Neural network modeling of pulsed-laser weld pool shapes in aluminum alloy welds

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Description

A model was developed to predict the weld pool shape in pulsed Nd:YAG laser welds of aluminum alloy 5754. The model utilized neural network analysis to relate the weld process conditions to four pool shape parameters: penetration, width, width at half-penetration, and cross-sectional area. The model development involved the identification of the input (process) variables, the desired output (shape) variables, and the optimal neural network architecture. The latter was influenced by the number of defined inputs and outputs as well as the amount of data that was available for training the network. After appropriate training, the best network was identified ... continued below

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7 p.

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Vitek, J.M.; Iskander, Y.S.; Oblow, E.M.; Babu, S.S.; David, S.A.; Fuerschbach, P.W. et al. November 1, 1998.

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Description

A model was developed to predict the weld pool shape in pulsed Nd:YAG laser welds of aluminum alloy 5754. The model utilized neural network analysis to relate the weld process conditions to four pool shape parameters: penetration, width, width at half-penetration, and cross-sectional area. The model development involved the identification of the input (process) variables, the desired output (shape) variables, and the optimal neural network architecture. The latter was influenced by the number of defined inputs and outputs as well as the amount of data that was available for training the network. After appropriate training, the best network was identified and was used to predict the weld shape. A routine to convert the shape parameters into predicted weld profiles was also developed. This routine was based on the actual experimental weld profiles and did not impose an artificial analytical function to describe the weld profile. The neural network model was tested on experimental welds. The model predictions were excellent. It was found that the predicted shapes were within the experimental variations that were found along the length of the welds (due to the pulsed nature of the weld power) and the reproducibility of welds made under nominally identical conditions.

Physical Description

7 p.

Notes

OSTI as DE99000362

Source

  • 5. international conference on trends in welding research, Pine Mountain, GA (United States), 1-5 Jun 1998

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  • Other: DE99000362
  • Report No.: ORNL/CP--98605
  • Report No.: CONF-980657--
  • Grant Number: AC05-96OR22464;AC04-94AL85000
  • DOI: 10.2172/290937 | External Link
  • Office of Scientific & Technical Information Report Number: 290937
  • Archival Resource Key: ark:/67531/metadc688069

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Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

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Creation Date

  • November 1, 1998

Added to The UNT Digital Library

  • July 25, 2015, 2:20 a.m.

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  • Jan. 22, 2016, 12:23 p.m.

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Vitek, J.M.; Iskander, Y.S.; Oblow, E.M.; Babu, S.S.; David, S.A.; Fuerschbach, P.W. et al. Neural network modeling of pulsed-laser weld pool shapes in aluminum alloy welds, report, November 1, 1998; Tennessee. (digital.library.unt.edu/ark:/67531/metadc688069/: accessed December 18, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.