A Dempster–Shafer approach for recognizing machine features from CAD models

Ji, Qiang; Marefat, Michael M.

doi:10.1016/s0031-3203(02)00260-1

Cited by 30 publications

(10 citation statements)

References 18 publications

(25 reference statements)

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“…This degree of belief is called the basic probability assignment (bpa) or probability mass function, m(.). The bpa ''expresses the degree to which the evidence confirms a hypothesis'' (Ji andMarefat, 2003, p. 1362). A bpa m(.)…”

Section: Assignment Of Belief Valuesmentioning

confidence: 99%

An application of the Dempster–Shafer theory of evidence to the classification of knee function and detection of improvement due to total knee replacement surgery

Jones

Beynon

Holt

et al. 2006

Journal of Biomechanics

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Section: Assignment Of Belief Valuesmentioning

confidence: 99%

An application of the Dempster–Shafer theory of evidence to the classification of knee function and detection of improvement due to total knee replacement surgery

Jones

Beynon

Holt

et al. 2006

Journal of Biomechanics

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“…The limitations are usually relieved by adopting hybrid methods, and construct rich feature libraries. The methods of Gravankar and Henderson [11], Yuen and Venuvinod [35], and Ji and Marefat [19] belong to the method. The method of Stefano et al [9] proposed a B-rep model-based method that also utilizes semantics of the features of a component from a geometric representation.…”

Section: Geometric Feature Recognitionmentioning

confidence: 99%

Development of an Agile Concept for MBSE for Future Digital Products through the Entire Life Cycle Management Called Munich Agile MBSE Concept (MAGIC)

Salehi¹

2019

CAD&A

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The value of (semi-)automatically recognizing the meaningful regions on a Computer Aided Design (CAD) model is well documented and has been traditionally studied as Geometric feature recognition. However, the effective tagging of the regions, especially tagging with contextually meaningful (i.e. semantic) keywords, has been elusive. In this paper, we focus on the non-geometric aspects of the tagging problem and present a semantic tagging framework. Specifically, given the contextually augmented instances created from the unidentified regions on a CAD model, the framework tags the instances with semantic keywords defined in the semantic model. This will provide the direct link between a CAD model and semantic models, and thus allow high-level reasoning to support design and manufacturing tasks. The feasibility of the approach has been verified by applying the developed framework to tag the regions on turbine blade models. This paper concludes with the future challenges of the approach.

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“…The missing arcs are defined as virtual links. Ji and Marefat [9] used Bayesian probabilistic rules and Dempster-Shafer theory for handling uncertainties and finding the exact set of missing arcs. The method was limited to polyhedral objects and did not guarantee the recovery of the correct set of missed links.…”

Section: A Multiple Interpretations To Complex Parts With Intersectimentioning

confidence: 99%

Combining Dynamic Machining Feature With Function Blocks for Adaptive Machining

Liu

Wang

2016

IEEE Trans. Automat. Sci. Eng.

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Feature-based technologies are widely researched for manufacturing automation. However, in current feature models, features once defined remain constant throughout the whole manufacturing lifecycle. This static feature model is inflexible to support adaptive machining when facing frequent changes to manufacturing resources. This paper presents a new machining feature concept that facilitates responsive changes to the dynamics of machining features in 2.5/3D machining. Basic geometry information for feature construction of complex parts with various intersecting features is represented as a set of meta machining features (MMF). Optimum feature definition is generated adaptively by choosing optimum merging strategies of MMFs according to the capabilities of the selected machine tool, cutter, and cutting parameters. A composite function block for dynamic machining feature modelling is designed with Basic Machining Feature Function Block, Meta Machining Feature Extraction Function Block and Feature Interpreter Function Block. Once changes of the selected machining resources occur, they are informed as input events and machining features are then updated automatically and adaptively based on the event-driven model of function blocks. An example is provided to demonstrate the feasibility and benefits of the developed methodology.Note to Practitioners-In this paper, an important problem in most feature-based machining systems is addressed: when the selected machine tool, cutters or cutting parameters are changed due to reasons like product changeover, urgent job insertion, and broken tools, the adjustment or optimization to the machining plan is carried out based on the existing feature model. Influences of the machining resources changes to the feature model are ignored. Thus, it is impossible to get the real optimal machining result. The proposed concept of dynamic machining feature integrates the feature definition of a part to the capabilities of the selected machining resources. By applying the event-driven model of function blocks, features can be generated adaptively and automatically during the whole dynamic manufacturing lifecycle.Index Terms-Adaptive machining, dynamic feature, function block.

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A Dempster–Shafer approach for recognizing machine features from CAD models

Cited by 30 publications

References 18 publications

An application of the Dempster–Shafer theory of evidence to the classification of knee function and detection of improvement due to total knee replacement surgery

An application of the Dempster–Shafer theory of evidence to the classification of knee function and detection of improvement due to total knee replacement surgery

Development of an Agile Concept for MBSE for Future Digital Products through the Entire Life Cycle Management Called Munich Agile MBSE Concept (MAGIC)

Combining Dynamic Machining Feature With Function Blocks for Adaptive Machining

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