Connectionist models for part-family classifications

Chakraborty, Kanad; Roy, Utpal

doi:10.1016/0360-8352(93)90007-k

Cited by 20 publications

(6 citation statements)

References 4 publications

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“…The ® rst category includes works such as those of Kaparthi and Suresh (1991) , Moon and Roy (1992) , Chakraborty and Roy (1993) , Escobedo et al (1993) and Bahrami et al (1995) . Kaparthi and Suresh (1991) addressed the problem of generating GT codes based on shape-based data.…”

Section: Neural Network Methods For Part± Machine Groupingmentioning

confidence: 99%

“…Moon and Roy (1992) advocated the introduction of a feature-based solid modelling scheme for part representation using a backpropagation rule. Chakraborty and Roy (1993) utilized Kohonen's (1984) self-organizing feature map based on feature information extracted from CAD databases. A similar system based on the ART1 network was presented by Escobedo et al (1993).…”

Section: Neural Network Methods For Part± Machine Groupingmentioning

confidence: 99%

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Sequence-dependent clustering of parts and machines: A Fuzzy ART neural network approach

Slomp¹,

Kaparthi²

1999

International Journal of Production Research

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This study addresses the problem of identifying families of parts having a similar sequence of operations. This is a prerequisite for the implementation of cellular manufacturing, group technology, just-in-time manufacturing systems, and for streamlining material¯ows in general. A pattern recognition approach based on arti® cial neural networks is proposed, and it is shown that the Fuzzy ART neural network can be e ectively utilized for this application. First, a representation scheme for operation sequences is developed, followed by an illustrative example. A more comprehensive experimental veri® cation, based on the mixture-model approach is then performed to evaluate its performance. The experimental factors include size of the part± machine matrix, proportion of voids, proportion of exceptional elements, and vigilance threshold. It is shown that this neural network is e ective in identifying good clustering solutions, consistently and with relatively fast execution times.

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Section: Neural Network Methods For Part± Machine Groupingmentioning

confidence: 99%

Section: Neural Network Methods For Part± Machine Groupingmentioning

confidence: 99%

Sequence-dependent clustering of parts and machines: A Fuzzy ART neural network approach

Slomp¹,

Kaparthi²

1999

International Journal of Production Research

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“…Thirty-six parts (Fig. 7), nine of them adopted from [9], are used to demonstrate the classification capability of this approach.…”

Section: Methodsmentioning

confidence: 99%

“…Chakraborty and Roy [9] used "Kohonen's self-organising feature maps" [12] to cluster parts into families, then the obtained part families are used as the training input to construct a workpiece classifier based on a back-propagation neural network model [11]. That is, each part family needs a classifier built to find if a workpiece is a member of this family or not.…”

Section: Introductionmentioning

confidence: 99%

A neural network approach to the classification of 3D prismatic parts

Jen

1996

Int J Adv Manuf Technol

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“…The network with the trained weights is then employed as the basis for classifying new inputs. The most popular technique of this type is the back propagation algorithm [137][138][139][140].…”

Section: Neural Networkmentioning

confidence: 99%

Virtual production system

Ko¹

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This manuscript has been reproduced from the microfilm master. UMI films the text directly from the original or copy submitted. Thus, some thesis arxl dissertation copies are in typewriter face, wtiile ottiers may be from any type of computer printer.The quality of this reproduction is dependent upon tlw quality of ttie copy submitted. Brolwn or irvlistinct print cotored or poor quality illustrations and photographs, print bleedUirough, substandard margins, and improper alignment can adversely aftect reproduction.In the unlikely event that the author did not send UMI a complete manuscript and there are missing pages, these «mN be noted. Also, if unauthorized copyright material had to t)e removed, a rwte will indicate the deletion.Oversize materials (e.g., maps, drawings, charts) are reproduced t»y sectioning the original, k)eginning at the upper left-hand comer and continuing from left to right in equal sections with small overlaps.

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Connectionist models for part-family classifications

Cited by 20 publications

References 4 publications

Sequence-dependent clustering of parts and machines: A Fuzzy ART neural network approach

Sequence-dependent clustering of parts and machines: A Fuzzy ART neural network approach

A neural network approach to the classification of 3D prismatic parts

Virtual production system

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