基于图像特征融合的粉末床缺陷检测方法

doi:10.7527/S1000-6893.2020.24430

论文

本期目录 | 过刊浏览 | 高级检索

前一篇 | 后一篇

基于图像特征融合的粉末床缺陷检测方法

师彬彬, 陈哲涵

北京科技大学机械工程学院, 北京 100083

收稿日期:2020-06-17 修回日期:2020-07-16 发布日期:2020-10-16
通讯作者: 陈哲涵 E-mail:chenzh_ustb@163.com
基金资助:
国家自然科学基金（61803023）

Defect detection method of powder bed based on image feature fusion

SHI Binbin, CHEN Zhehan

School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China

Received:2020-06-17 Revised:2020-07-16 Published:2020-10-16
Supported by:
National Natural Science Foundation of China (61803023)

摘要/Abstract

摘要： 针对单一特征对粉末床缺陷表达不明确导致检测效果不佳的问题，提出了一种基于特征融合的增材制造过程粉末床缺陷视觉检测方法。该算法分别使用SIFT方法、灰度共生矩阵和Hu不变矩提取尺度空间特征、纹理特征和几何特征，借助词袋模型对每张图像构建3组视觉单词直方图，通过串行融合3组视觉单词直方图得到新的特征矩阵，采用特征选择对融合后特征矩阵进行降维，并传入随机森林分类器中进行训练。实验结果表明，不同特征对粉末床不同类型缺陷检测具有不同的贡献，优化特征融合参数后，算法平均准确率达到97.46%，缺陷检测效果明显提升。

关键词: 特征融合, 视觉检测, 粉末床缺陷, 增材制造, 质量控制

Abstract: A visual detection method for powder bed defects in the additive manufacturing process based on feature fusion is proposed to improve the poor detection effect due to unclear expression of single features for powder bed defects. The algorithm firstly extracts the scale-space features, texture features, and geometric features from each powder bed image using methods of SIFT, gray-level co-occurrence matrix and Hu invariant moments; then, these images are translated to the feature matrix by serially fusing the three visual word histograms constructed from the three kinds of features by the bag-of-words model; finally, the feature matrix of each image is reduced by feature selection, and passed to the random forest classifier for model training. Experimental results show that different features have different contributions to the detection of different types of defects; through optimizing the feature fusion parameters, the average accuracy of the proposed algorithm reaches 97.46%, significantly improving the defect detection effect.

Key words: feature fusion, visual detection, powder bed defects, additive manufacturing, quality control

中图分类号:

师彬彬, 陈哲涵. 基于图像特征融合的粉末床缺陷检测方法[J]. 航空学报, 2021, 42(10): 524430-524430.

SHI Binbin, CHEN Zhehan. Defect detection method of powder bed based on image feature fusion[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021, 42(10): 524430-524430.

参考文献

[1] MARCO G, VITTORIO L, QUIRICO S, et al. In-process monitoring of selective laser melting:Spatial detection of defects via image data analysis[J]. Journal of Manufacturing Science and Engineering,2017,139(5):051001.
[2] KAHNERT M, LUTZMANN S, ZAEH M F. Layer formations in electron beam sintering[C]//Solid Freeform Fabrication Symposium. 2007:88-99.
[3] Z? H M F, LUTZMANN S. Modelling and simulation of electron beam melting[J]. Production Engineering, 2010,4(1):15-23.
[4] RAUSCH A M, KVNG V E, POBEL C, et al. Predictive simulation of process windows for powder bed fusion additive manufacturing:influence of the powder bulk density[J]. Materials,2017,10(10):1117.
[5] MURR L E, MARTINEZ E, GAYTAN S M, et al. Microstructural architecture, microstructures, and mechanical properties for a nickel-base superalloy fabricated by electron beam melting[J]. Metallurgical and Materials Transactions A, 2011,42(11):3491-3508.
[6] CUNNINGHAM R, NARRA S P, OZTURK T, et al. Evaluating the effect of processing parameters on porosity in electron beam melted Ti-6Al-4V via synchrotron X-ray microtomography[J]. JOM, 2016,68(3):765-771.
[7] DEPOND P J, GUSS G, LY S, et al. In situ measurements of layer roughness during laser powder bed fusion additive manufacturing using low coherence scanning interferometry[J]. Materials & Design, 2018, 154:347-359.
[8] CASATI R, LEMKE J, VEDANI M. Microstructure and fracture behavior of 316L austenitic stainless steel produced by selective laser melting[J]. Journal of Materials Science & Technology,2016,32(8):738-744.
[9] PUEBLA K, MURR L E, GAYTAN S M, et al. Effect of melt scan rate on microstructure and macrostructure for electron beam melting of Ti-6Al-4V[J]. Materials Sciences and Applications, 2012,3(5):259-264.
[10] CRAEGHS T, CLIJSTERS S, YASA E, et al. Online quality control of selective laser melting[C]//Solid Freeform Fabrication Proceedings, 2011:212-226.
[11] KLESZCZYNSKI S, JACOBSMVHLEN J Z, REINARZ B, et al. Improving process stability of laser beam melting systems[C]//Fraunhofer Direct Digital Manufacturing Conference, 2014:187-192.
[12] JACOBSMUHLEN J Z, KLESZCZYNSKI S, SCHNEIDER D, et al. High resolution imaging for inspection of Laser Beam Melting systems[C]//Instrumentation and Measurement Technology Conference (I2MTC), 2013:707-712.
[13] NEEF A, SEYDA V, HERZOG D, et al. Low coherence interferometry in selective laser melting[J]. Physics Procedia, 2014, 56:82-89.
[14] SCIME L, BEUTH J. Anomaly detection and classification in a laser powder bed additive manufacturing process using a trained computer vision algorithm[J]. Additive Manufacturing, 2018,19:114-126.
[15] SCIME L, BEUTH J. A multi-scale convolutional neural network for autonomous anomaly detection and classification in a laser powder bed fusion additive manufacturing process[J]. Additive Manufacturing, 2018, 24:273-286.
[16] 林椹尠, 李妮, 惠小强. 基于空间金字塔的BoW模型图像分类方法[J]. 西安邮电大学学报, 2018, 23(3):31-37. LIN Z X, LI N, HUI X Q. An image classification method of BoW model based on spatial pyramid[J]. Journal of Xi'an University of Posts and Telecommunications, 2018, 23(3):31-37(in Chinese).
[17] 闵信军. 基于灰度共生矩阵和视觉信息的布匹瑕疵检测方法研究[D]. 镇江:江苏大学, 2018:23-26. MIN X J. Research on detection methods of fabric defects based on gray level co-occurrence matrix and visual information[D]. Zhenjiang:Jiangsu University, 2018:23-26(in Chinese).
[18] 孙枭文. 基于纹理特征和Hu不变矩的KELM滤光片缺陷识别研究[J]. 甘肃科学学报, 2019, 31(5):17-22. SUN X W. Study on the identification of KELM filter defects based on texture characteristics and hu invariant moment[J].Journal of Gansu Sciences, 2019, 31(5):17-22(in Chinese).
[19] 陈静, 张艳新, 姜媛媛. 融合多特征与随机森林的纹理图像分类方法[J]. 传感器与微系统, 2019, 38(12):58-61. CHEN J, ZHANG Y X, JIANG Y Y. Texture image classification method combining multi-features and random forest[J]. Sensors and Microsystems, 2019, 38(12):58-61(in Chinese).
[20] 肖旎旖. 基于相关性和冗余性分析的特征选择算法研究[D]. 大连:大连理工大学, 2013:5-6. XIAO Y N. The research of feature selection algorithm based on analysis of relevancy and redundancy[D]. Dalian:Dalian University of Technology, 2013:5-6(in Chinese).
[21] 张鹏. 基于视觉的激光选区熔化成形铺粉质量在线监控系统研究[D]. 武汉:华中科技大学, 2017:36-51. ZHANG P. Research of a motoring system of deposition quality in selective laser melting based on machine vision[D]. Wuhan:Huazhong University of Science & Technology, 2017:36-51(in Chinese).

E-mail：hkxb@buaa.edu.cn

关于我们

期刊社服务

专业学科

封面文章

友情链接

主管单位：中国科学技术协会主办单位：中国航空学会北京航空航天大学

基于图像特征融合的粉末床缺陷检测方法

Defect detection method of powder bed based on image feature fusion

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	司瑞, 陈勇. 民用飞机增材制造技术应用发展趋势[J]. 航空学报, 2024, 45(5): 529677-529677.
[2]	唐论, 余圣甫, 郑博, 史玉升, 陈颖. 圆柱面点阵自生Al₂O₃铝合金粉芯丝材开发及应用[J]. 航空学报, 2023, 44(9): 626864-626864.
[3]	张卫红, 周涵, 李韶英, 朱继宏, 周璐. 航天高性能薄壁构件的材料-结构一体化设计综述[J]. 航空学报, 2023, 44(9): 627428-627428.
[4]	林忠钦, 于忠奇, 戴冬华, 樊晓光, 余圣甫, 顾冬冬, 李淑慧, 史玉升. 复杂高筋薄壁构件旋压-增材复合制造技术发展与展望[J]. 航空学报, 2023, 44(9): 627493-627493.
[5]	奉志强, 谢志军, 包正伟, 陈科伟. 基于改进YOLOv5的无人机实时密集小目标检测算法[J]. 航空学报, 2023, 44(7): 327106-327106.
[6]	冒国韬, 邓天民, 于楠晶. 基于多尺度分割注意力的无人机航拍图像目标检测算法[J]. 航空学报, 2023, 44(5): 326738-326738.
[7]	顾孟奇, 朱家才, 郭万林, 薛松. 可重复使用运载火箭结构疲劳耐久性与可靠性展望[J]. 航空学报, 2023, 44(23): 628299-628299.
[8]	李晓航, 周建江. 基于自适应记忆长度的多尺度模态融合网络[J]. 航空学报, 2023, 44(22): 628977-628977.
[9]	张志强, 勾青泽, 路学成, 王浩, 曹轶然, 郭志永. 高强铝合金CMT+P电弧增材制造熔滴过渡行为[J]. 航空学报, 2023, 44(13): 427881-427881.
[10]	薛远亮, 金国栋, 谭力宁, 许剑锟. 基于多尺度融合的自适应无人机目标跟踪算法[J]. 航空学报, 2023, 44(1): 326107-326107.
[11]	刘芳, 孙亚楠. 基于自适应融合网络的无人机目标跟踪算法[J]. 航空学报, 2022, 43(7): 325522-325522.
[12]	刘芳, 韩笑. 基于多尺度深度学习的自适应航拍目标检测[J]. 航空学报, 2022, 43(5): 325270-325270.
[13]	李涤尘, 鲁中良, 田小永, 张航, 杨春成, 曹毅, 苗恺. 增材制造——面向航空航天制造的变革性技术[J]. 航空学报, 2022, 43(4): 525387-525387.
[14]	姚燕生, 周瑞根, 张成林, 梅涛, 吴敏. 增材制造复杂金属构件表面抛光技术[J]. 航空学报, 2022, 43(4): 525202-525202.
[15]	张佩宇, 周鑫, 李应红. 单晶涡轮叶片高能束修复研究进展[J]. 航空学报, 2022, 43(4): 525610-525610.