Digital Twins in Advanced Coating Manufacturing
- July 22, 2026
Manufacturing industries are increasingly moving towards intelligent, connected, and data-driven production environments. In sectors where thermal spray coatings and advanced surface engineering play a critical role, maintaining consistent coating quality and predicting performance over time remain significant industrial challenges. Variations in process parameters, operating conditions, and material behaviour can influence coating durability, maintenance requirements, and overall equipment reliability. As industrial systems become more complex, companies are seeking digital solutions capable of improving process control while reducing costs and downtime[1],[2].
Among the most promising innovations in this area are Digital Twins. A digital twin is a virtual replica of a physical system, process, or component that continuously integrates real-world data to simulate behaviour, predict performance, and support decision-making[3]. In advanced coating manufacturing, digital twins can provide real-time insights into coating degradation, wear evolution, process optimisation, and maintenance scheduling. Combined with Artificial Intelligence and predictive analytics, these technologies are transforming how industries monitor and manage coated components throughout their lifecycle[4].
The role of Digital Twins in coating technologies
Thermal spray coatings are used in demanding industrial sectors such as aerospace, automotive, steel, chemical processing, and energy production. Their performance depends on a wide combination of variables, including coating composition, deposition parameters, operating temperature, mechanical loading, corrosion exposure, and wear conditions. Traditionally, monitoring coating behaviour has relied heavily on experimental testing and periodic inspections. While effective, these approaches are often time-consuming, expensive, and reactive rather than predictive[5].
Digital twins introduce a more proactive strategy. By integrating sensor data, modelling tools, and operational information, digital twins can simulate the real-time behaviour of coated components under industrial conditions. This allows engineers to detect anomalies earlier, predict degradation trends, optimise maintenance intervals, and improve process efficiency[6]. Instead of relying only on historical data or trial-and-error methods, industries can use digital twins to make faster and more informed decisions[7].
Challenges in implementing Digital Twins
Despite their potential, implementing digital twins in coating manufacturing is not without challenges. One major difficulty is the integration of large volumes of heterogeneous data coming from sensors, laboratory testing, industrial trials, and operational environments. Ensuring data quality and interoperability between digital systems remains a critical issue[8].
Another challenge is the complexity of coating degradation mechanisms. Wear, corrosion, thermal fatigue, and adhesion failures often interact simultaneously, making predictive modelling particularly demanding. Accurate digital twins require advanced computational models capable of representing these multi-physics phenomena under realistic industrial conditions[9].
In addition, industries must address cybersecurity, data ownership, and workforce training as digitalisation expands across manufacturing environments. Successful adoption depends not only on technological capability but also on organisational readiness and collaboration between materials scientists, software developers, and industrial operators[10].
CoBRAIN’s approach to digitalisation and decision-making
CoBRAIN contributes to this digital transformation by combining advanced materials development with Artificial Intelligence and data-driven decision-support methodologies. Rather than focusing solely on coating synthesis, the project integrates modelling, experimental validation, sustainability considerations, and digital tools into a unified framework.
A central component of this strategy is the Sustainable Decision Support System (SDSS). The SDSS supports engineers and industrial users in selecting optimal coating solutions based on performance requirements, environmental criteria, and industrial conditions. By integrating laboratory data, industrial testing results, and sustainability indicators, the SDSS acts as a bridge between research and practical industrial implementation.
In the context of digital twin development, tools such as the SDSS represent an important step towards smarter and more connected manufacturing ecosystems. Structured digital platforms can support predictive maintenance strategies, optimise coating selection processes, and improve lifecycle management across industrial applications.
Industrial and market impact
The industrial demand for smart manufacturing technologies continues to grow rapidly across Europe and worldwide. Digital twins are increasingly recognised as key enablers of Industry 4.0 and Industry 5.0 strategies, particularly in sectors where reliability, efficiency, and sustainability are critical. For coating-intensive industries, the integration of predictive digital tools can significantly reduce downtime, maintenance costs, and material waste while improving operational safety and asset longevity[11].
At the same time, sustainability pressures and stricter environmental regulations are pushing industries towards more efficient lifecycle management practices. Digital twins can contribute directly to these goals by enabling condition-based maintenance, extending equipment lifetime, and supporting data-driven optimisation of industrial processes. By improving process optimisation and reducing unnecessary maintenance interventions, digital twins can also contribute to lower material consumption and reduced environmental impact.
Conclusions
Digital twins are reshaping the future of advanced coating manufacturing by enabling more predictive, efficient, and intelligent industrial operations. By combining real-time monitoring, modelling, and Artificial Intelligence, these technologies allow industries to move beyond reactive maintenance towards proactive and optimised lifecycle management.
CoBRAIN supports this transition through its integrated approach to advanced coatings, sustainability assessment, and digital decision-making. The project demonstrates how data-driven tools such as the SDSS can contribute to smarter coating selection, improved industrial reliability, and more sustainable manufacturing practices. As digitalisation continues to expand across industrial environments, digital twins are expected to become an increasingly important component of future surface engineering strategies.
References
- Yu-jie Zhou, et all., Intelligent thermal spraying: A comprehensive review of innovation driven by artificial intelligence and machine learning, Materials today Communications, 49, 2025
- Han Gao, et all., Machine learning in thermal spraying: applications, challenges, and future perspectives, Surface Science and Technology, 4 (17), 2026
- Tao, F., et all., Digital Twin in Industry: State-of-the-Art, IEEE Transactions on Industrial Informatics, 15(4): 2405-2415, 2019
- Sabrine Ben Amor, et all., Digital Twin Implementation in Additive Manufacturing: A Comprehensive Review, Processes, 12 (6):1062, 2024
- Wolfgang Rannetbauer, et. all., Enhancing Predictive Quality in HVOF Coating Technology: A Comparative Analysis of Machine Learning Techniques, Procedia Computer Science, 232: 1377-1387, 2024
- Dalzochio, J., et all., Machine learning and reasoning for predictive maintenance in Industry 4.0: Current status and challenges, Computers in Industry, 123 (9-12): 103298, 2020
- Muhammad Qamar Khan, et all., Impact of Digital Twins on Real Practices in Manufacturing Industries, Inventions, 10(6), 106, 2025
- Alexander Wuttke, et all., Review and Classification of Challenges in Digital Twin Implementation for Simulation-Based Industrial Applications, Winter Simulation Conference 2025, Seattle, WA
- Qimuge Saren, et all., An accuracy and performance-oriented accurate digital twin modeling method for precision microstructures, Journal of Intelligent Manufacturing, 35:2887–2911, 2024
- Youde Wu, et all., A digital twin-based multidisciplinary collaborative design approach for complex engineering product development, Advanced Engineering Informatics 52(1):101635, 2022
- David, I., et all., Interoperability of Digital Twins: Challenges, Success Factors, and Future Research Directions. In: Margaria, T., Steffen, B. (eds) Leveraging Applications of Formal Methods, Verification and Validation. Application Areas. ISoLA 2024. Lecture Notes in Computer Science, vol 15223. Springer
