A Systematic Review of Computer Vision Techniques for Quality Control in End-of-Line Visual Inspection of Antenna Parts

The rapid evolution of wireless communication technologies has underscored the critical role of antennas in ensuring seamless connectivity.Antenna defects,ranging from manufacturing imperfections to environmental wear,pose significant challenges to the reliability and performance of communication systems.This review paper navigates the landscape of antenna defect detection,emphasizing the need for a nuanced understanding of various defect types and the associated challenges in visual detection.This review paper serves as a valuable resource for researchers,engineers,and practitioners engaged in the design and maintenance of communication systems.The insights presented here pave the way for enhanced reliability in antenna systems through targeted defect detection measures.In this study,a comprehensive literature analysis on computer vision algorithms that are employed in end-of-line visual inspection of antenna parts is presented.The PRISMA principles will be followed throughout the review,and its goals are to provide a summary of recent research,identify relevant computer vision techniques,and evaluate how effective these techniques are in discovering defects during inspections.It contains articles from scholarly journals as well as papers presented at conferences up until June 2023.This research utilized search phrases that were relevant,and papers were chosen based on whether or not they met certain inclusion and exclusion criteria.In this study,several different computer vision approaches,such as feature extraction and defect classification,are broken down and analyzed.Additionally,their applicability and performance are discussed.The review highlights the significance of utilizing a wide variety of datasets and measurement criteria.The findings of this study add to the existing body of knowledge and point researchers in the direction of promising new areas of investigation,such as real-time inspection systems and multispectral imaging.This review,on its whole,offers a complete study of computer vision approaches for quality control in antenna parts.It does so by providing helpful insights and drawing attention to areas that require additional exploration.

Pre-Compensation for Continuous-Path Running Trajectory Error in High-Speed Machining of Parts with Varied Curvature Features

Parts with varied curvature features play increasingly critical roles in engineering, and are often machined under high-speed continuous-path running mode to ensure the machining efficiency. However, the continuous-path running trajectory error is significant during high-feed-speed machining, which seriously restricts the machining precision for such parts with varied curvature features. In order to reduce the continuous-path running trajectory error without sacrificing the machining efficiency, a pre-compensation method for the trajectory error is proposed. Based on the formation mechanism of the continuous-path running trajectory error analyzed, this error is estimated in advance by approximating the desired toolpath with spline curves. Then, an iterative error pre-compensation method is presented. By machining with the regenerated toolpath after pre-compensation instead of the uncompensated toolpath, the continuous-path running trajectory error can be effectively decreased without the reduction of the feed speed. To demonstrate the feasibility of the proposed pre-compensation method, a heart curve toolpath that possesses varied curvature features is employed. Experimental results indicate that compared with the uncompensated processing trajectory, the maximum and average machining errors for the pre-compensated processing trajectory are reduced by 67.19% and 82.30%, respectively. An easy to implement solution for high efficiency and high precision machining of the parts with varied curvature features is provided.

Development Trend of NC Machining Accuracy Control Technology for Aeronautical Structural Parts

High-performance five-axis computer numerical control machine tools are widely used in the processing of Aeronautical Structural parts. With the increase of service life, the precision of CNC machine tools equipped by aeronautical manufacturing enterprises is declining day by day, while the new generation of aircraft structural parts are developing towards integration, large-scale, complexity, thin-walled and lightweight. It is very easy to produce dimension overshoot and surface quality defects due to unstable processing technology. The machining accuracy of aircraft structural parts is also affected by complex factors such as cutting load, cutting stability, tool error, workpiece deformation, fixture deformation, etc. Because of the complexity of structure and characteristics of Aeronautical Structural parts, the consistency and stability of cutting process are poor. It is easy to cause machining accuracy problems due to tool wear, breakage and cutting chatter. Relevant scholars have carried out a lot of basic research on NC machining accuracy control and achieved fruitful results, but the research on NC machining accuracy control of Aeronautical structural parts is still less. This paper elaborates from three aspects: error modeling method of NC machine tools, error compensation method, prediction and control of machining accuracy, and combines the characteristics of Aeronautical Structural parts, the development trend and demand of NC machining accuracy control technology are put forward.

Research on intelligent search-and-secure technology in accelerator hazardous areas based on machine vision

Prompt radiation emitted during accelerator operation poses a significant health risk,necessitating a thorough search and securing of hazardous areas prior to initiation.Currently,manual sweep methods are employed.However,the limitations of manual sweeps have become increasingly evident with the implementation of large-scale accelerators.By leveraging advancements in machine vision technology,the automatic identification of stranded personnel in controlled areas through camera imagery presents a viable solution for efficient search and security.Given the criticality of personal safety for stranded individuals,search and security processes must be sufficiently reliable.To ensure comprehensive coverage,180°camera groups were strategically positioned on both sides of the accelerator tunnel to eliminate blind spots within the monitoring range.The YOLOV8 network model was modified to enable the detection of small targets,such as hands and feet,as well as larger targets formed by individuals near the cameras.Furthermore,the system incorporates a pedestrian recognition model that detects human body parts,and an information fusion strategy is used to integrate the detected head,hands,and feet with the identified pedestrians as a cohesive unit.This strategy enhanced the capability of the model to identify pedestrians obstructed by equipment,resulting in a notable improvement in the recall rate.Specifically,recall rates of 0.915 and 0.82were obtained for Datasets 1 and 2,respectively.Although there was a slight decrease in accuracy,it aligned with the intended purpose of the search-and-secure software design.Experimental tests conducted within an accelerator tunnel demonstrated the effectiveness of this approach in achieving reliable recognition outcomes.

Machining Deformation Prediction of Thin-Walled Part Based on Finite Element Analysis

For the problems of machining distortion and the low accepted product during milling process of aluminum alloy thin-walled part,this paper starts from the analysis of initial stress state in material preparation process,the change process of residual stress within aluminum alloy pre-stretching plate is researched,and the distribution law of residual stress is indirectly obtained by delamination measurement methods,so the effect of internal residual stress on machining distortion is considered before finite element simulation. Considering the coupling effects of residual stress,dynamic milling force and clamping force on machining distortion,a threedimensional dynamic finite element simulation model is established,and the whole cutting process is simulated from the blank material to finished product,a novel prediction method is proposed,which can availably predict the machining distortion accurately. The machining distortion state of the thin-walled part is achieved at different processing steps,the machining distortion of the thin-walled part is detected with three coordinate measuring machine tools,show that the simulation results are in good agreement with experimental data.

Mapping relationship analysis of welding assembly properties for thin-walled parts with finite element and machine learning algorithm

The finite element(FE)-based simulation of welding characteristics was carried out to explore the relationship among welding assembly properties for the parallel T-shaped thin-walled parts of an antenna structure.The effects of welding direction,clamping,fixture release time,fixed constraints,and welding sequences on these properties were analyzed,and the mapping relationship among welding characteristics was thoroughly examined.Different machine learning algorithms,including the generalized regression neural network(GRNN),wavelet neural network(WNN),and fuzzy neural network(FNN),are used to predict the multiple welding properties of thin-walled parts to mirror their variation trend and verify the correctness of the mapping relationship.Compared with those from GRNN and WNN,the maximum mean relative errors for the predicted values of deformation,temperature,and residual stress with FNN were less than 4.8%,1.4%,and 4.4%,respectively.These results indicate that FNN generated the best predicted welding characteristics.Analysis under various welding conditions also shows a mapping relationship among welding deformation,temperature,and residual stress over a period of time.This finding further provides a paramount basis for the control of welding assembly errors of an antenna structure in the future.

Improve Neural Machine Translation by Building Word Vector with Part of Speech

Neural Machine Translation(NMT)based system is an important technology for translation applications.However,there is plenty of rooms for the improvement of NMT.In the process of NMT,traditional word vector cannot distinguish the same words under different parts of speech(POS).Aiming to alleviate this problem,this paper proposed a new word vector training method based on POS feature.It can efficiently improve the quality of translation by adding POS feature to the training process of word vectors.In the experiments,we conducted extensive experiments to evaluate our methods.The experimental result shows that the proposed method is beneficial to improve the quality of translation from English into Chinese.

Simulation and Experimental Study on Improving Electrochemical Machining Stability of Highly Convex Structures on Casing Surfaces Using Backwater Pressure

Casing parts are regarded as key components of aero-engines.Most casing parts are attached to convex structures of diferent shapes,whose heights range from hundreds of microns to tens of millimeters.Using profling blocky electrodes for electrochemical machining(ECM)of casing parts is a commonly adopted method,especially when highly convex structures.However,with an increase in the convex structure height,the fow felds of the machining areas become more complex,and short circuits may occur at any time.In this study,a method to improve the fow feld characteristics within a machining area by adjusting the backwater pressure is proposed and validated through simulation and experiment analyses.The simulation results demonstrated that the back-pressure method can signifcantly improve the uniformity of the fow feld around the convex structure compared with the extraction and open outlet modes.Subsequently,the back-pressure value was optimized at 0.5 MPa according to the simulation results.The experimental results showed that using the optimized back-pressure parameters,the cathode feed-rate increased from 0.6 to 0.7 mm/min,and a 16.1 mm tall convex structure was successfully machined.This indicates that the back-pressure method is suitable and efective for electrochemical machining of highly convex structures with blocky electrodes.In this study,we propose a method to improve the electrochemical machining stability of a convex structure on a casing surface using backwater pressure,which has achieved remarkable results.

Development of micro wire electrical discharge machining platform and its application

A machining platform of micro wire electrical discharge machining (MWEDM) was developed. The key technology of MWEDM mainly includes granite basement, micro energy pulse generator, detection and servo control system, constant tension winding system and V-block guide wire mechanism. Utilizing micro wire electrode with 30μm in diameter, the MWEDM can machine the micro slot with the minimum size of 38μm wide, and the surface roughness is smaller than 0.1μm, the machining precision is less than 0.5μm, the white layer is no more than 2μm with main cut. All kinds of complex micro parts, such as micro gear, micro bearing bracket and micro shaped holes, can also be machined by using this platform.

A Petri Net Model for Part Sequencing and Robot Moves Sequence in A 2-Machine Robotic Cell

This paper deals with part sequencing and optimal robot moves sequence in 2-machine robotic cells according to Petri net graph. We have assumed that the robotic cell is capable of producing same and different parts. We have considered a new motion cycle for robot moves sequence which is the development of existing motion cycles in 2-machine robotic cells. The main goal of this study is to minimize the cycle time by determining the optimal part sequencing and robot moves sequence in the robotic cell. So, we have proposed a model based on Petri network.

SolidCAM中iMachining模块在薄壁零件加工中的应用研究

薄壁零件的数控加工对设备性能有较高的要求,利用Solid CAM中的i Machining模块对薄壁零件进行数控铣削编程、仿真并加工。本文在主轴转速、进给速度相等的情况下针对走刀路径、轴向切削量、径向切削量等工艺参数以及加工效率方面与传统的分层铣削方式进行了综合对比,为i Machining模块在薄壁件编程加工方面的研究提供一些参考。

基于机器视觉的飞机故障检查系统

针对当前飞机维修检查工作以人工目视检查为主、效率低且存在人为因素影响的情况,设计一套基于图像识别与机器深度学习的飞机部件表面无损检测系统。收集并整理了某航空公司一线飞机维修员拍摄的飞机机身及发动机部件图片,对图片集进行预处理,包括通道提取、Sobel滤波处理及二值化;最后用Blob分析对处理后的图像进行特征提取与系统分析。系统运行速度快、准确率高且可连续自动识别图像。利用机器视觉技术对飞机部件表面进行无损检测不仅可以提高生产效率,同时可以去除人为因素对航空器飞行安全的影响,使得飞机的飞行安全得到进一步提升。实践证明,该系统性能稳定可靠,具有极高的推广应用价值。

基于旋量理论的镜像加工运动学建模及轨迹自动修调研究

针对镜像加工过程中结构变形致使加工精度难以控制的问题,建立了基于旋量理论的镜像加工剩余壁厚运动学模型,综合考虑零件加工要求及镜像约束关系,提出了镜像加工“支撑-铣削”轨迹生成方法,建立了针对工件变形补偿的镜像支撑轨迹修调模型,实现了镜像加工“支撑-铣削”轨迹自动修调。栅格壁板镜像加工验证试验结果表明,提出的镜像加工“支撑-铣削”轨迹生成和自动修调方法可以满足镜像加工剩余壁厚要求。

基于双目视觉的堆叠零件识别与定位研究

针对堆叠零件识别定位耗时长与精度低等问题,设计了一种高效的识别定位方法。在识别方面,提出了一种分类器融合方法。通过使用此方法训练出一个权重可动态调整的分类器模型,成功实现了对堆叠零件的分类识别。在定位方面,采用改进随机Hough变换的方法提取目标边缘。利用堆叠零件的边缘几何特征,重建出堆叠零件的空间姿态信息。最后,对单个特征分类器和组合特征分类器进行了分类识别实验和模型评估。实验结果表明,特征融合后的分类器展现出更高的识别精度和更稳定的准确率波动,表明其具有更好的分类鲁棒性。

基于机器视觉的飞机电镀部件曲表面无损检测

[目的]为解决机器视觉在曲表面无损检测中算法复杂的问题,以及克服传统光源在曲表面检测中的局限性,开发了一套基于格栅光源的飞机电镀部件曲表面无损检测系统。[方法]该系统通过格栅光对电镀部件曲表面进行成像,随后进行图像分析,包括图像导入、预处理、Blob分析等步骤。[结果]该系统能够快速、准确地检测出电镀部件曲表面存在的缺陷,检测效率高且稳定可靠。[结论]该系统在飞机制造企业及航空公司飞机维护一线的初步工程应用中取得了良好效果,有望进一步推广。

数控技术在农机零部件加工中的优化及应用

随着农业机械加工及制造行业的快速发展,为了满足农业机械多样化设计要求,数控节能加工技术通过精密的计算机控制,能够实现对农机零部件加工过程中各个参数的准确控制。针对传统农机零部件加工过程能耗大及难以控制的问题,基于带精英策略的非支配排序遗传算法NSGA-Ⅱ对加工工艺参数进行多目标优化,并进行实验对比分析。研究结果对于提高农机零部件加工精度、降低能耗、减少加工时间等提供技术参考与借鉴。

叶轮零件增减材复合加工工艺规划及虚拟仿真加工研究

针对叶轮零件结构复杂、加工难度大等问题,对其结构及工艺特点进行分析,制定增减材复合加工工艺规划,并基于NX软件设计增材和减材加工的刀具轨迹,最后通过在VERICUT中构建的虚拟仿真系统对加工过程进行仿真,验证增减材复合加工工艺及NC程序的正确性。虚拟仿真加工结果表明,该方法可为复杂零件高效、高质量加工提供一种新的思路和参考。

基于直驱机床的航空整体叶轮加工效率优化方法研究

为解决航空叶轮在复杂五轴联动加工过程中的效率和精度控制难题,本文基于直驱机床围绕加工参数对表面粗糙度和加工时间的影响进行了深入分析。首先通过与传统机床进行加工对比,证明了直驱机床的精度与速度方面的优势;其次针对原点坐标的研究揭示了原点位置对加工效率的影响,尤其是当原点接近A、C轴中心时,加工效率显著提高;最后通过正交试验法研究了转速、每齿进给量、切宽这3个关键因素,并发现每齿进给量是影响表面粗糙度的主导因素,其次是切宽,而转速的影响较小。在保证粗糙度小于1.6μm的前提下,以转速20000 r/mim、每齿进给量0.03 mm、切宽0.6 mm的参数组合获得最优结果。基于优化的工艺参数不仅提升了加工效率,节约了43.3%的加工时间,还确保了加工质量,为航空叶轮的精密加工提供了重要的技术参考,同时为基于直驱机床的复杂零件高效加工提供实践指导和理论依据。

某型复杂工程机械变速箱体复合工艺约束机加工线平衡研究

为解决机加工生产线平衡问题所包含的加工任务刀具需求、机床类型需求、加工方位约束、“紧密”型和“或”型约束等复杂条件,建立了机加工生产线平衡数学模型,并采用含多重筛选机制的粒子群算法进行了求解。首先,建立了满足此复杂实际约束条件的机加工生产线平衡问题数学模型;然后,采用粒子的位置坐标作为粒子群搜索的权重信息,进行了加工任务、集中任务的选取,并设计了多重筛选机制构造启发式任务集生成规则;采用此规则对待分配加工任务进行了多重筛选,以得到可供直接分配的加工任务集合,粒子群算法(PSO)从此集合中依次选取了加工任务,构成了完整的解,并形成了具体的任务分配方案;最后,为提高程序的实用性和可视性,设计了加工任务的甘特图生成模块,通过对某复杂工程机械变速箱体零件的实际案例研究,将简化后的任务信息代入算法进行了求解。研究结果表明:该方法实现了多组平衡率高于90%的优化结果,在节拍时间为1120 s时,得到94.66%的较高平衡率,排产方案表格内容与甘特图显示一致;算法推演结果满足设定的多种复合约束条件,通过与人工排产对比说明了该算法的有效性并具有较好的经济性、实用性;对柔性生产案例进行探讨,证明该算法运算结果具备一定的生产柔性。

一种锁芯片零件加工连续级进冲压模具的设计分析

为了实现锁芯片零件的全自动化加工,针对一种锁芯片零件,设计了一种连续级进冲压加工模具,解决了该零件打凹、冲孔及落件多道工序的加工问题,并且大大提高了该零件生产的自动化程度、生产效率及加工精度。