Machining planning: a model of an expert level planning process
Machining planning: a model of an expert level planning process
Hybrid blackboard model for feature interactions in process planning
ICC&IE '94 Proceedings of the 17th international conference on Computers and industrial engineering
An IT view on perspectives of computer aided process planning research
Computers in Industry
Hybrid feature recognition method for setup planning from STEP AP-203
Robotics and Computer-Integrated Manufacturing
Defining, recognizing and representing feature interactions in a feature-based data model
Robotics and Computer-Integrated Manufacturing
Machining precedence of 2½D interacting features in a feature-based data model
Journal of Intelligent Manufacturing
Topological model for machining of parts with complex shapes
Computers in Industry
Feature-based generation of machining process plans for optimised parts manufacture
Journal of Intelligent Manufacturing
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Today, feature-based process planning has been popular in academia and industry with its ability to rigorously integrate design and manufacturing. To date, research on feature sequencing is mainly focused on using expert systems or knowledge-based systems, geometric based approaches, unsupervised-learning or artificial neural network, and genetic algorithms. The approach presented in this paper, however, is a hybrid one using both knowledge-based rules and geometric reasoning rules. In addition to feature sequencing rules formulation, our research contributions consist of: (1) determining machining precedence constraints by a set of defined knowledge-based rules, (2) grouping machining features into setups based on tool approaching directions, and (3) sequencing features within each setup through geometric reasoning. The sequence of materials (features) to be removed depends on two types of interactions: adjacent interaction and volumetric interaction. A set of rules for geometric reasoning is therefore developed to generate feature sequence. The developed approach has been implemented as the Sequence Generator module in a Distributed Process Planning system and is validated through a case study.