Digital Twin Applications in Manufacturing

An engineer looking at a digital twin on a tablet with the machine in the background

Digital Twin Applications in Manufacturing

Introduction to Digital Twins


Developments and advancements in digital technologies have challenged traditional manufacturing and become the basis of what is known as the Fourth Industrial Revolution or Industry 4.0. Technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), Cyber-Physical Systems (CPS) and Big Data are driving the manufacturing industry towards smart manufacturing through the easy interconnection of intelligent components inside the shop floor [1].

The increasing availability of real-time operational data, thanks to IoT technologies, and the boost of AI capabilities in learning and reasoning represent drivers toward realising a vision of physical products or processes having accompanying virtual representations that evolve through their entire life cycle [2]. These virtual representations are known as Digital Twins (DTs) and represent real-time digital models of physical objects. The physical object can range from a single component inside a CNC machine to a whole factory.

There are big expectations about the benefits of Digital Twins, but the technology is still in its infancy, and much work needs to be done before we see widespread use of DTs.

What is a Digital Twin?


The first definition of Digital Twin was introduced by NASA as “an integrated multi-physics, multi-scale, probabilistic simulation of a vehicle or system that uses the best available physical models, sensor updates, fleet history, etc., to mirror the life of its flying twin. It is ultra-realistic and may consider one or more important and interdependent vehicle systems”.

But 34 years before this concept was coined, it could be argued that the first operational use of a “Digital” Twin occurred. In the Apollo 13 mission, a simulator, originally used for training, was continuously updated with the latest operational information from the actual ship and used to come up with solutions to bring the badly damaged vessel and its crew back to Earth. For more information check out the great blog article [3]

This episode highlights one of the most differentiating characteristics of a DT: the concept of continuous twinning, or mirroring of the operational conditions of the physical twin. Real-time data is continuously collected from sensors and transmitted to the virtual models to drive the simulation and prediction. Then, the parameters of the virtual models are fed back to optimise the physical entities. In the closed-loop interaction process, the physical entities and virtual models co-evolute [4].

As noted in [12], DTs are not standalone applications, but are integrated into the organization’s existing enterprise application suite and require a significant effort to produce results.


Digital Twin Application in Manufacturing


Digital Twins can be used in all manufacturing processes, from product design, manufacturing planning and execution to prognostics and health management.

Product Design


During the designing phase, digital twins are created to reflect the expectations in the designer’s mind and the constraints in the physical world. This representation enables designers to optimise the products by iteratively improving the design models. Additionally, digital twin driven verification can validate the performance of the product under real-life circumstances. Taking advantage of digital twins can result in the reduction of production lead time and the need for expensive tests and physical prototypes.

Manufacturing Planning and Execution


Digital twins can play an important role during the manufacturing planning process, especially for the scheduling of resources such as operators, material and equipment. Before the manufacturing process starts, different production schedules can be simulated and evaluated in the virtual model until satisfactory planning is settled. During the manufacturing process, the interaction and iteration between the virtual models and the physical systems allow for real-time monitoring and adjustment of manufacturing processes. For example, in manufacturing, AStar — Singapore’s Agency for Science, Technology, and Research — works with companies to equip their machine equipment with digital twins that automatically adjust its operation, such as correcting a wobbling piece on a spindle. This removes the need for extensive diagnosis and repair and can significantly reduce downtime [5].

Prognostics and Health Management


Prognostics and Health Management (PHM) is necessary to monitor the equipment condition, predict and diagnose equipment faults and component lifetime [6]. The development of a high-fidelity digital twin may support the PHM decision-making process thanks to the integrated real-time operations status of the equipment and the health status of the components. For instance, when there is a failure, the fault can be visually diagnosed and analysed by the operators and technicians. Based on this an action plan to repair the broken-down equipment is developed. And even before a product has failed, based on the real-time data from a physical machine and historical data, the machine digital twin is able to accurately predict the machine’s remaining life, faults, etc [7]. Virtual models are equally used to execute maintenance and repair strategies before a failure and determine if they are effective, executable, and optimal [8].

Digital Twin Applications for Machine Tools


Digital twins can be used in the lifecycle of machine tools in two ways; one is throughout the development and commissioning of the machine tool. Commissioning refers to the phase when contributions from mechanical, electrical and control engineers come together for the first time to form the machine. Virtual commissioning of a machine enables engineers to test, refine and optimise mechanical, electrical, and logical designs, and the integration between them, before hardware is assembled on the shop floor [9]. After the machine is tested, produced and delivered, service technicians can use the digital twin to perform quality control inspections to track down any malfunction and offer solutions without having to physically interact with the machine. This application allows machine builders to reduce development periods and product launch times.

The second way digital twins can be adopted in the machining sector is during the machine tool operation. During the operation, the operator uses a digital twin to ensure that CNC programmes are running smoothly without any faults and will deliver the desired part on the first go. It can also allow operator to ensure that a new NC programme will not cause any collisions between a tool or clamp and the machine or workpiece [10].

Benefits of Digital Twins


As mentioned in the application of digital twins in manufacturing and machining, the main benefits of this technology are focused on the optimisation of production scheduling, minimising production lead times, assessing asset utilization and performance and identifying potential bottlenecks in the manufacturing process.



Digital twin is one of the core concepts associated to the Industry 4.0 wave. This technology represents an opportunity for manufacturing companies to continue improving, thanks to the continuous evaluation of their processes, and to increase the speed of innovation thanks to the reduction of physical prototypes and testing costs. And even though the reduction of data acquisition costs and the ease of connectivity of devices (IoT) has made paradigm of the digital twin affordable for all [11], the DT is still mostly at a conceptual stage, in terms of demonstrating wide industrial adoption and becoming well-defined engineering practice within the industry [2].


[1]         E. Negri, L. Fumagalli, and M. Macchi, “A Review of the Roles of Digital Twin in CPS-based Production Systems,” Procedia Manuf., vol. 11, no. June, pp. 939–948, 2017.

[2]         C. Koulamas and A. Kalogeras, “Cyber-physical systems and digital twins in the industrial internet of things,” Computer (Long. Beach. Calif)., vol. 51, no. 11, pp. 95–98, 2018.


[4]         E. H. Glaessgen and D. S. Stargel, “The digital twin paradigm for future NASA and U.S. Air force vehicles,” Collect. Tech. Pap. – AIAA/ASME/ASCE/AHS/ASC Struct. Struct. Dyn. Mater. Conf., pp. 1–14, 2012.

[5] F. Simon and A. Enrique, “How Digital Twin Can Help Manufacturers Transform,” 2017.

[6]         Q. Qi, F. Tao, Y. Zuo, and D. Zhao, “Digital Twin Service towards Smart Manufacturing,” Procedia CIRP, vol. 72, pp. 237–242, 2018.

[7]         E. J. Tuegel, A. R. Ingraffea, T. G. Eason, and S. M. Spottswood, “Reengineering aircraft structural life prediction using a digital twin,” Int. J. Aerosp. Eng., vol. 2011, 2011.

[8]         Q. Qi and F. Tao, “Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison,” IEEE Access, vol. 6, pp. 3585–3593, 2018.

[9]         ECS, “Strategic Research Agenda for Electronic Components and Systems 2020.” pp. 123–148, 2020.

[10]      Siemens, “Digital twins of machine tools | Digitalization topics | Siemens Global.” [Online]. Available: [Accessed: 26-Jun-2020].

[11]      H. Professor Ben, “Industry 4.0 and Digital Twins: Key lessons from NASA — The Future Factory®: Business Transformation Training.” [Online]. Available: [Accessed: 26-Jun-2020].