Evaluation of surgical laser optical feedback on uneven surfaces using robotic manipulator

Dmitrii B. Smirnov; Elena A. Melnichenko; Semen V. Bushuev; Andrei A. Vorotnikov; Yuri V. Poduraev

doi:10.1117/12.2655685

16 December 2022 Evaluation of surgical laser optical feedback on uneven surfaces using robotic manipulator

Dmitrii B. Smirnov, Elena A. Melnichenko, Semen V. Bushuev, Andrei A. Vorotnikov, Yuri V. Poduraev

Proceedings Volume 12312, Advanced Laser Processing and Manufacturing VI; 123120Q (2022) https://doi.org/10.1117/12.2655685
Event: SPIE/COS Photonics Asia, 2022, Online Only

Abstract

In order to improve the performance of robotic laser medical operations, various methods are used to control the distance from the laser head or objective optics to the surface to be treated, and algorithms for correcting robot movements are built on their basis, which makes it possible to achieve high-precision processing. This study evaluated data of the reflected laser emission feedback sensor on uneven surfaces for use in robotic laser processing automation. As an uneven surface, a produced on a 3D printer detail with different profiles was used, which had been measured on conturograph before the experiments. Then, data were obtained from the feedback sensor of laser when passing along these profiles using a CMM and a robot. In the course of comparing the obtained results, it was found that the values of standard deviations from the real (highly-precise measured) surface do not exceed 600 μm. This value can be reduced by exclusion the identified problems that affect the data, the solution or leveling of which will be given in the further works of the authors. In general, it can be concluded that this method can be applied in robotic laser processing.

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Citation Download Citation

Dmitrii B. Smirnov, Elena A. Melnichenko, Semen V. Bushuev, Andrei A. Vorotnikov, and Yuri V. Poduraev "Evaluation of surgical laser optical feedback on uneven surfaces using robotic manipulator", Proc. SPIE 12312, Advanced Laser Processing and Manufacturing VI, 123120Q (16 December 2022); https://doi.org/10.1117/12.2655685

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