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Prediction Model for Liquid-Assisted Femtosecond Laser Micro Milling of Quartz without Taper

The strong nonlinear absorption effect and “cold” processing characteristics of femtosecond lasers make them uniquely advantageous and promising for the micro- and nanoprocessing of hard and brittle materials, such as quartz. Traditional methods for studying the effects of femtosecond laser paramete...

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Autores principales: Yuan, Hongbing, Chen, Zhihao, Wu, Peichao, Deng, Yimin, Cao, Xiaowen, Zhang, Wenwu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9503145/
https://www.ncbi.nlm.nih.gov/pubmed/36144021
http://dx.doi.org/10.3390/mi13091398
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author Yuan, Hongbing
Chen, Zhihao
Wu, Peichao
Deng, Yimin
Cao, Xiaowen
Zhang, Wenwu
author_facet Yuan, Hongbing
Chen, Zhihao
Wu, Peichao
Deng, Yimin
Cao, Xiaowen
Zhang, Wenwu
author_sort Yuan, Hongbing
collection PubMed
description The strong nonlinear absorption effect and “cold” processing characteristics of femtosecond lasers make them uniquely advantageous and promising for the micro- and nanoprocessing of hard and brittle materials, such as quartz. Traditional methods for studying the effects of femtosecond laser parameters on the quality of the processed structure mainly use univariate analysis methods, which require large mounts of experiments to predict and achieve the desired experimental results. The method of design of experiments (DOE) provides a way to predict desirable experimental results through smaller experimental scales, shorter experimental periods and lower experimental costs. In this study, a DOE program was designed to investigate the effects of a serious of parameters (laser repetition frequency, pulse energy, scan speed, scan distance, scan mode, scan times and laser focus position) on the depth and roughness (Ra) of the fabricated structure through the liquid-assisted femtosecond laser processing of quartz. A prediction model between the response variables and the main parameters was defined and validated. Finally, several blind holes with a size of 50 × [Formula: see text] and a depth of 200 [Formula: see text] were fabricated by the prediction model, which demonstrated the good consistency of the prediction model.
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spelling pubmed-95031452022-09-24 Prediction Model for Liquid-Assisted Femtosecond Laser Micro Milling of Quartz without Taper Yuan, Hongbing Chen, Zhihao Wu, Peichao Deng, Yimin Cao, Xiaowen Zhang, Wenwu Micromachines (Basel) Article The strong nonlinear absorption effect and “cold” processing characteristics of femtosecond lasers make them uniquely advantageous and promising for the micro- and nanoprocessing of hard and brittle materials, such as quartz. Traditional methods for studying the effects of femtosecond laser parameters on the quality of the processed structure mainly use univariate analysis methods, which require large mounts of experiments to predict and achieve the desired experimental results. The method of design of experiments (DOE) provides a way to predict desirable experimental results through smaller experimental scales, shorter experimental periods and lower experimental costs. In this study, a DOE program was designed to investigate the effects of a serious of parameters (laser repetition frequency, pulse energy, scan speed, scan distance, scan mode, scan times and laser focus position) on the depth and roughness (Ra) of the fabricated structure through the liquid-assisted femtosecond laser processing of quartz. A prediction model between the response variables and the main parameters was defined and validated. Finally, several blind holes with a size of 50 × [Formula: see text] and a depth of 200 [Formula: see text] were fabricated by the prediction model, which demonstrated the good consistency of the prediction model. MDPI 2022-08-26 /pmc/articles/PMC9503145/ /pubmed/36144021 http://dx.doi.org/10.3390/mi13091398 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yuan, Hongbing
Chen, Zhihao
Wu, Peichao
Deng, Yimin
Cao, Xiaowen
Zhang, Wenwu
Prediction Model for Liquid-Assisted Femtosecond Laser Micro Milling of Quartz without Taper
title Prediction Model for Liquid-Assisted Femtosecond Laser Micro Milling of Quartz without Taper
title_full Prediction Model for Liquid-Assisted Femtosecond Laser Micro Milling of Quartz without Taper
title_fullStr Prediction Model for Liquid-Assisted Femtosecond Laser Micro Milling of Quartz without Taper
title_full_unstemmed Prediction Model for Liquid-Assisted Femtosecond Laser Micro Milling of Quartz without Taper
title_short Prediction Model for Liquid-Assisted Femtosecond Laser Micro Milling of Quartz without Taper
title_sort prediction model for liquid-assisted femtosecond laser micro milling of quartz without taper
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9503145/
https://www.ncbi.nlm.nih.gov/pubmed/36144021
http://dx.doi.org/10.3390/mi13091398
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