基于数字孪生的智能车间系统仿真加速测试方法

为解决当前制造系统软件可靠性仿真测试时间长、测试环境难以搭建等问题,提出采用数字孪生技术与智能车间系统仿真加速测试相结合的方法;建立智能车间高保真数字孪生模型替代现实生产车间系统用于制造系统软件的可靠性仿真测试,首先要构建包含产品、设备资源、工艺流程等系统级仿真模型;同时,为仿真车间生产事件流程,在模型中,还需结合生产实际情况,设置设备间通信协议、通信数据以及生产线事件及队列顺序,真实模拟系统运行环境;通过构建步进电机产线数字孪生模型,仿真加工装配流程,运行智能车间系统软件,采用仿真时钟推进机制开展加速测试,验证了该方法的有效性和实用性,对开展工业系统软件高保真快速测试评估具有一定的借鉴意义。     

Digital twin-driven intelligent production line for automotive MEMS pressure sensors

The equipment and technological processes used in manufacturing electronic products are gradually being automated and networked. Currently, digital twin technology continues to evolve and mature. The electronics manufacturing industry is undergoing an intelligent and digital transformation. Micro-electro-mechanical system (MEMS) sensors have been widely used in the automotive field due to their small size, low cost, and high reliability. In this study, a new intelligent production line for automotive MEMS pressure sensors driven by digital twin is individually designed. The intelligent production line system consists of physical production lines, digital production lines, twin data, and data service systems. The technology of multi-source heterogeneous data acquisition is used to process and analyze data collected in real time in a physical production line. Based on the technology of parallel control, the physical and digital production lines are synchronized. To obtain optimal process parameters, a process database is established through the analysis of the key processes of the production line. Three types of automotive MEMS pressure sensors are successfully manufactured in the constructed digital twin-driven intelligent production line. The intelligent production line can realize 24-h unattended operation. The product yield is above 98 %, and the takt time is less than 16 s.     

Bi-level dynamic scheduling architecture based on service unit digital twin agents

Pure reactive scheduling is one of the core technologies to solve the complex dynamic disturbance factors in real-time. The emergence of CPS, digital twin, cloud computing, big data and other new technologies based on the industrial Internet enables information acquisition and pure reactive scheduling more practical to some extent. However, how to build a new architecture to solve the problems which traditional dynamic scheduling methods cannot solve becomes a new research challenge. Therefore, this paper designs a new bi-level distributed dynamic workshop scheduling architecture, which is based on the workshop digital twin scheduling agent and multiple service unit digital twin scheduling agents.Within this architecture, scheduling a physical workshop is decomposed to the whole workshop scheduling in the first level and its service unit scheduling in the second level. On the first level, the whole workshop scheduling is executed by its virtual workshop coordination (scheduling) agent embedded with the workshop digital twin consisting of multi-service unit digital twins. On the second level, each service unit scheduling coordinated by the first level scheduling is executed in a distributed way by the corresponding service unit scheduling agent associated with its service unit digital twin. The benefits of the new architecture include (1) if a dynamic scheduling only requires a single service unit scheduling, it will then be performed in the corresponding service unit scheduling without involving other service units, which will make the scheduling locally, simply and robustly. (2) when a dynamic scheduling requires changes in multiple service units in a coordinated way, the first level scheduling will be executed and then coordinate the second level service unit scheduling accordingly. This divide-and-then-conquer strategy will make the scheduling easier and practical.The proposed architecture has been tested to illustrate its feasibility and practicality.     

人工智能技术在矿山智能化建设中的应用初探

介绍了人工智能技术的相关概念、发展概述及其在煤炭行业发展中的应用,指出目前人工智能技术在矿山应用只是点状结合和浅度结合,没有实现人工智能技术和矿山某个生产或管理系统层面的深度融合。概述了智能矿山的发展历程,指出智能矿山是人工智能技术、大数据技术、物联网技术和矿山实体的深度融合体,利用智能通信、智能控制和智能计算技术实现数字化矿山的计算、处理,构建数字孪生矿山,通过数字孪生矿山和物理矿山的智能交互演化,达到对煤矿安全、高效、绿色的生产控制。构建了将人工智能技术和矿山深度融合的包括设备层、智能层、应用层的智能矿山三层构架:应用层处于智能矿山的最高层,其中的数字孪生矿山子层相当于“数字大脑”,实现矿山最高层次的智能控制;智能层中的智能体要求子系统不仅仅是应用人工智能技术处理子系统所产生的数据,而是从架构上就要将智能计算、智能通信、智能控制融为一体。展望了智能矿山建设的发展趋势:智能化矿山需要加强人工智能技术和矿山融合度的深入研究,将现有的基于人工智能的故障检测、诊断及超前干预技术应用到机器人系统中,智能计算、智能通信、智能控制融合的巡检机器人将是最早能推广的井下智能体之一;智能化矿山需要进一步加强复杂巨系统建模技术的研究,只有建立了矿山的复杂巨系统模型,才能实现采矿活动和环境的协同互动,实现采煤活动的精准控制,复杂巨系统模型的缺乏将是未来智能矿山建设亟需解决的问题。     

断路器柔性装配车间数字孪生系统设计

为优化断路器装配车间的产线结构和作业方法,结合数字孪生技术,提出一种基于多机器人运动控制的断路器柔性自动化车间装配方案。面向自动化装配单元,结合工业机器人的柔性装配工艺及方法,对实体装配车间进行全物理属性的数字化建模,同时建立多机器人的运动学控制模型,将机器人的三维运动模型应用于虚拟孪生场景。通过数据的交互传递,实现物理单元与虚拟单元的实时链接,将车间机器人的运动轨迹、装配状态、作业运送流程等数据信息进行实时显示,从而实现断路器柔性装配数字孪生系统的搭建与同步映射。实验结果证明,所提方案对实现断路器柔性装配有显著效果。     

Edge computing-based real-time scheduling for digital twin flexible job shop with variable time window

Production scheduling is the central link between enterprise production and operation management and is also the key to realising efficient, high-quality and sustainable production. However, in real-world manufacturing, the frequent occurrence of abnormal disturbance leads to the deviation of scheduling, which affects the accuracy and reliability of scheduling execution. The traditional dynamic scheduling methods (TDSMs) cannot solve this problem effectively. This paper presents a real-time digital twin flexible job shop scheduling (R-DTFJSS) method with edge computing to address the issue. Firstly, an overall framework of R-DTFJSS is proposed to realise real-time scheduling (RS) through real-time interaction between physical workshop (PW) and virtual workshop (VW). Secondly, the implementation process of R-DTFJSS is designed to realise real-time operation allocation. Then, to obtain the optimal RS result, an improved Hungarian algorithm (IHA) is adopted. Finally, a case simulation from an industrial case of a cooperative enterprise is described and analysed to verify the effectiveness of the proposed R-DTFJSS method. The results show that compared with the TDSMs, the R-DTFJSS method can effectively deal with unexpected and frequent abnormal disturbances in the production process.     

A digital twin-driven approach towards traceability and dynamic control for processing quality

Processing quality is the basis for ensuring product quality, and reflects the development needs and application value of realizing intelligent manufacturing. Aiming at the low efficiency of quality problems traceability, poor timeliness and unpredictability of quality control in the machining process, digital twin technology can provide a new intelligent solution based on interaction and integration between physical workshop and virtual workshop. Therefore, a digital twin-driven approach towards traceability and dynamic control for processing quality is proposed in the paper. Firstly, a Bayesian network model for the analysis of factors affecting processing quality (BN_PQ) is introduced, which determines the relevance and influence weight of each factor to processing quality. Secondly, in order to integrate multi-source heterogeneous data to trace the processing quality, a multi-level scalable information model and association mechanism are established. Moreover, the construction method of the IoT system in manufacturing unit for dynamic control of processing quality are introduced, in which the collection method of real-time data is discussed. The contents of digital twin data for processing quality constraints (DTD_PQ) and the management method are elaborated. Then, the digital twin-driven dynamic control method of processing quality is proposed. The conceptual model of the digital twin database and the operating logic for dynamic control of processing quality are described in detail. Finally, the interactive operation and core technologies of DTD_PQ towards traceability and dynamic control of processing quality are analyzed. By choosing examples of machining the connecting rod of diesel engine and the prototype system that has been developed, the effectiveness of the proposed method is verified.     

基于数字孪生的周公宅水库防洪“四预”系统构建与应用

以构建数字孪生流域、开展智慧化模拟、支撑精准化决策为实现路径,利用天空地一体化空间数据底板、多维智能感知数据、以及专业模型网格数据和计算结果数据等几类多源异构数据,进行数据汇聚和融合,采用WebGL孪生引擎进行可视化展示,构建数字孪生流域;聚焦流域防洪数字化场景,通过山区集总式水文模型和洪水演进模型对洪水过程进行预报和模拟,形成包括预报调度一体化、区域联防预警一体化、模拟仿真预演、决策建议方案等功能的四预孪生系统。本文以周公宅水库为具体研究对象,探索了具有水库工程的中小流域数字孪生建设的相关关键技术和应用场景构建,提供了建设思路和案例参考。     

基于增强现实的制造数据记录系统设计与实现

针对传统制造业数字化水平不高的问题,提出了基于增强现实的生产数据记录系统;分析了车间数字化转型的软件架构需求,应用了 RESTful风格的前后端分离软件架构,将系统数据库和前端应用解耦,实现了基于HTTP协议的网络通讯;通过包含BLSTM的深度学习算法,对设备仪表图像进行检测识别,实现了制造过程关键生产数据智能化获取;通过对生产流程的分析和多维信息之间的联系,建立了基于增强现实的引导式生产数据记录应用;生产实践证明,系统能够实现生产任务的引导和过程数据的记录,工人工作效率提高,系统响应迅速,有利于车间生产过程的数字化转型.     

线性规划在车间动态排产中的应用与实现

本文基于线性规划思想提出了一套快速实现车间最优动态排产的方法,该方法在确保订单不延迟的情况下,能够有效合理地安排生产过程。本文通过对车间工序建立数学模型,应用改进的算法对所有订单进行评估,确定所有订单的每道工序的最早发生时间和最迟发生时间,从而实现车间最优动态排产。本算法适用于严格按订单组织生产的企业,企业可以利用该算法对订单进行评估,确定是否可以接受该订单以及订单最佳投产时间。本文提出的方法已经在四川省某制造业信息化示范企业投入实际应用。     

流程工业数字孪生关键技术探讨

流程工业是制造业的重要组成部分, 是国民经济发展的重要基础, 主要包括化工、冶金、石化等行业, 其安全高效的生产对国家而言具有重要的战略意义. 然而, 流程工业物理化学变化反应复杂、流程间能质流严重耦合、多目标冲突、在线实验风险大, 给生产流程系统建模与高效协同优化带来极大困难, 严重制约了生产质量和资源利用率的进一步提升. 随着信息技术与人工智能的发展, 建立虚实结合、协同优化运行的流程工业数字孪生生产线所需技术逐渐成熟, 其在流程工业的应用价值与潜力日益凸显. 本文首先阐述数字孪生在流程工业应用的必要性与重要性, 并通过边界定义法将数字孪生与信息物理系统(Cyber-physical system, CPS)、工业互联网等概念进行对比分析,从而明确数字孪生的基本内涵与功能边界. 其次描述流程工业抽象模型和数字孪生理论模型间的映射关系, 并分析了如何用数字孪生技术解决流程工业系统建模与高效协同优化的瓶颈问题. 最后, 从数字孪生系统构建的角度探讨数字孪生发展的关键技术, 并以一条炼铁生产线为例, 展示数字孪生技术在实际工业中的应用解决方案.     

数字孪生太浦河防洪及供水“四预”业务应用设计与实现

本文基于太浦河二、三维数据底板，根据太浦河物理河道特点、水闸、泵站等工程设计参数、影响区域范围等，提出建立数字孪生太浦河防洪及供水“四预”业务系统，构建太浦河防洪及供水知识平台，实现数字孪生流域与物理流域的交互。系统通过建立太浦河历史水雨情、工情、水质等信息与调度预案方案间的映射关系，以事件为驱动进行系统设计，在自动综合分析区域代表站水情、水质监测、水利工程调度情况、水雨情信息等基础上，触发相应事件驱动机制，针对不同的事件，“四预”系统会呈现针对性的功能和界面，结合模型滚动预测预报成果，关联提示调度方案并实现自动推荐应急处置措施，为综合调度提供智能决策支持，并流场动态可视化技术，确定太浦河各个二维网格水体流速、流向，实现太浦河流场的仿真动画模拟。系统为精准应对2022年“梅花”台风防御及上海市抗咸潮保供水提供了技术支持，为数字孪生流域建设提供参考。     

智能制造下产品数字孪生体全生命周期研究

针对信息物理融合系统在智能制造领域中的应用新范式,提出了产品数字李生体全生命周期的理论框架。数字孪生的出现为信息物理融合理念的实现提供可行的思路和途径。从数字孪生概念的发展背景出发,分别从物理对象和物理对象的发展过程分析数字李生和数字李生体的定义,在此基础上进一步分析了数字李生体全生命周期的内涵和特征。数字李生体全生命周期分为数字胚胎、数字化映射体和孪生体互长三个阶段,作为物理对象全生命周期的另一条生命线.可以为物理对象全生命周期演化提供指导。最后,利用数字孪生体全生命周期在智能制造背景下的产品全生命周期中发挥的作用映证数字李生体全生命周期的概念,为数字孪生体全生命周期在其他领域的应用提供参考。     

企业离散式智能设备预测性维护综述

针对智能设备的大量使用且缺乏根据监测大数据进行故障自动分析、判断与处理的问题,研究了基于物联网技术、大数据技术、边云协同技术的智能设备预测性维护框架和模式.提出针对非智能设备安装传感器实现设备智能化的方法.指出边缘计算负责设备工况数据的实时采集、分析,可快速甄别设备故障并实时报警;云计算聚焦同类设备运行海量历史数据的挖掘和分析,形成故障自动预测分析和诊断模式并下载至智能边缘设备.在研究了模型驱动、数据驱动、概率统计驱动、数字孪生和概率数字孪生驱动等故障预测模式后,提出了采用数据驱动的多层级数据融合模式,为制定企业性智能设备维保方案提供借鉴作用.     

From simple digital twin to complex digital twin part II: Multi-scenario applications of digital twin shop floor

The shop floor has always been an important application object for the digital twin. It is well known that production, process, and product are the core business of the shop floor. Therefore, the digital twin shop floor covers multi-dimensional information and multi-scale application scenarios. In this paper, the digital twin shop floor is constructed according to the modeling method of the complex digital twin proposed in Part I. The digital twin shop floor is firstly divided into several simple digital twins that focus on scenarios of different scales. Two simple application scenarios are constructed, including tool wear prediction and spindle temperature prediction. Main functions in different application scenarios, such as data acquisition, data processing, and data visualization, are implemented and encapsulated as components to construct simple digital twins. Secondly, ontology models, knowledge graphs, and message queues are used to assemble these simple digital twins into the complex digital twin shop floor. And two complex application scenarios are constructed, including machining geometry simulation considering spindle temperature and production scheduling considering tool wear. The implementation of the complex digital twin shop floor demonstrates the feasibility of the proposed modeling method.     

基于数字孪生的产线设备实时监控方法与实现

为推动国家智能制造发展,面向生产过程中设备实时监控困难、透明性差、管控效率低、跨学科交叉情况复杂等问题,融合MBSE思想,提出一种基于数字孪生的产线设备监控方法并实现。首先,提出基于数字孪生的监控方法架构;基于SysML建模语言对产线系统和设备进行统一描述建模,建立结构图和行为图,形成数据模型;通过SysML模型与OPC UA联合,以位移数据和任务数据双通道驱动的方式进行虚实映射,并建立异常报警追溯机制;以仓储系统中核心设备堆垛机为例,构建其SysML模型、数字孪生模型,并以仓储产线实时缓存数据库redis为数据源,通过OPC UA获取并实时更新数据驱动数字孪生模型,实现其三维可视化监控,验证了方法的可行性和实时性     

The rapid construction method of the digital twin polymorphic model for discrete manufacturing workshop

The modeling process is resource-intensive, time-consuming, and expensive due to the large number and variety of production and auxiliary equipment in discrete manufacturing plants. The popular twin-model construction method based on a single type of equipment cannot meet the demand for rapid construction and high-quality model mapping in large discrete manufacturing plants. This paper proposes a strategy for developing a digital twin polymorphic model (DTPM) for discrete manufacturing workshops to meet complex manufacturing business scenarios, improve the richness of digital twin model varieties, construction efficiency and intelligence, and form an efficient equipment model construction method. The complete element information and real-time dynamic data of the minimal component unit (Functional Components, FC) of DTPM are clustered and analyzed. In addition, properties of the functional component information model are characterized from several dimensions, such as geometry, physics, and behavior. Furthermore, the FC with highly concentrated primary components is established based on the object-oriented derivation inheritance and attribute reuse hybrid drive technique. FC drastically reduces the duration of the digital twin model development process through encapsulation, inheritance, polymorphism, and other technologies. On this premise, the DTPM construction method that deeply decouples the physical structure and functional methods is proposed. DTPM integrates adaptive information interaction technology to accomplish intelligent data connection, dynamic data updating, and digitally accurate mapping of static-dynamic-virtual multidimensional models in discrete production workshops. Lastly, the method's validity is confirmed by creating a large discrete production workplace. The results indicate that the proposed technique may significantly enhance the model variety and building efficiency of large-scale digital twin workshop systems.     

面向智能矿山与新工科的数字孪生技术研究

将数字孪生与人工智能(AI)技术相结合,提出了基于数字孪生+AI的智能矿山建设新思路。探索了智能矿山技术发展路径,研究了数字孪生技术的特征、应用领域及发展趋势,指出数字孪生是数字化矿山发展的必然趋势。提出了基于数字孪生+AI的智能矿山理论架构,构建了矿山数字孪生模型,模型自下而上分别为矿山全要素物理实体、矿山信息物理融合层、矿山数字孪生模型、矿山孪生数据交互层、矿山应用智能服务层,据此实现智能矿山的泛在感知、协同控制和智能决策与优化。从应用实际需求出发,探讨了智能矿山模型构建技术、智能开采数字孪生体技术、矿山智能控制技术、矿山设备故障预测、基于数字孪生的人机交互等关键技术。通过研究数字孪生在智能矿山中的应用,为AI技术在智能矿山应用落地提供思路,为未来智能矿山新工科建设提供理论借鉴。     

遗传算法在刹车片生产计划排产中的应用与实践

生产计划的排产问题是影响车间生产效率的主要因素之一.合理的排产计划能够高效地使用车间现有的生产资源,提高车间生产能力,降低生产成本.本文首先分析了刹车片生产运行状况,针对热压成型生产车间的多品种变批次生产模式,建立了排产数学模型;其次,设计了一种联合均值-遗传算法,对热压成型车间的排产数学模型进行求解;最后通过实验仿真,对本文设计的算法进行验证,实验结果表明联合均值-遗传算法能够很好的解决当前刹车片生产计划的排产问题.     

北大港水库数字孪生建设方案与要点探讨

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