Overview Of Allied Signal's XCUT System

Brooks, S.C.; Wolf, Mark L.

doi:10.1016/b978-0-444-81600-9.50022-5

Cited by 8 publications

(3 citation statements)

References 6 publications

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“…As noted earlier, there are many academic and research prototype feature identification systems~Allada & Anand, 1995;Ji & Marefat, 1997;Han et al, 2000;Shah et al, 2001!. Rather than create a special-case feature recognizer, we use one of the few available industrial systems, the Feature-Based Machining Husk~FBMach; Brooks & Wolf, 1994;Brooks & Greenway, 1995! from Honeywell, Federal Manufacturing Technologies~Kansas City, MO!.…”

Section: Potential To Return Multiple Feature Interpretationsmentioning

confidence: 99%

An approach to a feature-based comparison of solid models of machined parts

Cicirello

Regli

2002

AIEDAM

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Solid models are the critical data elements in modern computer-aided design environments, because they describe the shape and form of manufactured artifacts. Their growing ubiquity has created new problems in how to effectively manage the many models that are now stored in the digital libraries for large design and manufacturing enterprises. Existing techniques from the engineering literature and industrial practice, such as group technology, rely on human-supervised encodings and classification; techniques from the multimedia database and computer graphics/vision communities often ignore the manufacturing attributes that are most significant in the classification of models. This paper presents our approach to comparing the manufacturing similarity assessments of solid models of mechanical parts based on machining features. Our technical approach is threefold: perform machining feature extraction, construct a model dependency graph (MDG) from the set of machining features, and partition the models in a database using a measure of similarity based on the MDGs. We introduce two heuristic search techniques for comparing MDGs and present empirical experiments to validate our approach using our testbed, the National Design Repository.

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Section: Potential To Return Multiple Feature Interpretationsmentioning

confidence: 99%

An approach to a feature-based comparison of solid models of machined parts

Cicirello

Regli

2002

AIEDAM

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“…There have been many efforts to classify machining features and devise feature hierarchies [11]- [16]. However, the feature community has not yet reached agreement on a canonical set of features for any application, and it is doubtful whether it is even worthwhile trying to achieve such a consensus.…”

Section: B Feature Classification and Standardizationmentioning

confidence: 99%

Manufacturing feature recognition from solid models: a status report

Han

Pratt

Regli

2000

IEEE Trans. Robot. Automat.

224

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The field of solid modeling has developed a variety of techniques for unambiguous representations of three-dimensional objects. Feature recognition is a sub-discipline of solid modeling that focuses on the design and implementation of algorithms for detecting manufacturing information from solid models produced by computer-aided design (CAD) systems. Examples of this manufacturing information include features such as holes, slots, pockets and other shapes that can be created on modern computer numerically controlled machining systems. Automated feature recognition has been an active research area in solid modeling for many years and is considered to be a critical component for integration of CAD and computer-aided manufacturing. This paper gives an overview of the state-of-the-art in feature recognition research. Rather than giving an exhaustive survey, we focus on the three of the major algorithmic approaches for feature recognition: graph-based algorithms, volumetric decomposition techniques, and hint-based geometric reasoning. For each approach, we present a detailed description of the algorithms being employed along with some assessments of the technology. We conclude by outlining important open research and development issues.

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“…Automated process planning strives to bridge the CAD/CAM gap by producing machine-interpretable process plans as inputs to CAM NC systems from the design CAD model of the part (Brooks and Wolf, 1994). Despite the advances in CAD/CAM and Concurrent Engineering, however, there is still a limited communication between CAD and CAPP systems (Grabowski and Fu, 1997).…”

Section: Introductionmentioning

confidence: 99%

Resource capability modelling for responsive process planning in extended manufacturing facilities

Ratchev

1998

Globalization of Manufacturing in the Digital Communications Era of the 21st Century

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The paper reports on the development of a decision making methodology for responsive process planning in extended manufacturing facilities using resource capability modelling. The methodology is based on a distributed interactive planning architecture using autonomaus decision making agents and data models. The distributed process planning environment and decision making methodology aims to provide the designers and planners with a design support tool for collaborative process plan generation and rapid assessment of the manufacturing feasibility and resource availability at different stages of the product development process.

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Overview Of Allied Signal's XCUT System

Cited by 8 publications

References 6 publications

An approach to a feature-based comparison of solid models of machined parts

An approach to a feature-based comparison of solid models of machined parts

Manufacturing feature recognition from solid models: a status report

Resource capability modelling for responsive process planning in extended manufacturing facilities

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