A Zhanwen, Guisheng Zou, Wenqiang Li, Yue You, Bin Feng, Zimao Sheng, Chengjie Du, Yu Xiao, Jinpeng Huo, Lei Liu
{"title":"基于深度学习的超快激光钻孔微孔阵列多特征提取","authors":"A Zhanwen, Guisheng Zou, Wenqiang Li, Yue You, Bin Feng, Zimao Sheng, Chengjie Du, Yu Xiao, Jinpeng Huo, Lei Liu","doi":"10.2351/7.0001162","DOIUrl":null,"url":null,"abstract":"An efficient quality evaluation method is crucial for the applications of high-quality microhole arrays drilled with ultrafast lasers. The vision-based feature extraction was used as a data acquisition method to evaluate the drilling quality in terms of the geometric quality of the hole shape. However, the morphological features such as the recast layer, microcracks, and debris on the surface are difficult to consider in the quality evaluation since simultaneous recognition of multiple features remains challenging. Herein, we successfully recognized and extracted multiple features by deep learning, thus achieving the quality evaluation of microhole arrays in terms of both geometrical and surface qualities. Microhole arrays of various sizes and surface quality are fabricated on copper, stainless steel, titanium, and glass using different processing parameters. Then, the images of the microhole arrays are prepared as the dataset to train the deep learning network by labeling the typical features of microholes. The well-trained deep learning network has efficient and powerful recognition ability. Typical features such as the hole profile, recast layer, microcracks, and debris can be recognized and extracted simultaneously; thereby the geometric and surface quality of the microhole are obtained. We also demonstrate the implementation of the method with a fast quality evaluation of an array of 2300 microholes based on a statistical approach. The methods presented here extend the quality evaluation of microhole arrays by considering both geometric and surface qualities and can also be applied to quality monitoring in other ultrafast laser micromachining.","PeriodicalId":50168,"journal":{"name":"Journal of Laser Applications","volume":"14 1","pages":"0"},"PeriodicalIF":1.7000,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Deep learning driven multifeature extraction for quality evaluation of ultrafast laser drilled microhole arrays\",\"authors\":\"A Zhanwen, Guisheng Zou, Wenqiang Li, Yue You, Bin Feng, Zimao Sheng, Chengjie Du, Yu Xiao, Jinpeng Huo, Lei Liu\",\"doi\":\"10.2351/7.0001162\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An efficient quality evaluation method is crucial for the applications of high-quality microhole arrays drilled with ultrafast lasers. The vision-based feature extraction was used as a data acquisition method to evaluate the drilling quality in terms of the geometric quality of the hole shape. However, the morphological features such as the recast layer, microcracks, and debris on the surface are difficult to consider in the quality evaluation since simultaneous recognition of multiple features remains challenging. Herein, we successfully recognized and extracted multiple features by deep learning, thus achieving the quality evaluation of microhole arrays in terms of both geometrical and surface qualities. Microhole arrays of various sizes and surface quality are fabricated on copper, stainless steel, titanium, and glass using different processing parameters. Then, the images of the microhole arrays are prepared as the dataset to train the deep learning network by labeling the typical features of microholes. The well-trained deep learning network has efficient and powerful recognition ability. Typical features such as the hole profile, recast layer, microcracks, and debris can be recognized and extracted simultaneously; thereby the geometric and surface quality of the microhole are obtained. We also demonstrate the implementation of the method with a fast quality evaluation of an array of 2300 microholes based on a statistical approach. The methods presented here extend the quality evaluation of microhole arrays by considering both geometric and surface qualities and can also be applied to quality monitoring in other ultrafast laser micromachining.\",\"PeriodicalId\":50168,\"journal\":{\"name\":\"Journal of Laser Applications\",\"volume\":\"14 1\",\"pages\":\"0\"},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2023-09-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Laser Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2351/7.0001162\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MATERIALS SCIENCE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Laser Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2351/7.0001162","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
Deep learning driven multifeature extraction for quality evaluation of ultrafast laser drilled microhole arrays
An efficient quality evaluation method is crucial for the applications of high-quality microhole arrays drilled with ultrafast lasers. The vision-based feature extraction was used as a data acquisition method to evaluate the drilling quality in terms of the geometric quality of the hole shape. However, the morphological features such as the recast layer, microcracks, and debris on the surface are difficult to consider in the quality evaluation since simultaneous recognition of multiple features remains challenging. Herein, we successfully recognized and extracted multiple features by deep learning, thus achieving the quality evaluation of microhole arrays in terms of both geometrical and surface qualities. Microhole arrays of various sizes and surface quality are fabricated on copper, stainless steel, titanium, and glass using different processing parameters. Then, the images of the microhole arrays are prepared as the dataset to train the deep learning network by labeling the typical features of microholes. The well-trained deep learning network has efficient and powerful recognition ability. Typical features such as the hole profile, recast layer, microcracks, and debris can be recognized and extracted simultaneously; thereby the geometric and surface quality of the microhole are obtained. We also demonstrate the implementation of the method with a fast quality evaluation of an array of 2300 microholes based on a statistical approach. The methods presented here extend the quality evaluation of microhole arrays by considering both geometric and surface qualities and can also be applied to quality monitoring in other ultrafast laser micromachining.
期刊介绍:
The Journal of Laser Applications (JLA) is the scientific platform of the Laser Institute of America (LIA) and is published in cooperation with AIP Publishing. The high-quality articles cover a broad range from fundamental and applied research and development to industrial applications. Therefore, JLA is a reflection of the state-of-R&D in photonic production, sensing and measurement as well as Laser safety.
The following international and well known first-class scientists serve as allocated Editors in 9 new categories:
High Precision Materials Processing with Ultrafast Lasers
Laser Additive Manufacturing
High Power Materials Processing with High Brightness Lasers
Emerging Applications of Laser Technologies in High-performance/Multi-function Materials and Structures
Surface Modification
Lasers in Nanomanufacturing / Nanophotonics & Thin Film Technology
Spectroscopy / Imaging / Diagnostics / Measurements
Laser Systems and Markets
Medical Applications & Safety
Thermal Transportation
Nanomaterials and Nanoprocessing
Laser applications in Microelectronics.