WO2020087725A1 - 一种铝车身自冲铆接铆模失效视觉检测方法 - Google Patents

一种铝车身自冲铆接铆模失效视觉检测方法 Download PDF

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WO2020087725A1
WO2020087725A1 PCT/CN2018/123747 CN2018123747W WO2020087725A1 WO 2020087725 A1 WO2020087725 A1 WO 2020087725A1 CN 2018123747 W CN2018123747 W CN 2018123747W WO 2020087725 A1 WO2020087725 A1 WO 2020087725A1
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riveting
image acquisition
acquisition device
die
riveting die
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PCT/CN2018/123747
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English (en)
French (fr)
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刘蕾
梁端
汤伟
汤东华
潘伟涛
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安徽巨一自动化装备有限公司
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Publication of WO2020087725A1 publication Critical patent/WO2020087725A1/zh

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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B21—MECHANICAL METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING METAL
    • B21J—FORGING; HAMMERING; PRESSING METAL; RIVETING; FORGE FURNACES
    • B21J15/00—Riveting
    • B21J15/10—Riveting machines
    • B21J15/28—Control devices specially adapted to riveting machines not restricted to one of the preceding subgroups
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B21—MECHANICAL METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING METAL
    • B21J—FORGING; HAMMERING; PRESSING METAL; RIVETING; FORGE FURNACES
    • B21J15/00—Riveting
    • B21J15/02—Riveting procedures
    • B21J15/025—Setting self-piercing rivets

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  • the invention relates to a self-pierce riveting riveting die failure detection method, and more particularly to a visual detection method of an aluminum body self-piercing riveting riveting die failure.
  • SPR riveting Semi-hollow self-piercing riveting, or Self-Piercing Riveting, referred to as SPR riveting, is a mechanical cold forming connection process, using semi-hollow rivets made of special materials, using SPR self-piercing riveting guns, for two or more types of Metal or non-metallic materials are connected.
  • the semi-hollow rivet penetrates the first layer plate, and the hollow structure at the tail of the rivet expands and penetrates without puncturing the bottom plate under the action of the riveting die, thereby forming an internal interlock with the bottom plate to connect different materials Together.
  • the quality of SPR riveting is affected by the size parameters of the riveting die.
  • the riveting die plays an important role in the expansion and deformation of the rivet feet during the SPR riveting process.
  • the defects of the riveting die will cause riveting quality problems. Therefore, the SPR riveting in the production process needs to be The riveting die of the gun is monitored for defects.
  • the riveting mold surface Scratches, oil stains, light intensity, failure modes of riveting mold cracks and other interference factors will affect the detection effect.
  • the threshold comparison method cannot detect defects with different shapes and large grayscale ranges; therefore, it is used in practice During the process, there will be false alarms and missed inspections, and the accuracy is poor, which affects normal production.
  • the present invention is to avoid the deficiencies of the above-mentioned prior art, and to provide a visual inspection method for the failure of self-piercing riveting dies of aluminum car bodies to realize the capture of the subtle features of the surface of the rivet and further distinguish cracks, scratches, stains and many interferences
  • the characteristics of the factors can reduce the misjudgment and missed detection, realize the real-time online defect monitoring of the riveting die, and then improve the quality of SPR riveting.
  • the riveting die is an online riveting die fixed on the lower arm end of the C-arm of the riveting gun;
  • An image acquisition device is provided, and the optical axis in the image acquisition device forms a non-zero angle with the central axis of the riveting rod of the rivet gun, so that the image acquisition device makes way for the C-shaped cavity of the C-arm of the rivet gun during measurement;
  • a corner prism is fixedly arranged at the front end of the lens of the image acquisition device, and the lens in the image acquisition device uses the corner prism to acquire the riveting die image to realize the riveting die image acquisition.
  • the feature of the visual inspection method for the failure of the self-piercing riveting die of the aluminum car body of the present invention is also that the image acquisition device adopts a high-pixel industrial camera with not less than 300,000 pixels.
  • the feature of the visual detection method for the failure of the self-piercing riveting die of the aluminum car body of the present invention is also that the optical axis in the image acquisition device and the central axis of the riveting rod of the riveting gun form an angle of 90 °, and the corner prism is a 90 ° corner prism.
  • the feature of the visual detection method for the failure of the self-piercing riveting die of the aluminum body of the present invention is also that: a light source emitter with a current stabilizer is provided in the image acquisition device, and the current stabilizer is used to ensure the stability of the light source voltage and the spectrum emitted by the light source emitter The frequency is consistent.
  • the characteristic of the visual detection method for the failure of the self-piercing riveting die of the aluminum car body of the invention is that: an image acquisition device is fixedly set during the detection process; when the riveting gun completes the riveting work, the robot moves the C-arm of the riveting gun to move the riveting die Go directly under the corner prism, and carry out riveting mold image acquisition.
  • the neural network that has completed the feature learning is used to determine the riveting mold defects, and the visual detection of the failure of the self-piercing riveting mold of the aluminum body is realized.
  • the invention uses a prism, so that the image acquisition device does not have to be placed between the rivet rod and the rivet mold, so that a higher pixel industrial camera and lens can be used to capture the subtle features of the surface of the rivet mold to ensure a high-pixel camera And lens is used in the real-time monitoring process of riveting die defects, feature capture is accurate and reliable, which provides a guarantee for improving the accuracy of monitoring results.
  • the present invention introduces a deep learning neural network processing method for visual processing.
  • the collection of a large number of riveting mold defect pictures obtained for feature learning enables the detection device to distinguish the characteristics of cracks, scratches, stains and many interference factors, which is effective Reduce misjudgment and missed inspection, realize real-time online defect monitoring of riveting die, and then improve the quality of SPR riveting.
  • FIG. 1 is a schematic structural diagram of a visual inspection device for failure of a self-piercing riveting die for an aluminum car body in the present invention
  • the visual inspection method for the failure of the self-piercing riveting die of the aluminum body in this embodiment is:
  • the riveting die 2 is an online riveting die fixed on the lower arm end of the C-arm 1 of the riveting gun; an image acquisition device 5 is provided, and the optical axis in the image acquisition device 5 forms a non-zero angle with the central axis of the riveting rod of the riveting gun , Make the image acquisition device 5 give way to the C-shaped cavity of the riveting gun C-arm 1 during the measurement; a corner prism 3 is fixedly arranged at the front end of the lens of the image acquisition device 5, and the lens in the image acquisition device 5 is acquired by the corner prism 3 Online riveting die image, to achieve online riveting die image acquisition.
  • the corresponding measures also include:
  • the image acquisition device uses a high-pixel industrial camera of not less than 300,000 pixels; set the optical axis of the image acquisition device 5 and the central axis of the rivet rod of the riveting gun to form an angle of 90 °, and the corner prism 3 is a 90 ° corner prism;
  • the image acquisition device 5 is provided with a light source emitter 4 with a current stabilizer, and the current stabilizer is used to ensure that the voltage of the light source is stable, and the spectral frequency emitted by the light source emitter is consistent.
  • the image acquisition device 5 is fixedly set during the detection process; when the riveting gun completes the riveting work, the robot holds the gun to move the C-arm 1 of the riveting gun, so that the online riveting die moves directly under the corner prism 3, and the image riveting die image acquisition is carried out .
  • image processing is performed as follows:
  • the neural network that has completed the feature learning is used to determine the riveting mold defects, and the visual detection of the failure of the self-piercing riveting mold of the aluminum body is realized.
  • the computer 6 can be set as a public machine, and through data transmission, one public machine can be connected to multiple image acquisition devices.
  • the robot After the riveting work of the riveting gun is completed, the robot holds the gun and moves the riveting mold below the prism. After reaching a specific position, the image acquisition device captures the image of the riveting mold reflected by the prism. The robot sends a signal to control the light source emitter lighting through the PLC and the high-pixel camera to take pictures of the riveting mold.
  • the voltage stabilizer is used to ensure that the voltage of the light source is stable, and the spectral frequency emitted by the light source is consistent, which will not affect the collected pictures.
  • the prism refracts the light reflected by the riveting mold under the light source at a certain angle, so that it is not captured by the lens that is not on the same axis of the riveting rod of the riveting gun.
  • the photoelectric sensor is used to convert the optical signal captured by the lens into an electrical signal, which is transmitted to the public machine in real time through the data transmission line.
  • the robot moves the riveting gun to the riveting work position again.
  • the public machine determines whether there is a defect in the collected pictures, and does not process the riveting mold when it is normal; the public machine will send an error to the robot when the defect occurs, stop the robot, and remind the staff to replace the riveting mold in time to ensure the quality of the riveting.

Abstract

本发明公开了一种铝车身自冲铆接铆模失效视觉检测方法，铆模是固定设置在铆枪C型臂的下臂端的在线铆模，图像采集装置中的光轴与铆枪铆杆的中轴线形成不为零的夹角，使图像采集装置在测量中针对铆枪C型臂的C形腔形成让位；在图像采集装置的镜头前端固定设置转角棱镜，图像采集装置中的镜头利用转角棱镜获取铆模图像，实现铆模图像采集。本发明使用棱镜，使图像采集设备不必放置在铆枪铆杆与铆模之间，因而可以使用更高像素的工业相机和镜头捕捉铆模表面的细微特征，为提高监测结果的准确性提供了保障。

Description

一种铝车身自冲铆接铆模失效视觉检测方法 技术领域
本发明涉及自冲铆接铆模失效检测方法，更具体地说是一种铝车身自冲铆接铆模失效视觉检测方法。
背景技术
半空心自冲铆接，即Self-Piercing Riveting，简称SPR铆接，是一种机械冷成型连接工艺，使用特殊材料制成的半空心铆钉，利用SPR自冲铆枪，对两种或两种以上的金属或非金属材料板材进行连接的。在SPR铆接过程中，半空心铆钉穿透首层板件，在铆模的作用下铆钉尾部的中空结构扩张刺入而并不刺穿底层板材，从而与底层板形成内部互锁将不同材料连接在一起。
SPR铆接质量受到铆模尺寸参数的影响，铆模在SPR铆接过程中对铆钉钉脚的扩张形变过程起到重要作用，铆模缺陷会带来铆接质量问题，因此需要对生产过程中的SPR铆枪的铆模进行缺陷监测。
针对铆模缺陷监测，现有技术中最为广泛使用的仍然是人工监测，但其监测效率低，存在漏检和误判的情况，也不能实时在线发现受损铆模。另一种方式是针对铆模进行拍照，并与正常铆模图片进行阈值比对，以此判定铆模是否失效；由于追求封装机体积小，在目前的技术水平下，仅能安装使用30万像素的工业相机，铆模体积小，表面出现的裂纹仅有几十微米，因此30万的像素无法实现对于细小裂纹的特征的捕捉；同时，采用传统的拍照处理的方式，铆模表面出现的划痕、油污痕迹、光照强度、铆模裂纹的失效形式以及其它干扰因素都会对检测效果产生影响，阈值对比方法无法检测出形状大小不一、灰度值区间大的缺陷特征；因此在实际使用过程中会出现误报和漏检，准确性较差，影响正常生产。
发明内容
本发明是为避免上述现有技术所存在的不足，提供一种铝车身自冲铆接铆模失效视觉检测方法，实现铆模表面细微特征的捕捉，并进一步区分裂纹、划痕、污渍以及诸多干扰因素的特征，减少误判和漏检，实现铆模的实时在线缺陷监测，进而提高SPR铆接质量。
本发明为解决技术问题采用如下技术方案：
本发明铝车身自冲铆接铆模失效视觉检测方法的特点是：
铆模是固定设置在铆枪C型臂的下臂端的在线铆模；
设置图像采集装置，图像采集装置中的光轴与铆枪铆杆的中轴线形成不为零的夹角，使图像采集装置在测量中针对铆枪C型臂的C形腔形成让位；
在图像采集装置的镜头前端固定设置转角棱镜，图像采集装置中的镜头利用转角棱镜获 取铆模图像，实现铆模图像采集。
本发明铝车身自冲铆接铆模失效视觉检测方法的特点也在于：图像采集装置采用不低于30万像素的高像素工业相机。
本发明铝车身自冲铆接铆模失效视觉检测方法的特点也在于：设置图像采集装置中的光轴与铆枪铆杆的中轴线形成90°的夹角，转角棱镜为90°转角棱镜。
本发明铝车身自冲铆接铆模失效视觉检测方法的特点也在于：在图像采集装置中设置带有稳流器的光源发射器，利用稳流器确保光源电压稳定，使光源发射器发出的光谱频率一致。
本发明铝车身自冲铆接铆模失效视觉检测方法的特点也在于：检测过程中固定设置图像采集装置；铆枪在完成铆接工作时，由机器人持枪移动铆枪C型臂，使铆模移动到转角棱镜的正下方，实施铆模图像采集。
本发明铝车身自冲铆接铆模失效视觉检测方法的特点也在于：
通过采集获得大量铆模缺陷图像，针对采集获得的大量铆模缺陷图像采用神经网络进行特征学习，完成包括缺陷特征和干扰特征在内的特征学习；
针对由图像采集装置在线采集获得的被监测铆模的铆模图像，利用已完成特征学习的神经网络判断铆模缺陷，实现铝车身自冲铆接铆模失效视觉检测。
与已有技术相比，本发明有益效果体现在：
1、本发明使用棱镜，使图像采集设备不必放置在铆枪铆杆与铆模之间，从而可以使用更高像素的工业相机和镜头，去捕捉铆模表面的细微特征，确保高像素的相机和镜头使用在铆模缺陷的实时监测过程中，特征捕捉准确可靠，为提高监测结果的准确性提供了保障。
2、本发明针对视觉处理引入了深度学习神经网络的处理方法，通过采集获得的大量的铆模缺陷图片进行特征学习，使得检测装置可以区分裂纹、划痕、污渍以及诸多干扰因素的特征，有效减少误判和漏检，实现铆模的实时在线缺陷监测，进而提高SPR铆接质量。
附图说明
图1本发明中铝车身自冲铆接铆模失效视觉检测装置结构示意图；
图中标号：1铆枪C型臂，2铆模，3转角棱镜，4带有稳流器的光源发射器，5图像采集装置，6计算机。
具体实施方式
参见图1，本实施例中铝车身自冲铆接铆模失效视觉检测方法是：
铆模2是固定设置在铆枪C型臂1的下臂端的在线铆模；设置图像采集装置5，图像采集装置5中的光轴与铆枪铆杆的中轴线形成不为零的夹角，使图像采集装置5在测量中针对铆枪C型臂1的C形腔形成让位；在图像采集装置5的镜头前端固定设置转角棱镜3，图像 采集装置5中的镜头利用转角棱镜3获取在线铆模的图像，实现在线铆模图像采集。
具体实施中，相应的措施也包括：
图像采集装置采用不低于30万像素的高像素工业相机；设置图像采集装置5中的光轴与铆枪铆杆的中轴线形成90°的夹角，转角棱镜3为90°转角棱镜；在图像采集装置5中设置带有稳流器的光源发射器4，利用稳流器确保光源电压稳定，使光源发射器发出的光谱频率一致。
检测过程中固定设置图像采集装置5；铆枪在完成铆接工作时，由机器人持枪移动铆枪C型臂1，使在线铆模移动到转角棱镜3的正下方，实施在线铆模的图像采集。
具体实施中，按如下方式进行图像处理：
通过采集获得大量铆模缺陷图像，针对采集获得的大量铆模缺陷图像采用神经网络进行特征学习，完成包括缺陷特征和干扰特征在内的特征学习；
针对由图像采集装置在线采集获得的被监测铆模的铆模图像，利用已完成特征学习的神经网络判断铆模缺陷，实现铝车身自冲铆接铆模失效视觉检测。
在线监测中，计算机6可以设置为公共机，通过数据传输，一台公共机可以连接多部图像采集装置。
系统工作流程。
铆枪在铆接工作完成后，由机器人持枪将铆模移动到棱镜下方，到达特定位置处后，图像采集装置捕捉通过棱镜反射的铆模图像。由机器人发出信号通过PLC控制光源发射器照明、控制高像素相机对铆模进行拍照。通过电压稳流器确保光源电压稳定，确保光源发出的光谱频率一致，不会对采集到的图片产生影响。棱镜对光源照射下的铆模所反射的光折射一定角度，使不处于铆枪铆杆同一轴线上的镜头所捕获。利用光电传感器将镜头捕获到的光信号转换为电信号，通过数据传输线实时传递给公共机。当图像采集结束之后机器人将铆枪重新移动到铆接工作位。公共机对采集到的图片判定是否存在缺陷，铆模正常则不做处理；出现缺陷公共机将报错发给机器人，使机器人停止工作，提醒工作人员及时的跟换铆模，以保证铆接质量。

Claims (6)

  1. 一种铝车身自冲铆接铆模失效视觉检测方法，其特征是：
    铆模(2)是固定设置在铆枪C型臂(1)的下臂端的在线铆模；
    设置图像采集装置(5)，图像采集装置(5)中的光轴与铆枪铆杆的中轴线形成不为零的夹角，使图像采集装置(5)在测量中针对铆枪C型臂(1)的C形腔形成让位；
    在图像采集装置(5)的镜头前端固定设置转角棱镜(3)，图像采集装置(5)中的镜头利用转角棱镜(3)获取铆模图像，实现铆模图像采集。
  2. 根据权利要求1的铝车身自冲铆接铆模失效视觉检测方法，其特征是：图像采集装置(5)采用不低于30万像素的高像素工业相机。
  3. 根据权利要求1的铝车身自冲铆接铆模失效视觉检测方法，其特征是：设置图像采集装置(5)中的光轴与铆枪铆杆的中轴线形成90°的夹角，转角棱镜(3)为90°转角棱镜。
  4. 根据权利要求1的铝车身自冲铆接铆模失效视觉检测方法，其特征是：在图像采集装置(5)中设置带有稳流器的光源发射器(4)，利用稳流器确保光源电压稳定，使光源发射器发出的光谱频率一致。
  5. 根据权利要求1的铝车身自冲铆接铆模失效视觉检测方法，其特征是：检测过程中固定设置图像采集装置(5)；铆枪在完成铆接工作时，由机器人持枪移动铆枪C型臂(1)，使铆模移动到转角棱镜的正下方，实施铆模图像采集。
  6. 根据权利要求1、2、3或4的铝车身自冲铆接铆模失效视觉检测方法，其特征是：
    通过采集获得大量铆模缺陷图像，针对采集获得的大量铆模缺陷图像采用神经网络进行特征学习，完成包括缺陷特征和干扰特征在内的特征学习；
    针对由图像采集装置在线采集获得的被监测铆模的铆模图像，利用已完成特征学习的神经网络判断铆模缺陷，实现铝车身自冲铆接铆模失效视觉检测。
PCT/CN2018/123747 2018-10-31 2018-12-26 一种铝车身自冲铆接铆模失效视觉检测方法 WO2020087725A1 (zh)

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