Beyond Technical Performance: A Multi-Criteria Approach to Industrial Coating Selection
- October 7, 2026
Selecting an industrial coating has traditionally focused on technical performance. How hard is the coating? How resistant is it to wear? Can it withstand corrosion, high temperatures, or mechanical stress? These questions remain fundamental. However, modern industrial applications are becoming increasingly complex, and technical performance is no longer the only factor that determines whether a coating is the right choice. Today, industries must also consider material availability, cost, process requirements, safety, environmental performance, resource efficiency, and expected service lifetime. A coating that performs exceptionally well according to one criterion may not necessarily be the most appropriate solution when all relevant factors are considered together. This raises an important question:
What does “the right coating” actually mean?
Answering this requires moving past isolated mechanical metrics to establish a multi-objective selection methodology that weights engineering capability against operational realities. [1]
Performance is only one piece of the puzzle
Different industrial applications place very different demands on coating systems. A coating used in a high-wear component may need exceptional hardness and wear resistance. Another application may prioritise corrosion protection, thermal stability, adhesion, or resistance to repeated thermal cycles.
At the same time, industrial users need to consider how a coating is produced and maintained. The relevant criteria may include: technical performance, material composition and availability, deposition technology, process requirements, production and maintenance costs, environmental impacts, resource efficiency, safety considerations, component lifetime, and compatibility with the intended application. This creates a multi-dimensional decision problem. The technically highest-performing coating is not necessarily the most sustainable, the most economical, or the most appropriate for a specific industrial environment. This industrial dilemma highlights why evaluating a coating requires assessing both its operational performance and its environmental-economic lifecycle impacts. [2]
From “best material” to “best solution”
The growing number of available materials and coating technologies makes selection increasingly challenging. Different coating compositions can offer different combinations of hardness, toughness, corrosion resistance, wear behaviour, processability, cost, and sustainability. Similarly, the choice of thermal spray process can influence both coating performance and resource consumption. This means that coating selection cannot always be reduced to a simple ranking. Instead, the question becomes:
Which coating provides the most appropriate balance of properties for this particular application?
This shift from identifying the “best material” to identifying the “best solution” is particularly important when sustainability objectives are included in the decision.
From data to decisions
Advanced coating development produces increasingly large and diverse datasets. Researchers and engineers may have access to information from: material composition, powder characteristics, computational modelling, process parameters, experimental testing, coating microstructure, mechanical performance, wear and corrosion behaviour, lifecycle considerations, and sustainability assessments. However, having more data does not automatically make decision-making easier. The challenge is to transform these different types of information into knowledge that can support an engineering decision. Digital decision-support systems can help by organising and connecting information from different sources and allowing users to compare alternative solutions against defined requirements, thereby enabling multi-objective process control and predictive quality regulation directly from experimental and diagnostic data streams. [3]
CoBRAIN’s Sustainable Decision Support System
This is where the CoBRAIN Sustainable Decision Support System (SDSS) becomes particularly relevant. The SDSS is designed to support coating selection by bringing together information related to materials, processes, technical performance, application requirements, and sustainability considerations. Instead of evaluating a coating according to a single property, the system supports a more holistic assessment of alternative solutions. This reflects one of the central principles of CoBRAIN: advanced coating development should connect materials engineering, computational methods, experimental evidence, and sustainability rather than treating them as independent activities. The SDSS therefore acts as a bridge between complex scientific and industrial information and the practical decisions that coating engineers and industrial users need to make.
Supporting application-specific decisions
The value of a decision-support approach lies in its ability to consider the context in which a coating will actually be used. A coating suitable for an automotive component may not be the optimal solution for a steel-processing application. Similarly, a material that performs well under laboratory conditions may not be the most appropriate choice when industrial processing requirements, cost, sustainability, and component lifetime are considered together. By structuring and combining relevant information, digital tools can help users move towards more transparent and informed comparisons. This becomes particularly important as industries seek to replace conventional materials with safer, more sustainable alternatives while maintaining demanding technical performance.
Artificial Intelligence can further strengthen this transition. Recent research shows that Machine Learning is increasingly being investigated for material composition optimisation, process-parameter optimisation, coating-performance prediction, defect detection, quality control, and digital-twin applications in thermal spraying. [4] The objective is not simply to collect more information, but to identify patterns and relationships that may be difficult to recognise through conventional approaches. Combined with structured decision-support systems, these capabilities can help create a more systematic pathway from experimental data to engineering decisions. [5]
Towards smarter coating selection
The future of coating selection is therefore likely to be increasingly multi-criteria and data-driven. Instead of asking only: “Which coating performs best?”, engineers will increasingly need to ask:
“Which coating offers the best overall solution for this application?”
That means balancing performance, durability, cost, safety, resource use, environmental impact, and industrial feasibility. CoBRAIN’s Sustainable Decision Support System (SDSS) actively facilitates this transformation by centralizing these multi-dimensional datasets within an integrated, ontology-driven digital framework.
Conclusions
Choosing an industrial coating is becoming a more complex engineering decision. Technical performance remains essential, but it is increasingly only one part of the equation. Industrial users also need to consider sustainability, safety, cost, resource efficiency, process requirements, and application-specific needs.
CoBRAIN addresses this challenge through its Sustainable Decision Support System, which connects technical and sustainability-related information to support more informed coating selection. As advanced materials and coating technologies continue to evolve, the ability to transform complex data into practical decisions will become increasingly important. The right coating is not necessarily the one that performs best in isolation. It is the one that provides the right balance of performance, sustainability, and industrial value for the application that matters.
References
- Rannetbauer, W. et al. (2025). A practical guide to optimizing industrial thermal spraying through comparative multi-objective optimization. Journal of Mathematics in Industry, 15, 12. DOI: 10.1186/s13362-025-00177-w
- Müller, C.J. & Schleifenbaum, J.H. (2026). Life-Cycle Assessment of a Hybrid HS-DED Manufacturing Route for Punching Dies with MMC Coatings. Journal of Thermal Spray Technology. DOI: 10.1007/s11666-026-02311-5
- Bocklisch, F. et al. (2026). Intelligent Decision Support in Thermal Spraying Through AI Modeling of Small and Smart Data: A Case Study on HVAF- and HVOF-Sprayed WC-CoCr Coatings. Journal of Thermal Spray Technology, 35, 2223–2250. DOI: 10.1007/s11666-026-02258-7
- Gao, H. et al. (2026). Machine learning in thermal spraying: applications, challenges, and future perspectives. Surface Science and Technology, 4, 17. DOI: 10.1007/s44251-025-00113-5
- Zhou, Y.-J. et al. (2025). Intelligent thermal spraying: A comprehensive review of innovation driven by artificial intelligence and machine learning. Materials Today Communications, 49, 113804. DOI: 10.1016/j.mtcomm.2025.113804
