Hole Depth Prediction in a Femtosecond Laser Drilling Process Using Deep Learning
In high-aspect ratio laser drilling, many laser and optical parameters can be controlled, including the high-laser beam fluence and number of drilling process cycles. Measurement of the drilled hole depth is occasionally difficult or time consuming, especially during machining processes. This study...
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Format: | Article |
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MDPI AG
2023-03-01
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Series: | Micromachines |
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Online Access: | https://www.mdpi.com/2072-666X/14/4/743 |
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author | Dong-Wook Lim Myeongjun Kim Philgong Choi Sung-June Yoon Hyun-Taek Lee Kyunghan Kim |
author_facet | Dong-Wook Lim Myeongjun Kim Philgong Choi Sung-June Yoon Hyun-Taek Lee Kyunghan Kim |
author_sort | Dong-Wook Lim |
collection | DOAJ |
description | In high-aspect ratio laser drilling, many laser and optical parameters can be controlled, including the high-laser beam fluence and number of drilling process cycles. Measurement of the drilled hole depth is occasionally difficult or time consuming, especially during machining processes. This study aimed to estimate the drilled hole depth in high-aspect ratio laser drilling by using captured two-dimensional (2D) hole images. The measuring conditions included light brightness, light exposure time, and gamma value. In this study, a method for predicting the depth of a machined hole by using a deep learning methodology was devised. Adjusting the laser power and the number of processing cycles for blind hole generation and image analysis yielded optimal conditions. Furthermore, to forecast the form of the machined hole, we identified the best circumstances based on changes in the exposure duration and gamma value of the microscope, which is a 2D image measurement instrument. After extracting the data frame by detecting the contrast data of the hole by using an interferometer, the hole depth was predicted using a deep neural network with a precision of within 5 μm for a hole within 100 μm. |
first_indexed | 2024-03-11T04:44:15Z |
format | Article |
id | doaj.art-0696c5977bd6444fa68b3cb1ab0f41d2 |
institution | Directory Open Access Journal |
issn | 2072-666X |
language | English |
last_indexed | 2024-03-11T04:44:15Z |
publishDate | 2023-03-01 |
publisher | MDPI AG |
record_format | Article |
series | Micromachines |
spelling | doaj.art-0696c5977bd6444fa68b3cb1ab0f41d22023-11-17T20:28:43ZengMDPI AGMicromachines2072-666X2023-03-0114474310.3390/mi14040743Hole Depth Prediction in a Femtosecond Laser Drilling Process Using Deep LearningDong-Wook Lim0Myeongjun Kim1Philgong Choi2Sung-June Yoon3Hyun-Taek Lee4Kyunghan Kim5Department of Mechanical Engineering, Inha University, Incheon 22212, Republic of KoreaDepartment of Mechanical Engineering, Chungnam University, Daejeon 34134, Republic of KoreaDepartment of Laser and Electron Beam Application Group, Korea Institute of Machinery & Materials, Daejeon 34103, Republic of KoreaDepartment of Mechanical Engineering, Inha University, Incheon 22212, Republic of KoreaDepartment of Mechanical Engineering, Inha University, Incheon 22212, Republic of KoreaDepartment of Laser and Electron Beam Application Group, Korea Institute of Machinery & Materials, Daejeon 34103, Republic of KoreaIn high-aspect ratio laser drilling, many laser and optical parameters can be controlled, including the high-laser beam fluence and number of drilling process cycles. Measurement of the drilled hole depth is occasionally difficult or time consuming, especially during machining processes. This study aimed to estimate the drilled hole depth in high-aspect ratio laser drilling by using captured two-dimensional (2D) hole images. The measuring conditions included light brightness, light exposure time, and gamma value. In this study, a method for predicting the depth of a machined hole by using a deep learning methodology was devised. Adjusting the laser power and the number of processing cycles for blind hole generation and image analysis yielded optimal conditions. Furthermore, to forecast the form of the machined hole, we identified the best circumstances based on changes in the exposure duration and gamma value of the microscope, which is a 2D image measurement instrument. After extracting the data frame by detecting the contrast data of the hole by using an interferometer, the hole depth was predicted using a deep neural network with a precision of within 5 μm for a hole within 100 μm.https://www.mdpi.com/2072-666X/14/4/743laser micromachiningdeep learningfemtosecond lasersilicon nitrideblind hole |
spellingShingle | Dong-Wook Lim Myeongjun Kim Philgong Choi Sung-June Yoon Hyun-Taek Lee Kyunghan Kim Hole Depth Prediction in a Femtosecond Laser Drilling Process Using Deep Learning Micromachines laser micromachining deep learning femtosecond laser silicon nitride blind hole |
title | Hole Depth Prediction in a Femtosecond Laser Drilling Process Using Deep Learning |
title_full | Hole Depth Prediction in a Femtosecond Laser Drilling Process Using Deep Learning |
title_fullStr | Hole Depth Prediction in a Femtosecond Laser Drilling Process Using Deep Learning |
title_full_unstemmed | Hole Depth Prediction in a Femtosecond Laser Drilling Process Using Deep Learning |
title_short | Hole Depth Prediction in a Femtosecond Laser Drilling Process Using Deep Learning |
title_sort | hole depth prediction in a femtosecond laser drilling process using deep learning |
topic | laser micromachining deep learning femtosecond laser silicon nitride blind hole |
url | https://www.mdpi.com/2072-666X/14/4/743 |
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