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.     

Interval approach challenges Monte Carlo simulation

Intervals are used to represent imprecise numerical values. Modelling uncertain values with precise bounds without considering their probability distribution is infeasible in many applications. As a solution, this paper proposes the use of probability density functions instead of intervals; we consider evaluation of an arithmetical function of random variables. Since the result density cannot in general be solved algebraically, an interval method for determining itsguaranteed bounds is developed. This possibility challenges traditional Monte Carlo methods in which only stochastic characterizations for the result distribution, such as confidence bounds for fractiles, can be determined.     

A Monte Carlo simulation approach for effective assessment of flyrock based on intelligent system of neural network

One of the undesirable phenomena in the surface mines, which results in various hazards for human and facilities, is flyrock. It seems that the careful study of the subject and its effects on the environment can affect the control of flyrock hazards in the studied area. Therefore, the use of intelligent models and methods which are capable of predicting and simulating the risk of flyrock can be considered as an appropriate solution in this regard. The current research was conducted using nonlinear models and Monte Carlo (MC) simulation. The data used in this study consist of 260 samples of rock thrown from a mine in Malaysia. The parameters used in these models include hole’s diameter (D), hole’s depth (HD), burden to spacing (BS), stemming (ST), maximum charge per delay (MC), and powder factor (PF). At first, multiple regression analysis (MRA) and artificial neural network (ANN) models were used in order to develop a non-linear relationship between dependent and independent parameters. The ANN model was an appropriate predictor of flyrock in the mine. Then using the best implemented model of ANN, the flyrock environmental phenomenon was simulated using MC technique. MC simulation showed a proper level of accuracy of flyrock ranges in the mine. Using this simulation, it can be concluded with 90% accuracy that the Flyrock phenomenon does not exceed 331 m. Under these conditions, this simulation can be used for various areas requiring risk assessment. Finally, a sensitive analysis was carried out on data. This analysis showed MC has the greatest effect on flyrock.     

Tolerance analysis based on Monte Carlo simulation: a case of an automotive water pump design optimization

Successful products are those presenting the highest quality at a fair cost. Although different approaches can be used to define the concept of quality, functional reliability is always a major requirement, due to implications such as safety and user losses regarding maintenance expenses, and product availability. Intelligent designs are robust and result in a fair cost. Robust designs are those insensitive to sources of variation occurring during the product life, keeping their performance under variable use conditions, like thermal and stress effects. The robustness approach is a function of two main design criteria: low complexity and tolerance design. Design for manufacture and assembly is closely related to decreasing complexity. Tolerance design is a tool in which the unavoidable manufacturing variations are considered during product development. This work presents a proposal for an intelligent design in an actual application by considering design simplification through the reduction of parts for an automotive water pump. The tolerance analysis is performed by means of a powerful statistical approach—a Monte Carlo simulation—in which process behavior is randomly simulated representing a high production volume. Additionally, service thermal effects are also contemplated, and assembly tests are proposed for automatic rejection of non-conforming parts, assuring high reliability and full compliance with functional requirements. This is an example of integrated design–manufacturing work aiming at both cost saving and improved reliability.

     

A reliability-based optimization method using sequential surrogate model and Monte Carlo simulation

This paper presents a sequential surrogate model method for reliability-based optimization (SSRBO), which aims to reduce the number of the expensive black-box function calls in reliability-based optimization. The proposed method consists of three key steps. First, the initial samples are selected to construct radial basis function surrogate models for the objective and constraint functions, respectively. Second, by solving a series of special optimization problems in terms of the surrogate models, local samples are identified and added in the vicinity of the current optimal point to refine the surrogate models. Third, by solving the optimization problem with the shifted constraints, the current optimal point is obtained. Then, at the current optimal point, the Monte Carlo simulation based on the surrogate models is carried out to obtain the cumulative distribution functions (CDFs) of the constraints. The CDFs and target reliabilities are used to update the offsets of the constraints for the next iteration. Therefore, the original problem is decomposed to serial cheap surrogate-based deterministic problems and Monte Carlo simulations. Several examples are adopted to verify SSRBO. The results show that the number of the expensive black-box function calls is reduced exponentially without losing of precision compared to the alternative methods, which illustrates the efficiency and accuracy of the proposed method.

     

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。     

A Monte Carlo approach for Galerkin radiosity

Galerkin radiosity solves the integral rendering equation by projecting the illumination functions into a set of higher-order basis functions. This paper presents a Monte Carlo approach for Galerkin radiosity to compute the coefficients of the basis functions. The new approach eliminates the problems with edge singularities between adjacent surfaces present in conventional Galerkin radiosity, the time complexity is reduced fromO(K ⁴) toO(K ²) for aK-order basis, and ideally specular energy transport can be simulated. As in conventional Galerkin radiosity, no meshing is required even for large or curved surfaces, thus reducing memory requirements, and no a posteriori Gouraud interpolation is necessary. The new algorithm is simple and can be parallelized on any parallel computer, including massively parallel systems.     

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.     

基于RMC的蒙特卡罗程序性能优化

蒙特卡罗MC方法是核反应堆设计和分析中重要的粒子输运模拟方法.MC方法能够模拟复杂几何形状且计算结果精度高,缺点是需要耗费大量时间进行上亿规模粒子模拟.如何提高蒙特卡罗程序的性能成为大规模蒙特卡罗数值模拟的挑战.基于堆用蒙特卡罗分析程序RM C,先后开展了基于TCMalloc动态内存分配优化、OpenMP线程调度策略优...     

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.     

Monte Carlo simulation program for ecosystems

A Monte Carlo computer simulation program is designed in order to describe the spatial and time evolution of a population of living individuals under preassigned environmental conditions of energy. The simulation is inspired by previous techniques developed in physics--in particular, in molecular dynamics and simulations of liquids--and it already provides some new insights regarding macroscopic deterministic models in ecology and concerning eventual control of artificial biomass production plants.     

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.     

结合先验知识与蒙特卡罗模拟的麻将博弈研究

针对内陆麻将缺乏统一平台和大量牌谱数据,难以设计出基于监督学习的博弈算法的问题,本文设计了一系列将规则、经验与蒙特卡罗方法相结合的博弈算法.首先,分别针对麻将博弈的弃牌模块、听牌模块、吃牌模块提出了弃牌优先级、听牌有效数、吃牌优先级的方法,完善了麻将AI的知识体系,设计了基础版博弈算法Fanfou_ba和优化版博弈算法...     

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.     

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。     

基于蒙特卡罗模拟修正的随机矩阵去噪方法

针对蕴含噪声信息较少的小组合股票市场,提出使用蒙特卡罗模拟修正的随机矩阵去噪方法。首先通过数据模拟生成随机矩阵,然后利用大量的模拟数据来同时修正噪声下界和上界,最终对噪声范围进行精确测定。运用道琼斯中国88指数和香港恒生50指数的数据进行实证分析,结果表明,与LCPB法、PG+法和KR法相比,在特征值、特征向量和反比参率方面, 蒙特卡罗模拟去噪方法修正后噪声范围的合理性及有效性得到很大的提升;对去噪前后的相关矩阵进行投资组合,得知在相同的期望收益率下,蒙特卡罗模拟去噪方法具有最小的风险值,能够为资产组合选择和风险管理等金融应用提供一定的参考。     

A variance-reduced electrothermal Monte Carlo method for semiconductor device simulation

This paper is concerned with electron transport and heat generation in semiconductor devices. An improved version of the electrothermal Monte Carlo method is presented. This modification has better approximation properties due to reduced statistical fluctuations. The corresponding transport equations are provided and results of numerical experiments are presented.     

群决策中随机信息的贝叶斯集结与统计模拟方法

针对方案属性值和权重都为随机变量的群决策问题,结合贝叶斯理论和随机模拟,提出一种集成专家主观概率分布集结和随机多属性决策方案选优的方法.该方法首先构建一个多元正态集结模型,将多个专家估计的属性值分布集结成单一分布,然后用随机模拟算法,生成不完全权重信息,并通过计算各方案获得特定排名的可信度因子,以及反映决策者风险偏好的整体排名可信度因子,得到各方案排序.实例分析验证了方法的有效性.     

二元自由基共聚合的Monte Carlo模拟

采用Monte Carlo模拟计算方法、可视化编程语言Visual Basic,以研究非稳态、考虑前末端效应、聚合反应可逆等情况下二元自由基共聚合反应,以期得到共聚物的瞬时组成、组成分布、序列分布等决定共聚物效能的重要参数。以数值模拟代替实际实验,不仅可节约成本,而且可减少工作量。模拟结果显示,Monte Carlo模拟值与实验值符合较好,说明本模拟过程模拟效果较好,结果可靠,可用来模拟未知共聚合体系,并为实际共聚物的合成提供帮助。