Navigating the Challenges of Implementing Digital Twin in Manufacturing

Fiona Catherine Jerry

Adopting digital twin in manufacturing comes with its own pros and cons. It is like a double-edged sword, providing the benefits of insights, optimization, and automation but also introducing potential liabilities related to data privacy and security. The digital twins can be used for physical assets, people, processes, places, devices and systems. The benefits of digital twins in manufacturing outweigh the potential drawbacks, and they can be applied across a variety of contexts. The true potential of it is still pretty vague, but limitless and promising

The true potential of it is still pretty vague, but limitless and promising. Manufacturing Industries can now monitor usage and performance data, as it streams in real-time They can stitch together the entire asset or product life cycle into digital threads. In doing so, they revolutionize manufacturing, R&D, supply chain management, service and logistics sectors by offering innovative products as a service. In terms of design, manufacturing, and support, businesses that use digital twin technology to collect data from various sources and use it to enhance results have a significant impact.

Digital twin technology has opened up exciting possibilities in various industries. As digital twin market size is forecast to reach over six billion U.S. dollars by 2025.

Despite its numerous benefits, implementing digital twin technology poses few challenges.

Addressing the Credibility Issue

Business owners seeking to implement digital twin technology are often wary of credibility issues, as the amount of available information on how credible a virtual twin is to its physical counterpart can be quite limited. Fortunately, creating reliable virtual twins isn’t too difficult; with the right sensors and processes in place, it is possible to capture incredibly detailed scans that replicate even the smallest details. For example, a virtual twin of a car engine could be created using sensors to capture detailed scans of the engine’s

components, as well as its performance under different conditions.

Ensuring Reliability and Accuracy

The accuracy of the digital twin simulations created by the manufacturing industries depends on the quality of the model used during the creation process. The more detailed the model is, the more accurate the simulation will be. Therefore, it is imperative to ensure that the model is as comprehensive as possible to achieve the desired results. Additionally, the virtual twin simulation must be regularly updated as the environment and conditions of the physical object are constantly changing. This helps ensure that the digital twin remains accurate and relevant. By taking these steps, companies can rest assured that digital twin simulations are reliable and accurate.

Assessing the Economic Feasibility

Given that digital twin deployments are expensive; their availability is restricted by the availability of such resources, which is limited in underdeveloped nations. The increased number of sensors required adds complexity to data transmission and processing. This makes it difficult to get to the point where the digital model must be filled with real-time data. This difficulty also limits practitioners' ability to achieve significant degrees of TRL when pilot systems are presented; digital twins are included in a commercial design, or full-scale deployment. It is critical that the digital twin technology be structured with several stakeholders in mind, not exclusively engineering personnel. It should be made accessible to technicians, analysts, and managers in order to enable them to use the tool. For instance, the digital twin could be used by a technician to diagnose a system issue in the field, by an analyst to review the system performance, or by a manager to check the system safety.

Overcoming the Obstacles in Communication Networks

Using 5G technology for smart cities, citing benefits such as the ability to connect many more sensors and devices, high-speed ubiquitous connectivity, improved reliability and redundancy, and ultra-low power consumption, is extremely valuable to enable real-time data connectivity and operational efficiency for the digital twin. By leveraging existing communication networks and technologies and investing in the research and development of faster, more efficient communication infrastructures can be achieved. For example, the rise of 5G networks has enabled the development of new communication technologies such as high-definition video streaming, Internet of Things (IoT) devices, and autonomous vehicle networks.

However, despite the challenges digital twin technology holds significant promise for a wide range of manufacturing industries. The benefits of creating a digital twin technology are too vast to be fully explored. For example, manufacturing companies can use digital twins to predict and improve the efficiency of their operations, down to the component level, while automotive companies can use them to simulate the performance of their vehicles. As smart manufacturing systems become more sophisticated, digital twin market strive for intelligence. For instance, digital twins can be used to create simulations of a production line to identify and optimize processes, anticipate and avoid problems, and reduce downtime.