A statistical tolerance analysis approach for over-constrained mechanism based on optimization and Monte Carlo simulation

Tolerancing decisions can profoundly impact the quality and cost of the mechanism. To evaluate the impact of tolerance on mechanism quality, designers need to simulate the influences of tolerances with respect to the functional requirements. This paper proposes a mathematical formulation of tolerance analysis which integrates the notion of quantifier: “For allacceptable deviations (deviations which are inside tolerances),there existsa gap configurationsuch asthe assembly requirements and the behavior constraints are verified” & “For allacceptable deviations (deviations which are inside tolerances), andfor alladmissible gap configurations, the assembly and functional requirements and the behavior constraints are verified”. The quantifiers provide a univocal expression of the condition corresponding to a geometrical product requirement. This opens a wide area for research in tolerance analysis. To solve the mechanical problem, an approach based on optimization is proposed. Monte Carlo simulation is implemented for the statistical analysis. The proposed approach is tested on an over-constrained mechanism.     

Improved algorithm for tolerance allocation based on Monte Carlo simulation and discrete optimization

The allocation of design and manufacturing tolerances has a significant effect on both manufacturing cost and quality. This paper considers nonlinearly constrained tolerance allocation problems. The purpose is to minimize the ratio between the sum of the manufacturing costs (tolerances costs) and the risk (probability of the respect of geometrical requirements). The techniques of Monte Carlo simulation and genetic algorithm are adopted to solve these problems. As the simplest and the popular method for non-linear statistical tolerance analysis, the Monte Carlo simulation is introduced into the frame. Moreover, in order to make the frame efficient, the genetic algorithm is improved according to the features of the Monte Carlo simulation. An illustrative example (hyperstatic mechanism) is given to demonstrate the efficiency of the proposed approach.     

基于Monte Carlo仿真的降低光谱散射方法比较研究

散射是影响光谱分析与检测技术精度的主要因素,使光谱分析的基本线性定律——朗伯比尔定律不再成立。基于Monte Carlo仿真对3种常用的降低光散射的方法进行比较分析,即偏振差法、附加吸收剂法和空间滤波法。3种方法都是通过提取出弱散射光,线性化光强衰减度与吸收系数关系。提出线性度可以由透射光的光程方差判断。弱散射光的光程更短,光程均值更接近介质厚度,光程方差更低,但是光强也更弱。对3种方法在不同散射系数的透射光时间响应、光强衰减度与吸收系数关系、光程方差以及光强进行比较,表明偏振差法相比其他两种方法效果更佳。     

Machining accuracy reliability analysis of multi-axis machine tool based on Monte Carlo simulation

Although machine tool can meet the specifications while it is new, after a long period of cutting operations, the abrasion of contact surfaces and deformation of structures will degrade the accuracy of machine tool due to the increase of the geometric errors in six freedoms. Therefore, how to maintain its accuracy for quality control of products is of crucial importance to machine tool. In this paper, machining accuracy reliability is defined as the ability to perform its specified machining accuracy under the stated conditions for a given period of time, and a new method to analyze the sensitivity of geometric errors to the machining accuracy reliability is proposed. By applying Multi-body system theory, a comprehensive volumetric model explains how individual geometric errors affect the machining accuracy (the coupling relationship) was established. Based on Monte Carlo mathematic simulation method, the models of the machining accuracy reliability and sensitivity analysis of machine tools were developed. By taking the machining accuracy reliability as a measure of the ability of machine tool and reliability sensitivity as a reference of optimizing the basic parameters of machine tools, an illustrative example of a three-axis machine tool was selected to demonstrate the effectiveness of the proposed method.     

High performance automotive chassis design: a topology optimization based approach

Automotive chassis design in view of car weight reduction is a challenging task due to the many performance targets that must be satisfied, in particular in terms of vehicle safety. In this paper a methodology for automotive chassis design in involving optimization techniques is presented. In particular, topology, topometry and size optimizations are coupled with fem analyses and adopted in cascade for reaching an optimum chassis configuration. The methodology is applied to the design process of a rear-central engine high performance vehicle chassis. The objective of the optimization process is the chassis weight reduction, yet in fulfilment of structural performance constraints as required by Ferrari standards. The results demonstrate the general applicability of the methodology presented for obtaining the general trusses layout and thicknesses distribution of the structure. The numerical model at this stage shows a significant weight reduction when compared to the chassis of the Ferrari F458 Italia.     

基于多假设粒子群优化的蒙特卡罗定位算法

针对传统蒙特卡罗定位(MCL)算法在结构化相似环境中容易出现定位失败的问题,提出一种基于多假设粒子群优化的改进蒙特卡罗定位方法(MPSO-CL).以激光传感器的观测信息作为适应度函数,对MCL算法的采样粒子进行多假设粒子群优化更新,使得采样粒子向当前群体中多个最优粒子方向移动,从而使得粒子迅速收敛到后验概率密度分布取值较大的区域,实现了移动机器人高效精确自主定位.实验结果表明,MPSO-MCL算法克服了相似环境中定位的粒子匮乏问题,并且提高了定位的精确度.     

Reliable kinetic Monte Carlo simulation based on random set sampling

Kinetic Monte Carlo (KMC) method has been widely used in simulating rare events such as chemical reactions or phase transitions. Yet lack of complete knowledge of transitions and the associated rates is one major challenge for accurate KMC predictions. In this paper, a reliable KMC (R-KMC) mechanism is proposed in which sampling is based on random sets instead of random numbers to improve the robustness of KMC results. In R-KMC, rates or propensities are interval estimates instead of precise numbers. A multi-event algorithm based on generalized interval probability is developed. The weak convergence of the multi-event algorithm towards the traditional KMC is demonstrated with a generalized Chapman–Kolmogorov equation.     

基于蒙特卡罗的光散射传输三维动态仿真

以典型偏振蒙特卡罗模型为基础,采用斯托克斯-穆勒(Stokes-Mueller)形式描述偏振光散射传输过程,利用VC和OpenGL工具设计实现了光散射传输过程的三维动态仿真系统,并对系统仿真的数据结果进行了分析.结果表明,此仿真系统可以直观地在三维空间中查看光子在介质中的散射传输过程和接收器的累积过程;且由仿真系统所得的数据结果分析,偏振信息具有一定的对称性.     

基于CVaR和Monte Carlo仿真的贷款组合决策模型

提供了一种新的贷款组合决策优化方法,该模型用更能反映贷款组合信用风险特征的CVaR作为风险度量。由于在实际中很难获取各笔贷款的历史数据,为此给出了一种基于Matlab语言的Monte Carlo仿真方法。从而使谊模型可以通过线性规划技术有效的进行求解。最后给出了一个例子。     

Comparison of the approximate solutions of a noisy non-linear system based on Monte Carlo simulation

This paper is concerned with the comparison of the approximate methods in order to evaluate the values of state variables of a noisy nonlinear system by Monte Carlo simulation. The approximate methods considered here are (1) the stochastic linearisation, (2) the first-order approximation and (3) the second-order approximation. It is shown that the order of the accuracies of the covariance based on the approximate equation of Riccati type does not always coincide with the order of the practical error covariance of the original system which is obtained by using Monte Carlo simulation. Therefore the comparison of the accuracies should not be carried out by the solution obtained from the covariance equation but by the practical error covariance of the original system directly. This result is demonstrated with numerical examples. The characteristics of the random numbers which are used for Monte Carlo simulation are examined by various kinds of tests.     

Quality-driven optimization of system architecture: Industrial case study on an automotive sub-system

Due to the complexity of today's embedded systems and time-to-market competition between companies developing embedded systems, system architects have to perform a complex task. To design a system which meets all its quality requirements becomes increasingly difficult because of customer demand for new innovative user functions. Methods and tools are needed to assist the architect during system design.     

Parallelization of a Monte Carlo particle transport simulation code

We have developed a high performance version of the Monte Carlo particle transport simulation code MC4. The original application code, developed in Visual Basic for Applications (VBA) for Microsoft Excel, was first rewritten in the C programming language for improving code portability. Several pseudo-random number generators have been also integrated and studied. The new MC4 version was then parallelized for shared and distributed-memory multiprocessor systems using the Message Passing Interface. Two parallel pseudo-random number generator libraries (SPRNG and DCMT) have been seamlessly integrated. The performance speedup of parallel MC4 has been studied on a variety of parallel computing architectures including an Intel Xeon server with 4 dual-core processors, a Sun cluster consisting of 16 nodes of 2 dual-core AMD Opteron processors and a 200 dual-processor HP cluster. For large problem size, which is limited only by the physical memory of the multiprocessor server, the speedup results are almost linear on all systems. We have validated the parallel implementation against the serial VBA and C implementations using the same random number generator. Our experimental results on the transport and energy loss of electrons in a water medium show that the serial and parallel codes are equivalent in accuracy. The present improvements allow for studying of higher particle energies with the use of more accurate physical models, and improve statistics as more particles tracks can be simulated in low response time.     

蒙特卡罗法用于多指标试验设计的优化计算

以一优化的问题为例,探讨应用蒙特卡罗法去计算多指标试验设计的优化问题。展示及解释了蒙特卡罗法的BASICI程序,比较蒙特卡罗法与网格法的优化结果,原来两种结果几乎相同。但实践表明,蒙特卡罗法既可用于多指标试验设计的优化计算,又方便灵活地实现多指标优化的目的。     

Nanodroplets on a solid plane: wetting and spreading in a Monte Carlo simulation

The wetting behavior and spreading dynamics of small polymer melt droplets in the course of transition from partial to complete wetting conditions on a flat structureless solid substrate have been studied by dynamic Monte Carlo simulation. From the density profiles of the drops we determine the contact angles at varying strength of the van der Waals surface forces in the whole interval of partial wetting. The validity of Young's equation is then tested whereby the surface tension of the melt/vapor interface is derived independently from interfacial fluctuation analysis, and the surface free energy of the melt at the substrate—from the anisotropy of the local pressure at the wall. The bending rigidity of the melt/vapor interface turns out negative, as recently predicted for short-range interactions.We carry out computer experiments which show that Tanner's law for the kinetics of drop spreading holds also on nanoscopic scales. The observed density profiles of spreading droplets confirm earlier predictions that the central cap-shaped region of the droplets shrinks at the expense of a transition region (“foot”) surrounded by a precursor film which is roughly one monolayer thick. At later times the precursor film breaks into individual polymer chains and advances in typically diffusive manner as found in laboratory experiments.Eventually we investigate the impact of line tension on nanodroplets behavior at varying strength of adhesion and demonstrate that the Gretz equation which incorporates line tension into Young's rule holds even on nanoscale and predicts important properties of the drops subject to droplet size.     

On the analysis of microbiological processes by Monte Carlo simulation techniques

The Barcelonagram is a Monte Carlo simulator recently designed in order to take account of the behaviour of living systems. In this paper we apply this technique to real bacterial growth in different and significant experimental conditions, namely (i) the growth of the Serratia marcescens in a minimal glucose-limited medium, (ii) the temperature effect on the anaerobic growth of the same strain, (iii) the growth of the Escherichia coli in a minimal medium and (iv) the normal specific growth rate of bacterial populations against the available substrate concentration. In the context of these different cases we discuss the diverse contributions of these simulated results to the understanding of the microbiological processes and the general reliability of the simulation considered as a third alternative besides both (and together with!) experience and mathematical modelling.     

Monte Carlo uncertainty analysis of an ANN-based spectral analysis method

Neural Computing and Applications - This work presents the uncertainty analysis of an artificial neural network (ANN)-based method, called multiharmonic ANN fitting method (MANNFM), which is able...     

基于网络排队模型的Monte Carlo多线程电梯交通流优化设计

采用Monte Carlo方法对串并联结合的复合电梯系统进行了分析,应用多线程技术模拟电梯交通流模型,并给出相应的算法流程.将该算法应用到电梯配置测评中,通过对多个仿真实例的比较,根据配置结构,给出各实例的相应性能指标.结果表明,以本模型为基础建立的电梯配置测评系统可以平衡乘客候梯时间和电梯负载之间的关系,对电梯系统的结构配置给出合理建议,证明了Monte Carlo方法在电梯群控系统测评和优化中的可行性和优越性.     

Monte Carlo simulation of neutron diffusion on SIMD architectures

An algorithm is described for the Monte Carlo simulation of neutron diffusion, including the treatment of fission neutrons, which is efficient on a computer with SIMD architecture. Tests carried out on the ICL DAP give results which compare favourably for timing with those obtained using standard UKAEA serial programs.     

基于Monte Carlo随机模拟的综合交通网络布局评价与决策方法

针对综合交通网络评价指标权重及属性值具有主观性和随机性的特点,提出了基于模拟运算的布局规划方案排序选优的群体随机决策方法.仿真生成满足集结的多个专家对指标重要性偏好排序统计分布的权重,同时考虑交通需求的不确定性对指标值的影响,结合客观熵权计算方案的综合评价值,由多次模拟得到的排序优势度确定方案的优劣差异.根据设计的仿真流程通过算例说明了方法应用的有效性,评价中考虑了主客观因素及随机性特征,可以为网络布局提供科学决策依据.