基于深度学习的超快激光钻孔微孔阵列多特征提取

IF 1.7 4区 工程技术 Q3 MATERIALS SCIENCE, MULTIDISCIPLINARY Journal of Laser Applications Pub Date : 2023-09-11 DOI:10.2351/7.0001162
A Zhanwen, Guisheng Zou, Wenqiang Li, Yue You, Bin Feng, Zimao Sheng, Chengjie Du, Yu Xiao, Jinpeng Huo, Lei Liu
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引用次数: 0

摘要

一种有效的质量评价方法对于高质量的超快激光微孔阵列的应用至关重要。采用基于视觉的特征提取作为数据采集方法,从孔形状的几何质量方面评价钻孔质量。然而,由于同时识别多个特征仍然具有挑战性,因此在质量评估中难以考虑重铸层、微裂纹和表面碎屑等形态学特征。在此,我们通过深度学习成功地识别和提取了多个特征,从而从几何质量和表面质量两个方面对微孔阵列进行了质量评价。采用不同的工艺参数,在铜、不锈钢、钛和玻璃上制备了不同尺寸和表面质量的微孔阵列。然后,制备微孔阵列图像作为数据集,通过标记微孔的典型特征来训练深度学习网络。经过良好训练的深度学习网络具有高效、强大的识别能力。可以同时识别和提取孔洞轮廓、重铸层、微裂纹和碎屑等典型特征;从而获得微孔的几何和表面质量。我们还演示了该方法的实现,并基于统计方法对2300个微孔阵列进行了快速质量评估。本文提出的方法从几何质量和表面质量两方面扩展了微孔阵列的质量评价,也可应用于其他超快激光微加工的质量监测。
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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.
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来源期刊
CiteScore
3.60
自引率
9.50%
发文量
125
审稿时长
>12 weeks
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.
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