A prediction-correction scheme for microchannel milling using femtosecond laser. (April 2017)

• Record Type: Journal Article
• Authors: Chen, Jianxiong; Zhou, Xiaolong; Lin, Shuwen; Tu, Yiliu
• Is Part Of: Optics and lasers in engineering. Volume 91(2017)
• Page Start: 115
• Page End: 123
• Publication Date: 2017-04
• Subjects: Microchannel milling -- Laser triggered plasma -- Online measurement -- Prediction-correction scheme; Lasers in engineering -- Periodicals; Optical measurements -- Periodicals; Optics -- Periodicals; Lasers en ingénierie -- Périodiques; Mesures optiques -- Périodiques; Optique -- Périodiques; 621.36605
• DOI: 10.1016/j.optlaseng.2016.11.012
• Languages: English
• ISSNs: 0143-8166

Abstract

Abstract: In this paper, a prediction-correction scheme is proposed to online measure and regulate the milling depth of microchannel using an indicator of laser triggered plasma. Firstly, a prediction model, with respect to the laser fluence and feedrate, is established with several calibration tests using the least square fitting method. It is utilized to change the focal position of objective to track the depth evolution of newly generated surface. Meanwhile, a scanning path for every milling layer with an offset in Z-axis at the beginning and the end of the trajectory, is developed to drive the plasma brightness periodically changing. Then, the milling depth could be obtained when the brightness reaches to the maximum value. By doing so, an online measurement method is presented to estimate the milling depth using the trend of plasma brightness. Furthermore, a correction model is developed to iteratively adjust the feedrate with the online estimated depth. Therefore, the microchannel milling process could be monitored and controlled in a closed-loop manner, in order to accurately regulate the milling depth. Finally, an online measurement and closed-loop microchannel milling is carried out on the self-developed micro-machining center. The effectiveness and correctness of the proposed method are verified by comparing the estimated depth with the actually measured results. Highlights: A closed-loop method is proposed to control the microchannel depth using laser triggered plasma. The microchannel milling process can be continuously monitored using the delivered light of plasma. A prediction model is established to estimate the depth evolution of microchannel milling. An online measurement method is presented to estimate the milling depth using the tendency of plasma brightness. A correction model is created to adjust the feedrate to iteratively regulate milling depth.