Application of Machine Vision and Machine Learning to Predict the Focal Position in Laser Machining
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-39090-6_31
Reference5 articles.
1. Z. Bai, J. Wei, Focusing error detection based on astigmatic method with a double cylindrical lens group. Opt. Laser Technol. 106, 145–151 (2018). https://doi.org/10.1016/j.optlastec.2018.04.005
2. X. Liang, Z. Bai, J. Wei, Movement flatness error measurement based on an astigmatic method. Appl. Opt. 56(15), 4347–4352 (2017). https://doi.org/10.1364/AO.56.004347
3. S.J. Xu, Y.Z. Duan, Y.H. Yu, Z.N. Tian, A.Q.D. Chen, Machine vision-based high-precision and robust focus detection for femtosecond laser machining. Opt. Express 29(19), 30952–30960 (2021). https://doi.org/10.1364/OE.438537
4. B.X. Cao, P.L. Hoang, S. Ahn, J. Kim, J. Noh, High-precision detection of focal position on a curved surface for laser processing. Precis. Eng. 50, 204–210 (2017). https://doi.org/10.1016/j.precisioneng.2017.05.008
5. B.X. Cao, M. Bae, H. Sohn, J. Choi, Y. Kim, J. Kim, J. Noh, Design and performance of a focus-detection system for use in laser micromachining. Micromachines 7(1), 2 (2016). https://doi.org/10.3390/mi7010002
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