The potential of neural networks is examined, and the effect of parallel processing on the solution of the stock-cutting problem is assessed. The conceptual model proposed integrates a feature-recognition network and a simulated annealing approach. The model uses a neocognitron neural network paradigm to generate data for assessing the degree of match between two irregular patterns. The information generated through the feature recognition network is passed to an energy function, and the optimal configuration of patterns is computed using a simulated annealing algorithm. Basics of the approach are demonstrated with an example.

Meeting Name

Rensselaer's Second International Conference on Computer Integrated Manufacturing, 1990


Engineering Management and Systems Engineering

Keywords and Phrases

Computerised Pattern Recognition; Cutting Stock Problems; Feature-Recognition Network; Manufacturing; Manufacturing Data Processing; Neural Nets; Neural Networks; Parallel Processing; Production Control; Simulated Annealing; Stock Control

Document Type

Article - Conference proceedings

Document Version

Final Version

File Type





© 1990 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

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