Abstract
The multi-axis laser manufacturing equipment is a piece of standard equipment suitable for complex components with hard and brittle material. The laser beam direction inevitably deviates from the coordinate axis of equipment due to the location of laser and optical parts, leading to component’s substrate over-cutting. This paper proposes a compensation method for the laser beam deflection to enhance the accuracy of the laser manufacturing. The deflection measurement was achieved by image and point cloud processing. The proposed method scans the point cloud model and fits the geometric feature to realize the orientation of the laser collimator relative to the equipment. The error of laser beam deflection calculates by the orientation of collimator and the spatial geometric relationship of the displacement and the laser trajectory. An error compensation model is constructed to compensate for dynamic error in the whole motion range during the calculation of equipment motion. The effectiveness of this method is verified through laser beam measurement, compensation and laser manufacturing for a complex component. The results show that the proposed method improves the processing accuracy (91.6 μm versus 316.6 μm) by data statistics.
Similar content being viewed by others
Abbreviations
- \(M_{c}\) :
-
The calibration relationship between the 3D scanner and the laser equipment.
- \(Pc_{i}\) :
-
Each point in the scanned point cloud model of the collimator.
- \(Pc^{\prime}_{i}\) :
-
The transformed point coordinate of \(Pc_{i}\).
- \(V_{d}\) :
-
The center axis of the cylindrical surface.
- \(V_{p}\) :
-
The normal vector of plane surface.
- \(\theta\) :
-
The direction angle between the laser beam and the central axis of the laser collimator.
- \(Ve_{i}\) :
-
The direction error at multiple positions.
- \(M_{lc}\) :
-
The attitude matrix \(M_{lc}\) of the laser collimator.
- \({}_{1}^{2} R\) :
-
The pose relationship from the laser beam coordinate system to the base coordinate system.
- \({}_{4}^{3} R\) :
-
The pose relationship from the turntable coordinate system to the swing table coordinate system.
- \({}_{1}^{4} R\) :
-
The pose relationship from the swing table coordinate system to the equipment coordinate system.
- \(L_{m}\) :
-
The movement range of equipment in Z-axis direction.
- \(Z_{0}\) :
-
The starting position of equipment movement in Z-axis direction
References
Andhi, I. K., & Huang, Y. M. (2022). Product quality prediction in pulsed laser cutting of silicon steel sheet using vibration signals and deep neural network. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-021-01881-1
Bi, C., Liu, Y., Fang, J. G., Guo, X., et al. (2015). Calibration of laser beam direction for optical coordinate measuring system. Measurement, 73, 191–199. https://doi.org/10.1016/j.measurement.2015.05.022
Cao, X., Jahazi, M., Immarigeon, J. P., & Wallace, W. (2006). A review of laser welding techniques for magnesium alloys. Journal of Materials Processing Technology, 171(2), 188–204. https://doi.org/10.1016/j.jmatprotec.2005.06.068
Chaki, S., Bathe, R. N., Ghosal, S., et al. (2018). Multi-objective optimisation of pulsed Nd:YAG laser cutting process using integrated ANN–NSGAII model. Journal of Intelligent Manufacturing, 29, 175–190. https://doi.org/10.1007/s10845-015-1100-2
Fitzsimons, E. D., Bogenstahl, J., Hough, J., et al. (2013). Precision absolute positional measurement of laser beams. Applied Optics, 52(12), 2527–2530. https://doi.org/10.1364/AO.52.002527
Guo, Y., Lu, W. F., & Fuh, J. Y. H. (2021). Semi-supervised deep learning based framework for assessing manufacturability of cellular structures in direct metal laser sintering process. Journal of Intelligent Manufacturing, 32, 347–359. https://doi.org/10.1007/s10845-020-01575-0
Hao, J. B., Meng, Q. D., Li, C. C., et al. (2019). Aircraft engineering and aerospace technology. Journal of Manufacturing Process, 43, 311–322. https://doi.org/10.1016/j.jmapro.2019.04.025
Heiderscheit, T., Shen, N. G., Wang, Q. H., Samanta, A., et al. (2019). Keyhole cutting of carbon fiber reinforced polymer using a long-duration nanosecond pulse laser. Optics and Lasers in Engineering, 120, 101–109. https://doi.org/10.1016/j.optlaseng.2019.03.009
Hou, D. X., Mei, X. S., Huang, W. W., et al. (2019). An online and vision-based method for fixtured pose measurement of non-datum complex component. IEEE Transactions on Instrumentation and Measurement, 69(6), 3370–3376. https://doi.org/10.1109/TIM.2019.2937530
Hu, M. F., Xie, J., Su, H. H., et al. (2018). Study on laser-assisted dry micro-ground surface of difficult-to-cut materials. International Journal of Advanced Manufacturing Technology, 94, 2919–2928. https://doi.org/10.1007/s00170-017-1093-4
Kang, J., Wu, B., & Wang, J. (2020). Calibration method of laser beam based on discrete point interpolation for 3D precision measurement. Optics Express, 28(19), 27588–27599. https://doi.org/10.1364/OE.403160
Lamikiz, A., Sanchez, J. A., Lopez, L. N., & Arana, J. L. (2007). Laser polishing of parts built up by selective laser sintering. International Journal of Machine Tools & Manufacture, 47(12–13), 2040–2050. https://doi.org/10.1016/j.ijmachtools.2007.01.013
Liu, X., Zhou, T., Pang, S., Xie, J., & Wang, X. (2017). Burr formation mechanism of ultraprecision cutting for microgrooves on nickel phosphide in consideration of the diamond tool edge radius. The International Journal of Advanced Manufacturing Technology, 94, 3929–3935. https://doi.org/10.1007/s00170-017-1079-2
Ma, R., Ji, L. F., & Yan, T. Y. (2020). Laser multi-focus precision cutting of thick sapphire by spherical aberration rectification. Optics and Lasers in Engineering. https://doi.org/10.1016/j.optlaseng.2019.105876
Ning, J. Q., Sievers, D. E., Garmestani, H., & Liang, Y. S. (2020). Analytical modeling of part porosity in metal additive manufacturing. International Journal of Mechanical Sciences., 172, 105428. https://doi.org/10.1016/j.ijmecsci.2020.105428
Pham, T. Q. D., Hoang, T. V., Tran, X. V., et al. (2022). Fast and accurate prediction of temperature evolutions in additive manufacturing process using deep learning. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-021-01896-8
Salonitis, K., Stournaras, A., Tsoukantas, G., Stavropoulos, P., & Chryssolouris, G. (2007). A theoretical and experimental investigation on limitations of pulsed laser drilling. Journal of Materials Processing Technology, 183(1), 96–103. https://doi.org/10.1016/j.jmatprotec.2006.09.031
Schuetze, D., Mueller, V., & Heinzel, G. (2014). Precision absolute measurement and alignment of laser beam direction and position. Applied Optics, 53(28), 6503–6507. https://doi.org/10.1364/AO.53.006503
Shen, Y., Zhang, X., Wang, Z., et al. (2020). A robust and efficient calibration method for spot laser probe on CMM. Measurement, 154(15), 107523. https://doi.org/10.1016/j.measurement.2020.107523
Simoni, F., Huxol, A., & Villmer, F. J. (2021). Improving surface quality inselective laser melting based tool making. Journal of Intelligent Manufactoring, 32, 1927–1938. https://doi.org/10.1007/s10845-021-01744-9
Sun, J., Zhang, J., Liu, Z., & Zhang, G. J. (2013). A vision measurement model of laser displacement sensor and its calibration method. Optics and Lasers in Engineering., 51(12), 1344–1352. https://doi.org/10.1016/j.optlaseng.2013.05.009
Vahaplar, K., Kalashnikova, A. M., et al. (2009). Ultrafast path for optical magnetization reversal via a strongly nonequilibrium state. Physical Review Letters., 103(11), 117201. https://doi.org/10.1103/PhysRevLett.103.117201
Wu, B., Duan, X., & Kang, J. H. (2019). A calibration method for spatial pose of a laser beam. Measurement Science and Technology. https://doi.org/10.1088/1361-6501/ab2b8e
Yang, T., Wang, Z., Wu, Z. G., et al. (2017). Calibration of Laser Beam Direction for Inner Diameter Measuring Device. Sensors., 17(2), 294–302. https://doi.org/10.3390/s17020294
Yuan, H. (2016). Design of control system software for laser micromachining seven-five axis machine. Wuhan, China: Huazhong University of Science & Technology.
Zeng, T., Lu, Y. F., Yang, H. X., et al. (2016). System for the measurement of the deviation of a laser beam from the vertical direction. Applied Optics., 55(10), 2692–2700. https://doi.org/10.1364/AO.55.002692
Zhang, Y., You, D., Gao, X., et al. (2020). Real-time monitoring of high-power disk laser welding statuses based on deep learning framework. Journal of Intelligent Manufacturing, 31, 799–814. https://doi.org/10.1007/s10845-019-01477-w
Acknowledgements
This research was supported by the National Key Development Program of China (Grant No.2016YFB1102500) for supporting this work.
Funding
The National Key Development Program of China, 2016YFB1102500, Dongxiang Hou.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service or company.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Hou, D., Wang, X., Song, Q. et al. A quality improvement method for complex component fine manufacturing based on terminal laser beam deflection compensation. J Intell Manuf 35, 331–341 (2024). https://doi.org/10.1007/s10845-022-02048-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10845-022-02048-2