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Artificial Neural Networks for Predicting the Performance of Novice CAD Users Based on Their Profiled Technical Attributes

[+] Author Affiliations
R. F. Hamade, A. H. Ammouri, H. A. Artail

American University of Beirut, Beirut, Lebanon

Paper No. IMECE2011-63409, pp. 97-103; 7 pages
doi:10.1115/IMECE2011-63409
From:
  • ASME 2011 International Mechanical Engineering Congress and Exposition
  • Volume 3: Design and Manufacturing
  • Denver, Colorado, USA, November 11–17, 2011
  • Conference Sponsors: ASME
  • ISBN: 978-0-7918-5489-1
  • Copyright © 2011 by ASME

abstract

This paper utilizes Artificial Neural Networks (ANN) to forecast the mechanical CAD performance of novice trainees involved in formal training. We utilize 3 Artificial Neural Networks, ANN, techniques: Feed-Forward Backpropagation, Elman Backpropagation, and Generalized Regression. We also compare their predictive capabilities compared to those of linear regression techniques. For this purpose, two kinds of data are utilized as input vectors for the predictive techniques: performance data and trainee attributes data. Such data has been previously published by Hamade and coworkers. Performance data is based on the following four quantitative measures of performance: (1) construction speed of the CAD model, (2) sophistication of the constructed CAD model, and the rates of improvement of (3) construction speed and (4) model sophistication. Trainees’ attributes identified as related to building CAD competence include: (1) technical and (2) character attributes and (3) learning styles. Strong correlations have been found between many of the trainees’ profiled attributes and trainee’s actual performance throughout and upon the conclusion of the training. Generally, the ANN methods as well as the linear regression techniques were found to be successful in discriminating the trainees based on their profiled attributes. However, the findings suggest that, of the networks considered, the Generalized Regression Neural Network (GRNN) gave the best prediction results by yielding the least prediction error practically across all performance measures. Therefore, GRNN can be used to predict the performance of the novice CAD users. This capability may be used to pre-assess the development of CAD skills as training progresses and may serve as basis to develop custom CAD training programs and to improve the efficiency and effectiveness of CAD training.

Copyright © 2011 by ASME

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