尾矿浆磁性物含量检测仪的研究

为了提高磁选柱的选别指标和自动控制水平,设计了一种尾矿浆磁性物含量检测仪.具体分析了电感线圈的检测原理和电感值的测量方法,确定了电感线圈与磁性物含量之间的非线性关系,并提出了检测仪的软硬件结构;同时,将该检测仪安装在磁选柱的溢出口处,用于在线检测溢流尾矿浆磁性物浓度.实际应用表明,该检测仪具有抗干扰能力强、工作稳定和精度高等特点,完全满足现场使用要求.     

基于嵌入式结构的便携式漏磁检测仪设计

针对基于工控机设计的漏磁检测仪体积大、功耗高、操作不便等问题,设计了一种基于嵌入式技术的便携式漏磁检测仪.该检测仪以S3C2440A嵌入式微处理器为核心,采用Windows CE 5.0嵌入式操作系统,通过霍尔传感器捕获缺陷漏磁信号,完成对铁磁材料工件机械结构缺陷的检测.实验结果表明:该仪器具有灵敏度高、结构简单、便于携带等优点,具有一定的推广价值.     

圆锥滚子轴承实际宽度高速在线检测仪

圆锥滚子轴承实际宽度的获取是轴承成品检测的一个重要环节,该参数是反映轴承成品质量最重要的参数之一.文中针对现有的轴承实际宽度检测仪存在的检测速度、精度和稳定性方面的不足,基于LabVIEW虚拟仪器技术和PLC控制技术,研制了一种圆锥滚子轴承实际宽度检测仪.该检测仪已经应用于工厂自动化检测线中,半年来运行稳定可靠,能够满足高速度、高精度的检测要求,测量重复性好.     

BP神经网络在气密性检测中的应用研究

设计了一种基于差压法原理的以ARM处理器为控制核心的通用型气密性检测仪,利用BP神经网络建立了泄漏量与各个影响因素之间关系的数学模型,并利用该模型对检测数据进行处理.试验结果表明:该检测仪提高了测试精度,并满足在线检测的要求.     

基于DSP的红外CO检测仪的设计

针对传统一氧化碳传感器存在中毒等问题,提出了一种红外一氧化碳气体检测仪的设计,该设计采用双光源双探测器气室结构,对杂质气体、粉尘和温漂进行有效补偿,基于红外检测原理和DSP技术,实现了液晶显示、声光报警和ZigBee通信等功能,零点调整和非线性修正减少了环境对测量精度的影响,试验表明:通过测得的CO浓度值与实际值对比,该检测仪的测量精度为±1％左右,从而得出结论:该检测仪能够满足安全生产的要求,具有较高的推广应用价值.     

一种新型的电能计量芯片检测仪

针对现有计量芯片检测工艺中存在的检测精度低,功能单一和成本高等问题,提出了一种新型的电能计量芯片检测仪。介绍了新型检测仪电子部分的基本构成,分析了各部分的基本功能和安装分布。基于该检测设备,可以提高芯片产品检测精度和效率,且能够适应不同计量芯片的检测要求和未来产品升级要求。     

嵌入式红外吸收甲烷检测仪的设计

为了能够准确地检测甲烷气体浓度,根据朗伯-比尔吸收定律,设计了以AT89LV51单片机为核心的甲烷检测仪,实现了数据的采集、处理、显示及报警控制.当被测气体中甲烷浓度超过预定数值时,将发出声光报警.实验结果表明该检测仪具有较高的灵敏度和测量精度,并具有结构简单、体积小、操作方便、实时检测和数据处理能力强的特点,可推广应用到其他气体测试系统.     

某型飞机武器控制系统计算机检测仪的设计与实现

为解决某型飞机武器控制系统计算机检测仪检测技术老旧且故障频发,不能满足作战和训练要求的问题,本文设计了一种新型的检测仪。基于该型飞机武器控制系统计算机的功能结构和维护保障规程,首先分析了该型计算机检测仪的功能需求;然后提出了基于Compact RIO平台和触摸屏式计算机的检测仪总体结构;最后设计了检测仪的硬件结构和匹配的软件设计思路。检测仪的硬件主要包括四个模块：控制模块、测量模块、显示模块和电源模块;软件实现采用LabVIEW图形化系统设计软件,设计过程分成开发模式和运行模式两阶段。该检测仪能够对武器控制系统中机载计算机的输入输出信号进行精确测量和分析,技术先进,且显示直观,能较好地提高维修保障效率。     

65米射电望远镜副面调整系统姿态精度监测与回零策略

上海天文台65米射电望远镜的副面调整机构为Stewart型并联机器人,为了及时发现该并联机器人因机械磨损或误差累积造成的精度下降问题,使用倾角传感器对并联机器人动平台姿态进行检测,求得动平台姿念均方根误差并将其与设计指标进行比较,从而用户可以判断是否需要进行维修或回零操作.为了提高并联机器人的易维护性,设计了光电传感器回零和磁尺(磁致伸缩位移传感器)回零两种回零方式,分析了两种回零方式以及通过回零操作对光电传感器和磁尺精度进行检测的原理.总结了该并联机器人需要进行回零操作的不同状况,并给出了相应的回零控制策略.实验证明本文提出的回零控制策略是解决并联机器人回零问题的一种有效方法.     

基于ATMEGA16的便携式瓦斯检测仪

针对目前常用瓦斯检测仪检测范围高时精度低,检测精度高时检测范围低等不足,设计了一种基于双检测回路的便携式瓦斯检测仪。该系统以ATMEGA16控制器为核心,利用催化燃烧式传感器和红外探测器组成双回路瓦斯检测电路,并将朗伯-比尔红外吸收定律运用到瓦斯检测原理中,提高了低瓦斯浓度时的测量精度,同时扩大了瓦斯浓度的测量范围。系统的无线收发模块可以和上位机通信实现信息共享,其开关机电路可以实现关机后仪器与电源完全断开,有效节约电池能量。实验表明该瓦斯浓度检测仪具有检测精度高,检测范围广,高效节能等特点,具有较高的应用价值。     

Digital Twin-driven machining process for thin-walled part manufacturing

Thin-walled parts are widely used in the aerospace, shipbuilding, and automotive industry, but due to its unique structure and high accuracy requirements, which leads to an increase in scrapped parts, high cost in production, and a more extended period in the trial machining process. However, to adapt to fast production cycles and increase the efficiency of thin-walled parts machining, this paper presents a Digital Twin-driven thin-walled part manufacturing framework to allow the machine operator to manage the product changes, make the start-up phases faster and more accurate. The framework has three parts: preparation, machining, and measurement, driven by Digital Twin technologies in detail. By establishing and updating the workpiece Digital Twin under a different status, various manufacturing information and data can be integrated and available to machine operators and other Digital Twins. It can serve as a guideline for establishing the machine tool and workpiece Digital Twin and integrating them into the machining process. It provides the machine operator opportunities to interact with both the physical manufacturing process and its digital data in real-time. The digital representation of the physical process can support them to manage the trial machining from different aspects. In addition, a demonstrative case study is presented to explain the implementation of this framework in a real manufacturing environment.     

数字制造环境下的加工过程仿真验证技术研究

生产线数字制造环境是数字化工厂的核心，而加工过程的仿真与验证技术构成生产线数字系统的底层结构与制造过程数字化分析的主要内容。分析了目前加工过程在几何仿真与物理仿真方面的研究情况、研究方法与存在问题，就该项技术向生产线数字制造环境融合的关键技术，即综合设备数字样机的完整数字加工环境的建立及加工过程仿真与上层制造环境的信息集成等进行分析与研究。     

Digital twin-based industrial cloud robotics: Framework,control approach and implementation

Industrial cloud robotics (ICR) integrates cloud computing with industrial robots (IRs). The capabilities of industrial robots can be encapsulated as cloud services and used for ubiquitous manufacturing. Currently, the digital models for process simulation, path simulation, etc. are encapsulated as cloud services. The digital models in the cloud may not reflect the real state of the physical robotic manufacturing systems due to inaccurate or delayed condition update and therefore result in inaccurate simulation and robotic control. Digital twin can be used to realize fine sensing control of the physical manufacturing systems by a combination of high-fidelity digital model and sensory data. In this paper, we propose a framework of digital twin-based industrial cloud robotics (DTICR) for industrial robotic control and its key methodologies. The DTICR is divided into physical IR, digital IR, robotic control services, and digital twin data. First, the robotic control capabilities are encapsulated as Robot Control as-a-Service (RCaaS) based on manufacturing features and feature-level robotic capability model. Then the available RCaaSs are ranked and parsed. After manufacturing process simulation with digital IR models, RCaaSs are mapped to physical robots for robotic control. The digital IR models are connected to the physical robots and updated by sensory data. A case is implemented to demonstrate the workflow of DTICR. The results show that DTICR is capable to synchronize and merge digital IRs and physical IRs effectively. The bidirectional interaction between digital IRs and physical IRs enables fine sensing control of IRs. The proposed DTICR is also flexible and extensible by using ontology models.     

基于Web Services的数字化制造软资源支撑系统开发

通过分析档案管理系统的开发流程和需求,针对数字化制造软资源管理业务中存在的手工管理为主、设计和生产信息不流畅等管理现状,构建了基于Web Services的数字化制造软资源管理业务流程,提出了基于Web Services的数字化制造软资源支撑系统框架,并设计和实现了该系统。实验结果表明,该系统能够对图纸、文件等纸质、电子文件进行一体化管理,基于Web Services技术是构建数字化制造软资源支撑系统的有效方式。     

Digital twin driven production progress prediction for discrete manufacturing workshop

In make-to-order manufacturing enterprises, accurate production progress (PP) prediction is an important basis for dynamic production process optimization and on-time delivery of orders. Digital twin technology offers an enabling tool for PP analysis. Although the production process can be observed, analyzed, and controlled in real-time by digital twin model (DTM), there exist some uncertain events, degradation of manufacturing elements, and abnormal disturbance in physical workshop (PW), which would cause the deviation between DTM and PW performance and affect the prediction accuracy of PP. Synchronous evolution of DTM for precision holding to ensure the consistency between DTM and the performance of PW, and guarantee the accuracy of DTM is still a challenging issue, especially when dealing with new dynamic samples for complex production environment of discrete manufacturing workshop (DMW). This article focuses on how to effectively construct DTM synchronous update methods based on dynamic sample data for DMW. This study proposes a representation model of performance degradation and an Adaboost-DNN-LSTM based synchronous update model with competitive election mechanism to enhance the accuracy of PP prediction with time in industrial environment. The experiment is conducted in the realistic production dataset, which demonstrates that the proposed synchronous evolution model has good performance for realizing the synchronization of the performance of physical workshop in industrial environment, and can greatly improve the prediction ability for PP.     

Developing a digital transformation process in the manufacturing sector: Egyptian case study

Digital transformation is of crucial importance in the manufacturing industry, especially after the COVID-19 pandemic because of the increasing need for remote working and socially distanced workplaces. However, there is a lack of a clear and well-defined process to implement digital transformation in manufacturing. This paper aims to identify the most critical stages to implementing digital transformation in the manufacturing sector. Twenty-one structured interviews with experienced specialists in digitalisation in the manufacturing sector in the Egyptian economy were held and used the Best–Worst Method to analyse the data as an analysis tool for a multiple criteria decision making (MCDM) approach. The digital transformation process comprises eight stages covering technology, management, communications, and customer elements. The main contribution of this work stage is the balance between the different elements of digital transformation—digital technologies, leadership and strategy, people and business processes—to create an integrated 8-step process of digital transformation in the manufacturing sector of developing economies such as the Egyptian economy.

     

A digital twin to train deep reinforcement learning agent for smart manufacturing plants: Environment,interfaces and intelligence

Filling the gaps between virtual and physical systems will open new doors in Smart Manufacturing. This work proposes a data-driven approach to utilize digital transformation methods to automate smart manufacturing systems. This is fundamentally enabled by using a digital twin to represent manufacturing cells, simulate system behaviors, predict process faults, and adaptively control manipulated variables. First, the manufacturing cell is accommodated to environments such as computer-aided applications, industrial Product Lifecycle Management solutions, and control platforms for automation systems. Second, a network of interfaces between the environments is designed and implemented to enable communication between the digital world and physical manufacturing plant, so that near-synchronous controls can be achieved. Third, capabilities of some members in the family of Deep Reinforcement Learning (DRL) are discussed with manufacturing features within the context of Smart Manufacturing. Trained results for Deep Q Learning algorithms are finally presented in this work as a case study to incorporate DRL-based artificial intelligence to the industrial control process. As a result, developed control methodology, named Digital Engine, is expected to acquire process knowledges, schedule manufacturing tasks, identify optimal actions, and demonstrate control robustness. The authors show that integrating a smart agent into the industrial platforms further expands the usage of the system-level digital twin, where intelligent control algorithms are trained and verified upfront before deployed to the physical world for implementation. Moreover, DRL approach to automated manufacturing control problems under facile optimization environments will be a novel combination between data science and manufacturing industries.     

Digital twin-based designing of the configuration,motion, control,and optimization model of a flow-type smart manufacturing system

Digital twins can achieve hardware-in-the-loop simulation of both physical equipment and cyber model, which could be used to avoid the considerable cost of manufacturing system reconfiguration if the design deficiencies are found in the deployment process of the traditional irreversible design approach. Based on the digital twin technology, a quad-play CMCO (i.e., Configuration design-Motion planning-Control development-Optimization decoupling) design architecture is put forward for the design of the flow-type smart manufacturing system in the Industry 4.0 context. The iteration logic of the CMCO design model is expounded. Two key enabling technologies for enabling the customized and software-defined design of flow-type smart manufacturing systems are presented, including the generalized encapsulation of the quad-play CMCO model and the digital twin technique. A prototype of a digital twin-based manufacturing system design platform, named Digital Twin System, is presented based on the CMCO model. The digital twin-based design platform is verified with a case study of the hollow glass smart manufacturing system. The result shows that the Digital Twin System-based design approach is feasible and efficient.     

CAXA在数控技术中的编程理论

本文通过阐述CAXA在数控技术中的编程理论,详细说明了用CAXA制造工程师实现数控加工的过程,并论述了数字化制造所特有的优越性,并为数控技术中的CAXA只要工程师在数字化制造过程提供了理论依据。