NDLI: Intelligent scheduling optimization using a rule-based artificial neural network

Content Provider	IEEE Xplore Digital Library
Author	Ruegsegger, S.M.
Copyright Year	1993
Description	Author affiliation: Case Western Reserve Univ., Cleveland, OH, USA (Ruegsegger, S.M.)
Abstract	Requirements for greater precision and reduced rejection/acceptance errors demand improved inspection methods that can be provided by implementing increased automation into the quality control (QC) process. Automated inspection planners can require significant human interface work deciphering the generated output, often requiring more work than the service they provide. The output of an automated inspection planner needs to produce a process plan that would resemble one created by an expert inspector. This paper discusses the implementation of an artificial neural network (ANN) to optimize the sequence of inspection points based upon inspection rule criteria. Using features from a feature-based CAD, the automated inspection planner uses the inspection rules to create "rule matrices" that the ANN will use to sequence the inspection points to be probed by a coordinate measurement machine (CMM). This is an extension of the familiar traveling salesman problem (TSP) solved by a Hopfield neural network.<>
Starting Page	800
Ending Page	806
File Size	609034
Page Count	7
File Format	PDF
ISBN	0780312953
DOI	10.1109/NAECON.1993.290840
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1993-05-24
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial intelligence Inspection Artificial neural networks Coordinate measuring machines Error correction Automation Quality control Humans Traveling salesman problems Hopfield neural networks
Content Type	Text
Resource Type	Article

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