Accurate positioning of a drilling and riveting cell for aircraft assembly

Modern aircraft assembly demands assembly cells or machines with higher machining efficiency and accuracy. Thus, a dual-machine drilling and riveting cell is developed in this paper. We firstly discuss its physical design, as well as the automatic drilling and riveting process. With the automatic drilling and riveting cell, drilling and riveting production line of aircraft panels can be expected. The frame chain of the drilling and riveting cell is constructed to link the assembly cell to its task space, which is the kinematics base. System calibrations, including task space calibration, the sensor calibration of an orientation alignment unit, the floating calibration of the implicit hand-eye relationship, are explored. For high positioning accuracy, a multi-sensor servoing method is proposed for cell positioning. An orientation-based laser servoing strategy, which uses the feedback of the orientation errors measured by laser displacement sensors, is used to align drilling direction and camera shooting direction. Besides, A single-camera-based visual servoing is applied to align the tool center point (TCP) to reference holes, to obtain their coordinates for drilling position modification. Experiments of multi-sensor servoing for cell positioning are performed on an automatic drilling and riveting machine developed for the panel assembly of an aircraft in China. With the cell positioning method, the automatic drilling and riveting cell can approximately achieve an accuracy of 0.05 mm, which can adequately fulfill the requirement for the assembly of the aircraft.     

A method for robot placement optimization based on two-dimensional manifold in joint space

This article addresses a method for placement determination of robotic drilling system on two-dimensional manifold in robot joint space. It has been proved that the feasibility of positioning error compensation on two-dimensional manifold, and that the continuity of the robot parameters in the two-dimensional space is the prerequisite to perform the compensation in previous study. It appears that there are bifurcations which might break the continuity of the robot parameters on the two-dimensional manifold due to improper placement. To avoid bifurcations, a performance index and a set of optimization procedure are proposed to achieve proper placement of robotic machining system. Experiments conducted on a KUKA robot have verified the effectiveness of the proposed placement optimization method. Experiment results indicated that positioning errors were significantly improved with the proposed method, which is beneficial for robotic machining accuracy.     

Multi-agent reinforcement learning for online scheduling in smart factories

Rapid advances in sensing and communication technologies connect isolated manufacturing units, which generates large amounts of data. The new trend of mass customization brings a higher level of disturbances and uncertainties to production planning. Traditional manufacturing systems analyze data and schedule orders in a centralized architecture, which is inefficient and unreliable for the overdependence on central controllers and limited communication channels. Internet of things (IoT) and cloud technologies make it possible to build a distributed manufacturing architecture such as the multi-agent system (MAS). Recently, artificial intelligence (AI) methods are used to solve scheduling problems in the manufacturing setting. However, it is difficult for scheduling algorithms to process high-dimensional data in a distributed system with heterogeneous manufacturing units. Therefore, this paper presents new cyber-physical integration in smart factories for online scheduling of low-volume-high-mix orders. First, manufacturing units are interconnected with each other through the cyber-physical system (CPS) by IoT technologies. Attributes of machining operations are stored and transmitted by radio frequency identification (RFID) tags. Second, we propose an AI scheduler with novel neural networks for each unit (e.g., warehouse, machine) to schedule dynamic operations with real-time sensor data. Each AI scheduler can collaborate with other schedulers by learning from their scheduling experiences. Third, we design new reward functions to improve the decision-making abilities of multiple AI schedulers based on reinforcement learning (RL). The proposed methodology is evaluated and validated in a smart factory by real-world case studies. Experimental results show that the new architecture for smart factories not only improves the learning and scheduling efficiency of multiple AI schedulers but also effectively deals with unexpected events such as rush orders and machine failures.     

An online multiple object tracker based on structure keeper net

We propose a novel online multiple object tracker taking structure information into account. State-of-the-art multi-object tracking (MOT) approaches commonly focus on discriminative appearance features, while neglect in different levels structure information and the core of data association. Addressing this, we design a new tracker fully exploiting structure information and encoding such information into the cost function of the graph matching model. Firstly, a new measurement is proposed to compare the structure similarity of two graphs whose nodes are equal. With this measurement, we define a complete matching which performs association in high efficiency. Secondly, for incomplete matching scenarios, a structure keeper net (SKnet) is designed to adaptively establish the graph for matching. Finally, we conduct extensive experiments on benchmarks including MOT2015 and MOT17. The results demonstrate the competitiveness and practicability of our tracker.

     

Mental imagery classification using one-dimensional convolutional neural network for target selection in single-channel BCI-controlled mobile robot

Neural Computing and Applications - This paper introduces the use of the one-dimensional convolutional neural network (1D-CNN) for end-to-end EEG decoding with application towards a BCI system with...     

EEG extended source imaging with structured sparsity and $$L_1$$ -norm residual

Neural Computing and Applications - It is a long-standing challenge to reconstruct the locations and extents of cortical neural activities from electroencephalogram (EEG) recordings, especially...     

A steel surface defect inspection approach towards smart industrial monitoring

Journal of Intelligent Manufacturing - With the advance in Industry 4.0, smart industrial monitoring has been proposed to timely discover faults and defects in industrial processes. Steel is widely...     

航空机轮加载测控系统软件的设计与实现

鉴于航空机轮加载试验过程中,要求上位机测控软件能够即时向下位机下达控制命令并且能够实时采集下位机上传的大量现场数据,给出了一种基于DataSocket技术和OPC协议的实时网络通信方法;针对程序执行过程中常出现的响应阻塞问题,结合LabWin-dows/CVI的多线程机制论述了一种多任务并发执行的方法;经过反复测试试验,由此所构建的上位机软件响应性能良好,其网络通信传输速率快、实时性高。     

紧凑型边界扫描技术的功能与现状

芯片一体化进程的加剧以及对功耗控制的关注日益增加,边界扫描技术面临新的挑战,这些挑战来源于调试、应用、测试等方面,紧凑型边界扫描技术对此提出了有效的解决方法;以IEEE1149.7标准以及国内外文献报道为依据,对精简引脚与增强功能边界扫描技术的测试规范进行系统的分析,重点对该技术的新功能、新特性和新应用进行详细介绍;针对符合IEEE1149.1标准的测试系统与被测系统如何升级以实现IEEE1149.7标准新功能的问题提出了相应的建议;最后总结了该技术的发展方向和应用前景。     

基于MCGS的步进电机控制系统设计

设计了一种分层式的步进电机控制系统,现场控制层以AT89C52单片机为主控芯片,通过软件编程实现对步进电机的启停、正反转及调速控制,上位机监控层在工控组态软件MCGS平台下开发,操作界面直观友好,可以方便地对现场步进电机控制系统发布控制命令和参数;为解决现场控制层与监控层间数据交互问题,开发了基于MCGS的51单片机驱动构件;实际运行结果表明,整个系统实时性、稳定性良好,完全达到设计要求。