The combination of smart manufacturing services and digital twin would radically change product design, manufacturing, usage, maintenance and other processes. Combined with the services, the digital twin will generate more efficient manufacturing planning and precise production control to help achieve smart manufacturing through the two-way connectivity between the virtual and physical worlds of manufacturing.
The advances in new generation information technologies, such as big data, high-performance computing, artificial intelligence etc., and their wide applications, are driving the manufacturing industry toward smart manufacturing
But how to converge the physical and digital worlds of manufacturing is still a major challenge. Digital twin creates high-fidelity virtual models for physical objects in digital way to simulate their behaviours.
In virtue of digital twin, complex manufacturing process can be integrated to achieve the closed-loop optimization of the product design, manufacturing, and smart services, etc.
Service plays an increasingly more important role in manufacturing. More and more manufacturers adopt service logistics for their business to compete and gain more revenues.
Services can shield the resources with differences, which are conducted by different vendors using various standards, and communication protocol/interfaces, and enable the interaction and integration between them.
With the characteristics of on-demand use, reconfiguration, and platform independence, services endow manufacturing with the advancement of large-scale sharing and collaboration.
In view of the concept of Everything-as-a-Service services could fully release the potential of digital twin. Through services, each component of the digital twin can be shared and used in a convenient “pay-as-you-go” manner, especially virtual models which are not easy to be created rapidly.
In the working process of digital twin, services are integral part, and a lot of actions require the support of third-party services. For example, multi-source data fusion requires algorithms, computing and storage services.
Due to tremendous potential of digital twin for disruptive development of industry, digital twin is receiving more and more attention from industry.
Because of the frequent use by major industry groups, some explanations and definitions of digital twin have been proposed. The most commonly used definition of digital twin is composed of three parts: physical products, virtual products and the connections between them.
Digital twin reflects two-way dynamic mapping of physical objects and virtual models By building digital twin system that integrates the manufacturing process, the innovation and efficiency from product design, production planning to manufacturing implementation, can be effectively enhanced.
For smart production, from small as a piece of equipment and a production line, to big as a shop floor or entire factory, all of them can be considered as a digital twin. Therefore, from the perspective of smart production, digital twin can be divided into three levels, i.e., unit level, system level, and system of system level.
The unit level, system level and system of systems level digital twin is a systematic model with rank going forward step by step. The system-level digital twin can be considered as the integration of multiple unit-level digital twin, which cooperate with each other. Multiple unit-level digital twins or multiple system-level digital twins constitute the systems of system-level digital twin, i.e., complex system. The unit-level and system-level digital twin meet the 3D definition of digital twin, i.e., physical entities, virtual models and the connections between them.
The unit level digital twin is the equipment. Equipment is the smallest unit participated in manufacturing activities. The optimization of manufacturing activities is achieved through the adjustment of equipment. With respect to system-level digital twin, a smart production line composed by machine tools, robot arms, etc. is system-level.
For unit-level and system-level digital twin, the virtual models are the ultra-high-fidelity mapping of physical equipment through the digital description from the perspectives of geometric shape, function and operating status of equipment and production line.
The basic attributes, real-time status and other data are transmitted to the virtual models to drive the simulation and prediction. Then, the parameters of the virtual models are fed back to optimize physical entities. In the closed-loop interaction process, the physical entities and virtual models co-develop.
For the system of system level e.g. shop-floor, accurate shop-floor management and reliable operations, which are inseparable from services, are very important for smart manufacturing. To further promote digital twin concepts and technologies, service is added and the role of data is valued. As a result, the three-dimensional structure of digital twin is extended to five-dimension, which are physical entities, virtual models, services, fusion data, and the connections among them.
Physical entities are the set of objective entities, which have specific functions to complete manufacturing tasks according to inputs and outputs
Virtual models are the digital images of the physical entities, which can completely and truly reflect the lifecycle of the physical entities.
Services integrate various functions such as management, control and optimization, to provide application services according to the requirements.
Fusion data is the core driver of the digital twin, including the data from physical entities, virtual models and service, as well as their fusion data.
The connections among them connect the parts in pairs, ensuring real-time interaction and iterative optimization. Based on the five-dimensional structure of digital twin, the digital twin shop-floor provides a new way to practice smart manufacturing.
Models and data are the cores of the digital twin. However, the creation of virtual models is complex and specialized project, so are the data fusion and analysis. For users who do not have relevant knowledge, it is difficult to build and use the digital twin. Therefore, sometimes models are able to be shared by users and data analysis are outsource.
Moreover, in the context of the manufacturing behavior, the physical resources involved in manufacturing are geographically distributed. With the characteristics of on-demand use, dynamic reconfiguration, and platform independence, services pave a way for frequently occurring problems.
The first and the most important step of service concept is to establish the information template, which consists of a variety of information For the physical objects, these information includes basic attributes e.g., name, ID, address, etc., time, cost, reliabilities, satisfaction, etc., capacities e.g., precision, size, process, etc., real-time status e.g., overload, idle, in maintenance, etc., as well as input and output.
Digital twin services consist of the equipment services, technology services, test services, data services, knowledge services, algorithms services, models services, simulation services, etc. In addition, there are many auxiliary services, such as financial services, logistics services, training services, equipment repair services and others. The services management includes searching, matching, scheduling, combination, transaction, fault-tolerance, etc.
A task is submitted to the management platform. Then, it is decomposed into subtasks that can be accomplished by a single service. The manufacturing service supply/demand matching and scheduling is carried out to select the optimal services. After the service transaction, the selected services are invoked and combined to complete the task collaboratively. Finally, the results are fed back to the users.
The digital twin services can be used in product design, production planning, manufacturing execution, equipment condition monitor, and other applications
In product design, it is the process of back-and-forth interactions between the expected, interpreted, and physical worlds. The digital twin driven design is to turn the expected product in the designer workspace into the digital representation in interpreted world based on the existing physical products.
To innovate products, designers have to study plenty of data to acquire valuable knowledge. However, the data about product is one of the most important assets, which is not easy to access. Besides, the designers also do not have the professional abilities to process massive data.
Service is an answer to these problems. Designers just simply submit their needs to the services management platform. Services managers will match the data services which designers need and the models and algorithms services that are used to process the data.
Combining and operating these services, the results are returned to the designers. As a result, designers acquire what they want in the “pay-as-you-go” manner. Moreover, after the function structure and components of product are designed, the design quality and feasibility need to be tested.
With digital twin, designers can quickly and easily forecast product behavior through verification of virtual products without having to wait until the product prototype is produced. But the virtual verification need the models of manufacturing site e.g., production line or shop-floor, etc., which designers do not have.
Model services can be used through services searching, matching and scheduling Through services, digital twin can be easy applied in product design, which can make product design more effectively to reduce the inconsistencies of expected behavior and design behavior, and greatly shorten design cycles and reduce costs.
In general, product manufacturing is the whole process from the input of raw materials to the output of finished products, which is executed in shop-floor. To reduce cost, production time, and improve efficiency, production planning to predefine the manufacturing process is necessary.
In the phase of production planning and manufacturing execution, digital twin provides an effective method to draw up the plan and optimize and execution process.
A production task is submitted to services management platform and resource services supply-demand matching and scheduling are carried out to find available resources. Then, based on the real-time status of physical resources e.g., machine tools, robot arms, etc., production plan is drawn up.
- Digital Twin offers a practical way to study smart manufacturing with focus on building digital twin for smart machine, smart workshop and smart factory.
- Digital Twin is digital mirror of physical world and maps performance of physical world
- Digital Twin introduces into production floor to open up bottlenecks through a combination of physical and virtual models
- Digital Twin can increase feasibility/quality of design project to include factory layout, material handling, buffer capacity, worker shift etc.
- Digital Twin helps to coordinate systematic factors and makes condition of designed factory strong.
- Digital Twin mirrors and feeds back physical design simulation stages to connect with suppliers, shareholders and design documents
- Digital Twin output allows designer to evaluate current design and decide if it can be approved
- Digital Twin replicates detail configurations of factory and validates if configurations can carry out volumes of throughput via simulation
- Digital Twin monitors final stage approved design and replicates it as virtual factory to confirm control of strategy and network design
- Digital Twin maps fidelity of uncertain design concept with 3D features