Abstract
A wide range of opportunities are emerging in the micro-system technology sector for laser micro-machining systems, because they are capable of processing various types of materials with micro-scale precision. However, few process datasets and machine-learning techniques are optimized for this industrial task. This study describes the process parameters of micro-laser milling and their influence on the final features of the microshapes that are produced. It also identifies the most accurate machine-learning technique for the modelization of this multivariable process. It examines the capabilities of laser micro-machining by performing experiments on hardened steel with a pulsed Nd:YAG laser. Arrays of micro-channels were manufactured using various scanning speeds, pulse intensities and pulse frequencies. The results are presented in terms of the main industrial requirements for any manufactured good: dimensional accuracy (in our case, depth and width of the channels), surface roughness and material removal rate (which is a measure of the productivity of the process). Different machine-learning techniques were then tested on the datasets to build highly accurate models for each output variable. The selected techniques were: k-Nearest Neighbours, neural networks, decision trees and linear regression models. Our analysis of the correlation coefficients and the mean absolute error of all the generated models show that neural networks are better at modelling channel depth and that decision trees are better at modelling material removal rate; both techniques were similar for width and surface roughness. In general, these two techniques show better accuracy than the other two models. The work concludes that decision trees should be used, if information on input parameter relations is sought, while neural networks are suitable when the dimensional accuracy of the workpiece is the main industrial requirement. Extensive datasets are necessary for this industrial task, to provide reliable AI models due to the high rates of noise, especially for some outputs such as roughness.
Similar content being viewed by others
References
Bartolo, P., Vasco, J., Silva, B., & Galo, C. (2006). Laser micromachining for mould manufacturing: I. The influence of operating parameters. Assembly Automation, 26(3), 227–234.
Bordatchev, E. V., & Nikumb, S. K. (2003). An experimental study and statistical analysis of the effect of laser pulse energy on the geometric quality during laser precision machining. Machine Science Technology, 7(1), 83–104.
Benardos, P. G., & Vosniakos, G. C. (2003). Predicting surface roughness in machining: A review. International Journal of Machine Tools and Manufacture, 43(8), 833–844.
Brousseau, E., & Eldukhri, E. (2011). Recent advances on key technologies for innovative manufacturing. Journal of Intelligent Manufacturing, 22(5), 675–691.
Bustillo, A., & Correa, M. (2012). Using artificial intelligence to predict surface roughness in deep drilling of steel components. Journal of Intelligent Manufacturing, 23(5), 1893–1902.
Bustillo, A., Díez-Pastor, J. F., Quintana, G., & García-Osorio, C. (2011a). Avoiding neural network fine tuning by using ensemble learning: application to ball-end milling operations. The International Journal of Advanced Manufacturing Technology, 57(5), 521–532.
Bustillo, A., Ukar, E., Rodriguez, J. J., & Lamikiz, A. (2011b). Modelling of process parameters in laser polishing of steel components using ensembles of regression trees. International Journal of Computer Integrated Manufacturing, 24(8), 735–747.
Campanelli, S. L., Ludovico, A. D., Bonserio, C., Cavalluzzi, P., & Cinquepalmi, M. (2007). Experimental analysis of the laser milling process parameters. Journal of Materials Processing Technology, 191(1–3), 220–223.
Cicală, E., Soveja, A., Sallamand, P., Grevey, D., & Jouvard, J. M. (2008). The application of the random balance method in laser machining of metals. Journal of Materials Processing Technology, 196(1–3), 393–401.
Chandrasekaran, M., Muralidhar, M., Krishna, C., & Dixit, U. (2010). Application of soft computing techniques in machining performance prediction and optimization: A literature review. International Journal of Advanced Manufacturing Technology, 46(5), 445–464.
Ciurana, J., Arias, G., & Ozel, T. (2009). Neural network modeling and particle swarm optimization (PSO) of process parameters in pulsed laser micromachining of hardened AISI H13 steel. Materials and Manufacturing Processes, 24(3), 358–368.
Correa, M., Bielza, C., & Pamies-Teixeira, J. (2009). Comparison of Bayesian networks and artificial neural networks for quality detection in a machining process. Expert Systems with Applications, 36, 7270–7279.
Desai, C. K., & Shaikh, A. (2012). Prediction of depth of cut for single-pass laser micro-milling process using semi-analytical, ANN and GP approaches. International Journal of Advanced Manufacturing Technology, 60(9–12), 865–882.
Dhara, S. K., Kuar, A. S., & Mitra, S. (2008). An artificial neural network approach on parametric optimization of laser micro-machining of die-steel. International Journal of Advanced Manufacturing Technology, 39(1–2), 39–46.
Díez-Pastor, J. F., Bustillo, A., Quintana, G., & García-Osorio, C. (2012). Boosting projections to improve surface roughness prediction in high-torque milling operations. Soft Computing, 16(8), 1427–1437.
Grzenda, M., Bustillo, A., Quintana, G., & Ciurana, J. (2012a). Improvement of surface roughness models for face milling operations through dimensionality reduction. Integrated Computer-Aided Engineering, 19(2), 179–197.
Grzenda, M., Bustillo, A., & Zawistowski, P. (2012b). A soft computing system using intelligent imputation strategies for roughness prediction in deep drilling. Journal of Intelligent Manufacturing, 23(5), 1733–1743.
Karazi, S. M., Issa, A., & Brabazon, D. (2009). Comparision of ANN and DoE for the prediction of laser-machined micro-channel dimensions. Optics and Lasers in Engineering, 47, 956–964.
Kaldos, A., Pieper, H. J., Wolf, E., & Krause, M. (2004). Laser machining in die making—a modern rapid tooling process. Journal of Materials Processing Technology, 155–156, 1815–1820.
Kumar, A., & Gupta, M. C. (2010). Laser machining of micro-notches for fatigue life. Optics and Lasers in Engineering., 48(6), 690–697.
Mahdavinejad, R. A., Khani, N., & Fakhrabadi, M. M. S. (2012). Optimization of milling parameters using artificial neural network and artificial immune system. Journal of Mechanical Science and Technology, 26(12), 4097–4104.
Pham, D. T., Dimov, S. S., & Petkov, P. V. (2007). Laser milling of ceramic components. International Journal of Machine Tools Manufacturing, 47(3–4), 618–626.
Quintana, G., Bustillo, A., & Ciurana, J. (2012). Prediction, monitoring and control of surface roughness in high-torque milling machine operations. International Journal of Computer Integrated Manufacturing, 25(12), 1129–1138.
Quintana, G., Garcia-Romeu, M. L., & Ciurana, J. (2011). Surface roughness monitoring application based on artificial neural networks for ball-end milling operations. Journal of Intelligent Manufacturing, 22, 607–617.
Rizvi, N. H., & Apte, P. (2002). Developments in laser micro-machining techniques. Journal of Materials Processing Technology, 127(2), 206–210.
Samant, A. N., & Dahotre, N. B. (2010). Three-dimensional laser machining of structural ceramics. Journal of Manufacturing Processes, 12(1), 1–7.
Semaltianos, N. G., Perrie, W., Cheng, J., French, P., Sharp, M., Dearden, G., et al. (2010). Picosecond laser ablation of nickel-based superalloy C263. Applied Physics A: Materials Science and Processing, 98(2), 345–355.
Yousef, B. F., Knopf, G. K., Bordatchev, E. V., & Nikumb, S. K. (2003). Neural network modeling and analysis of the material removal process during laser machining. International Journal of Advanced Manufacturing Technology, 22(1–2), 41–53.
Acknowledgments
This study was partially supported through grants from the European Commission project IREBID (FP7-PEOPLE-2009-IRSES-247476) and the Spanish Science and Innovation Minister project TECNIPLAD (DPI2009-09852).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Teixidor, D., Grzenda, M., Bustillo, A. et al. Modeling pulsed laser micromachining of micro geometries using machine-learning techniques. J Intell Manuf 26, 801–814 (2015). https://doi.org/10.1007/s10845-013-0835-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10845-013-0835-x