When I began working on this review, my intention was not simply to summarize the existing literature on thermal barrier coatings (TBCs). Instead, I wanted to explore a question that is becoming increasingly important in materials engineering: Can artificial intelligence fundamentally change how we design, manufacture, inspect, and maintain advanced coatings?
Thermal barrier coatings are indispensable in modern aerospace engines and gas turbines, where they protect metallic components from extreme temperatures and improve both efficiency and durability. Despite decades of research, many challenges remain, including coating degradation, thermally grown oxide (TGO) formation, CMAS attack, residual stresses, and limited process reproducibility. At the same time, manufacturing technologies are rapidly evolving. Artificial intelligence, additive manufacturing, digital twins, and real-time sensing are moving from research concepts to practical engineering tools. I realized that while these technologies were advancing quickly, the literature connecting them within the context of thermal barrier coatings remained fragmented. This observation became the motivation for writing this review.
One of the biggest challenges during the preparation of the manuscript was bringing together knowledge from several scientific disciplines. Thermal spray engineering, additive manufacturing, computational materials science, machine learning, and digital manufacturing each have their own terminology and research focus. Organizing these developments into a coherent review required extensive literature analysis and careful comparison of coating technologies, AI techniques, and their practical applications. The objective was to provide readers with a comprehensive roadmap rather than a collection of independent topics.
The manuscript evolved considerably during the peer-review process. The reviewers provided constructive suggestions that encouraged me to strengthen the comparisons between coating technologies, expand the discussion of AI-assisted process optimization and failure prediction, improve the quantitative analysis, and redesign several figures and summary tables. Although these revisions required substantial effort, they significantly improved the clarity, balance, and overall quality of the article. I am sincerely grateful to the editors and reviewers of The International Journal of Advanced Manufacturing Technology for their valuable feedback throughout this process.
Perhaps the most important lesson I learned while writing this review is that artificial intelligence is not replacing materials science, it is enhancing it. Machine learning models depend on high-quality experimental data, digital twins rely on accurate physical understanding, and intelligent manufacturing systems still require a strong foundation in coating materials, processing science, and failure mechanisms. The future of advanced coatings will therefore depend on combining data-driven methods with traditional materials engineering rather than viewing them as competing approaches.
I believe the next generation of thermal barrier coatings will be developed through the close integration of advanced manufacturing, in-situ monitoring, predictive modelling, and artificial intelligence. These technologies have the potential to optimize processing conditions in real time, detect defects before failure occurs, improve coating reliability, and extend component service life in demanding environments.
I hope this review will serve as a useful resource for researchers, engineers, and graduate students interested in advanced coatings, intelligent manufacturing, and high-temperature materials. More importantly, I hope it encourages further collaboration between the materials science and artificial intelligence communities as we work toward smarter and more sustainable engineering solutions.
I am delighted that this work has now been published as “Advanced Digitally-Controlled and AI-Enhanced Coating Techniques for Smart Thermal Barrier Coatings” in The International Journal of Advanced Manufacturing Technology. I look forward to seeing how the ideas discussed in this review inspire further research and accelerate the development of intelligent coating technologies for future high-temperature engineering applications.