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Proceedings ArticleDOI

Case-based manufacturing process planning with integrated support for knowledge sharing

TLDR
This work presents a new case-based planning methodology that extends the capability and effectiveness of previous approaches by incorporating the ability to learn new domain knowledge from other planners via an integrated knowledge sharing mechanism.
Abstract
Computer aided process planning is a key part of bridging the link between design and manufacturing. Case-based reasoning provides an attractive paradigm for process planning as it combines the generative and variant approaches and improves the effectiveness of the planner by applying old experiences to solving new planning problems. We present a new case-based planning methodology that extends the capability and effectiveness of previous approaches by incorporating the ability to learn new domain knowledge from other planners via an integrated knowledge sharing mechanism. In addition, our planner has the capability to use multiple cases in the process of constructing a new plan, providing more effective utilization of the planner's previous experiences. The planning algorithm is based on domain-independent partial-order planning, and provides a formal approach to case-based process planning.

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Citations
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Journal ArticleDOI

State of The Art-Intense Review on Artificial Intelligence Systems Application in Process Planning and Manufacturing

TL;DR: An attempt has been made for review on AI applications in Computer Aided Process Planning (CAPP) and manufacturing and role of Evolutionary Techniques (ET) in intelligent system development, execution of PP activities and manufacturing is described.
Journal ArticleDOI

An IT view on perspectives of computer aided process planning research

TL;DR: This paper first provides a general introduction to CAPP along with its background, and gives an overview of manufacturing features and feature recognition research.
Journal Article

Early fault classification in dynamic systems using case-based reasoning

TL;DR: A computational framework to deal with the problem of early fault classification using Case-Based Reasoning is presented and different techniques for case retrieval and reuse that have been applied at different times of fault evolution are illustrated.
Journal ArticleDOI

Qualitative representation of process trends for situation assessment based on cases

TL;DR: This paper proposes to use qualitative representations of signal trends as experienced cases and a similarity criterion among signal representations is defined by a Dynamic Time Warping approach.
Proceedings ArticleDOI

Expert supervision based on cases

TL;DR: The methodology of case base reasoning (CBR) is proposed to perform supervisory tasks in industrial processes by re-using the stored data to take advantage of experiences, registered in a suitable structure as cam, avoiding the tedious task of knowledge acquisition and representation needed by other reasoning techniques as expert systems.
References
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Journal ArticleDOI

Strips: A new approach to the application of theorem proving to problem solving

TL;DR: In this paper, the authors describe a problem solver called STRIPS that attempts to find a sequence of operators in a space of world models to transform a given initial world model in which a given goal formula can be proven to be true.
Proceedings Article

Generating project networks

TL;DR: The planner (NONLIN) and the Task Formalism (TF) used to hierarchically specify a domain are described, which can aid in the generation of project networks.
Journal ArticleDOI

A validation-structure-based theory of plan modification and reuse

TL;DR: A theory of plan modification applicable to hierarchical nonlinear planning is presented, which utilizes the validation structure of stored plans to yield a flexible and conservative plan modification framework.
Journal ArticleDOI

Partial-order planning: evaluating possible efficiency gains

TL;DR: This paper characterizes the types of domains that offer performance differentiation and the features that distinguish the relative overhead of three planning algorithms, arguing that the observed performance differences are best understood with an extension of Korf's taxonomy of subgoal collections.
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