|本期目录/Table of Contents|

[1]张梦奇,张志勇.基于 PLC 和机器视觉的铜跺拆捆系统[J].工业仪表与自动化装置,2025,(01):18-24.[doi:10.19950/j.cnki.CN61-1121/TH.2025.01.004]
　ZHANG Mengqi,ZHANG Zhiyong.Plc and machine vision-based copper stack unbinding system[J].Industrial Instrumentation & Automation,2025,(01):18-24.[doi:10.19950/j.cnki.CN61-1121/TH.2025.01.004]
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基于 PLC 和机器视觉的铜跺拆捆系统(PDF)

《工业仪表与自动化装置》[ISSN:1000-0682/CN:61-1121/TH]

卷:
期数:
2025年01期
页码:
18-24
栏目:
出版日期:
2025-02-15

文章信息/Info

Title:
Plc and machine vision-based copper stack unbinding system
文章编号:
1000-0682（2025）01-0018-07
作者:
张梦奇; 张志勇
（1.上海电力大学，电子与信息工程学院;2.上海乾庾智能科技有限公司，上海 201306）
Author(s):
ZHANG Mengqi;  ZHANG Zhiyong
(1.School of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai 201306, China;2.Shanghai Qianyu Intelligence Technology Co.,Ltd.,Shanghai 201306,China）
关键词:
PLC; 工业机器人; 机器视觉; YOLOv8n; SAM; HMI触摸屏
Keywords:
PLC;  industrial robot;  machine vision;  YOLOv8n;  SAM;  HMI touchscreen
分类号:
TP278
DOI:
10.19950/j.cnki.CN61-1121/TH.2025.01.004
文献标志码:
A
摘要:
针对目前电缆生产行业人工拆捆铜跺存在安全隐患以及效率低的问题，设计一套基于PLC和机器视觉的铜跺拆捆系统。首先，系统采用PLC作为控制核心，并结合工业机器人、双目相机和HMI触摸屏等设备实现对生产线的自动控制。其次，提出一种三维拆捆坐标定位算法，在目标检测算法YOLOv8n基础上结合图像分割大模型Segment Anything Model(SAM)对捆带进行检测与分割；基于捆带的分割结果结合缝隙距离测量策略和双目相机采集的深度信息，得到捆带与铜跺之间的缝隙距离，从而实现三维拆捆坐标的定位。最后，实验结果表明，捆带目标检测平均精度(Average precision, AP)达到93.7%，捆带图像分割平均交并比(Mean Intersection over Union, MloU)达到84.6%，三维拆捆坐标定位准确率(Accuracy,AC)达到95%，铜跺拆捆效率达到62个/时。因此，该系统能完成铜跺拆捆任务，且工作效率高，可靠性强，达到设计要求。
Abstract:
Design a copper stripping system based on PLC and machine vision to address the safety hazards and low efficiency in the current cable production industry. Firstly, the system adopts PLC as the control core, combined with industrial robots, binocular cameras, and an HMI touchscreen to achieve automatic control of the production line. Secondly, propose a three-dimensional stripping coordinate positioning algorithm. Based on the target detection algorithm YOLOv8n, combined with the image segmentation model "Segment Anything Model" (SAM), it detects and segments the bundles. Based on the segmentation results of the bundles, combined with the gap distance measurement strategy and the depth information collected by the binocular camera, the gap distance between the bundle and the copper strand is obtained, thus achieving three-dimensional stripping coordinate positioning. Finally, experimental results show that the average precision (AP) of bundle target detection reaches 93.7%, the mean Intersection over Union (MloU) of bundle image segmentation reaches 84.6%, the accuracy (AC) of three-dimensional stripping coordinate positioning reaches 95%, and the efficiency of copper stripping reaches 62 pieces per hour. Therefore, the system can complete the copper stripping task with high efficiency and strong reliability, meeting the design requirements.

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备注/Memo

备注/Memo:
收稿日期：2024-05-20第一作者：张梦奇(1999—)，男，安徽阜阳人，硕士研究生，研究方向为智能高端制造，自动化技术。
更新日期/Last Update: 1900-01-01