An artificial neural network approach to laser-based direct part marking of data matrix symbols

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, a 532 tun laser was utilized to create a direct-part marked Data Matrix symbol onto carbon steel substrates with different carbon content. The quality of the laser marked Data Matrix symbol was then evaluated according to the criteria outlined in the ISO/IEC 16022 bar code technology specification for Data Matrix.;Several experiments were conducted to explore the effects that different parameters have on the quality of the laser direct-part marked symbols. First, an experiment was conducted to investigate the effect of two different laser tool path patterns. In later experiments, parameters such as type of carbon steel, percent of laser tool path overlap, profile speed, average power and frequency were found to have significant effects on the quality of laser direct-part marked Data Matrix symbols. The analysis of the results indicated that contrast and print growth were the critical standard performance measures that limited laser direct-part marked Data Matrix symbols from achieving a higher final grade. No significant effects were found with respect to other standard performance measures (i.e., encode, axial uniformity, and unused error correction).;Next, the experimental data collected for contrast and print growth was utilized as training, validation and testing data sets in the modeling of artificial neural networks for the laser direct-part marking process. Two performance measures (i.e., mean squared error and correlation coefficient) were employed to assess the performance of the artificial neural network models. Single-output artificial neural network models corresponding to a specific performance measure 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.