and industrial significance in the carbody structural lightweighting design.

As a promising technique, surrogate-based design and optimization(SBDO) has been widely used in modern engineering design optimizations. Currently, static surrogate-based optimization methods have been successfully applied to expensive optimization problems. However, due to the low efficiency and poor flexibility, static surrogate-based optimization methods are difficult to efficiently solve practical engineering cases. At the aim of enhancing efficiency, a novel surrogate-based efficient optimization method is developed by using sequential radial basis function(SEO-SRBF). Moreover, augmented Lagrangian multiplier method is adopted to solve the problems involving expensive constraints. In order to study the performance of SEO-SRBF, several numerical benchmark functions and engineering problems are solved by SEO-SRBF and other well-known surrogate-based optimization methods including EGO, MPS, and IARSM. The optimal solutions, number of function evaluations, and algorithm execution time are recorded for comparison. The comparison results demonstrate that SEO-SRBF shows satisfactory performance in both optimization efficiency and global convergence capability. The CPU time required for running SEO-SRBF is dramatically less than that of other algorithms. In the torque arm optimization case using FEA simulation, SEO-SRBF further reduces 21% of the material volume compared with the solution from static-RBF subject to the stress constraint. This study provides the efficient strategy to solve expensive constrained optimization problems.

Many attentions for structural synthesis are paid to planar linkages and parallel mechanisms, while design novel pyramid deployable truss structure(PDTS) of satellite SAR mainly depends on experience of designer. To design novel configuration of PDTS, a two-step topology structure synthesis and analysis approach is proposed. Firstly, a conceptual configuration of PDTS is synthesized. Weighted graph and weighted adjacency matrix are established to realize topological description for PDTS. Graph properties are then summarized to distinguish differentia between PDTS and other type structures. According to graph properties, a procedure for synthesis conceptual configuration of PDTS is presented. Secondly, join relationship of components in a PDTS is analyzed. Kinematic chain and corresponding incidence/adjacency matrix are employed to analyze join relationship of PDTS. Properties and simplified rules of kinematic chain are extracted to construct kinematic chain. A procedure for construction kinematic chain of PDTS is then established. Finally, with this two-step approach all 11 rectangular pyramid deployable structures whose folded state is planar are discovered and their kinematic chains are constructed. Based on synthesis results, a novel deployable support structure for satellite SAR is designed. The proposed research can be applied to obtain some novel PDTSs, which is of great importance to design some novel deployable support structures for satellite SAR antenna.

The aerodynamic braking is a clean and non-adhesion braking, and can be used to provide extra braking force during high-speed emergency braking. The research of aerodynamic braking has attracted more and more attentions in recent years. However, most researchers in this field focus on aerodynamic effects and seldom on issues of position control of the aerodynamic braking board. The purpose of this paper is to explore position control optimization of the braking board in an aerodynamic braking prototype. The mathematical models of the hydraulic drive unit in the aerodynamic braking system are analyzed in detail, and the simulation models are established. Three control functions-constant, linear, and quadratic-are explored. Two kinds of criteria, including the position steady-state error and the acceleration of the piston rod, are used to evaluate system performance. Simulation results show that the position steady state-error is reduced from around 12–2 mm by applying a linear instead of a constant function, while the acceleration is reduced from 25.71–3.70 m/s2 with a quadratic control function. Use of the quadratic control function is shown to improve system performance. Experimental results obtained by measuring the position response of the piston rod on a test-bench also suggest a reduced position error and smooth movement of the piston rod. This implies that the acceleration is smaller when using the quadratic function, thus verifying the effectiveness of control schemes to improve to system performance. This paper proposes an effective and easily implemented control scheme that improves the position response of hydraulic cylinders during position control.

The current development of precision plastic injection molding machines mainly focuses on how to save material and improve precision, but the two aims contradict each other. For a clamp unit, clamping precision improving depends on the design quality of the stationary platen. Compared with the parametric design of stationary platen, structural scheme design could obtain the optimization model with double objectives and multi-constraints. In this paper, a SE-160 precision plastic injection molding machine with 1600 kN clamping force is selected as the subject in the case study. During the motion of mold closing and opening, the stationary platen of SE-160 is subjected to a cyclic loading, which would cause the fatigue rupture of the tie bars in periodically long term operations. In order to reduce the deflection of the stationary platen, the FEA method is introduced to optimize the structure of the stationary platen. Firstly, an optimal topology model is established by variable density method. Then, structural topology optimizations of the stationary platen are done with the removable material from 50%, 60% to 70%. Secondly, the other two recommended optimization schemes are given and compared with the original structure. The result of performances comparison shows that the scheme II of the platen is the best one. By choosing the best alternative, the volume and the local maximal stress of the platen could be decreased, corresponding to cost-saving material and better mechanical properties. This paper proposes a structural optimization design scheme, which can save the material as well as improve the clamping precision of the precision plastic injection molding machine.

Structure design and fabricating methods of three-dimensional(3D)artificial spherical compound eyes have been researched by many scholars.Micro-nano optical manufacturing is mostly used to process 3D artificial compound eyes.However,spherical optical compound eyes are less at optical performance than the eyes of insects,and it is difficult to further improve the imaging quality of compound eyes by means of micro-nano optical manufacturing.In this research,nonhomogeneous aspheric compound eyes(ACEs)are designed and fabricated.The nonhomogeneous aspheric structure is applied to calibrate the spherical aberration.Micro milling with advantages in processing three-dimensional micro structures is adopted to manufacture ACEs.In order to obtain ACEs with high imaging quality,the tool paths are optimized by analyzing the influence factors consisting of interpolation allowable error,scallop height and tool path pattern.In the experiments,two kinds of ACEs are manufactured by micro-milling with different too path patterns and cutting parameter on the miniature precision five-axis milling machine tool.The experimental results indicate that the ACEs of high surface quality can be achieved by circularly milling small micro-lens individually with changeable cutting depth.A prototype of the aspheric compound eye(ACE)with surface roughness(Ra)below 0.12?m is obtained with good imaging performance.This research ameliorates the imaging quality of 3D artificial compound eyes,and the proposed method of micro-milling can improve surface processing quality of compound eyes.

Fault diagnosis of various systems on rolling stock has drawn the attention of many researchers.However,obtaining an optimized sensor set of these systems,which is a prerequisite for fault diagnosis,remains a major challenge.Available literature suggests that the configuration of sensors in these systems is presently dependent on the knowledge and engineering experiences of designers,which may lead to insufficient or redundant development of various sensors.In this paper,the optimization of sensor sets is addressed by using the signed digraph(SDG)method.The method is modified for use in braking systems by the introduction of an effect-function method to replace the traditional quantitative methods.Two criteria are adopted to evaluate the capability of the sensor sets,namely,observability and resolution.The sensors configuration method of braking system is proposed.It consists of generating bipartite graphs from SDG models and then solving the set cover problem using a greedy algorithm.To demonstrate the improvement,the sensor configuration of the HP2008 braking system is investigated and fault diagnosis on a test bench is performed.The test results show that SDG algorithm can improve single-fault resolution from 6 faults to 10 faults,and with additional four brake cylinder pressure(BCP)sensors it can cover up to 67 double faults which were not considered by traditional fault diagnosis system.SDG methods are suitable for reducing redundant sensors and that the sensor sets thereby obtained are capable of detecting typical faults,such as the failure of a release valve.This study investigates the formal extension of the SDG method to the sensor configuration of braking system,as well as the adaptation supported by the effect-function method.

The active magnetic bearing(AMB)suspends the rotating shaft and maintains it in levitated position by applying controlled electromagnetic forces on the rotor in radial and axial directions.Although the development of various control methods is rapid,PID control strategy is still the most widely used control strategy in many applications,including AMBs.In order to tune PID controller,a particle swarm optimization(PSO)method is applied.Therefore,a comparative analysis of particle swarm optimization(PSO)algorithms is carried out,where two PSO algorithms,namely(1)PSO with linearly decreasing inertia weight(LDW-PSO),and(2)PSO algorithm with constriction factor approach(CFA-PSO),are independently tested for different PID structures.The computer simulations are carried out with the aim of minimizing the objective function defined as the integral of time multiplied by the absolute value of error(ITAE).In order to validate the performance of the analyzed PSO algorithms,one-axis and two-axis radial rotor/active magnetic bearing systems are examined.The results show that PSO algorithms are effective and easily implemented methods,providing stable convergence and good computational efficiency of different PID structures for the rotor/AMB systems.Moreover,the PSO algorithms prove to be easily used for controller tuning in case of both SISO and MIMO system,which consider the system delay and the interference among the horizontal and vertical rotor axes.