A functional feature modeling method

Cheng, Zhengrong; Ma, Yongsheng

doi:10.1016/j.aei.2017.04.003

Cited by 43 publications

(11 citation statements)

References 45 publications

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“…This provides the opportunity to semantically register feature-based 3D CAD models according to the unique purpose of the features in our specific domains of application. Linking design intent, parts' functionality, and features have been the subject of other studies, e.g., for robust CAD modeling [28,29]. In ASM parts, functionality and features are intertwined into design practices.…”

Section: Pose Registrationmentioning

confidence: 99%

Algebraic Filtering of Surfaces from 3D Medical Images with Julia

Jiřík¹,

DiCarlo²,

Liska³

et al. 2020

CADandA

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A semantic model difference identification (MDI) solution can be defined as an MDI solution that identifies and represents the differences between two compared models in terms of meaningful engineering information. Semantic representation of the differences between 3D CAD models is especially challenging due to the variety of modeling solutions used across industries, the non-uniqueness of modeling sequences and the use of low-level information, e.g. B-rep STEP files, in engineering communications. This work proposes an MDI method that represents the differences between 3D CAD models based on engineering semantics through features. Brake-and hydro-formed aerospace sheet metal parts are used as the application domain in which to propose and illustrate the proposed method. This method consists mainly of a pose registration stage and a difference identification stage. The pose registration method considers that all the features of a part serve specific functions, some of which are fundamental to the part's essential functionality, and that they are intertwined with the design intent of the part, which is particularly true for aerospace sheet metal parts. This provides the opportunity to semantically register feature-based 3D CAD models according to the unique purpose of the features in this specific domain of application. Difference identification is approached by primarily identifying and segregating the commonality between the compared 3D CAD models and then identifying the differences. The differences between 3D CAD models are classified as added, removed or differed features. Differed features are those features that are of the same type, but whose definition varies. As an outcome, the proposed method describes a way to fully pose-register 3D CAD models and identify their differences semantically based solely on their features, and, by extension, their design intent.

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Section: Pose Registrationmentioning

confidence: 99%

Algebraic Filtering of Surfaces from 3D Medical Images with Julia

Jiřík¹,

DiCarlo²,

Liska³

et al. 2020

CADandA

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show abstract

“…Rather than attempting to actually do such a comparison and make such a choice, a more scalable approach might be to find a way to automate such comparisons through a universal mechanism for defining the problem of consciousness or cognition, and through a universal mechanism for measuring fitness in solving that problem, so that it is more possible to compare the fitness of all available models to discover which component of each model offers the best understanding. Functional modeling approaches have long been used in various engineering domains [21], [22], [23], [24], [25], [26], [27], [28], because functional components with well defined interfaces remove the need to understand other work, creating the capacity for teams spread across a great variety of different disciplines to productively work on a single complex problem. The human-centric Functional Modeling Framework (FMF) described in this paper aims to provide a methodology for defining all conceptual entities according to the functions of those entities that can be observed in conscious human awareness.…”

Section: Introductionmentioning

confidence: 99%

A Human-Centric Functional Modeling Framework for Defining and Comparing Models of Consciousness and Cognition

Ae¹

2020

Preprint

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Borrowing techniques from complex systems and software engineering, this paper defines a Functional Modeling Framework to provide a simple common mechanism for representing models of consciousness or cognition that is independent of assumptions made by any specific model. All models of consciousness or cognition should of course fit into the complete set of functions of what consciousness, or cognition can do. Through attempting to represent all such functions (both computable and non-computable), this framework attempts to gain the capacity to represent all models of consciousness or cognition, even where the implementation mechanisms of those functions are unknown. Enabling different models of consciousness or cognition to be more easily compared is intended to enable research on consciousness and cognition to more reliably converge on a single understanding, even across massively collaborative research projects spanning multiple disciplines. Furthermore, if biological functionality can be considered as dynamically stable where it is persistent, then in aiming to provide a common approach for representing and comparing the functionalities of consciousness or cognition as dynamically stable systems, this framework provides a set of features through which it may be possible to see commonality between the functional components of a wide range of other dynamically stable biological or non-biological systems. If such commonality does exist, and if, as proposed, that commonality reflects deeper underlying physical and mathematical principles, then representing any researcher’s model of consciousness or cognition within this framework might help reveal applications of that researcher’s model across a wide range of other domains of study.

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“…In fact, in the perspective of CAD modeling, the modeling strategy above could only ensure the accuracy of geometric shape, but not necessarily meet the requirements of subsequent simulation and optimization, because their modeling strategy did not take the design/analysis intent [18][19][20][21], reusability [22,23] and model quality [24] into account. Although this does not affect the first surface construction, it will lead to cumbersome modification iteration and optimization [24][25][26]. In addition, the addendum surfaces mentioned above are mainly aimed at workpiece with closed contour, which is not suitable for hatch hollow parts with semi-opening contour.…”

Section: Introductionmentioning

confidence: 99%

Smoothing Parametric Design of Addendum Surfaces for Sheet Metal Forming

Tao

et al. 2020

Chin. J. Mech. Eng.

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The manual design of addendum surfaces on common CAD platforms is very tedious which requires many trialscorrections, which will certainly affect the construction efficiency and quality of addendum surfaces, and then affect the formability and quality of the workpiece in the process of sheet forming. In this paper, an automatic procedure based on parametric design method is proposed for the rapid construction of the addendum surfaces. The kernel of the parametric method is constructing boundary curves based on the shape of surfaces of workpiece and designing guide curves based on Hermite curve interpolation. By some simple parameters, the shape of the addendum surfaces could be controlled and adjusted easily. In addition, a minimum energy optimization method is employed to further optimize the constructed addendum surface. A finite element analysis for the sheet forming process is performed to evaluate the forming quality of constructed addendum surfaces. The instance illustrates that the addendum surface constructed by the proposed method could ensure both the overall smoothing of surfaces and the final forming quality, and it has a good effect on springback after forming. This research proposes a smoothing parametric design method for addendum surfaces construction which could construct and optimize addendum surfaces rapidly.

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A functional feature modeling method

Cited by 43 publications

References 45 publications

Algebraic Filtering of Surfaces from 3D Medical Images with Julia

Algebraic Filtering of Surfaces from 3D Medical Images with Julia

A Human-Centric Functional Modeling Framework for Defining and Comparing Models of Consciousness and Cognition

Smoothing Parametric Design of Addendum Surfaces for Sheet Metal Forming

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