Formalizing planning knowledge for hierarchical planning
Computational Intelligence
Theory and algorithms for plan merging
Artificial Intelligence
Complexity, decidability and undecidability results for domain-independent planning
Artificial Intelligence - Special volume on planning and scheduling
Advances in Feature-Based Manufacturing
Advances in Feature-Based Manufacturing
An Introduction to Automated Process Planning Systems
An Introduction to Automated Process Planning Systems
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
A Constraint Engine for Manufacturing Process Planning
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
A hierarchical manufacturing route planner based on heuristic algorithm: design and evaluation
Systems Analysis Modelling Simulation - Special issue: Advances in control and computer engineering
Design-to-fabrication automation for the cognitive machine shop
Advanced Engineering Informatics
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This article illustrates the complexities of real-world planning and how we can create AI planning systems to address them. Our system, IMACS (Interactive Manufacturability Analysis and Critiquing System), an automated designer's aid, evaluates the manufacturability of machined parts and suggests design modifications to improve manufacturability. Over the course of our efforts on IMACS, the manufacturing domain has continually challenged us to come up with working solutions that would scale to realistic problems. This article compares and contrasts IMACS's planning techniques with those used in classical AI planning systems and describes (1) how some of IMACS's planning techniques may be useful for AI planning in general, and (2) what challenges need to be overcome by AI planners so that they can be successfully used in manufacturing process planning. Similarities between AI and IMACS planning techniques indicate the large unrealized potential of AI planning techniques in solving real-world manufacturing problems. On the other hand, differences seem to indicate the need for domain-specific planning techniques. In particular, our experience suggests that process planning for complex machined parts cannot be easily accomplished by populating a general purpose planner with domain-specific knowledge. Instead, we needed to integrate the domain-specific knowledge into the planning algorithms themselves.