regarding literature

1. Journal of Manufacturing Systems 58 (2021) 118–131
Available online 13 June 2020
0278-6125/© 2020 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.
Digital twin-based assembly data management and process traceability for
complex products
Cunbo Zhuang, Jingcheng Gong, Jianhua Liu *
Laboratory of Digital Manufacturing, School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
A R T I C L E I N F O
Keywords:
Digital twin
Complex product
Workflow
Data Management
Process traceability
Product data package
A B S T R A C T
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) hierar­
chical 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.
1. Introduction
Along with the accelerating development of aerospace and aviation
technologies, in recent years, the number of production tasks for aero­
space and aviation products has also increased dramatically, resulting in
rapid growth in the amount of product data. The collecting and sorting
of this massive amount of data in real-time, and structurally managing
and analyzing these data to fulfill the process traceability and contin­
uous improvement of product design and process, have become key data
management topics in the aerospace and aviation sector.
Product Data Management (PDM) systems [1] and Product Lifecycle
Management (PLM) systems [2–4] have been adopted to achieve prod­
uct assembly data management and process traceability. The PDM sys­
tem mainly implements the data management and process management
in the product development phase. The PLM system, which is
product-centric and an extension of PDM, integrates and manages the
product lifecycle data on a unified platform. It enables users to collab­
oratively design, manufacture, and manage products at all stages of the
lifecycle [5]. In terms of PLM-based data management, Sudarsan built a
product information modeling framework, so that the product infor­
mation could be acquired, stored, used, and reused at all phases of the
product lifecycle [6]. By using an open standardized product metadata
model and Service-Oriented Architecture (SOA), Srinivasan built a
framework that integrated business and engineering processes to sup­
port PLM. The feasibility and effectiveness of the presented framework
was provided by two cases in the enterprise [7]. Lentes constructed an
ontology-based platform for manufacturing engineering and PLM,
which also can promote the reuse of knowledge [8].
The research described above manages distributed heterogeneous
data by constructing a single bill of materials (BOM) that acts as a single
product data source or an integrated information framework throughout
the product lifecycle. However, most of the research in this area is still at
* Corresponding author.
E-mail address: jeffliu@bit.edu.cn (J. Liu).
Contents lists available at ScienceDirect
Journal of Manufacturing Systems
journal homepage: www.elsevier.com/locate/jmansys
https://doi.org/10.1016/j.jmsy.2020.05.011
Received 25 October 2019; Received in revised form 18 May 2020; Accepted 18 May 2020

2. Journal of Manufacturing Systems 58 (2021) 118–131
119
the theoretical level, and little of it focuses on managing the large
amount of dynamic data generated in the reverse production processes.
It is difficult for research to meet the high standards and requirements of
dynamic data management for complex products. There are two reasons
for this.
(1) The assembly process of complex products involves high
complexity, strong dynamics, and many uncertainties, especially
in the model development stage. The assembly cycle can be as
long as 10 months and sometimes requires repeated adjustments.
In addition, the instability of the design process often leads to
frequent engineering changes and rework. This is why managing
and tracing the design, process, and execution data that is
dynamically generated during the assembly process has always
been a challenge for product assembly enterprises.
(2) The requirements of assembly quality control for complex prod­
ucts are stringent, and great value is placed on process trace­
ability, quality status analysis, and continuous improvement of
the product design. Achieving rapid assembly process tracing and
data value mining based on assembly data management is
another important topic.
In order to better express and utilize product lifecycle data and in­
formation, Grieves introduced the concept of “virtual digital represen­
tation equivalent to physical products” in a PLM course in 2003 [9].
Later, this concept was named Digital Twin (DT) in 2011 and has since
attracted the attention of researchers and companies worldwide in
various fields [10]. To better understand the concept of DT, NASA and
the U.S. Air Force Research Laboratory (UAFRL) pointed out that a DT
was an integrated multi-physics, multi-scale, probabilistic simulation of
an as-built system that utilizes the best available physical models,
updated sensor data, and historical data, to reflect the condition of the
corresponding flying twin [11]. Zhuang and Liu [12] reviewed the
background of DT and systematically expounded the connotation of
Product Digital Twin. On this basis, the concept of DT technology was
proposed, which refers to the process of using digital technology to
describe and model the physical entities. A digital twin refers to a virtual
mapping model that is consistent with the corresponding physical entity
and can simulate and mirror its behavior and performance. It is also
known as the Digital Twin Model (DTM). To date, researchers have put
forth much effort to apply DT technology in the PLM and product life­
cycle [13–16]. In the PLM, Tao et al. proposed a DT-driven product
design, manufacturing and service framework, and illustrated the
application methods and cases [17]. Grieves presented a DT-based fault
prediction and elimination approach for complex systems, which was
verified in NASA-related systems [18]. To promote the applications of
DT in the future, Qi et al. investigated and summarized the key enabling
technologies and tools for DT from the perspective of 5-dimensional DT
model [19]. In the product design phase, Tao et al. presented a DT-based
product design framework that enables the designers to customize,
assess, and accelerate the product development cycle [20]. In the
product manufacturing stage, Tao et al. proposed the implementation of
Digital Twin Shop-floor and clarified its system composition, operation
mechanism, characteristics, and key technologies. This provided a
theoretical reference for the realization of cyber-physical fusion on the
manufacturing shop-floor [21]. Zhang et al. introduced DT technology
to enhance dynamic scheduling and explored the DT-based machine
availability prediction, disturbance detection and performance evalua­
tion methods [22]. Söderberg et al. discussed the DT application in the
real-time geometry assurance in individualized production, based on an
example of a sheet metal assembly station [23]. Zhuang et al. proposed a
smart management and control approach for complex products based on
DT, constructed its framework and described the core techniques and
implementation process in detail [24]. Zhang et al. proposed a rapid
custom design and optimization method for the automated production
line of the hollow glass driven by DT technology, and built a
corresponding DT system through the synchronization between the
virtual digital manufacturing system model and physical equipment
[25]. Kong et al. presented a data construction method for the appli­
cations of shop-floor digital twin system [26]. In the product service
stage, Tuegel et al. sought to build a DT for each space vehicle, so as to
predict accurately the life of a spacecraft structure [27]. GE imple­
mented real-time monitoring, timely inspection, and predictive main­
tenance of engines based on DT in its Predix cloud service platform [28].
The above research show the application potential of DT technology
in PLM, and production is one of the most popular applied fields.
However, there is no corresponding approach to adopt DT in dynamic
product data management across multiple stages and process trace­
ability for complex products. As the extension of PLM, product DT em­
phasizes the data integration throughout the product lifecycle through
the product digital model, which provides a single data source for
product design, manufacturing, service, and engineering changes. It can
record all historical segments and processes of the product lifecycle,
thereby providing a new solution for process traceability to meet the
strict requirements of quality control and data utilization in enterprises
performing complex product assembly. Therefore, to solve the issues
that these enterprises face, a DT-based assembly data management and
process traceability approach for complex products is proposed in this
paper.
The rest of this paper is organized as follows: Section 2 analyzes the
evolutionary process of the complex product assembly data. Section 3
presents the framework of DT-based assembly data management and
process traceability for complex products. On this basis, Section 4 dis­
cusses several key technologies. Section 5 develops a system and verifies
the proposed approach by a case study of an assembly enterprise. Sec­
tion 6 presents the authors’ conclusions and discusses future research
issues.
2. Analysis of evolutionary process for the complex product
assembly data
The entire process of generation-to-archive of complex product as­
sembly data is undergoing a series of changes. These changes can be
considered granularity, period, and version, which respectively corre­
spond to product structure, product lifecycle, and product data version,
as shown in Fig. 1.
A complex product is composed of many assemblies, and an assembly
is made up of many parts. Hence, product assembly data is a collection of
data from the various components that make up the product in the
granularity dimension, which reflects the hierarchical characteristics of
the assembly data.
Whether it’s a finished product or an assembly, it has its own life­
cycle. The lifecycle corresponds to the period dimension, which has
three stages: product design, process planning, and assembly execution.
Assembly execution consists of three sub-stages: shop scheduling, on-site
assembly, and problem feedback. The data generated at each stage is
different. The period dimension reflects the dynamic characteristic of
the data.
If an engineering change occurs at any of the stages and necessitates
the revision of product design information or process information, the
version would be updated and new versions of data would be generated
along with the product lifecycle, resulting in the evolution of product
data in the version dimension. The version dimension reflects the
reverse feature of the data.
2.1. Granularity dimension
The evolutionary model of the product assembly data in the granu­
larity dimension is shown in Fig. 2. The complex product assembly
process consists of three levels: final assembly, subassembly, and part
assembly. A series of part assembly activities, such as cable assembly or
on-board equipment assembly, form a subassembly activity. A series of
C. Zhuang et al.

3. Journal of Manufacturing Systems 58 (2021) 118–131
120
subassembly activities form a final assembly activity, and the entire
product assembly process consists of a series of tandem and parallel final
assembly activities. By decomposing complex assembly activities into
more detailed activities, the refined management of complex product
assembly processes and the hierarchical management of assembly data
can be realized. Moreover, the implementation for the final assembly of
a product or subassembly of an assembly will undergo the five periods
listed in the horizontal axis in Fig. 2. Therefore, for every assembly and
product, the relevant assembly data is a collection of data from the five
periods.
2.2. Period dimension
The evolutionary model of the product assembly data in the period
Fig. 1. Reference model for classification of product assembly data.
Fig. 2. Evolutionary model of the product assembly data in the granularity dimension.
Fig. 3. Evolutionary model of the product assembly data in the period and version dimensions.
C. Zhuang et al.

4. Journal of Manufacturing Systems 58 (2021) 118–131
121
and version dimensions is shown in Fig. 3.
The information flow from the product design to the on-site assembly
is called the forward assembly process. In the product design stage,
according to the technical and functional requirements, the designer
creates a 3D model of the product, 2D drawing files, and a BOM, and
distributes them to the technologist. In the processing planning stage,
the technologist creates the assembly process flow in a Computer-aided
Process Planning (CAPP) system, and attaches the technical contents,
lists of materials, fixtures and devices, measurement requirements, and
3D simulation animation, for each process. The technical specifications
are then automatically generated and submitted to the PDM system for
approval. In the shop scheduling phase, after receiving and decomposing
the production plan, the shop floor dispatcher allocates the
manufacturing resources according to the approved technical specifi­
cations and the field checking results of 5M1E (man, machine, material,
method, measurement, and environment). Then, the daily work plans
and schedules are sent to on-site workers. In the on-site assembly stage,
based on the requirements of data acquisition, assembly workers and
inspectors collect dynamic assembly execution data, such as completion
data, implemented material data, measurement data, videos, and
pictures.
However, if a technical, quality-related or resource problem occurs,
the relevant personnel will record the problem data and feed it back to
the upstream designer, technologist, or dispatcher. For example, when a
technical problem arises on the shop floor, the designer, and the tech­
nologist first put forward the corresponding measures according to the
actual situation and fill in the technical problem treatment form. Then,
the designated person in charge modifies the design information and
process information of the original version, and sends the new version to
the shop-floor to guide the production again. Finally, the on-site
personnel refer to the new version and carry out assembly operation
and dynamic data collection until the plan is completed. The problem
feedback and processing is called the reverse assembly process. The
forward assembly process and the reverse assembly process form is
called a closed-loop product assembly process.
In the closed-loop product assembly process, the pattern of product
assembly data is gradually transformed from static product design data
and process data to dynamic assembly execution data and problem
feedback data. The content of the data is gradually expanded from static
data such as 2D drawings, 3D models, BOMs, technical specifications,
and simulation animations, to dynamic data such as completion data,
actual working hours, implemented material data, quality data, and
problem data, which reflects the dynamic evolution of product assembly
data along with the product assembly cycle.
2.3. Version dimension
The version of product assembly data refers to the data state that the
assembly data remains relatively stable for a certain period of time.
Nevertheless, if product design information or process information is
revised or changed, the version would be updated, resulting in the
evolution of product data in the version dimension.
3. Framework of DT-based assembly data management and
process traceability for complex products
Product assembly data is composed of data of different versions
(version dimension), data of different hierarchies (granularity dimen­
sion), and data of different stages (period dimension), including product
design, process planning, and assembly execution. To achieve complete
and accurate assembly data management and process traceability for
complex products, DT technology is introduced and then combined with
workflow technology in a framework, as shown in Fig. 4.
First, to achieve the product structure management, assembly BOM
Fig. 4. Framework of DT-based assembly data management and process traceability for complex products.
C. Zhuang et al.

5. Journal of Manufacturing Systems 58 (2021) 118–131
122
is built and then updated with the change of lifecycle stage. Each
component of the product is a tree node of assembly BOM that is
attached with its DT model, which consists of a 3D design model, process
model, and execution model. Then, workflow is introduced to organize
the static and dynamic assembly data of different versions and different
stages, including design data, process data, and execution data. These
assembly data of product or assemblies are associated with the corre­
sponding tree node of BOM. The detailed implementation is explained in
Section 4.1. Therefore, the association between data and its corre­
sponding model will be built through BOM.
On this basis, the collected real-time data from the shop floor can be
mapped into the corresponding DT model to create the synchronous
modeling of the product assembly process. Hence, through product DT
models, the technical state of the product can be monitored and recor­
ded. The corresponding key techniques are explained in Section 4.2.
Additionally, the hierarchical management of product assembly data
and process traceability also can be achieved based on the DT model.
Finally, the product assembly data package is automatically generated
for archiving and delivery. The implementation techniques are further
described in Sections 4.3 and 4.4.
4. Key techniques
4.1. Product assembly data organization and version management based
on workflow
Complex product assembly data includes data from three stages:
product design, process planning, and assembly execution. Achieving
the association, organization, and version management of data at each
of these stages is the premise of product assembly data management.
Hence, we introduce workflow technology to build three models: an
organizational model of product design data, an integrated
organizational model of assembly process and assembly execution data,
and a version association model of assembly data in the reverse process.
These three models lay the foundation for the product assembly data
management of each stage and each version.
4.1.1. Workflow-based organization of product design data
The organization of product design data is achieved on the PDM
platform by means of workflow management. A structural element in
the workflow is either called a process step or a process activity [29].
The goal of workflow management is to organize the data generated
during the execution of activities by a process activity such as a task
node or a process node. The generated data are called activity elements.
Activity elements consist of many types, which express the operation
methods, operation objects, and tools used in the process activity. The
workflow-based organizational model of product design data is shown in
Fig. 5.
In this model, the product design information carrier, that is the
specific process activity, includes design task, design flow, design flow
node, design subtask, subtask flow, and subtask flow node. First, the
product design task is driven by market demand analysis and order re­
quirements, for which the design flow is created. The design flow is
composed of many design flow nodes. Then, design subtasks are created
for each design flow node and corresponding subtask flow for each
design subtask. The subtask flow is also composed of a number of sub­
task flow nodes. Furthermore, design-related data such as 3D models, 2D
drawings, design documentation, BOM, and execution information
related to each process activity, are all associated with these specific
process activities, through which the product design data is organized.
4.1.2. Integrated organization of assembly process data and assembly
execution data based on workflow
The complex product assembly process is based on workflow, and the
Fig. 5. Organizational model of product design data based on workflow.
C. Zhuang et al.

6. Journal of Manufacturing Systems 58 (2021) 118–131
123
assembly sequence is determined by the assembly process flow. Since
the development model for complex products is a process of constant
experimentation, it includes characteristics such as process instability
and frequent production disturbances. The many unpredictable process
modifications include process revision, temporary process, and process
change, and even design modifications during the assembly process,
which affect the shop floor logistics, the assignment of equipment and
personnel, and assembly progress. For this reason, an integrated orga­
nizational model of assembly process data and execution data has been
established, as shown in Fig. 6, in which workflow is the core and as­
sembly activity nodes are the management objects. This model facili­
tates the integrated management of process data and execution data.
In the assembly process planning phase, the structural organization
of the process data, such as technical content, inspection and measure­
ment forms, process model, and simulation animation, is realized
through the assembly process flowchart and the assembly flow node.
The assembly process flowchart is associated with the corresponding
assembly BOM node, which is the foundation for model and BOM-based
process data management. The assembly flow node is a component of
the assembly flowchart and can represent a process or a specific as­
sembly activity.
In the assembly execution stage, the structural association manage­
ment of assembly execution data, including completion data, real-time
perception data, quality data, and resource usage data, is achieved
through the instantiated assembly flowchart and the instantiated as­
sembly flow node. Moreover, the execution data is the instantiated
mapping of process data. For example, the instantiated assembly flow­
chart for each physical product entity is an instantiated mapping object
of the assembly process flowchart; the instantiated assembly flow node
is an instantiated mapping object of the assembly flow node; the as­
sembly resource usage data is the instantiated mapping of the assembly
resource; and the actual measured data is an instantiated mapping of the
Fig. 6. Integrated organizational model of product assembly process data and execution data.
C. Zhuang et al.

7. Journal of Manufacturing Systems 58 (2021) 118–131
124
quality requirement data. Among these, the assembly resource usage
data are the most important compositions of implemented material data,
which includes assembly object usage data, fixture and tool usage data,
and main and auxiliary material usage data.
In addition, the real-time logistics data are collected by RFID and
barcode. The progress data, completion data, quality data, and imple­
mented material data are acquired by human-computer interactions. For
example, when an assembly or a part is installed on the product, the
operating worker collects the implemented material data. When an as­
sembly process is finished, the worker should electronically sign to
collect the completion data, and the checker should collect the quality-
related data. Only in this way can the next process start. The progress-
related data are calculated through the collected completion data.
4.1.3. Version management of assembly data in the reverse process for
complex products
The assembly process of complex products is often unstable. There
are many reverse processes on the assembly shop floor, such as reverse
operation, process change, and rework. Therefore, a large amount of
intermediate state data is generated. Managing these data is still a
complex problem for many enterprises.
The version of data records the evolution of the assembly data along
with the assembly cycle, which includes the design version, process
version, and assembly execution version. The new version generated is a
modification, addition, or replacement of the original version. The new
version is independent but interrelated with the original. Version asso­
ciation refers to the relationship between different versions of the same
object, which is a prerequisite for implementing the management and
traceability of intermediate state data. The design version consists of
two types: a small version that is upgraded in the multi-level approval
process of assemblies or parts-related design documents such as the 3D
model and 2D drawings; and a large version whose upgrade is necessi­
tated by changes in the issued design information because design
problems have occurred. The assembly process version also includes two
types: the assembly process planning stage, a small version whose
upgrading is driven by the modification of assembly process documen­
tation in the multi-level approval process; and the assembly execution
stage, a large version whose upgrading is driven by technical problems
such as design and process changes on the shop floor. The small version
is identified by a “lowercase letter + sequence number,” such as a.1 or
a.2. The large version is identified by a “capital letter,” such as A or B.
When the small version is updated from “approval” to “issued,” the
version is upgraded to a large version number, for example, from b.3 to
B.3. Since the production process can only be carried out after the
process is approved, the version number of the assembly execution data
is the same as that of the process data.
Current research has started to focus on the management of design
version and process version in the forward process, but there is relatively
little research on design and process version management in the reverse
process. The reverse process mainly includes design change, process
change, temporary process, and process revision. Design change refers
to the replacement of the original design structure with a new one.
Process change refers to the replacement of the old process with a new
one, i.e. the large version is changed, and the assembly personnel
perform the assembly operation under the guidance of the new process.
The temporary process is a supplement to the current process. Process
revision refers to the modification of the current process directly after
the confirmation of the inspections and technicians in the assembly
execution stage. The evolution of the design version in the reverse
process is relatively simple and only requires the upgrade to the large
version, for example, from the B version to the C version. The evolution
of the process version in the reverse process is more complicated. Hence,
the naming rules of “<design version number>. < process version
number>. < temporary process version number >. < process revision
version number>” are proposed to form a version association model
combining linear structure with tree structure. Each level includes a
design version number, process version number, temporary process
version number, and process revision version number, as shown in
Fig. 7.
The proposed model represents the hierarchical parent-child rela­
tionship among the design version, the process version, the temporary
process version, and the process revision version; the associated rela­
tionship between the large version and the small version; and the parent-
child relationship among the versions on the same level. For example,
design version B is the parent version of design version A, and process
version B is the parent version of process version A. The technical
problems, quality-related problems, and resource problems that arise
during the assembly process are the main reasons for the upgrading of
the design and process versions. The parent-child relationship between
the versions on the same level is achieved through the shop floor
problem of processing forms, and then the closed-loop version control
process is realized. The specific implementation process is shown in the
flowchart in Fig. 8.
4.2. Synchronous modeling of product assembly process based on DT
The assembly of a complex product is typically a discrete assembly
with high complexity, strong dynamics, many uncertainties, and
frequent production disruptions. Achieving the transparent supervision
of the assembly process and the tracing of historical fragments have long
been urgent issues that need to be resolved before an enterprise can
move into smart manufacturing. Therefore, on the basis of the real-time,
dynamic, and visualization characteristics of DT technology, we can
realize the synchronous modeling of the product assembly process. This
is realized by means of the true mapping between product virtual model
and actual measured data, so as to provide support for transparent su­
pervision and process traceability of the assembly process. The syn­
chronous modeling of the DT-based product assembly process based is
shown in Fig. 9.
Based on the real-time acquisition of logistics data, progress data,
completion data, quality-related data and implemented material data,
we establish the mapping relationship between the product’s 3D model
and physical data through a product structure tree, i.e., BOM. Then, the
synchronous modeling of assembly progress, technical state, and as­
sembly quality can be achieved based on product DT. For example, as­
sembly B is a component of product X, which has three assembly
processes. Self-made part C and standard part D are required in the first
process. When the operator starts the first process of assembly B, the
corresponding 3D model turns to red (prior to assembly, the corre­
sponding model is indicated by a dotted line and displayed as gray), and
the logistics process is monitored through the DT of the production line.
When the materials arrive, the operator begins to assemble the self-made
part C into assembly B. Once the assembly operation is completed, the
corresponding 3D model of part C in the assembly B model turns into
solid line driven by the collected implemented material data. At the
same time, the relevant parameters, such as name, code, specification,
place of origin, assembled personnel, and time, are displayed. Further­
more, the inspection and measurement system measures the assembled
precision and maps the data into the DT model to be displayed in real-
time, and then makes an intuitive comparison between the actual
value and the theoretical value. On this basis, the product assembly
precision and dynamic compensation of process parameters can be
predicted based on DT.
4.3. Hierarchical management and traceability of product assembly data
based on DT
Based on the above analysis of organization and version manage­
ment of the product assembly data and the synchronous modeling of
assembly process based on DT, a hierarchical management and trace­
ability approach of product assembly data based on DT in the granu­
larity dimension is proposed, as shown in Fig. 10.
C. Zhuang et al.

8. Journal of Manufacturing Systems 58 (2021) 118–131
125
This approach achieves the management of dynamic assembly data
and version for all the components in each stage of the product lifecycle
by means of constructing the product assembly structure tree, i.e., a
product assembly BOM covering product design, process planning, and
assembly execution. Hence, each physical product corresponds to a
product assembly BOM, the hierarchical structure of which, from top to
bottom, includes product, assembly, and part. To realize the organiza­
tion of data in the granularity dimension, subassembly flow is instanti­
ated and associated with the corresponding physical assembly, while
final assembly flow is instantiated and associated with the correspond­
ing physical product. The part data sets are associated with the corre­
sponding physical part. On this basis, the mapping relationship between
each model in the product DT and the corresponding node in the as­
sembly BOM is established in order to realize hierarchical management
and traceability of product assembly data.
On the one hand, the application of product DT and assembly BOM
for data management conforms to the internal business process of the
assembly enterprise and is easy to understand and use. On the other
hand, it reduces the complexity of management by transforming the
management of large amounts of data within the enterprise to the
management of product objects and models.
4.4. Generation of assembly data package for complex products
The product assembly data package is the basis and data source of
product ex-factory review, quality traceability, file management, and
continuous improvement of product design and assembly processes. The
assembly data package of complex products consists of final product
assembly data, sub-assembly data and data related to parts. The product
final assembly data includes product design data, final assembly process
data and final assembly execution data, such as resumption of the final
assembly process. Data related to parts is provided by the parts manu­
facturer. Assemblies of a product generally consist of purchased as­
semblies, outsourced assemblies, and self-manufactured assemblies.
With regard to data collection, storage, and management, differences
exist for different types of assemblies.
Fig. 7. Version management model for product design and process in the reverse processes.
Fig. 8. Correlation and closed-loop control model of version management on the same level.
C. Zhuang et al.

9. Journal of Manufacturing Systems 58 (2021) 118–131
126
Outsourced and purchased assemblies are produced by other man­
ufacturers. They are put into the warehouse after check and acceptance.
Relevant data is managed in the form of files placed in folder directories.
Designated personnel classify files according to archiving requirements
and send them to the server. The system creates a data record in the
database for each independent file, including its storage path, associated
BOM node, type, and source. For key outsourced components, assembly
data packages are required along with data related to quality, progress,
and cost monitoring and controlling. These control data should be
collected and stored structurally to ensure the traceability of product
quality and the guarantee of progress and cost. Using a missile as an
example, the seeker is completed by other manufacturers. To achieve the
Fig. 9. Synchronous modeling of the product assembly process based on DT.
Fig. 10. DT-based hierarchical management and traceability model of product assembly data.
C. Zhuang et al.

10. Journal of Manufacturing Systems 58 (2021) 118–131
127
quality, progress and cost control of the final assembly of a missile, it is
necessary for final assembly plants to collect the quality data, progress
data, and cost data for the seeker in a timely basis from the outsourced
manufacturer during the final assembly process.
(2) The self-manufactured assemblies are produced independently by
the enterprise, and the data come from the enterprise’s own information
management systems, such as MES, ERP, and PDM. Data interaction
among these information systems is implemented through the assembly
BOM and DT model, so that the data packages for the self-manufactured
assemblies can be generated.
The algorithm flowchart for generating assembly data packages for
complex products is shown in Fig. 11.
Input: a specific physical product PP
Output: assembly data package PPADP of the product PP
Step 1: Create product design data sets PDc, process data sets PPc =
{PPMain, PPTemp}, assembly execution data sets EPPc = {EPPMain,
EPPTemp}, and purchased and outsourced assemblies’ data sets DPoa.
Obtain assembly BOM PABOM of product PP, assembly process resume
APMT, product design data DIP, final assembly flow AFT and final as­
sembly flow nodes AFNT = {AFNT1, AFNT2, • • •, AFNTn}, n ∈ Z+
associ­
ated with product PP. PABOM, APMT and DIP are stored in PDc.
Step 2: Take any AFNTh ∈ AFNT, obtain the associated assembly
ASSEMBLYh that is self-manufactured. Obtain the subassembly process
resume APMh and design data DPh of ASSEMBLYh, as well as the latest
version of subassembly flow AFhP which is also called the main flow.
Store them in PDc.
Step 3: Obtain the 3D simulation animation and process description
that are associated with the main flow AFhP. Store them in PPMain, and
obtain the subassembly nodes AFNhP = {AFNhP1,AFNhP2,• • •,AFNhPm},
m ∈ Z+
.
Step 4: Take any AFNhPk ∈ AFNhP, retrieve process data AFNInfohPk
related to AFNhPk, store AFNhPk and AFNInfohPk in the process data set
PPMain, and remove AFNhPk from AFNhP. If AFNhP = ∅, obtain the latest
version of the temporary process set AFhTemp = {AFhTemp1, AFhTemp2, • • •,
AFhTempl}, l ∈ Z+
of AFhP, and go to step 5; otherwise go to step 4.
Step 5: Take any AFhTempx ∈ AFhTemp, retrieve temporary process data
AFInfohTemp related to AFhTempx, store AFhTempx and AFInfohTemp in the
process data set PPTemp. Retrieve temporary process instance EFNhTempx
of AFhTempx.
Step 6: Retrieve the execution data EFNInfohTempx related to
EFNhTempx, and store it in the set PPETemp with EFNhTempx. Remove
EFNhTempx from EFNhTemp. If EFNhTemp = ∅, go to step 7; otherwise go to
step 6.
Step 7: If the version of temporary process AFhTempx is not A, obtain
its parent version PAAFhTempx, then go to step 5; otherwise, remove
AFhTempx from AFhTemp. If AFhTemp = ∅, retrieve instantiated flow EFNhP =
{EFNhP1, EFNhP2, • • •, EFNhPm}, m ∈ Z+
of AFhP, go to step 8; otherwise
go to step 5.
Step 8: Take any EFNhPi ∈ EFNhP, and obtain the record set of process
revision MEFNhPi = {MEFNhPi1, MEFNhPi2, • • •, MEFNhPiy}, y ∈ Z+
of
Fig. 11. Algorithm flowchart of generating assembly data package for complex products.
C. Zhuang et al.

11. Journal of Manufacturing Systems 58 (2021) 118–131
128
EFNhPi. If MEFNhPi ∕
= ∅, go to step 9; otherwise go to step 8.
Step 9: Take any MEFNhPij ∈ MEFNhPi. If MEFNhPij is the latest revi­
sion, assign the content in MEFNhPij to EFNhPi. Go to step 10; otherwise
remove MEFNhPij from MEFNhPi and go to step 9.
Step 10: Take any EFNhPi ∈ EFNhP and obtain assembly execution
data EFNInfohPi associated with EFNhPi including completion data, actual
working hour, implemented material, quality data, videos and pictures,
and model animation. Store EFNhPi and EFNInfohPi in the assembly
execution data set PPEMain and remove EFNhPi from EFNhP. If EFNhPi = ∅,
go to step 11; otherwise go to step 10.
Step 11: If the version of the main process flow AFhP is not “A”,
obtain its parent version PAAFhP, and go to step 3. Otherwise, remove
AFNTh from AFNT. If AFNTh = ∅, go to step 12; otherwise go to step 2.
Step 12: Obtain all purchased and outsourced assemblies set OA = {
OA1, OA2, • • •, OAr}, r ∈ Z+
in PABOM. Take any OAq ∈ OA, retrieve
data package DPoaq of OAq and store it in the purchased and outsourced
assemblies’ data package set DPoaq. Remove OAq from OA. If OA = ∅, go
to step 13; otherwise go to step 12.
Step 13: Output the assembly data package PPADP = {PDc, PPc,
EPPc, DPoa} of the product PP. The algorithm ends.
The algorithm flow of generating the product assembly data package
is as follows.
1) Obtain product assembly BOM, final assembly process resume,
design data, and final assembly process flow.
2) Obtain self-made assembly that corresponds to each node of the final
assembly flow, as well as its subassembly process resume and design
data.
3) Obtain associated temporary process and the subassembly flow.
4) Retrieve the instances of the main flow and temporary process.
5) Retrieve the execution data associated with each instance, such as
completion data, actual working hour, implemented material, qual­
ity data, videos and pictures, and model animations.
6) Retrieve the parent version of the main flow and temporary process
iteratively until the version is “A.”
7) Through product assembly BOM, obtain all the outsourced and
purchased assemblies and their associated data packages.
8) Summarize all of the data and output the product assembly data
package.
5. Case study
Based on the above research results, we developed the Digital Twin-
based Assembly Process Management and Control System (DT-APMCS)
using Microsoft’s. Net Framework 3.5 and Visual Studio 2008 to verify
the proposed approach. This system consists of key functions such as
management of product assembly BOM, synchronous mapping of the
product assembly process, integrated management of product assembly
data, generation of product assembly data package, and integration with
other systems.
5.1. Management of product assembly BOM
The product assembly BOM is a bridge for data integration and
interaction between the DT-APMCS and PDM, ERP and other informa­
tion management systems. It provides a single data source for the
management of product assembly data, and the generation of the
product assembly data package. The interface for managing the product
assembly BOM is shown in Fig. 12.
5.2. Synchronous mapping of product assembly process
DT-based synchronous mapping of the product assembly process is a
means to record the historical segments of the assembly process and
achieve process traceability. Monitoring and recording of the product
assembly process can be achieved by establishing mapping between
real-time data and the corresponding 3D model. Using the collected
implemented material data and quality-related data as an example, the
interface for synchronous mapping of the product assembly process is
shown in Fig. 13.
5.3. Integrated management of product assembly data
The interface for managing product assembly data is shown in
Fig. 14. The assembly data of different versions and different stages are
all associated with the corresponding tree node of the assembly BOM
and its DT model. Therefore, all of the necessary data, including design,
process, and execution data, can be queried and traced through the as­
sembly BOM or its corresponding DT model. The basic attributes of the
data itself also can be used to obtain the relevant data.
Fig. 12. Interface for managing the product assembly BOM.
C. Zhuang et al.

12. Journal of Manufacturing Systems 58 (2021) 118–131
129
5.4. Generation of product assembly data package
Fig. 15 shows the generation of part of the assembly data package for
a specific product entity, including the 3D model (bottom left); the as­
sembly process card that consists of the directory of process files, as­
sembly flow, and process content; assembly process animation (middle
and lower); the technical problem processing form (first right); the
quality control card of the final assembly process (second right); and the
actual measurement data form in the shop floor (third right).
5.5. Integration with other systems
To build the assembly BOM for a product and generate the product
assembly data package, we should integrate DT-APMCS with other in­
formation systems such as ERP and PDM. The interfaces with the PDM
system achieve the interaction and transmission of product BOM,
product 3D model, and other product design data. The interfaces with
the ERP system achieve the interaction and transmission of the pro­
duction plan and procurement information. The transmission method
and form of data includes address links, XML, and entity files.
5.6. Comparison with other developed systems for PLM and application
effect
Compared with other systems developed for PLM, the DT-APMCS has
the following differences in function.
1) Process traceability is significant for complex products. In the DT-
APMCS, the historical segments of the assembly process can be
recorded using the function of DT-based synchronous mapping of the
product assembly process, thereby achieving model-based assembly
process monitoring and traceability. The proposed approach is
intuitive, accurate, and fast in process traceability. It is also a
necessary supplement to the method of taking videos and photos.
Generally, other developed systems for PLM either lack the function
of process traceability or realize it only through videos and photos
taken on the shop floor.
2) The assembly process of a complex product has the characteristics of
high complexity, strong dynamics, and need of frequent rework and
repair. Therefore, the process generates a significant amount of dy­
namic design, process, and execution data, particularly the inter­
mediate state data. In the DT-APMCS, these dynamic data can be
acquired, managed, and traced through the product DT model to
meet the exacting requirements of complex product data manage­
ment. Generally, the other developed systems for PLM cannot be
used for the management of complex product assembly data, because
they are unable to completely acquire and trace the dynamic as­
sembly data of the different stages and versions generated in reverse
processes.
Currently the developed system of DT-APMCS is used by some as­
sembly companies that produce satellites, including one located in
Shanghai. At present, according to the statistics, the proportion of
electronic data collection has covered about 80 % of the assembly
businesses, shortened the time for processing technical problems from
2.5 h to 1 h. The running time of obtaining and displaying the results of
data statistics and tracking is within 5 s. The output of product assembly
data package is within 2 min. The time delay for the synchrony of the
physical product and its counterpart is within 3 s.
6. Conclusions and future work
DT technology has attracted the attention of academic researchers
and industrial practitioners because of its ability to achieve the fusion of
virtual and physical space, and because it shows extensive application
prospects. However, due to the characteristics of high complexity,
strong randomness, process instability, and extremely strict data man­
agement and process traceability for complex product assembly
Fig. 13. Interface for synchronous mapping of product assembly process.
Fig. 14. Interface for managing product assembly data.
C. Zhuang et al.

13. Journal of Manufacturing Systems 58 (2021) 118–131
130
processes, achieving the assembly data management and process
traceability of complex products has been a challenge. DT technology
provides a novel way to tackle this issue.
There are three main contributions of this paper. The first is intro­
ducing DT technology into the assembly process of complex products
and proposing an assembly data management and process traceability
approach based on DT; this approach manages the integration of product
assembly data and generates a package of product assembly data accu­
rately. The proposed approach provides a novel and feasible way to
achieve the management of assembly data, process traceability, and
generation of the data package for complex products. It will play an
important role in improving the quality management and data man­
agement in the assembly process of these products.
The second contribution is the introduction of workflow to organize
the dynamic data of each stage and each version in the reverse process,
which lays a foundation for the management of DT-based product as­
sembly data.
The third contribution is the development of the DT-APMCS system
and a case study that shows the effectiveness of the proposed approach.
This paper explores the application of DT to manage assembly data
and improve process traceability for complex products. The future
research will focus on two areas: accurately predicting the product
quality and progress during the assembly process based on the syn­
chronous modeling of product assembly process in order to assist
decision-making; and establishing the mapping relationship between
manufacturing BOM and service BOM to open up the data link between
the manufacturing and service stage in order to make the management
of DT-based product lifecycle data and closed-loop feedback for design
optimization a reality.
Declaration of Competing Interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Acknowledgements
The authors would like to express our sincere gratitude to the
anonymous reviewers for the invaluable comments and suggestions that
have improved the quality of the paper. We also thank LetPub for its
linguistic assistance during the preparation of this manuscript. This
research is financially supported in part by the National Natural Science
Foundation, China (No. 51935003), and in part by the National Defense
Fundamental Research Foundation, China (No. JCKY2018210C005, No.
JCKY2016204A502).
References
[1] Ouyang C, Chang MJ. Developing an agent-based PDM/ERP collaboration system.
Int J Adv Manuf Technol 2006;30(3):369–84.
[2] Shen W, Hao Q, Mak H, Neelamkavil J, Xie H, Dickinson J, et al. Systems
integration and collaboration in architecture, engineering, construction, and
facilities management: a review. Int J Adv Sci Eng Inf 2010;24(2):196–207.
[3] Daaboul J, Duigou JL, Penciuc D, Eynard B. An integrated closed-loop product
lifecycle management approach for reverse logistics design. Prod Plan Control
2016;27(13):1062–77.
[4] Tao F, Qi Q, Liu A, Kusiak A. Data-driven smart manufacturing. J Manuf Syst 2018;
48:157–69.
[5] Huang S, Fan Y. Overview of product lifecycle management. Comput Integr Manuf
Syst 2004;10(1):1–9.
[6] Sudarsan R, Fenves SJ, Sriram RD, Wang F. A product information modeling
framework for product lifecycle management. Comput Aided Des 2005;37(13):
1399–411.
[7] Srinivasan V. An integration framework for product lifecycle management. Comput
Aided Des 2011;43(5):464–78.
[8] Lentes J, Zimmermann N. amePLM: a platform providing information provision in
engineering. Int J Prod Res 2017;55(13):3832–41.
[9] Grieves M. Digital twin: manufacturing excellence through virtual factory
replication. 2014. www.apriso.com/library/Whitepaper_Dr_Grieves_DigitalTwin_
ManufacturingExcellence.php.
Fig. 15. Output of product assembly data package.
C. Zhuang et al.

14. Journal of Manufacturing Systems 58 (2021) 118–131
131
[10] Grieves M. Virtually perfect: driving innovative and lean products through product
lifecycle management. Cocoa Beach, FL: Space Coast Press; 2011.
[11] Glaessgen EH, Stargel DS. The digital twin paradigm for future NASA and US air
force vehicles. In: 53rd Structures, Structural Dynamics and Materials Conference.
Special Session: Digital Twin; 2012. p. 1–14.
[12] Zhuang C, Liu J, Xiong H, Ding X, Liu S, Weng G. Connotation, architecture and
trends of product digital twin. Comput Integr Manuf Syst 2017;23(4):753–68.
[13] Tao F, Zhang H, Liu A, Nee AYC. Digital twin in industry: state-of-the-art. IEEE T
Ind Inform 2019;15(4):2405–15.
[14] Yi Y, Yan Y, Liu X, Ni Z, Feng J, Liu J. Digital twin-based smart assembly process
design and application framework for complex products and its case study. J Manuf
Syst 2020. https://doi.org/10.1016/j.jmsy.2020.04.013.
[15] Huang S, Wang G, Yan Y, Fang X. Blockchain-based data management for digital
twin of product. J Manuf Syst 2020;54:361–71.
[16] Coronado PDU, Lynn R, Louhichi W, Parto M, Wescoat E, Kurfess T. Part data
integration in the shop floor digital twin: mobile and cloud technologies to enable a
manufacturing execution system. J Manuf Syst 2018;48:25–33.
[17] Tao F, Cheng J, Qi Q, Zhang M, Zhang H, Sui F. Digital twin-driven product design,
manufacturing and service with big data. Int J Adv Manuf Technol 2018;94:
3563–76.
[18] Grieves M, Vickers J. Digital twin: mitigating unpredictable, undesirable emergent
behavior in complex systems. Trans-disciplinary perspectives on complex systems.
Berlin, Germany: Springer-Verlag; 2017.
[19] Qi Q, Tao F, Hu T, Anwer N, Liu A, Wei Y, et al. Enabling technologies and tools for
digital twin. J Manuf Syst 2019. https://doi.org/10.1016/j.jmsy.2019.10.001.
[20] Tao F, Sui F, Liu A, Qi Q, Zhang M, Song B, et al. Digital twin-driven product design
framework. Int J Prod Res 2019;57(12):3935–53.
[21] Tao F, Zhang M. Digital twin shop-floor: a new shop-floor paradigm towards smart
manufacturing. IEEE Access 2017;5:20418–27.
[22] Zhang M, Tao F, Nee AYC. Digital twin enhanced dynamic job-shop scheduling.
J Manuf Syst 2020. https://doi.org/10.1016/j.jmsy.2020.04.008.
[23] Söderberg R, Wärmefjord K, Carlson JS, Lindkvist L. Toward a digital twin for real-
time geometry assurance in individualized production. CIRP Ann-Manuf Tech
2017;66(1):137–40.
[24] Zhuang C, Liu J, Xiong H. Digital twin-based smart production management and
control framework for the complex product assembly shop-floor. Int J Adv Manuf
Technol 2018;96:1149–63.
[25] Zhang H, Liu Q, Xin C, Zhang D, Leng J. A digital twin-based approach for
designing and multi-objective optimization of hollow glass production line. IEEE
Access 2017;5:26901–11.
[26] Kong T, Hu T, Zhou T, Ye Y. Data construction method for the applications of
workshop digital twin system. J Manuf Syst 2020. https://doi.org/10.1016/j.
jmsy.2020.02.003.
[27] Tuegel EJ, Ingraffea AR, Eason TG, Spottswood SM. Reengineering aircraft
structural life prediction using a digital twin. Int J Aerospace Eng 2011. https://
doi.org/10.1155/2011/154798.
[28] Warwick Graham. GE advances analytical maintenance with digital twins. Aviation
Week & Space Technology; 2015. 10–19.
[29] Workflow Management Coalition. Workflow management coalition: terminology
and glossary, WfMC-TC-1011. Brussels: Workflow Management Coalition; 1999.
p. 8–11.
C. Zhuang et al.