Closed-Loop Analysis in Semantic Tolerance Modeling

Wang, Yan

doi:10.1115/1.2900715

Cited by 10 publications

(4 citation statements)

References 32 publications

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“…By incorporating quantifiers, the QCSP formulation can capture design intent as well as material properties, process sequences, and other semantics. With the advantage of quantified variables, QCSPs have recently been applied in mechanical design [46][47][48][49][50], control [51][52][53][54][55], scheduling [56,57], and planning [58,59]. Notice that CSP can be regarded a special case of QCSP, in which all variables are existentially quantified.…”

Section: Csp and Qcspmentioning

confidence: 99%

Sensitivity Analysis in Quantified Interval Constraint Satisfaction Problems

Wang

Cheng

et al. 2015

Journal of Mechanical Design

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Interval is an alternative to probability distribution in quantifying uncertainty for sensitivity analysis (SA) when there is a lack of data to fit a distribution with good confidence. It only requires the information of lower and upper bounds. Analytical relations among design parameters, design variables, and target performances under uncertainty can be modeled as interval-valued constraints. By incorporating logic quantifiers, quantified constraint satisfaction problems (QCSPs) can integrate semantics and engineering intent in mathematical relations for engineering design. In this paper, a global sensitivity analysis (GSA) method is developed for feasible design space searching problems that are formulated as QCSPs, where the effects of value variations and quantifier changes for design parameters on target performances are analyzed based on several proposed metrics, including the indeterminacy of target performances, information gain of parameter variations, and infeasibility of constraints. Three examples are used to demonstrate the proposed approach.

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Section: Csp and Qcspmentioning

confidence: 99%

Sensitivity Analysis in Quantified Interval Constraint Satisfaction Problems

Wang

Cheng

et al. 2015

Journal of Mechanical Design

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“…have the similar semantic properties. More information of generalized interval can be found in [41,42,43].…”

Section: / × −mentioning

confidence: 99%

Multiscale Variability and Uncertainty Quantification Based on a Generalized Multiscale Markov Model

Wang

2010

Volume 1: 36th Design Automation Conference, Parts a and B

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Variability is inherent randomness in systems, whereas uncertainty is due to lack of knowledge. In this paper, a generalized multiscale Markov (GMM) model is proposed to quantify variability and uncertainty simultaneously in multiscale system analysis. The GMM model is based on a new imprecise probability theory that has the form of generalized interval, which is a Kaucher or modal extension of classical set-based intervals to represent uncertainties. The properties of the new definitions of independence and Bayesian inference are studied. Based on a new Bayes’ rule with generalized intervals, three cross-scale validation approaches that incorporate variability and uncertainty propagation are also developed.

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“…Vector loops provide vectorial tolerance representation and analysis for the surfaces of different components. However, only five standard surfaces are typically considered (plane, cylinder, sphere, cone, and torus) [8][9][10][11]. Consisting of a hypothetical volume of points, a T-Map contains all the possible locations for geometrical features, and is used to illustrate the accumulation of product variations [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Assembly Tolerance Design Based on Skin Model Shapes Considering Processing Feature Degradation

Zhang

Qiu

et al. 2019

Applied Sciences

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To increase the reliability and accuracy of tolerance design, more and more research works are considering not only orientation and position deviations; they are also forming errors in tolerance modeling. As a direct cause of form errors in industrial mass production, the processing features of the machining system degrade over time. Under the Industry 4.0 paradigm, an assembly tolerance design method based on Skin Model Shape is proposed to take the effect of degrading processing features into consideration. A continuous-time multi-dimensional Markov process is trained through maximum likelihood estimation based on the nodal sampling point set on the machined surface. Degradation of the machined surface is modeled based on the joint probability distribution of nodal displacements. Assembly force constraints and assembly entity constraints are applied to spatial assembly simulations. Tolerance synthesis takes the manufacturing cost and assembling probability as design objectives. A design example of the rotary feed component in a five-axis machine tool is proposed for explanation and verification.

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Closed-Loop Analysis in Semantic Tolerance Modeling

Cited by 10 publications

References 32 publications

Sensitivity Analysis in Quantified Interval Constraint Satisfaction Problems

Sensitivity Analysis in Quantified Interval Constraint Satisfaction Problems

Multiscale Variability and Uncertainty Quantification Based on a Generalized Multiscale Markov Model

Assembly Tolerance Design Based on Skin Model Shapes Considering Processing Feature Degradation

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