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.     

Digital twin modeling method based on biomimicry for machining aerospace components

High-performance aerospace component manufacturing requires stringent in-process geometrical and performance-based quality control. Real-time observation, understanding and control of machining processes are integral to optimizing the machining strategies of aerospace component manufacturing. Digital Twin can be used to model, monitor and control the machining process by fusing multi-dimensional in-context machining process data, such as changes in geometry, material properties and machining parameters. However, there is a lack of systematic and efficient Digital Twin modeling method that can adaptively develop high-fidelity multi-scale and multi-dimensional Digital Twins of machining processes. Aiming at addressing this challenge, we proposed a Digital Twin modeling method based on biomimicry principles that can adaptively construct a multi-physics digital twin of the machining process. With this approach, we developed multiple Digital Twin sub-models, e.g., geometry model, behavior model and process model. These Digital Twin sub-models can interact with each other and compose an integrated true representation of the physical machining process. To demonstrate the effectiveness of the proposed biomimicry-based Digital Twin modeling method, we tested the method in monitoring and controlling the machining process of an air rudder.     

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.     

Hybrid learning-based digital twin for manufacturing process: Modeling framework and implementation

Digital twin (DT) and artificial intelligence (AI) technologies are powerful enablers for Industry 4.0 toward sustainable resilient manufacturing. Digital twins of machine tools and machining processes combine advanced digital techniques and production domain knowledge, facilitate the enhancement of agility, traceability, and resilience of production systems, and help machine tool builders achieve a paradigm shift from one-time products provision to on-going service delivery. However, the adaptability and accuracy of digital twins at the shopfloor level are restricted by heterogeneous data sources, modeling precision as well as uncertainties from dynamical industrial environments. This article proposes a novel modeling framework to address these inadequacies by in-depth integrating AI techniques and machine tool expertise using aggregated data along the product development process. A data processing procedure is constructed to contextualize metadata sources from the design, planning, manufacturing, and quality stages and link them into a digital thread. On this consistent data basis, a modeling pipeline is presented to incorporate production and machine tool prior knowledge into AI development pipeline, while considering the multi-fidelity nature of data sources in dynamic industrial circumstances. In terms of implementation, we first introduce our existing work for building digital twins of machine tool and manufacturing process. Within this infrastructure, we developed a hybrid learning-based digital twin for manufacturing process following proposed modeling framework and tested it in an external industrial project exemplarily for real-time workpiece quality monitoring. The result indicates that the proposed hybrid learning-based digital twin enables learning uncertainties of the interaction of machine tools and machining processes in real industrial environments, thus allows estimating and enhancing the modeling reliability, depending on the data quality and accessibility. Prospectively, it also contributes to the reparametrization of model parameters and to the adaptive process control.     

Digital twin-based assembly data management and process traceability for complex products

Complex products such as satellites, missiles, and aircraft typically have demanding requirements for dynamic data management and process traceability. The assembly process for these complex products involves high complexity, strong dynamics, many uncertainties, and frequent rework and repair, especially in the model development stage. Achieving assembly data management and process traceability for complex products has always been a challenge. A recently proposed solution involves one-to-one mapping of the corresponding physical entity, also known as the digital twin method. This paper proposes a digital twin-based assembly data management and process traceability approach for complex products. First, the dynamic evolutionary process of complex product assembly data was analyzed from three dimensions: granularity, period and version. Then, a framework of digital twin-based assembly data management and process traceability for complex products was constructed. Some core techniques are: 1) workflow-based product assembly data organization and version management; 2) synchronous modeling of the product assembly process based on digital twin; and 3) hierarchical management and traceability of product assembly data based on digital twin. On this basis, an algorithm flowchart for generating a product assembly data package was created, which includes product assembly data management, assembly process traceability, and generation of a product assembly data package. Furthermore, the Digital Twin-based Assembly Process Management and Control System (DT-APMCS) was designed to verify the efficiency of the proposed approach. Some aerospace-related assembly enterprises are currently using DT-APMCS and achieving satisfactory results. Finally, a summary of our work is given, and the future research work is also discussed.     

Digital twin-driven surface roughness prediction and process parameter adaptive optimization

In the process of parts machining, the real-time state of equipment such as tool wear will change dynamically with the cutting process, and then affect the surface roughness of parts. The traditional process parameter optimization method is difficult to take into account the uncertain factors in the machining process, and cannot meet the requirements of real-time and predictability of process parameter optimization in intelligent manufacturing. To solve this problem, a digital twin-driven surface roughness prediction and process parameter adaptive optimization method is proposed. Firstly, a digital twin containing machining elements is constructed to monitor the machining process in real-time and serve as a data source for process parameter optimization; Then IPSO-GRNN (Improved Particle Swarm Optimization-Generalized Regression Neural Networks) prediction model is constructed to realize tool wear prediction and surface roughness prediction based on data; Finally, when the surface roughness predicted based on the real-time data fails to meet the processing requirements, the digital twin system will warn and perform adaptive optimization of cutting parameters based on the currently predicted tool wear. Through the development of a process-optimized digital twin system and a large number of cutting tests, the effectiveness and advancement of the method proposed in this paper are verified. The organic combination of real-time monitoring, accurate prediction, and optimization decision-making in the machining process is realized which solves the problem of inconsistency between quality and efficiency of the machining process.     

Digital twin-enabled machining process modeling

Considering the new generation of information technology, the digitalization and intellectualization of the machining process have become the major core in intelligent manufacturing. The complex and diverse requirements, as well as the processing sites force the machining sequence to move towards cyber-physical integration. This paper presents a multidimensional modeling approach for machining processes, by introducing Digital Twin (DT) technology. The method is oriented towards the design and execution phases of the machining process and is used to support intelligent machining. The working mechanism of modeling, simulation, prediction and control of machining process is described based on the interpretation of the modeling and application methods of machining process design, inspection process, fault diagnosis and quality prediction, as based on digital twin technology. Finally, key components of diesel engines are targeted as test objects, demonstrating increased material removal rate by 5.1%, reduced deformation by 22.98% and 30.13%, respectively, verifying the effectiveness of the applied framework and the proposed method.     

Digital twin-driven clamping force control for thin-walled parts

Clamping quality is one of the main factors that will affect the deformation of thin-walled parts during their processing, which can then directly affect parts’ performance. However, traditional clamping force settings are based on manual experience, which is a random and inaccurate manner. In addition, dynamic clamping force adjustment according to clamping deformation is rarely considered in clamping force control process, which easily causes large clamping deformation and low machining accuracy. To address these issues, this study proposes a digital twin-driven clamping force control approach to improve the machining accuracy of thin-walled parts. The total factor information model of clamping system is built to integrate the dynamic information of the clamping process. The virtual space model is constructed based on finite element simulation and deep neural network algorithm. To ensure bidirectional mapping of physical-virtual space, the workflow of clamping force control and interoperability method between digital twin models are elaborated. Finally, a case study is used to verify the effectiveness and feasibility of the proposed method.     

From simple digital twin to complex digital twin Part I: A novel modeling method for multi-scale and multi-scenario digital twin

In recent years, the digital twin has attracted widespread attention as an important means of digitalization and intelligence. However, the digital twin is becoming more and more complex due to the expansion of need on the simulation of multi-scale and multi-scenario in reality. The instance of digital twin in references mostly concentrates a particular application, while it is still a lack of a method for constructing the complex digital twin in the total elements, the variable scale of working environments, changeable process, not even the coupling effects. In this paper, a novel modeling method for such a complex digital twin is proposed based on the standardized processing on the model division and assembly. Firstly, the complex model of digital twin is divided into several simple models according to the composition, context, component, and code in 4C architecture. Composition and context make the digital twin focus on the effective elements in a specific scale and scenario. Component and code develop the digital twin in standard-based modularization. Secondly, assemble the simple models of digital twins into the complex model through information fusion, multi-scale association and multi-scenarios iterations. Ontology establishes the complete information library of the entities on different digital twins. Knowledge graph bridges the structure relationship between the different scales of digital twins. The scenario iterations realize the behavior interaction and the accuracy calculation results. It provides an implementable method to construct a complex model of digital twin, and the reuse of components and code also enables rapid development of digital twins.     

A digital twin-driven approach for the assembly-commissioning of high precision products

High precision products (HPPs) with multidisciplinary coupling are widely used in aerospace, marine, chemical and other fields. Since the internal structure of HPPs is complex and compact, the assembly process requires high precision and involves multidisciplinary coupling. Traditional assembly process of HPPs is based on manual experience, which results in low assembly efficiency and poor-quality consistency. Given the above problems, this research proposes a digital twin-driven assembly-commissioning approach for HPPs. Firstly, this paper introduces the theoretical framework of digital twin-driven assembly-commissioning. Secondly, we introduce the construction method of assembly-commissioning total factor information model based on digital twin technology; the fusion method of twin data and the interoperability method between digital twin models; in addition, the assembliability prediction and assembly-commissioning process optimization methods. Finally, a case study product is used to verify the effectiveness and feasibility of the proposed method.     

Digital twin-driven virtual commissioning of machine tool

The commissioning of Computerized Numerical Control Machine Tools (CNCMTs) is particularly important and the commissioning quality directly affects its product processing. However, traditional commissioning methods are not suitable for complex and changeable machining conditions during operation, and the derived commissioning results have limited effectiveness. Therefore, this paper proposes a digital twin-driven virtual commissioning method to simulate the machining processes in a virtual environment and perform virtual commissioning to obtain better commissioning results. Firstly, a digital twin model is constructed using a multi-domain unified modeling language combined with a virtual-real mapping strategy to describe the response characteristics of CNCMTs. Secondly, a complex machining scene is simulated based on the twin model, and a virtual commissioning strategy and platform are constructed in this environment. Finally, the effectiveness of the proposed method is verified by taking the spindle system of CNCMTs as an example. The experimental results show a 13% short in response time and a 54% reduction in total systematic error along with a decrease in commissioning time.     

Digital twin-based smart assembly process design and application framework for complex products and its case study

With rapid advances in new generation information technologies, digital twin (DT), and cyber-physical system, smart assembly has become a core focus for intelligent manufacturing in the fourth industrial evolution. Deep integration between information and physical worlds is a key phase to develop smart assembly process design that bridge the gap between product assembly design and manufacturing. This paper presents a digital twin reference model for smart assembly process design, and proposes an application framework for DT-based smart assembly with three layers. Product assembly station components are detailed in the physical space layer; two main modules, communication connection and data processing, are introduced in the interaction layer; and we discuss working mechanisms of assembly process planning, simulation, predication, and control management in the virtual space layer in detail. A case study shows the proposed approach application for an experimental simplified satellite assembly case using the DT-based assembly application system (DT-AAS) to verify the proposed application framework and method effectiveness.     

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。     

基于LabVIEW和MATLAB的数字孪生供热系统平台的设计与仿真

数字孪生技术充分利用物理结构、传感器更新、设备运行历史等数据通过集成多领域、多物理量、多可能性的模拟过程，在虚拟空间中进行镜像，以此表达相对应的实物装置的整个生命周期过程。从智慧供热的发展历史来看，依托现代工业系统理念，提出了基于"数字孪生"的智能供热系统结构。首先介绍了数字孪生的基本结构，给出了数字孪生的构建方式基于虚拟仪器Labview的大数据采集、处理、归档、仿真；然后以采集的数据为基础，得到供热系统的孪生模型，叙述了数字孪生技术解决的关键问题；最后，通过Labview仿真平台调用Matlab中神经网络智能算法，得到基于大数据采集以及经过智能算法优化后的参数，同时系统将参数反馈给物理实体设备，从而完成孪生模型的仿真、优化、反馈过程。通过热网系统优化仿真案例验证了Labview和Matlab混合编程在建立的孪生供热平台上、应用的有效性。     

Predicting part deformation based on deformation force data using Physics-informed Latent Variable Model

Part deformation prediction and control is a crucial issue for obtaining tight dimensional accuracy so as to ensure product quality with high performance, and deformation prediction is the fundamental of the deformation control. However, existing machining deformation prediction methods are based on the prediction or measurement of residual stress and suffering from two challenges: (i) the measurement accuracy of residual stress field is limited by physical principle and (ii) low prediction in accuracy. In order to address these issues, this paper presents a method for predicting part machining deformation based on deformation force using the proposed Physics-informed Latent Variable Model involved physics knowledge. Deformation force is introduced to represent the inner unbalanced residual stress state of the workpiece, and it is a much easier and more accurate signal compared with residual stress. Machining deformation is predicted by fusing the data-driven method and the prior knowledge of deformation mechanical relationship by taking advantage of the latent variable. The proposed method was verified both in simulation and actual machining environment, and accurate machining deformation prediction has been achieved. The proposed method can be readily extended to the prediction problems involved with difficult-to-measure physical quantities.     

Product redesign using functional backtrack with digital twin

Digital twin, as a new industrial technology, provides great opportunities in various stages of product development. Product redesign is widely required in the process of product improvement, which is greatly depends on the functional analysis of product. Although traditional functional analysis can identify product design problems, the analyzed information is extremely detailed and verbose, which hinders the opportunity of product innovation. To expand the solution space for improving the innovation chance and ensuring solution quality of the product in the physical space, a digital twin is introduced in the redesign process. This study proposes a product redesign method using the functional backtrack obtained from a relational function model (RFM) to the hierarchical function model (HFM) with the digital twin. Based on a selected target product, the proposed method constructs the product RFM (sub-field) that originates from the reverse fishbone and relationships between components. Related parameters of components are obtained. A digital twin entity is built using the RFM (sub-field) and parameters based on the target physical product, and functions are extracted in the form of “verb + noun.” The RFM is formed considering four relations between functions. Furthermore, functions in the RFM are divided into various levels using the Dempster–Shafer theory based on functional levels and boundaries. In addition, the HFM is formed to indicate the level of problem functions and range area of the solution space. Components and parameters of harmful functions are obtained based on the digital twin entity. Creative ideas of product redesign are generated using the theory of inventive problem solving (TRIZ) to solve inventive problems at different functional levels. Technique for order preference by similarity to an ideal solution (TOPSIS) is introduced to evaluate and select solutions. Finally, the feasibility and effectiveness of the proposed method are verified in the redesign of an antenna mounted on vehicles.     

A novel knowledge graph-based optimization approach for resource allocation in discrete manufacturing workshops

Dynamic personalized orders demand and uncertain manufacturing resource availability have become the research hotspots of intelligent resource optimization allocation. Currently, the data generated from the manufacturing industry are rapidly expanding. Such data are multi-source, heterogeneous and multi-scale. Transforming the data into knowledge to optimize the allocation between personalized orders and manufacturing resources is an effective strategy to improve the cognitive intelligent production level of enterprises. However, the manufacturing processes in resource allocation is diversity. There are many rules and constraints among the data. And the relationship among data is more complicated. There lacks a unified approach to information modeling and industrial knowledge generation from mining semantic information from massive manufacturing data. The research challenge is how to fully integrate the complex data of workshop resources and mine the implicit semantic information to form a viable knowledge-driven resource allocation optimization method. Such method can then efficiently provide the relevant engineering information needed for resource allocation. This research presented a unified knowledge graph-driven production resource allocation approach, allowing fast resource allocation decision-making for given order inserting tasks, subject to the resource machining information and the device evaluation strategy. The workshop resource knowledge graph (WRKG) model was presented to integrate the engineering semantic information in the machining workshop. A distributed knowledge representation learning algorithm was developed to mine the implicit resource information for updating the WRKG in real-time. Moreover, a three-staged resource allocation optimization method supported by the WRKG was proposed to output the device sets needed for a specific task. A case study of the manufacturing resource allocation process task in an aerospace enterprise was used to demonstrate the feasibility of the proposed approach.     

A framework for machining optimisation based on STEP-NC

Inappropriate machining conditions such as cutting forces cause tool failures, poor surface quality and worst of all machine breakdowns. This may be avoided by using optimal machining parameters, e.g. feed-rate, and continuing to monitor it throughout the machining process. To optimize feed-rate, we propose a system that consists of an optimisation module, a process control module and a knowledge based evaluation module. STEP-NC is the underlying data model for optimisation. Given the nominal powers, the cutting force can be estimated based on the higher-level production information such as workpiece properties, tool materials and geometries, and machine capabilities. The main function of the Process Control module is process monitoring and control. The output is the desired actual feed-rate. Finally, the actual feed-rate is recorded and evaluated in the Knowledge Based Evaluation module.     

面向复杂产品研制的MBSE体系架构及其发展趋势研究

基于模型的系统工程(model based systems engineering,MBSE)是一种将系统工程理论与数字化技术相结合的复杂产品研制技术,能够以系统工程思维有效驱动复杂产品研制流程,并以系统模型方式形式化地表达系统复杂交互作用.对此,首先以复杂产品研制背景、MBSE国内外发展状况为理论基础,总结并介绍目前MBSE在复杂产品研制过程中存在的问题;其次,以MBSE模型域、技术域、功能域、跨域支撑要素和应用域为研究层面,结合人工智能、数字孪生、数字主线等数字化技术综合考虑MBSE工程实践所需的关键要素,提出一种面向复杂产品研制的MBSE体系架构并进行详细论述;最后,基于该体系架构研判目前MBSE发展形势,并以数据模型为核心生产要素的角度进一步探讨未来MBSE的发展趋势.     

Manufacturing cost modelling for concurrent product development

This research work aims to develop an intelligent knowledge-based system that accomplishes an environment to assist inexperienced users to estimate the manufacturing cost modelling of a product at the conceptual design stage of the product life cycle. Therefore, a quicker response to customers’ expectations is generated. This paper discusses the development process of the proposed system for cost modelling of machining processes. It embodies a CAD solid modelling system, user interface, material selection, process/machine selection, and cost estimation techniques. The main function of the system, besides estimating the product cost, is to generate initial process planning includes generation and selection of machining processes, their sequence and their machining parameters. Therefore, the developed system differs from conventional product cost estimating systems, in that it is structured to support concurrent engineering. Manufacturing knowledge is represented by hybrid knowledge representation techniques, such as production rules, frames and object oriented. To handle the uncertainty in cost estimation model that cannot be addressed by traditional analytical methods, a fuzzy logic-based knowledge representation is implemented in the developed system. Based on the analysis of product life cycle, the estimated cost included material, processing, machine set-up and non-productive costs. A case study is discussed and demonstrated to validate the proposed system.