Integration of geometric variation and part deformation into variation propagation of 3-D assemblies

This paper introduces a novel modelling method for variation propagation calculation of 3-D assemblies taking into account geometric variation and part deformation, which are neglected in most models in tolerance analysis. Initially, numerical studies are carried out in order to illustrate the characteristics of strain distribution in components and contact forces on the mating surfaces of a 3-D assembly. According to these characteristics, a linear equivalent model using springs to represent the elastic mating surfaces with geometric variation was presented. Then, the equilibrium criterions corresponding to actual contact situations and iterative searching algorithm of the equilibrium status of contacting were developed. The proposed modelling and calculation method were finally applied to the assembly of two machined parts, on which finite-element analyses and experimental tests were conducted to validate the effectiveness and accuracy. This linear contact model also shows an important advantage on modelling and calculating efficiency, which enable the practical application to variation propagation calculation in both tolerance design and assembly process.     

A performance analytical model of automated material handling system for semiconductor wafer fabrication system

To effectively analyse and evaluate the performances of closed-loop automated material handling system (AMHS) with shortcut and blocking in semiconductor wafer fabrication system, a modified Markov chain model (MMCM) has been proposed. The system characteristics, such as vehicle blockage and system’s shortcut configuration, are well considered in the MMCM. The state space explosion problem and computational challenge due to the increase of AMHS scale can be effectively eliminated. With production data from Interbay material handling system of a 300-mm semiconductor wafer fabrication line, the proposed MMCM is compared with simulation analysis model. The results demonstrate that the proposed MMCM is an effective modelling methodology for AMHS’s performance analysis at system design stage.     

A primary influence vertex approach to identify driving factors in complex integrated agri-industrial systems – an example from sugarcane supply and processing systems

Integrated agri-industrial systems (IAISs), such as sugarcane supply and processing systems, are complex systems and hence generally difficult to understand and manage. The large number factors in IAISs coupled with the complex interrelationships among the factors make it challenging to identify the points of intervention for improving their overall performance. Several approaches, such as the network theory and the Theory of Constraints have been used to identify important factors in systems with variations in success. This paper demonstrates a primary influence vertex approach for identifying and ranking the factors that drive the performance of IAISs. The approach is based on comprehensive causal network analyses and was tested in four relatively diverse large-scale sugarcane milling operations in South Africa. Results from the analyses were found to be consistent with the literature and external knowledge of the milling areas as at the time of the study. It is concluded that the approach can proffer a sound basis from which deeper rooted problems in systems can be identified on an ongoing basis. It is, however, recommended that the approach should be systematically compared with other relevant methods that are used to analyse complex systems.     

Isogeometric analysis of fluid‐saturated porous media including flow in the cracks

An isogeometric model is developed for the analysis of fluid transport in pre‐existing faults or cracks that are embedded in a fluid‐saturated deformable porous medium. Flow of the interstitial fluid in the porous medium and fluid transport in the discontinuities are accounted for and are coupled. The modelling of a fluid‐saturated porous medium in general requires the interpolation of the displacements of the solid to be one order higher than that of the pressure of the interstitial fluid. Using order elevation and Bézier projection, a consistent procedure has been developed to accomplish this in an isogeometric framework. Particular attention has also been given to the spatial integration along the isogeometric interface element in order to suppress traction oscillations that can arise for certain integration rules when a relatively high dummy stiffness is used in a poromechanical model. © 2016 The Authors. International Journal for Numerical Methods in Engineering Published by John Wiley & Sons Ltd.     

A Change-Point Approach for Phase-I Analysis in Multivariate Profile Monitoring and Diagnosis

Process monitoring and fault diagnosis using profile data remains an important and challenging problem in statistical process control (SPC). Although the analysis of profile data has been extensively studied in the SPC literature, the challenges associated with monitoring and diagnosis of multichannel (multiple) nonlinear profiles are yet to be addressed. Motivated by an application in multioperation forging processes, we propose a new modeling, monitoring, and diagnosis framework for phase-I analysis of multichannel profiles. The proposed framework is developed under the assumption that different profile channels have similar structure so that we can gain strength by borrowing information from all channels. The multidimensional functional principal component analysis is incorporated into change-point models to construct monitoring statistics. Simulation results show that the proposed approach has good performance in identifying change-points in various situations compared with some existing methods. The codes for implementing the proposed procedure are available in the supplementary material.     

Powell–Sabin B‐splines and unstructured standard T‐splines for the solution of the Kirchhoff–Love plate theory exploiting Bézier extraction

The equations that govern Kirchhoff–Love plate theory are solved using quadratic Powell–Sabin B‐splines and unstructured standard T‐splines. Bézier extraction is exploited to make the formulation computationally efficient. Because quadratic Powell–Sabin B‐splines result in ‐continuous shape functions, they are of sufficiently high continuity to capture Kirchhoff–Love plate theory when cast in a weak form. Unlike non‐uniform rational B‐splines (NURBS), which are commonly used in isogeometric analysis, Powell–Sabin B‐splines do not necessarily capture the geometry exactly. However, the fact that they are defined on triangles instead of on quadrilaterals increases their flexibility in meshing and can make them competitive with respect to NURBS, as no bending strip method for joined NURBS patches is needed. This paper further illustrates how unstructured T‐splines can be modified such that they are ‐continuous around extraordinary points, and that the blending functions fulfil the partition of unity property. The performance of quadratic NURBS, unstructured T‐splines, Powell–Sabin B‐splines and NURBS‐to‐NURPS (non‐uniform rational Powell–Sabin B‐splines, which are obtained by a transformation from a NURBS patch) is compared in a study of a circular plate. Copyright © 2015 John Wiley & Sons, Ltd.     

Achieving robust design through statistical effect screening

This work proposes a method for statistical effect screening to identify design parameters of a numerical simulation that are influential to performance while simultaneously being robust to epistemic uncertainty introduced by calibration variables. Design parameters are controlled by the analyst, but the optimal design is often uncertain, while calibration variables are introduced by modeling choices. We argue that uncertainty introduced by design parameters and calibration variables should be treated differently, despite potential interactions between the two sets. Herein, a robustness criterion is embedded in our effect screening to guarantee the influence of design parameters, irrespective of values used for calibration variables. The Morris screening method is utilized to explore the design space, while robustness to uncertainty is quantified in the context of info‐gap decision theory. The proposed method is applied to the National Aeronautics and Space Administration Multidisciplinary Uncertainty Quantification Challenge Problem, which is a black‐box code for aeronautic flight guidance that requires 35 input parameters. The application demonstrates that a large number of variables can be handled without formulating simplifying assumptions about the potential coupling between calibration variables and design parameters. Because of the computational efficiency of the Morris screening method, we conclude that the analysis can be applied to even larger‐dimensional problems. (Approved for unlimited, public release on October 9, 2013, LA‐UR‐13‐27839, Unclassified.) Copyright © 2015 John Wiley & Sons, Ltd.     

Theoretical analysis of shunting effect in resistance spot welding (RSW) of AA2219

Shunting effect is the loss of electrical current via the secondary circuit provided due to existence of previous nugget in a series of welding spots. This phenomenon is important for products containing intermittent spots. In this study, a theoretical model is developed for shunting effect in resistance spot welding for aluminum alloy 2219. Welding distance together with welding current and time is included in the model to analyze the shunting effect on final nugget quality. Thermal and electrical interactions are considered in the model as well as geometrical aspects of the process. Temperature dependence of material properties, integration through the whole volume of the nugget and heat-affected zone, and assuming a simple cooling mechanism are the most important modifications designated in the proposed theoretical model. Predicted results of nugget diameter are compatible with experimental and finite element analysis results.     

An efficient hybrid reliability analysis method with random and interval variables

Random and interval variables often coexist. Interval variables make reliability analysis much more computationally intensive. This work develops a new hybrid reliability analysis method so that the probability analysis (PA) loop and interval analysis (IA) loop are decomposed into two separate loops. An efficient PA algorithm is employed, and a new efficient IA method is developed. The new IA method consists of two stages. The first stage is for monotonic limit-state functions. If the limit-state function is not monotonic, the second stage is triggered. In the second stage, the limit-state function is sequentially approximated with a second order form, and the gradient projection method is applied to solve the extreme responses of the limit-state function with respect to the interval variables. The efficiency and accuracy of the proposed method are demonstrated by three examples.     

Identification of Pivotal Causes and Spreaders in the Time‐Varying Fault Propagation Model to Improve the Decision Making under Abnormal Situation

Under abnormal conditions, timely and effective decisions of system recovery and protective measures are of great significance for safety‐critical systems. The knowledge of the roles that network nodes play in the spreading process is crucial for developing efficient maintenance decisions; for singling out and preferential control, the ‘pivotal spreaders’ may be a way to maximize the chances to timely hinder the fault pervasion. Inspired by the inhomogeneous topological nature of a complex fault propagation network, this study is devoted to exploring the spreading capabilities of nodes regarding both structural connectivity and causal influence strength, so as to provide decisions of preferential recovery actions under specific fault scenarios. Specifically, the dynamic betweenness centrality and nonsymmetrical entropy are incorporated to adaptively measure the system‐wide fault diffusion risk of a set of controllable fault events. In order to model the dynamics and uncertainties involved in the complex fault spreading process, we introduce the model of a dynamic uncertain causality graph, based on which solutions of time‐varying structure decomposition and causality reduction are adopted to improve the reasoning efficiency. Verification experiments consisting of simulated calculation cases and generator faults of a nuclear power plant show empirically the effectiveness and applicability of this method in large‐scale engineering practice. Copyright © 2014 John Wiley & Sons, Ltd.