Artificial Neural Network Approach to Data Matrix Laser Direct Part Marking |
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Authors: | Witaya Jangsombatsiri J David Porter |
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Affiliation: | (1) Department of Industrial Technology, Mississippi Valley State University, Itta Bena, MS 38941, USA;(2) Department of Industrial and Manufacturing Engineering, Oregon State University, Corvallis, OR 97331, USA |
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Abstract: | Certain applications have recently appeared in industry where a traditional bar code printed on a label will not survive because
the item to be tracked has to be exposed to harsh environments. Laser direct-part marking is a manufacturing process used
to create permanent marks on a substrate that could help to alleviate this problem. In this research, artificial neural networks
were employed to model the laser direct-part marking process of Data Matrix symbols on carbon steel substrates. Several experiments
were conducted to study the laser direct-part marking process and to generate data to serve as training, validation and testing
data sets in the artificial neural networks modeling process. Two performance measures, mean squared error and correlation
coefficient, were utilized to assess the performance of the artificial neural network models. Single-output artificial neural
network models corresponding to four performance measures specific to the Data Matrix bar code symbology were found to have
good learning and predicting capabilities. The single-output artificial neural network models were compared to equivalent
multiple linear regression models for validation purposes. The prediction capability of the single-output artificial neural
network models with respect to laser direct-part marking of Data Matrix symbols on carbon steel substrates was superior to
that of the multiple linear regression models. |
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Keywords: | Artificial neural networks automatic identification and data capture data matrix direct-part marking Nd:YAG laser |
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