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                    工業鑄件缺陷無損檢測技術的應用進展與展望

                    張輝 張鄒銓 陳煜嶸 吳天月 鐘杭 王耀南

                    張輝, 張鄒銓, 陳煜嶸, 吳天月, 鐘杭, 王耀南. 工業鑄件缺陷無損檢測技術的應用進展與展望. 自動化學報, 2021, x(x): 1?22 doi: 10.16383/j.aas.c210161
                    引用本文: 張輝, 張鄒銓, 陳煜嶸, 吳天月, 鐘杭, 王耀南. 工業鑄件缺陷無損檢測技術的應用進展與展望. 自動化學報, 2021, x(x): 1?22 doi: 10.16383/j.aas.c210161
                    Zhang Hui, Zhang Zou-Quan, Chen Yu-Rong, Wu Tian-Yue, >Zhong Hang, Wang Yao-Nan. Application advance and prospect of nondestructive testing technology for industrial casting defects. Acta Automatica Sinica, 2021, x(x): 1?22 doi: 10.16383/j.aas.c210161
                    Citation: Zhang Hui, Zhang Zou-Quan, Chen Yu-Rong, Wu Tian-Yue, >Zhong Hang, Wang Yao-Nan. Application advance and prospect of nondestructive testing technology for industrial casting defects. Acta Automatica Sinica, 2021, x(x): 1?22 doi: 10.16383/j.aas.c210161

                    工業鑄件缺陷無損檢測技術的應用進展與展望

                    doi: 10.16383/j.aas.c210161
                    基金項目: 國家重點研發計劃(2018YFB1308200), 國家自然科學基金(61971071, 6202780012), 長沙市科技計劃項目 (kq1907087), 湖南省創新型省份建設專項經費 (2020SK3007), 博士后創新人才支持計劃 (BX20200122)資助
                    詳細信息
                      作者簡介:

                      張輝:湖南大學機器人學院教授. 分別于2004年、2007年和2012年獲得湖南大學學士、碩士和博士學位. 主要研究方向為工業機器視覺和數字圖像處理. 本文通訊作者. E-mail: zhanghuihby@126.com

                      張鄒銓:長沙理工大學電氣與信息工程學院碩士研究生. 主要研究方向為深度學習和視覺檢測. E-mail: zouquan_zhang@163.com

                      陳煜嶸:湖南大學電氣與信息工程學院博士研究生. 于2020年獲得美國匹茲堡大學碩士學位. 主要研究方向為圖像處理, 機器學習和領域自適應. E-mail: chenyurong1998@outlook.com

                      吳天月:長沙理工大學電氣與信息工程學院碩士研究生. 主要研究方向為深度學習和視覺檢測. E-mail: yue_wuwuwu@163.com

                      鐘杭:湖南大學博士后. 分別于2013年、2016年和2020年獲得湖南大學學士、碩士和博士學位. 主要研究方向為機器人控制, 視覺伺服和路徑規劃. E-mail: zhonghang@hnu.edu.cn

                      王耀南:中國工程院院士, 湖南大學機器人學院教授. 1995年獲得湖南大學博士學位. 主要研究方向為機器人學, 智能控制和圖像處理. E-mail: yaonan@hnu.edu.cn

                    • 收稿日期 2021-02-26 錄用日期 2021-06-06 Manuscript?received?February?26,?2021;?accepted?June?6,?2021 國家重點研發計劃 (2018YFB1308200),?國家自然科學基金 (61971071,?6202780012),?長沙市科技計劃項目?(kq1907087),?湖南省創新型省份建設專項經費?(2020SK3007),?博士后創新人才支持計劃?(BX20200122) 資助 Supported?by?National?Key?Research?and?Development?Program?of?China?(2018YFB1308200),?National?Natural?Science Foundation?of?China?(61971071,?6202780012),?Changsha?science and?technology?project?(kq1907087),?Special?funds?for?the?construction?of?innovative?provinces?in?Hunan?Province (2020SK3007),?Postdoctoral?innovative?talent?support?program (BX20200122) 本文責任編委?徐德 Recommended?by?Associate?Editor?XU?De 1.?湖南大學機器人學院?長沙?410082 2.?長沙理工大學電氣與
                    • 信息工程學院?長沙?410114 1.?School?of?Robotics,?Hunan?University,?Changsha?410082 2.?School?of?Electrical?and?Information?Engineering,?Changsha University?of?Science?and?Technology,?Changsha?410114

                    Application Advance and Prospect of Nondestructive Testing Technology for Industrial Casting Defects

                    Funds: Supported by National Key Research and Development Program of China (2018YFB1308200), National Natural Science Foundation of China (61971071, 6202780012), Changsha science and technology project (kq1907087), Special funds for the construction of innovative provinces in Hunan Province (2020SK3007), Postdoctoral innovative talent support program (BX20200122)
                    More Information
                      Author Bio:

                      ZHANG Hui Professor at the School of Robotics, Hunan University. He received his bachelor, master and Ph.D. degrees from Hunan University in 2004, 2007 and 2012, respectively. His research interest covers industrial machine vision and digital image processing. Corresponding author of this article

                      ZHANG Zou-Quan Master student at the School of Electrical and Information Engineering, Changsha University of Science and Technology. His research interest covers deep learning and visual inspection

                      CHEN Yu-Rong Ph.D. candidate at the School of Electrical and Information Engineering, Hunan University. His research interest covers deep learning and visual inspection.) received master degree in Electrical and Computer Engineering, University of Pittsburgh in 2020. His research interest covers image processing, machine learning and domain adaption

                      WU Tian-Yue Master student at the School of Electrical and Information Engineering, Changsha University of Science and Technology. Her research interest covers deep learning and visual inspection

                      ZHONG Hang Postdoctoral fellow at the School of Electrical and Information Engineering, Hunan University. He received his bachelor, master and Ph.D. degrees from Hunan University in 2013, 2016 and 2020, respectively. His research interest covers robotics control, visual servo and path planning

                      WANG Yao-Nan Academician at Chinese Academy of Engineering, professor at the School of Robotics, Hunan University. He received his Ph.D. degree from Hunan University in 1995. His research interest covers robotics, intelligent control and image processing

                    • 摘要: 鑄造產業一直是人類現代生產生活中重要的、不可替代的產業, 鑄件產品既是工業制造產品, 也是大型機械的組成部分. 隨著經濟水平和工業自動化程度的不斷提升, 人們對于鑄件的需求量呈指數爆炸式增長, 鑄件價值輻射到各行各業. 與此同時, 鑄件在鑄造、服役過程中經常會出現各種缺陷, 而傳統低效的人工檢測方法難以保障工業界對中高端鑄件的性能需求. 因此亟需對鑄件檢測技術進行革新. 本文首先對鑄件鑄造過程以及服役過程中各類缺陷的形成機理進行分析. 然后闡述了基于聲學、光學、電磁學等主流檢測技術及其常規信號處理方法、磁粉檢測技術與滲透檢測技術等其他檢測技術, 并對近年來新興的基于神經網絡的信號處理方法進行了說明. 在此基礎上, 分析了近年來鑄件缺陷無損檢測技術以及基于神經網絡的信號處理方法的研究現狀. 最后, 對鑄件缺陷無損檢測技術及應用的發展趨勢進行了展望.
                      1)  收稿日期 2021-02-26 錄用日期 2021-06-06 Manuscript?received?February?26,?2021;?accepted?June?6,?2021 國家重點研發計劃 (2018YFB1308200),?國家自然科學基金 (61971071,?6202780012),?長沙市科技計劃項目?(kq1907087),?湖南省創新型省份建設專項經費?(2020SK3007),?博士后創新人才支持計劃?(BX20200122) 資助 Supported?by?National?Key?Research?and?Development?Program?of?China?(2018YFB1308200),?National?Natural?Science Foundation?of?China?(61971071,?6202780012),?Changsha?science and?technology?project?(kq1907087),?Special?funds?for?the?construction?of?innovative?provinces?in?Hunan?Province (2020SK3007),?Postdoctoral?innovative?talent?support?program (BX20200122) 本文責任編委?徐德 Recommended?by?Associate?Editor?XU?De 1.?湖南大學機器人學院?長沙?410082 2.?長沙理工大學電氣與
                      2)  信息工程學院?長沙?410114 1.?School?of?Robotics,?Hunan?University,?Changsha?410082 2.?School?of?Electrical?and?Information?Engineering,?Changsha University?of?Science?and?Technology,?Changsha?410114
                    • 圖  1  基于稀疏表示檢測算法流程圖

                      Fig.  1  The flow chart of detection algorithm based on sparse representation

                      圖  2  拉伸桿和航空工件原圖像及CT重建的三維圖像

                      Fig.  2  Original images of stretching rods and aerial parts and three dimensional images rebuilt by CT

                      圖  3  鋁鑄件缺陷與無損傷處的高光譜數據

                      Fig.  3  Hyperspectral data of aluminum casting defects and no damage

                      圖  4  凝固擴展裂紋的超聲波信號

                      Fig.  4  Ultrasonic transmission normalized signal and reflection normalized signal of solidification propagation crack

                      圖  5  裂紋的幾何尺寸與遠場渦流探測信號關系

                      Fig.  5  The relationship between the crack geometry size and the far-field eddy current detection signal

                      圖  6  合金樣品的缺陷區域熱成像3D模型

                      Fig.  6  Thermal imaging 3D model of defect area of alloy sample

                      圖  7  機器視覺系統示意圖

                      Fig.  7  Schematic diagram of machine vision system

                      圖  8  圖像識別缺陷的類別、位置和區域

                      Fig.  8  Image recognition defect category, location and area

                      圖  9  幀間深度卷積神經網絡結構圖

                      Fig.  9  Inter-frame DCNN structure diagram

                      圖  10  高端鑄件缺陷檢測技術展望概述

                      Fig.  10  Overview of the prospect of high-end casting defect detection technology

                      表  1  鑄件缺陷類型以及各傷損示意圖

                      Table  1  Types of casting defects and diagrams of each damage

                      缺陷
                      種類
                      成因影響因素特征示例
                      氣孔合金凝固時氣體析出氣體溶解度、澆鑄溫度、壓射速度、砂粒度[7]在鑄件內部、表面處有光滑孔眼,有時附有一層氧化膜
                      縮孔鑄件凝固過程中,合金成分的液態收縮、凝固收縮以及固態收縮鑄件復雜度、澆注溫度、冒口位置、鑄造壓力[12,14,18]在鑄件厚斷面內部,交界面內部及厚斷面處,形狀多為長尾狀或凸形,孔內粗糙不平,晶粒粗大
                      鑄造
                      裂紋
                      鑄件表面或內部因各種原因發生斷裂,或機械加工產生的微缺陷離心轉速、涂料、澆鑄溫度及速度在鑄件上有穿透或穿透的裂紋,開裂處金屬表皮未氧化
                      夾雜物鑄造合金在熔煉過程中雜質顆粒保留在固體金屬內澆注時間、碳含量鑄件內部出現不規則孔洞, 內含有明顯細粒
                      偏析凝固過程液相或固相的物理運動鑄件厚度、澆注溫度同一鑄件上化學成分、金相
                      組織和性能不一致
                      疲勞
                      裂紋
                      在鑄件內部產生永久性累計損傷循環應力、循環應變疲勞擴展區裂紋表面光滑, 脆性斷裂區表面粗糙
                      下載: 導出CSV

                      表  2  鑄件缺陷無損檢測與評估技術對比

                      Table  2  Comparison of non-destructive testing and evaluation techniques for rail defects

                      物理學分類檢測技術鑄件類型缺陷類型優點缺點
                      基于光學的無損檢測技術X射線二維成像技術所有鑄件孔洞類缺陷、夾雜類缺陷可探測復雜異形鑄件、結果直觀且便于存儲、對氣孔類缺陷檢測良好[21-27]檢測環境要求高、速度慢、成本高、無法表征完整的缺陷輪廓及形態[21-27]
                      X射線三維成像技術所有鑄件孔洞類缺陷、夾雜類缺陷及裂紋可探測復雜異形鑄件、結果直觀且便于存儲、對三維缺陷表達能力強 [15, 28-38]檢測環境要求高、速度慢、成本高[28-38]
                      機器視覺檢測技術所有鑄件表面缺陷硬件成本低、可檢測復雜鑄件表面缺陷、結果直觀且便于存儲、檢測速度快[41-47]系統抗干擾能力弱、成像質量易受外界因素影響 [41-47]
                      高光譜檢測
                      技術
                      所有鑄件孔洞類缺陷可探測復雜異形鑄件、能獲得缺陷更詳盡的特征、預測潛在缺陷[48, 49]成像過程極長、圖像所占內存量大、結果無法直觀地判別缺陷[48, 49]
                      基于聲學的無損檢測技術超聲檢測技術所有鑄件內部缺陷探測速度快、檢測成本低、穿透能力強、對環境無污染[50,51]需要耦合劑、對鑄件表面光滑度有要求、信號信噪比低[50, 51]
                      相控陣超聲檢測技術所有鑄件內部缺陷探測速度快、聲束角度及深度人為可控[52-56]需要耦合劑、對鑄件表面光滑度有要求[52-56]
                      全聚焦相控陣超聲技術所有鑄件內部缺陷探測速度快、可高分辨率成像[57-59]需要耦合劑、探測手段尚未成熟[57-59]
                      激光超聲檢測技術復雜鑄件內部缺陷無需耦合劑、可探測復雜鑄件、穿透能力強、對細微裂紋敏感、能檢測缺陷位置及大小 [60-66]探測手段尚未成熟
                      基于電磁學的無損檢測技術渦流檢測技術鐵磁性鑄件表面及近表面缺陷無需耦合劑、可在高溫下檢測、探測速度快、檢測電信號便于數據比較與存儲 [67-69]只能探測結構簡單鑄件、難以定量定性地評估缺陷[67-69]
                      遠場渦流技術鐵磁性鑄件表面缺陷及內部裂紋無需耦合劑、探測速度快、對管狀類鑄件缺陷檢測效果極佳[70-75]只能檢測管狀鑄件[70-75]
                      脈沖渦流檢測技術鐵磁性鑄件表面及內部缺陷無需耦合劑、探測速度快、能對缺陷定量評估[76-81]易受頻率影響,檢測時效性低、對微小裂紋異常敏感[76-81,96]
                      脈沖渦流熱成像技術金屬型鑄件表面及內部缺陷無需耦合劑、檢測結果直觀、精度高、檢測面積大[83-88]對鑄件本身會有一定損耗[83-88]
                      其他無損檢測技術磁粉檢測技術鐵磁性鑄件表面及近表面缺陷檢測結果直觀、成本低、對表面細微缺陷敏感[91, 95]需要磁懸液、對鑄件表面光滑度有要求、人工參與度高[89, 91, 95]
                      滲透檢測技術所有鑄件表面及近表面缺陷可探測復雜鑄件、檢測結果直觀、成本低[94, 95]使用試劑對人與環境有害、檢測流程復雜、速度慢、人工操作、檢測環境有要求[90, 94, 95]
                      下載: 導出CSV

                      表  3  基于深度學習的鑄件缺陷檢測研究現狀

                      Table  3  Research status of casting defect detection based on deep learning

                      方法實驗對象檢測目標結果分析數據來源
                      新的空間注意力雙線性卷積神經網絡所有鑄件氣孔及人工鉆孔缺陷精度高達93.30%,可以有效地學習并鑒別特征文獻[105]中的表3
                      基于深度學習特征匹配的鑄件缺陷三維定位方法精密鑄件0.3?1 mm 大小的渣孔缺陷在實現自動定位的基礎上精度優于傳統方法,平均定位誤差低于傳統平移視差法8.69%文獻[106]中的表3
                      采用特征金字塔網絡提取特征,結合區域特征聚集方式的ROI Align汽車鑄鋁件微小孔洞類缺陷與Faster R-CNN相比,使用FPN后,平均精度提高40.9%; 使用ROI Align后,精度提高了23.6%文獻[103]中的表3?6
                      自適應深度與感受野選擇語義分割的網絡鋁合金鑄件海綿收縮、低密度異物、高密度異物、孔洞類缺陷此方法的mIoU比最新的語義分割模型Dense-ASPP高出3.85%文獻[100]中的表3圖7
                      基于對象級注意機制和雙線性池化構建有效的CNN模型鑄鋁件一般缺陷對于每個定量指標(準確率、精確度、召回率), 提出的模型均優于其他經典深度學習分類模型文獻[102]中的表3?5
                      通用特征網絡(General feature net-work, GFN)與微妙特征網絡(Subtle feature extraction, SFN)結合的網絡模型汽車鑄鋁件20種鑄造缺陷該模型在實際X射線圖像的每個指標上均優于其他分類模型文獻[107]中的表1
                      基于視覺注意力機制和特征圖深度學習的魯棒跟蹤檢測方法三類工件縮孔、孔隙率鑄件缺陷的誤檢率和漏檢率均小于4%,缺陷檢測的準確率大于96%文獻[104]中的圖7
                      基于深度殘差網絡的鑄件外觀缺陷檢測方法汽車制動支架暗孔、淺坑、裂紋、缺口、凸起、凹陷ResNet-34ASoftReLu方法的準確率達到93.7%, 遠遠高于傳統檢測方法文獻[108]中的表1-2
                      基于自適應神經模糊推理系統的閥門鑄件影像智能故障診斷系統閥門鑄件裂紋、氣體夾雜物、縮孔缺陷分類的平均準確性為80%文獻[109]中的表3
                      3D卷積神經網絡結合非線性拓撲尺寸參數和經驗模型二十種鑄造模型縮孔、夾雜物、裂紋所提出的CNN在平均精度上優于現有方法的10%至20%文獻[110]中的表2
                      改進的Faster R-CNN算法鋼帶六類表面缺陷以20 fps的速度實現了98.32 %的平均精度均值, 97.02 %的查全率和99 %的檢測率文獻[111]中的表2表3
                      基于選擇性注意機制和深度學習特征匹配的缺陷動態跟蹤檢測方法一般鑄件渣孔誤檢率和漏檢率均低于3 %, 缺陷檢測準確率超過97 %. 與極線約束跟蹤等方法比較, 準確率提高5個百分點以上文獻[112]中的表2、表3與表4
                      用于識別X射線圖像中鑄件缺陷的基于Mask-RCNN的體系結構汽車鑄鋁件一般缺陷超過了缺陷檢測算法在GD-Xray數據集上的最高性能, mAP達95.7 %文獻[113]中的表4
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                    出版歷程
                    • 收稿日期:  2021-02-26
                    • 錄用日期:  2021-06-06
                    • 網絡出版日期:  2021-07-27

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