New mass optimization technique to achieve low mass BIW designs using optimal material layout methodology on the frontal vehicle crash

Journal of Mechanical Science and Technology - BIW is the car body made of sheet metals welded together. Numerous researches were performed across the world to optimize and save mass on the car...     

Multiple-response optimization for micro-endmilling process using response surface methodology

In the present trend, new fabrication methods for producing miniaturized components are gaining popularity due to the recent advancements in micro-electro mechanical systems. Micro-machining differs from the traditional machining with the small size tool, resolution of x?Cy and z stages. This paper focuses RSM for the multiple response optimization in micro-endmilling operation to achieve maximum metal removal rate (MRR) and minimum surface roughness. In this work, second-order quadratic models were developed for MRR and surface roughness, considering the spindle speed, feed rate and depth of cut as the cutting parameters, using central composite design. The developed models were used for multiple-response optimization by desirability function approach to determine the optimum machining parameters. These optimized machining parameters are validated experimentally, and it is observed that the response values are in good agreement with the predicted values.     

Design optimization to improve the performance of the aircraft composite structures

Journal of Mechanical Science and Technology - Specific properties such as the stiffness and strength-to-weight ratio of the sandwich structure have a great impact on aircraft performance. The...     

优化过程知识引导的挖掘机动臂结构优化设计新策略

针对现有智能优化算法求解挖掘机动臂结构优化问题效率低、易陷入局部最优等问题,提出一种优化过程知识挖掘、处理和利用策略.构建基于任务知识指导的优化过程知识挖掘、处理和利用机制,探讨了运用优化过程知识引导遗传算法数值优化搜索的途径,并建立群体成员分组的知识利用算子以及选择、交叉、变异操作的知识利用算子.以中型液压挖掘机动臂结构优化任务为例,以所构建的知识利用算子引导遗传算法的数值优化搜索,通过与现有遗传算法进行对比,表明优化过程知识的利用可以有效提高优化效率并改善优化效果.     

New knowledge-based genetic algorithm for excavator boom structural optimization

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 conflgurability 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, arc 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.     

Numerical analysis and design optimization of engine room to improve cooling performance for a mid-class excavator

Excavators perform high-load operations in mainly a fixed place, and the heat balance is determined by the performance of the cooling system. Prediction of the cooling system performance is difficult due to non-uniformity of the air flow field inside the engine room and complicated boundary conditions. In the present study, CFD analysis of the engine room was performed for a preliminary evaluation of the cooling system performance of an excavator, and the analysis technique was validated by comparison with heat balance test results for an actual vehicle. CFD analysis was performed according to the design of experiment method for three types of internal design variables and two types of external design variables. A sensitivity analysis of the engine room design variables was performed and a regression model for the major design variables was proposed. Based on the regression model, optimization was conducted for the engine room layout for performance improvement.

     

Adaptive flow optimization of a turbocharger compressor to improve engine low speed performance

To improve the engine overall performance, an adaptive flow optimization procedure is proposed in this paper to synthesize turbocharger compressor optimum designs. Two objective functions are involved in the adaptive optimization. They are the traditional compressor design and the compressor design with consideration of improving engine overall performance. The two-step decomposition approach is chosen to generate optimum designs. The optimized designs not only satisfy turbomachinery and engine constraints but also have optimum objective function values in the two fields. Performance sensitivity analysis of compressor main design variables is performed for the flow optimization design process. A centrifugal compressor is redesigned for a turbocharged gasoline engine, as an example, based on the adaptive flow optimization process. The calculating results show a more than 5% increase of isentropic efficiency in comparison with the base line compressor, resulting in a more than 19% increase of engine torque at low speed conditions.     

用软件来提高多核处理器性能的方法分析

分析了多核处理器的硬件体系结构,并分析了影响多核处理器性能的最关键两个因素:二级缓存的命中率和处理器线路的利用率。主要分析了如何通过软件方法来提升多核处理器的性能,也就是通过设计操作系统进程调度算法来实现。分析了如何提高上述两个因素的方法。使用这些方法可以将多核处理器的吞吐率提高27%~45%。     

基于提高主传动系统快速响应目标的平旋盘方案优化研究

根据平旋盘的安装位置、功能特点,从机床主传动控制指令能被快速响应的角度对数控平旋盘总方案进行设计优化。并借助于有限元分析软件,对主旋转件平旋盘体采用方案试算法进一步结构优化,通过方案及关键零件结构优化,提高配备平旋盘机床的主传动系统快速响应。     

提升高温应变计寿命及精度的结构优化方法研究

高温应变电测技术广泛应用于航空发动机热端部件应力状态测量,如何将高温应变计的各部分结构参数进行合理搭配,以提高应变计灵敏度及使用寿命,在工程中具有十分重要的价值。首先,针对影响高温应变计测量误差与疲劳寿命这两个目标,分别建立简支梁-测量误差模型与悬臂梁-疲劳寿命模型;然后,经有限元分析得到各个参数变化对应变计测量误差以及疲劳寿命的影响规律,进而筛选出待优化的参数;接着,采用遗传算法与响应面法相结合对高温应变计进行优化;最后,通过试验对最终优化结果进行验证。结果表明,单参数分析法能直观反映各个参数对应变计灵敏度与寿命的影响规律,基于响应面模型和多目标遗传算法的优化,可得出高温应变计栅丝直径、栅丝长度、栅丝间距、栅丝弯数、基底厚度5个参数的最佳参数组合;优化后的测量误差为0.255%,疲劳寿命达到2.384 6×10~7次循环;试验验证结果表明,多目标优化后测量误差降低了89.2%,疲劳寿命提升了10.14%。     

Lightweight design of airlift provision for Korean light tactical vehicle using approximate optimization

Journal of Mechanical Science and Technology - A tactical vehicle should have air transportability for rapid attack and should satisfy its basic role for ground transportation. Airlift provision...     

Process parameter optimization for fused deposition modeling using response surface methodology combined with fuzzy inference system

Fused deposition modeling (FDM) is gaining distinct advantages because of its ability to fabricate the 3D physical prototypes without the restrictions of geometric complexities, while when it comes to accuracy and efficiency, the advantages of FDM is not distinct, and so how to improve them is worthy of study. Focusing on process parameter optimization, such parameters as line width compensation, extrusion velocity, filling velocity, and layer thickness are selected as control factors, input variables, and dimensional error, warp deformation, and built time are selected as output responses, evaluation indexes. Experiment design is assigned according to uniform experiment design, and then the three output responses are converted with fuzzy inference system to a single comprehensive response. The relation between the comprehensive response and the four input variables is derived with second-order response surface methodology, the correctness of which is further validated with artificial neural network. Fitness function is created using penalty function and is solved with genetic algorithm toolbox in Matlab software. With confirmation test, the results are obtained preferring to the results of the experiment 1 with the best comprehensive response among the 17 experiment runs, which confirms that the proposed approach in this study can effectively improve accuracy and efficiency in the FDM process.     

Multi-response optimization of process parameters based on response surface methodology for pure titanium using WEDM process

This paper presents an investigation on WEDM of pure titanium (grade-2). An attempt has been made to model the four response variables, i.e., machining rate, surface roughness, dimensional deviation and wire wear ratio in WEDM process using response surface methodology. The experimental plan is based on Box–Behnken design. The six parameters, i.e., pulse on time, pulse off time, peak current, spark gap voltage, wire feed and wire tension have been varied to investigate their effect on output responses. These responses have been optimized using multiresponse optimization through desirability. The ANOVA has been applied to identify the significance of developed model. The test results confirm the validity and adequacy of the developed RSM model. Finally, the optimum parametric setting has been designed for the optimization of process.     

An identification method for joint structural parameters using an FRF-based substructuring method and an optimization technique

A new method is proposed to identify the joint structural parameters of complex systems using a frequency response function (FRF)-based substructuring method and an optimization technique. The FRF method is used to estimate the joint parameters indirectly by minimizing the difference between the reference and calculated responses using a gradient-based optimization technique with analytical gradient information. To assess the robustness of the identification method with respect to noisy input data, FRFs contaminated by uniformly distributed random noise were tested in a numerical example. The effects of the random noise and the magnitude of the connection stiffness values on the accuracy of the method were investigated while identifying the joint parameters. When the FRFs were contaminated with random noise, the proposed procedure performed well when used to identify the stiffness values, but the accuracy of identification is deteriorative when used to identify the damping coefficients. The joint parameters of a real bolted structure were also identified by the proposed method. The results show that it can be applied successfully to real structures, and that a hybrid approach using both calculated and measured FRFs in the substructure model can enhance the quality of the identification results.     

Nonlinear PID control to improve the control performance of the pneumatic artificial muscle manipulator using neural network

A novel actuator system which has achieved increased popularity to provide these advantages such as high strength and power/weight ratio, low cost, compactness, ease of maintenance, cleanliness, readily available, cheap power source, inherent safety and mobility assistance to humans performing tasks has been the utilization of the pneumatic artificial muscle (PAM) manipulator, in recent times. However, the complex nonlinear dynamics of the PAM manipulator makes it a challenging and appealing system for modeling and control design. The problems with the time variance, compliance, high hysteresis and nonlinearity of pneumatic systems have made it difficult to realize precise position control with high speed. In order to realize satisfactory control performance, the effect of nonlinear factors contained in thePAM manipulator must be considered. The purpose of this study is to improve the control performance of thePAM manipulator using a nonlinearPID controller. Superb mixture of conventionalPID controller and the neural network, which has powerful capability of learning, adaptation and tackling nonlinearity, brings us a novel nonlinearPID controller using neural network. This proposed controller is appropriate for a kind of plants with nonlinearity uncertainties and disturbances. The experiments were carried out in practicalPAM manipulator and the effectiveness of the proposed control algorithm was demonstrated through the experiments, which suggests its superior performance and disturbance rejection.     

New approach for robust multi-objective optimization of turning parameters using probabilistic genetic algorithm

Limited by techniques, the process of remanufacturing exists masses of uncertainties which have a great impact on the remanufactured parts quality, how to achieve a higher quality of mechanical products by using limited remanufactured parts precision, has become one of the key issues of remanufacturing industry. Firstly, with a target to reduce uncertainties and improve the quality of automatic products, a method of tolerance grading allocation for remanufactured parts is proposed based on the uncertainty analysis of the remanufacturing assembly. The dimensional tolerances of the mechanical parts are divided into positive and negative two groups. We use selective assembly method to reduce assembling deviation. Then, the method is proven by mathematical formulas that the remanufactured parts variance can be expanded to two times, and the tolerances can be liberalized 40 % through tolerance grading allocation method. It is also the theoretical basis for improving the reuse radio and quantitatively describing the tolerance liberalization in this paper. Finally, feasibility research on this method is studied from the angle of cost–benefit. Furthermore, a tolerance grading allocation example of remanufactured engine piston assembly in a power corporation shows the validity and practicality of the proposed method.     

Multiparameter optimization for the nonlinear performance improvement of centrifugal pumps using a multilayer neural network

To increase efficiency at the design point of a centrifugal pump, this study adopted an artificial neural network in the construction of an accurate nonlinear function between the optimization objective and the design variables of impellers. Modified particle swarm optimization was further applied to refine the mathematical model globally. The database, which consisted of 200 sets of impellers, were generated from the Latin hypercube sampling method, and their corresponding efficiencies were obtained automatically from numerical simulation. Design variables were the distributions of blade angles, and results established that the difference between the numerical performance curve and the experimental results was acceptable. Optimization with a two-layer feedforward network improved the pump efficiency at the design point by 0.454 %. Flow complexity improved as the blade curvature increased. The application of the multilayer neural network could provide a meaningful reference to single- and multi-objective optimization of complex and nonlinear pump performance.

     

An attempt to improve the poor performance characteristics of coconut oil for industrial lubricants

Coconut oil is used as the base fluid for metal cutting applications because of its high natural properties compared to other vegetable oils. The major hindrances to extensive utilization of coconut oil compared to mineral oils for various metal cutting operations are its high pour point, low oxidation and thermal stability therefore further improvement in these aspects is much-needed. In this work, a systematic approach to improve the cold flow behavior, oxidation and thermal stability of the coconut oil by the combination of chemical additive i.e 2,6 Di-tetra butyl phenol and diluents such as Poly alpha olefin4(PAO4), Poly (Ethylene co-vinyl acetate) (PPD) has been carried out. Based on the obtained results an improvement of cold flow behavior (least pour point) was observed with the addition of PAO4 and PPD with varying weight percentage combinations. Apart from this addition of 2, 6 Di-tetra butyl phenol as anti-oxidant (AO) in various weight percentage has led to an improvement of oxidation and thermal stability to the primarily modified coconut oil.

     

Features of electromagnetic methods for testing the wear resistance of medium-carbon structural steel subjected to laser or bulk hardening and tempering

Features of applying attachable eddy-current transducers of two types (with a flat end surface and a protruding ferrite rod core with localities 5–6 and 3–4 mm in diameter, respectively) for testing the structural state, hardness, and abrasive wear resistance of structural steel 45X (0.45 mass % C and 0.85% Cr), which was hardened under the action of continuous laser radiation, have been studied. The feasibilities of the eddy-current and coercimetric techniques for evaluating the wear resistance of a medium-carbon steel subjected to laser or bulk hardening and tempering in the temperature range 75–600°C have been studied.