Collaborative simulation method with spatiotemporal synchronization process control

When designing a complex mechatronics system, such as high speed trains, it is relatively difficult to effectively simulate the entire system’s dynamic behaviors because it involves multi-disciplinary subsystems. Currently, a most practical approach for multi-disciplinary simulation is interface based coupling simulation method, but it faces a twofold challenge: spatial and time unsynchronizations among multi-directional coupling simulation of subsystems. A new collaborative simulation method with spatiotemporal synchronization process control is proposed for coupling simulating a given complex mechatronics system across multiple subsystems on different platforms. The method consists of 1) a coupler-based coupling mechanisms to define the interfacing and interaction mechanisms among subsystems, and 2) a simulation process control algorithm to realize the coupling simulation in a spatiotemporal synchronized manner. The test results from a case study show that the proposed method 1) can certainly be used to simulate the sub-systems interactions under different simulation conditions in an engineering system, and 2) effectively supports multi-directional coupling simulation among multi-disciplinary subsystems. This method has been successfully applied in China high speed train design and development processes, demonstrating that it can be applied in a wide range of engineering systems design and simulation with improved efficiency and effectiveness.     

Standard uncertainty evaluation of multivariate polynomial

The uncertainty propagation law helps to infer the uncertainty of unobservable variables from known or assumed relationship with observable variable. Currently only analytical linear approximation or Monte Carlo simulation methods is widely adopted. The former method is limited to weakly nonlinear systems while the latter does not provide any analytical expression that links the uncertainties of input to uncertainties of output quantities. This paper proposes procedures to evaluate the standard uncertainty of multivariate polynomial using basic algebraic manipulation and tabulated Mellin transform. Case studies are presented whereby the effectiveness and practicality of the proposed method is demonstrated. The proposed method can be readily automated using computer algebra systems, thus negating the need for practitioners to perform the actual complex computation. The work theoretically enriches and extends the validity of the analytic approach in the existing uncertainty evaluation framework, thus enabling the analytic evaluation of uncertainty for many nonlinear cases which was previously an impossible task.     

Use of design of experiments and Monte Carlo method for instruments optimal design

A common issue in the design of measurement instruments is the comparison between different solutions in terms of components of the measurement chain, data processing or even measurement principles; the predicted instrumental uncertainty is the driving parameter for such a comparison. While in many situations the linearization of the measuring model allows using the standard ISO GUM procedure, in complex cases it might be necessary to proceed with Monte Carlo simulations as per ISO GUM supplement 1. This paper describes a method that combines the factorial design of experiments (DOE) and the ISO GUM supplement 1 uncertainty evaluation method to guide the instrument designer in the instrument configuration optimization. The proposed approach allows estimating, in the design phase, the overall instrumental uncertainty for different configurations, the instrument sensitivity to the accuracy in the measurements of its inputs and the effects on systematic and random measurement errors deriving from the choice of all instrumental variables. The use of data populations selected with the DOE criteria allows recovering valuable parameters equivalent to the sensitivity factors of the GUM linearized approach. The data analysis allows separating the critical factors that must be accurately controlled from those only weakly affecting the measurement uncertainty. The method has been applied to a case study where the metrological performances of a system devoted to the measurement of the acoustic radiation emitted by a vibrating panel in a reverberant enclosure had to be assessed.     

Identification of key design parameters of high-speed train for optimal design

As a complex mechatronic system, the running stability, safety, and comfort of high-speed train are affected by many design variables. It is of great difficulty to identify a set of effective design parameters to optimize its running performance. The current simulation systems like the SIMPACK can simulate the running dynamics, but cannot be used effectively for optimal design of the train and rail system because there are too many design variables being supposed to be dealt with. Therefore, there is a need to make a software solution from simulation analysis to optimal design so that the computer-aided design (CAD) and engineering (CAE) can be integrated into an integral design process. This paper presents a new method to identify the key design variables against the running performance indicators based on the sensitivity analysis, which in turn bases itself on simulation-oriented surrogate models. In this way, the optimal design of a high-speed train can be successfully conducted because (1) the surrogate model can reduce the simulation time greatly and (2) the design variable space with the key variables will be reduced significantly. The research shows that this method is of practical significance for speeding up the design of high-speed train or similar complex mechatronic systems.     

Implementation of unscented transform to estimate the uncertainty of a liquid flow standard system

First-order partial derivatives of a mathematical model are an essential part of evaluating the measurement uncertainty of a liquid flow standard system according to the Guide to the expression of uncertainty in measurement (GUM). Although the GUM provides a straight-forward method to evaluate the measurement uncertainty of volume flow rate, the first-order partial derivatives can be complicated. The mathematical model of volume flow rate in a liquid flow standard system has a cross-correlation between liquid density and buoyancy correction factor. This cross-correlation can make derivation of the first-order partial derivatives difficult. Monte Carlo simulation can be used as an alternative method to circumvent the difficulty in partial derivation. However, the Monte Carlo simulation requires large computational resources for a correct simulation because it considers the completeness issue whether an ideal or a real operator conducts an experiment to evaluate the measurement uncertainty. Thus, the Monte Carlo simulation needs a large number of samples to ensure that the uncertainty evaluation is as close to the GUM as possible. Unscented transform can alleviate this problem because unscented transform can be regarded as a Monte Carlo simulation with an infinite number of samples. This idea means that unscented transform considers the uncertainty evaluation with respect to the ideal operator. Thus, unscented transform can evaluate the measurement uncertainty the same as the uncertainty that the GUM provides.     

Large-scale computer simulation and systems design of computer-integrated manufacturing

It is a well known fact that the design of computer-integrated manufacturing systems is an extremely comprehensive task due to the need to consider the complex as a whole. Based on the results of a case study, it is suggested that the system requirements of CIM design creates a need for large-scale simulation, as this kind of simulation is well suited to reflect the complex interactions which are inherent in a CIM environment. The system's definition and the construction of a large-scale simulation model for a particular type of manufacturing operation are discussed. Some experimental results from the model, which reveal certain overall systems behaviours of the manufacturing operation under investigation, are also presented.     

Digital twin-based analysis of volumetric error mapping procedures

The evaluation of the volumetric accuracy of a machine tool is an open challenge in the industry, and a wide variety of technical solutions are available in the market and at research level. All solutions have advantages and disadvantages concerning which errors can be measured, the achievable uncertainty, the ease of implementation, possibility of machine integration and automation, the equipment cost and the machine occupation time, and it is not always straightforward which option to choose for each application. The need to ensure accuracy during the whole lifetime of the machine and the availability of monitoring systems developed following the Industry 4.0 trend are pushing the development of measurement systems that can be integrated in the machine to perform semi-automatic verification procedures that can be performed frequently by the machine user to monitor the condition of the machine. Calibrated artefact based calibration and verification solutions have an advantage in this field over laser based solutions in terms of cost and feasibility of machine integration, but they need to be optimized for each machine and customer requirements to achieve the required calibration uncertainty and minimize machine occupation time.This paper introduces a digital twin-based methodology to simulate all relevant effects in an artefact-based machine tool calibration procedure, from the machine itself with its expected error ranges, to the artefact geometry and uncertainty, artefact positions in the workspace, probe uncertainty, compensation model, etc. By parameterizing all relevant variables in the design of the calibration procedure, this simulation methodology can be used to analyse the effect of each design variable on the error mapping uncertainty, which is of great help in adapting the procedure to each specific machine and user requirements. The simulation methodology and the analysis possibilities are illustrated by applying it on a 3-axis milling machine tool.     

电阻层析成像有限元仿真模型分析与设计

本文根据电阻层析成像(ERT)中常采用的16电极相邻激励模式,以及有限元计算中广泛采用的三角形网格元素划分.比较分析了5种不同拓扑类捌模型的对称性误差和结构性误差.仿真结果表明不同类型模型各有优缺点,实际应用中应结合具体情况与系统要求选择理想有限元模型.在此基础上,讨了网格分布形式、有限元质量等因素对ERT有限元仿真不确定度的影响,并探讨了提高ERT计算精度的有限元网格设计方法.仿真实验表明,传统的按等间隔原理剖分的有限元模型并不合理,而通过适当调整有限元剖分密度等措施可降低ERT有限元计算的不确定度.     

制造系统人因仿真参考模型及若干关键技术

人因仿真是对制造系统进行人因分析的重要手段。针对制造系统设计中人因的评价问题,提出了指导人因仿真实施的通用参考模型与系统方法,并给出了各个层次人因仿真的特点和应用领域。其次,给出了面向任务的人因仿真步骤和方法集,研究了建立任务网络模型、工作失误仿真以及工作任职需求仿真等关键技术。最后结合一个三维激光焊接生产单元的实例说明了人因仿真技术在自治生产单元设计中的应用。     

Travel-time models for flow-rack automated storage and retrieval systems

In this paper, closed-form travel-time expressions for flow-rack automated storage and retrieval systems are developed. The expressions, which are based on a continuous approach, are compared for accuracy, via simulation, with exact models which are based on a discrete approach. There is no significant difference between the results obtained from the continuous-approach-based closed-form expressions and the ones from the discrete-approach-based exact solutions. The closed-form expressions are easy to calculate due to their simplistic forms, even without a computer, while the exact solutions are extremely complex. On the basis of computation time, the proposed closed-form expressions are extremely practical when compared with the discrete-approach-based expressions, which require extensive computation time. The closed-form travel-time expressions developed in this study can be used to (1) establish performance standards for existing AS/RS, (2) evaluate throughput performance for flow-rack AS/RS alternative design configurations, and (3) compare different storage techniques for improved system performance. Due to their simplistic, yet accurate, definitions, the closed-form expressions, as well as the results of this study, are applicable to industry.     

Applying CBR to machine tool product configuration design oriented to customer requirements

Product customization is a trend in the current market-oriented manufacturing environment. However, deduction from customer requirements to design results and evaluation of design alternatives are still heavily reliant on the designer’s experience and knowledge. To solve the problem of fuzziness and uncertainty of customer requirements in product configuration, an analysis method based on the grey rough model is presented. The customer requirements can be converted into technical characteristics effectively. In addition, an optimization decision model for product planning is established to help the enterprises select the key technical characteristics under the constraints of cost and time to serve the customer to maximal satisfaction. A new case retrieval approach that combines the self-organizing map and fuzzy similarity priority ratio method is proposed in case-based design. The self-organizing map can reduce the retrieval range and increase the retrieval efficiency, and the fuzzy similarity priority ratio method can evaluate the similarity of cases comprehensively. To ensure that the final case has the best overall performance, an evaluation method of similar cases based on grey correlation analysis is proposed to evaluate similar cases to select the most suitable case. Furthermore, a computer-aided system is developed using MATLAB GUI to assist the product configuration design. The actual example and result on an ETC series machine tool product show that the proposed method is effective, rapid and accurate in the process of product configuration. The proposed methodology provides a detailed instruction for the product configuration design oriented to customer requirements.     

Reliability-based design optimization of time-dependent systems with stochastic degradation

A major hurdle in the application of reliability-based design optimization (RBDO) to time-dependent systems is the continual interplay between calculating time-variant reliability (to ensure reliability policies are met) and moving the design point to optimize some objective function, such as cost, weight, size and so forth. In most cases the reliability can be obtained readily using so-called fast integration methods. However, this option is not available when certain stochastic processes are invoked to model gradual damage or deterioration. In this case, sampling methods must be used. This paper provides a novel way to obviate this inefficiency. First, a meta-model is built to relate time-variant system reliability to the entire design space (and noise space if required). A design of experiments paradigm and Monte Carlo simulation using the mechanistic model determines the corresponding system reliability accurately. A moving least-squares meta-model relates the data. Then, the optimization process to find the best design point, accesses the meta-model to quickly evaluate objectives and reliability constraints. Case-studies include a parallel Daniel's system and a series servo control system. The meta-model approach is simple, accurate and very fast, suggesting an attractive means for RBDO of time-dependent systems.

     

Effects of aliasing on numerical integration

During the course of processing acceleration data from mechanical systems it is often desirable to integrate the data to obtain velocity or displacement waveforms. However, those who have attempted these operations may be painfully aware that the integrated records often yield unrealistic residual values. This is true whether the data has been obtained experimentally or through numerical simulation such as Runge–Kutta integration or the explicit finite element method. In the case of experimentally obtained data, the integration errors are usually blamed on accelerometer zero shift or amplifier saturation. In the case of simulation data, incorrect integrations are often incorrectly blamed on the integration algorithm itself. This work demonstrates that seemingly small aliased content can cause appreciable errors in the integrated waveforms and explores the unavoidable source of aliasing in both experiment and simulation—the sampling operation. Numerical analysts are often puzzled as to why the integrated acceleration from their simulation does not match the displacement output from the same simulation. This work shows that these strange results can be caused by aliasing induced by interpolation of the model output during sampling regularisation.     

A computer simulation platform for the estimation of measurement uncertainties in dimensional X-ray computed tomography

The knowledge of measurement uncertainty is of great importance in conformance testing in production. The tolerance limit for production must be reduced by the amounts of measurement uncertainty to ensure that the parts are in fact within the tolerance. Over the last 5 years, industrial X-ray computed tomography (CT) has become an important technology for dimensional quality control. In this paper a computer simulation platform is presented which is able to investigate error sources in dimensional CT measurements. The typical workflow in industrial CT metrology is described and methods for estimating measurement uncertainties are briefly discussed. As we will show, the developed virtual CT (VCT) simulator can be adapted to various scanner systems, providing realistic CT data. Using the Monte Carlo method (MCM), measurement uncertainties for a given measuring task can be estimated, taking into account the main error sources for the measurement. This method has the potential to deal with all kinds of systematic and random errors that influence a dimensional CT measurement. A case study demonstrates the practical application of the VCT simulator using numerically generated CT data and statistical evaluation methods.     

Design solution evaluation for metal forming product development

In the current metal forming product development paradigm, the simultaneous and optimal design of product, process and forming system is a non-trivial issue as there are many affecting factors which interact and interplay each other. In the up-front design process, the systematic evaluation and verification of design solution is critical as this could shift the product development paradigm from traditionally trial-and-error and heuristic know-how to more scientific calculation, analysis and simulation. To ensure the efficient and accurate assessment and evaluation of design solution generation, state-of-the-art technologies need to be developed. In this paper, a methodology for systematic evaluation and verification of the simultaneous design of metal forming product, process, and forming system is presented. The factors which affect these designs are first articulated and how they interact and interplay are described. The importance of the systematic evaluation of designs is, thus, figured out. In addition, the role that CAE simulation plays in this process is explained. To evaluate the design, detailed evaluation criteria are developed and how the criteria are used through CAE simulation technology to reveal the behaviors and performances of designs is articulated. Through case studies, the developed technology is illustrated and its efficiency is finally verified.     

阵列式近红外目标模拟器的设计与实现

为了能够实现逼真的红外仿真环境,设计了一套红外成像仿真系统。该系统通过对红外发光二极管的特性研究,利用脉动光提高其发射距离,借助适当的光学装置,实现小功率发光器件模拟红外目标的功能。该系统可用于红外目标仿真,对红外成像系统的性能做出整体评价;也可以对不同红外成像系统的性能进行比较分析。通过对某型导弹动态测试,表明了该系统方案的可行性、实用性。     

压水堆核电厂专设安全系统泵试验管线设计优化

通过解决国内某压水堆核电厂专设安全系统(安全注入系统和安全壳喷淋系统)泵试验管线振动高的工程实例,提出压水堆核电厂专设安全系统泵试验管线设计注意事项,可为电厂工程设计人员解决实际问题提供参考,本文所提的问题和方法对其他具有类似运行工况的大型泵试验管线的设计具有借鉴的意义。     

Development of a fuzzy FMEA based product design system

The demand for high-quality and low-cost products with short development time in the dynamic global market has forced researchers and industries to focus on various effective product development strategies. The authors are carrying out research studies to explore the applicability of fuzzy logic and knowledge-based systems technologies to today’s competitive product design and development, with an emphasis on the design of high quality products at the conceptual design stage. A framework of a fuzzy FMEA (failure modes and rffects analysis) based evaluation approach for new product concepts is proposed in this paper. Based on the proposed approach and methodologies, a prototype system named EPDS-1, which can assist inexperienced users to perform FMEA analysis for quality and reliability improvement, alternative design evaluation, materials selection, and cost assessment, thus helping to enhance robustness of new products at the conceptual design stage. This paper presents the underlying concepts of the development and shows the practical application with the prototype system with a case study.     

A rough set approach to design concept analysis in a design chain

The inherent dynamic relationships among design tasks performed concurrently at different organizations characterize the complexities of a design chain where designers with diverse expertise need to collaborate across organizational boundaries. To ensure timely completion of inter-related design tasks, metrics to facilitate the early evaluation of design concepts are crucial. The ability to evaluate and select suitable design concepts at an early stage will ensure better solutions and greater savings in time and effort further downstream. This paper proposes a new approach based on the rough set theory to design concept analysis. The approach aims at early detection of design inadequacy. A so-called information system is constructed using the information gleaned from design concepts and design capabilities, and analyzed using the rough set theory to derive a set of design rules for design concept analysis. The approach embodies a technique for handling attributes with unavailable information, which is a frequent occurrence in design. This paper presents details of the proposed approach, the novel technique, and a case study.