The Heriot-Watt FeatureFinder: CIE97 results

Little, Gordon R.; Tuttle, Ronald F.; Clark, D. E. R.; Corney, Jonathan

doi:10.1016/s0010-4485(98)00059-1

Cited by 16 publications

(4 citation statements)

References 4 publications

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“…The concept of a face set from an inner loop on a single face can be extended conceptually to a loop of edges straddling several faces. This type of face set is new, though in some cases it could be equivalent to depressions from multiple faces proposed in the Heriot-Watt FeatureFinder [20]. This face set is referred to as multiple-face inner loop set and an example is shown in Fig.…”

Section: Individual Feature Detection Methodsmentioning

confidence: 99%

Systematic development and evaluation of composite methods for recognition of three-dimensional subtractive features

Owodunni

Hinduja

2005

Proceedings of the Institution of Mechanical Engineers, Part B:

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This paper describes a systematic procedure for developing composite feature detection systems from six methods for detecting three-dimensional depression features. The six methods, proposed by the authors in earlier papers, correspond to all the possible ways of grouping faces together from the simplest to the most complex grouping. All the possible ways of combining the six feature detection methods are considered and arranged in a tree structure. The possible composites are reduced to 20, using a tree pruning technique based on the criteria that the features detected should be the same (i.e. consistent), irrespective of the ordering of the faces in the B-rep model and that all faces of the component should be detected (i.e. complete coverage). A test bed for these 20 composites has been developed, implemented, and tested using carefully selected components from the public domain. The performance of these 20 composites is evaluated on the basis of suitability of the features as input to a machining application with minimal or no additional geometric reasoning, thus enabling the most promising composites to be identified.

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Section: Individual Feature Detection Methodsmentioning

confidence: 99%

Systematic development and evaluation of composite methods for recognition of three-dimensional subtractive features

Owodunni

Hinduja

2005

Proceedings of the Institution of Mechanical Engineers, Part B:

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“…It is over ten years since the special panel session for FR at the ASME 'Computers in Engineering Conference' in 1997 presented a comparison among different systems (Abdel-Malek et al 1998, Han and Rosen 1998, Little et al 1998, Sonthi and Gadh 1998, Wang and Kim 1998) and a set of standard test components became available at NIST (Regli and Gaines 1997). Yet no easy way of comparing functionality has yet appeared.…”

Section: Introductionmentioning

confidence: 99%

A review of machining feature recognition methodologies

Verma

Rajotia

2010

International Journal of Computer Integrated Manufacturing

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In a continuing quest to decrease the time interval between conceptualisation of a product and its first production, the use of information technology in design, analysis and manufacturing practice has been actively researched. The design engineer designs a part and sends the final design to the manufacturing engineer, who re-interprets the design and plans the manufacturing activities to produce the part. These two sections generally work in isolation from each other, resulting in high lead-time, duplication of data, inconsistent product data and sometimes redesign of a product. Feature recognition is a process of reinterpreting a design model database for automating downstream manufacturing activities. Active research in this field has developed numerous techniques such as syntactic pattern recognition, graph theory, volume decomposition, artificial intelligence and hint-based, and neural network-based systems. This paper presents a critical review of strengths and weaknesses of these approaches.

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“…On the one hand, a great deal of research work has been done on the recognition of machining feature from product models [3][4], but most of them can recognize regular and 2.5 dimensional features only, and it is still difficult in the recognition of three-dimensional or sculptured surface features. The methods of feature recognition can be summarized as follows [5]: (1) hint-based reasoning [6,7]; (2) convex decomposition [8]; (3) face-edge graph based approach [9]; (4) curvature region approach [10] and (5) cellbased decomposition [11]. On the other hand, a feature database needs to be created so that the process planning can be performed based on it.…”

Section: Introductionmentioning

confidence: 99%

New approach to process planning using feature-based techniques

Zhang

2008

2008 IEEE International Conference on Industrial Technology

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In this paper, we proposed a new strategy to process planning for parts with complex manufacturing procedure. Different from previous works, on the one hand, features are defined as high-level features and low-level ones. In the high-level features, only some key parameters such as types of operations, manufacturing resources used like machining tools, cutters and instruments need to be taken into consideration. In contrast, the alterative operation plans in detail are given as the low level features, and the relation between the high-level feature and the low-level ones is specified. On the other hand, the features are extended including machining features and non-machining ones like jointing and forming according to the requirements of manufacturing tasks. By this way, the process plans need to be performed at different levels so that the successful manufacturing experiences can be collected and reused easily. One of advantages of the proposed method is that the manufacturing data embedded in the feature can be transformed and integrated with the traditional, document-based manufacturing process; another advantage is the process plan proposed could be easily changed to other machine tools if some unpredictable issues happened in the shop floor. A prototype has been developed and applied in the Shaanxi Heavy Cart Limited Company, at the northwest of China.

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The Heriot-Watt FeatureFinder: CIE97 results

Cited by 16 publications

References 4 publications

Systematic development and evaluation of composite methods for recognition of three-dimensional subtractive features

Systematic development and evaluation of composite methods for recognition of three-dimensional subtractive features

A review of machining feature recognition methodologies

New approach to process planning using feature-based techniques

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