Graph-based heuristics for recognition of machined features from a 3D solid model
Computer-Aided Design
Automatically generating abstractions for planning
Artificial Intelligence
GPS, a program that simulates human thought
Computers & thought
Building MRSEV models for CAM applications
Advances in Engineering Software - Special issue: feature-based design and manufacturing
Fast planning through planning graph analysis
Artificial Intelligence
Geometric Reasoning for Recognition of Three-Dimensional Object Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric Reasoning for Recognition of Three-Dimensional Object Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spatial Reasoning for the Automatic Recognition of Machinable Features in Solid Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Effector-based goal and operator construction: a model for the design of effector adaptive planners
Effector-based goal and operator construction: a model for the design of effector adaptive planners
An approach to a feature-based comparison of solid models of machined parts
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
| Hi-index | 0.00 |
In this work, we describe an approach to automated manufacturing feature extraction and operations planning that combines the two processes to benefit both. When viewed in planning terms, if feature extraction is the process of identifying planning goals, and operations planning is the the process of selecting and sequencing planning operators, then what we are doing can be viewed as combining the normally separate processes of finding goals and developing instantiated operators to satisfy them. Thus, we call this approach Ebgoc (Effector-Based Goal and Operator Construction), and we have implemented it in a computer program called Mediator. Effectors are the physical equipment, such as tools and machines, that are used to transform the initial materials into the desired end product (goal). We say that this approach is effector-based (rather than feature-based) because we do not recognize a fixed set of features, but instead we geometrically derive the set of shapes that can be machined with the currently available set of effectors. Thus, each shop can customize Mediator so that it identifies machinable volumes appropriate for the resource in that specific shop. This approach gives Mediator several important properties. It can 1) effectively handle feature interactions (e.g., volumetric intersections), 2) be customized to produce features appropriate to specific shop resources, 3) identify areas that the current resources cannot machine, and 4) handle nonstandard, user-defined tooling.