Graph-based heuristics for recognition of machined features from a 3D solid model
Computer-Aided Design
Principles of computer science
Principles of computer science
Building a feature-based object description from a boundary model
Computer-Aided Design
Applying the perceptron to three-dimensional feature recognition
Applying the perceptron to three-dimensional feature recognition
A neural network approach for datum selection in computer-aided process planning
Computers in Industry
Automatic classification of block-shaped parts based on their 2D projections
Computers and Industrial Engineering
Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
How to be a gray box: dynamic semi-physical modeling
Neural Networks
Novel ANN-based feature recognition incorporating design by features
Computers in Industry
Rule Extraction from Recurrent Neural Networks: A Taxonomy and Review
Neural Computation
Constraint-based approach to investigate the process flexibility of food processing equipment
Computers and Industrial Engineering
A feasible approach to the integration of CAD and CAPP
Computer-Aided Design
A review on evolution of production scheduling with neural networks
Computers and Industrial Engineering
Advances in collaborative CAD: the-state-of-the art
Computer-Aided Design
Learning pattern classification-a survey
IEEE Transactions on Information Theory
Software tool used for holes modelling in AutoCAD environment
EMESEG'10 Proceedings of the 3rd WSEAS international conference on Engineering mechanics, structures, engineering geology
Simulated rolling method for the recognition of outer profile faces of aircraft structural parts
Advances in Engineering Software
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In recent years, collaborative research between academia and industry has intensified in finding a successful approach to take the information from a computer generated drawings of products such as casting dies, and produce optimal manufacturing process plans. Core to this process is feature recognition. Artificial neural networks have a proven track record in pattern recognition and there ability to learn seems to offer an approach to aid both feature recognition and process planning tasks. This paper presents an up-to-date critical study of the implementation of artificial neural networks (ANN) applied to feature recognition and computer aided process planning. In providing this comprehensive survey, the authors consider the factors which define the function of a neural network specifically: the net topology, the input node characteristic, the learning rules and the output node characteristics. In additions the authors have considered ANN hybrid approaches to computer aided process planning, where the specific capabilities of ANN's have been used to enhance the employed approaches.