# From Pairwise Comparisons of Complex Behavior to an Overall Performance Rank: A Novel Alloy Design Strategy

Rafael Herschberg, Lisa Rateau, Laure Martinelli, Fanny Balbaud-Célérier, Jean Dhers, Anna Fraczkiewicz, Franck Tancret, Gérard Ramstein

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Rafael Herschberg, Lisa Rateau, Laure Martinelli, Fanny Balbaud-Célérier, Jean Dhers, et al. From Pairwise Comparisons of Complex Behavior to an Overall Performance Rank: A Novel Alloy Design Strategy. Metals, 2024, 14 (12), pp.1412. 10.3390/met14121412. emse-04918636

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Article

# From Pairwise Comparisons of Complex Behavior to an Overall Performance Rank: A Novel Alloy Design Strategy

Rafael Herschberg<fnref n="1" />, Lisa Rateau <fnref n="1" />,<fnref n="2" />,<fnref n="3" />, Laure Martinelli <fnref n="4" />,*, Fanny Balbaud-Célérier <fnref n="4" />, Jean Dhers <fnref n="3" />, Anna Fraczkiewicz <fnref n="2" />, Gérard Ramstein <fnref n="5" /> and Franck Tancret <fnref n="1" />,*

**Abstract:** A method is developed to exploit data on complex materials behaviors that are impossible to tackle by conventional machine learning tools. A pairwise comparison algorithm is used to assess a particular property among a group of different alloys tested simultaneously in identical conditions. Even though such characteristics can be evaluated differently across teams, if a series of the same alloys are analyzed among two or more studies, it is feasible to infer an overall ranking among materials. The obtained ranking is later fitted with respect to the alloy's composition by a Gaussian process. The predictive power of the method is demonstrated in the case of the resistance of metallic materials to molten salt corrosion and wear. In this case, the method is applied to the design of wear-resistant hard-facing alloys by also associating it with a combinatorial optimization of their composition by a multi-objective genetic algorithm. New alloys are selected and fabricated, and their experimental behavior is compared to that of concurrent materials. This generic method can therefore be applied to model other complex material properties—such as environmental resistance, contact properties, or processability—and to design alloys with improved performance.

**Keywords:** alloy design; machine learning; optimization; molten salt corrosion; wear

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**Citation:** Herschberg, R.; Rateau, L.; Martinelli, L.; Balbaud-Célérier, F.; Dhers, J.; Fraczkiewicz, A.; Ramstein, G.; Tancret, F. From Pairwise Comparisons of Complex Behavior to an Overall Performance Rank: A Novel Alloy Design Strategy. *Metals* **2024**, *14*, 1412. https://doi.org/10.3390/met14121412

Academic Editor: Seyedmehdi Hosseini

Received: 29 October 2024

Revised: 29 November 2024

Accepted: 3 December 2024

Published: 10 December 2024

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**Copyright:** © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## 1. Introduction and Background

Historically, materials research has been made through trial and error, making it a very lengthy and costly procedure [1]. However, thanks to an explosion of the availability of experimental data coupled with advances in artificial intelligence, a new paradigm was born: materials informatics, which can improve and accelerate materials design and development [2]. For instance, regression by machine learning, e.g., using neural networks [3], Gaussian processes [4], support vector machines [5], or genetic programming [6], can be exploited to fit some characteristics of existing alloys as a function of composition and/or processing parameters and then to predict those of new materials in view of alloy design by combinatorial optimization [7]. Even though this approach has already been proven successful, there are still a few obstacles to address. First is the lack of databases large enough to cover the landscape of some specific properties, for which it is hard to make good predictions or where the machine learning algorithms might over fit the data [8,9]. Then since only a few selected data points are finally published in the scientific literature—where failed experimental results and abandoned projects never get to see the light of day—the final machine learning model does not consider all the possible data, making it biased [10].

<footnote n="1">1 Nantes Université, CNRS, Institut des Matériaux de Nantes Jean Rouxel, IMN, 44000 Nantes, France; rafael.herschberg@cnrs-imn.fr (R.H.); lisa rateau@emse.fr (L.R.)</footnote>

<footnote n="2">2 MINES Saint-Etienne, Université de Lyon, CNRS, UMR 5307 LGF, Centre SMS, 42023 Saint-Etienne, France; fraczkie@emse.fr</footnote>

<footnote n="3">3 Framatome, 69006 Lyon, France; jean.dhers@newcleo.com</footnote>

<footnote n="4">4 CEA, Service de Recherche en Corrosion et Comportement des Matériaux, Université Paris-Saclay, 91191 Gif-sur-Yvette, France; laure.martinelli@cea.fr (L.M.); fanny.balbaud@cea.fr (F.B.-C.)</footnote>

<footnote n="5">5 Nantes Université, CNRS, Laboratoire des Sciences du Numérique de Nantes (LS2N), 44306 Nantes, France; gerard.ramstein@univ-nantes.fr</footnote>

<footnote>* Correspondence: franck.tancret@univ-nantes.fr</footnote>
Some promising advances have been made in hopes of solving this problem, such as the creation of centralized and highly specialized databases [11–13], while others have tried a holistic approach by coupling the automatization of experimental processing and characterization with modeling techniques [14,15], but such datasets remain largely inaccessible. In addition, certain complex material characteristics—like processability (e.g., castability, forgeability, weldability, etc.), environmental resistance (e.g., corrosion, hydrogen embrittlement, irradiation, etc.), or contact properties (e.g., friction, wear, etc.)—are not frequently assessed in a standardized fashion for a large variety of alloys, which results in a high experimental scatter across teams. Consequently, in such cases, no dataset exists or can even be assembled with a large amount of robust data collected in equivalent conditions to allow for a conventional machine learning treatment.

## 1.1. *Structure of Comparative Data*

The objective of this study is to present a strategy that tackles the above-mentioned obstacles, without the need for a centralized public database or an in-house experimental setup for data entry purposes through the exploitation of scarce and scattered pairwise comparative data. Indeed, it seems appealing and relevant to exploit data on the performance of materials that have been evaluated in identical conditions. Of course, each batch of experiments may have been implemented differently, but step by step and by proximity, a global rank may be inferred among all materials. An illustrative example is presented in Table 1 where, on each line, a comparison has been reported between two alloys previously tested in equal conditions in the case of corrosion experiments in molten salts.

<table><caption>**Table 1.** Pairwise comparison of alloys tested in identical experimental conditions in terms of the mass change of alloy *i* (Δ*m*<sub>*i*</sub>) relative to alloy *j* (Δ*m*<sub>*j*</sub>) during corrosion experiments in molten salts.</caption><thead><tr><th>Ref.</th><th>Alloy <i>i</i></th><th>Alloy <i>j</i></th><th>Molten Salt</th><th>T (°C)</th><th>Δ<i>m</i><sub><i>i</i></sub>/Δ<i>m</i><sub><i>j</i></sub></th></tr></thead><tbody><tr><td>[16]</td><td>Hastelloy N</td><td>MoNiCr</td><td>FLiNaK</td><td>650</td><td>1.28</td></tr><tr><td>[17]</td><td>Inconel 625</td><td>Hastelloy N</td><td>Li<sub>2</sub>BeF<sub>4</sub></td><td>750</td><td>1.45</td></tr><tr><td>[18]</td><td>Hastelloy C276</td><td>Inconel 625</td><td>NaCl-KCl-MgCl<sub>2</sub></td><td>700</td><td>1.57</td></tr><tr><td>[18]</td><td>SS316</td><td>Hastelloy C276</td><td>NaCl-KCl-MgCl<sub>2</sub></td><td>700</td><td>2.26</td></tr><tr><td>[19]</td><td>P91</td><td>SS316</td><td>NaNO<sub>3</sub>-KNO<sub>3</sub></td><td>600</td><td>20.82</td></tr></tbody></table>

In this example, the first alloy *i* is worse than the second alloy *j*, assuming that a lower mass variation indicates better corrosion resistance by a measurable ratio Δ*m*<sub>*i*</sub>/Δ*m*<sub>*j*</sub> (although this value is highly sensitive to the testing setup). From this simple example, the rank P91 > SS316 > Hastelloy C276 > Inconel 625 > Hastelloy N > MoNiCr can be easily inferred, where the symbol ">" means "worse than." If an arbitrary overall performance score S<sub>1</sub> is attributed to the best alloy (MoNiCr), it could also be possible to do the same for the others in Table 1 (where a higher score would reflect a worse performance), relying, for instance, on the relative weight variations. Hastelloy N would then have a score S<sub>2</sub>, Inconel 625 a score S<sub>3</sub>, and so forth until S<sub>6</sub> for P91, with S<sub>6</sub> > S<sub>5</sub> > S<sub>4</sub> > S<sub>3</sub> > S<sub>2</sub> > S<sub>1</sub>.

Such a situation would be easy to implement without the need of a particular algorithm. Afterward, the rank, or the score, may be fitted as a function of alloy composition by a standard machine learning regression tool and used for material design, e.g., by combinatorial optimization. However, data on complex characteristics, such as processability, environmental resistance, or contact properties, are usually scattered and sensitive to experimental conditions so a situation such as in Table 1 is almost never found. Moreover, it may happen that, among a group of alloys (e.g., A, B, C, D, E, and F), the pairwise comparisons do not yield an obvious ranking (e.g., A > B, B > C, C > D, D > C, D > B, C > E, E > F, etc.). In such a scenario, even if a real overall hierarchy exists, it would be very arduous to assign ranks or scores manually, as in the previous example, especially if there is a vast amount of pairwise comparisons.
## 1.2. *Envisioned Method*

Such a task can be undertaken by specialized algorithms, called incomplete pairwise comparison algorithms, since they can establish a ranking among a set of objects when all available pairs have not been compared, which is usually the case in materials problems. The chosen algorithm is named SpringRank (SR), originally developed by De Bacco et al. [20] and intended to infer rankings of nodes in directed networks. It has been used, for instance, to model faculty hiring networks [21] and the ranking of American universities [22]. However, to the authors' knowledge, incomplete pairwise comparison algorithms have never been used as tools for materials design. The approach developed here, whose originality is the association of different tools within the specific context of alloy modeling and design, has several steps, which are reflected in the structure of the present publication. The first step is the use of a pairwise comparison algorithm to rank and score alloys according to their relative reported performance in complex situations. The second step consists in using a machine learning tool—a Gaussian process—to fit the obtained rank or score as a function of alloy composition so as to obtain a predictive tool of the relative alloy performance. The last step, when applied, is a multi-objective optimization of composition to design alloys with optimal performance. After a presentation of the method—pairwise comparison, machine learning, and optimization algorithms—it is applied to two different case studies—molten salt corrosion of structural alloys and wear resistance of hard-facing alloys (Works on the modeling and design of hard-facing alloys were performed between and 2019 and 2023 [23]; those on molten salt corrosion in 2023–2024 within projects ANR-22-PEXD-0003 and ANR-22-PEXD-0005)—from which trends solely based on chemical content can be identified. Lastly, the presented strategy will be validated by experimental results for the case of wear resistance.

# 2. General Method

## 2.1. *Data Processing*

Data processing also presented distinct steps that are common to each case study. Firstly, a database was built from publications available in the scientific literature, where at least two different alloys were tested in identical experimental conditions. Secondly, a list of alloys similar to Table 1 was built where alloy *i* had a worse performance than alloy *j* and its $s_{ij}$ was calculated. (In the specific case of molten salt corrosion, $s_{ij}$ was equal to $\Delta m_i / \Delta m_j$.) Lastly, the list of pairwise comparisons was fed into the SR algorithm in order to determine the rank of all alloys. The size of the database and its impact on the final result are discussed separately for each case study.

## 2.2. *Pairwise Comparison Algorithm*

A pairwise comparison algorithm starts with the basic assumption that all actors (i.e., alloys) are compared in a pairwise fashion and that there exists a hierarchical rank among them. This idea is used to describe the system as a directed network (see Figure 1 in the case of molten salt corrosion; data will be described later), where each individual *i* is a node and each pairwise comparison between objects *i* and *j* is a weighted and directed edge. The algorithm then builds an adjacency matrix *A*, where $A_{ij}$ denotes the comparison between *i* and *j* individuals, where *i* is ranked above *j* with a given ratio $s_{ij}$. Then, SR models each edge as a physical spring and, by virtually stretching the network, finds the node's real-valued position that minimizes the total energy of the system, limiting inconsistencies in the ranking [24] and predicting pairwise comparisons from nodes that are not directly connected by an edge. Lastly, it provides an overall ranking of all actors by assigning them a score $S_i$. One of the main advantages of this algorithm is its computing efficiency: for both case studies, it took only a few seconds to predict an overall rank.
<figure><img src="image_5.png" /><figcaption>**Figure 1.** Graphical representation of the pairwise comparisons between the alloys studied in the literature (blue: Ni-based alloys, black: ferritic-martensitic steels, green: BCC HEA, red: FCC HEAs, yellow: austenitic stainless steels (SS), pink: Co-based alloys). The size of nodes is proportional to their centrality degree. Nodes labeled A1 to A5 represent some reference alloys, i.e., those that have been compared the most (see Table 2).</figcaption></figure>

<table><caption>**Table 2.** Reference alloys from the database in terms of their centrality degree.</caption><thead><tr><td>Ref.</td><td>Alloy</td><td>Centrality</td></tr></thead><tbody><tr><td>(A1)</td><td>SS 304</td><td>62</td></tr><tr><td>(A2)</td><td>Inconel 625</td><td>58</td></tr><tr><td>(A3)</td><td>Inconel 600</td><td>57</td></tr><tr><td>(A4)</td><td>Hastelloy N</td><td>52</td></tr><tr><td>(A5)</td><td>SS 316</td><td>48</td></tr></tbody></table>

## 2.3. Gaussian Process for Regression

An important aspect of SR is that it only needs a relative performance index $s_{ij}$ for each pairwise comparison. This means that other information, such as the chemical composition and the microstructure, are not taken into account for the final ranking. The score or rank $S_i$ is subsequently linked to the average chemical content of each alloy i by treating it as a regression problem using a Gaussian process (GP).

A detailed description of GPs can be found elsewhere [25]. A GP is a Bayesian machine learning tool that can perform a flexible regression of an output (the score or rank attributed by SR) as a function of a set of input variables (the alloy's chemical composition) based on a statistical analysis of the data. This method has been used, for instance, to model the yield strength of Ni-based alloys [26], the stability of high entropy alloys (HEAs) [27], or the surface tension of liquid metals [28]. GPs are particularly well adapted to cases where data is scarce and/or scattered [25]. This means that a GP would be the tool of choice in the present case where, as will be explained later, on the one hand, a limited amount of data is available and, on the other, the rank or score inferred by SR may be highly noisy.
## 2.4. Multi-Objective Optimization Algorithm

The search for alloys that present the best compromise between several predicted properties was made with a modified version of the multi-objective "non-dominated sorting genetic algorithm" NSGA-II [29]. As in most genetic algorithms, the original code works with a population (i.e., a group of alloys), where each one presents a particular genetic code (i.e., chemical composition) with a resulting set of characteristics (i.e., properties). At each generation, the alloys are ranked in a multi-objective perspective through a non-dominated sorting procedure based on a set of targets and constraints. Then, "parents" are selected from the population by using a binary tournament selection based on the rank and crowding distance, from which N "children" are produced by crossing couples of parents and then mutating genes (total population of 2N, typically a few hundred). At the end of an iteration, the Darwinian survival-of-the-fittest standard is applied, where only the N best individuals are kept using the same non-dominated sorting procedure, consequently becoming the parents for the next generation. After several iterations—which takes a few hours depending on the size of the population and the performance of the predictive models used (see later explanation on the use of Thermo-Calc, e.g.)—the genetic algorithm converges to an optimal solution and finds a Pareto set. In other words, it provides a group of alloys where a particular characteristic cannot be further improved without the deterioration of the remaining ones. For example, this approach has been applied to the design of superior Ni-based superalloys [30] and the discovery of strong and light HEAs [31].

# 3. Results

## 3.1. *Case Study 1: Molten Salt Corrosion*

### 3.1.1. Overall Context: Current State of Corrosion Resistance Data in the Scientific Literature

Molten salt reactors (MSRs) are a type of Generation IV nuclear fission reactor in which the primary coolant is a molten salt mixture [32]. Their design presents several operational and safety advantages over their solid fuel counterparts, such as the continuous removal of Xe after shutdown and more compact components of the primary loop, among others [33]. Moreover, MSRs are also promising in terms of recycling nuclear fuel and improving safety while maintaining an economic edge [34]. Nonetheless, the extreme conditions to which the structural materials are being subjected make it imperative to manufacture alloys with outstanding characteristics, such as a stable microstructure, high-temperature strength, and irradiation resistance, although the most limiting factor probably is their resistance to corrosion in molten salts [35]. The latter might be, for instance, fluorides or chlorides [36]. Unfortunately, corrosion experiments in molten salts are extremely sensitive to testing conditions. It has been shown that the alloy's performance is dependent on the experimental setup, such as the exposure time [37], the salt's composition [38,39], the cover gas [40,41], the relative velocity between the sample and the molten salt [42,43], the crucible [44,45], the temperature [46,47], the alloy's microstructure [48–53], and small variations in the alloy's chemical composition [54–56]. Moreover, it must be taken into account that some of these are difficult to measure, such as the purity of the salt [57] and the real-time evolution of its chemical composition [58].

There have been different data-centric strategies that tackle the modeling of corrosion-resistant alloys, though not particularly under molten salts. For example, Ozdemir et al. [59] used a random forest algorithm to screen the whole compositional space of the HfNbTaTiZr high entropy alloy system and identified a particular composition that presented improved properties for biomedical applications. Their approach for predicting the corrosion potential was validated through subsequent experimental examination. Likewise, Roy et al. [60] also developed an ML model coupled with a descriptors optimization for the prediction of the corrosion rate of multi-principal element alloys in aqueous solutions. They observed that, even though their approach narrowed down the compositional space of potential alloys, they needed a larger dataset and data of better quality in order to improve their predictions of the corrosion rate. On the other hand, Sasidhar et al. [61] recognized that
predicting pitting potential from numerical values exclusively—such as alloy composition, pH, test temperature, and ion concentration—was a rather simplistic approach. So they developed a model that had three distinct components: numerical (developed in [62]), categorial (which took into account the microstructure and type of material), and textual (which included, among others, the description of the test method and heat treatment that were interpreted via natural language processing). Their model helped them understand the contributions of different atomic species in different metallic systems toward pitting resistance. Nevertheless, all such approaches [59–62] were limited to rather conventional data, i.e., where a single property was consistently measured, for limited alloy categories and/or where testing parameters had to be included as inputs, irrespective of whether data was collected manually or automatized using language processing [61]. This makes such methods rather restrictive compared to the present one, which can potentially embrace a very wide set of alloys and conditions provided that comparative pairs of data are available, independently of experimental conditions.

In the following sections of the paper, it will be showed how, by leveraging sparsce and diverse data from scientific literature, the proposed method can identify the chemical elements that contribute the most to molten salt corrosion. In addition, it does not require explicitly enviromental factors as inputs (e.g., salt purity, temperature, exposition time, etc.), which in turn reduces the amount of variables necessary for the ML model. Lastly, the objective of the present method is not to accurately predict the mass loss or corrosion rate of a given alloy tested with a given experimental setup, but rather to be able to correctly identify the alloying elements that contribute the most to the materials' performance.

Nonetheless, few attempts have been made toward the design of alloys specifically resistant to molten salt corrosion. For instance, Wang et al. [63] coupled automated characterization with machine learning in order to study the corrosion mechanisms of Cr-Fe-Mn-Ni single-phase face-centered cubic (FCC) HEAs during four days in a LiCl-KCl eutectic salt (44 wt% LiCl—56 wt% KCl) with an addition of 2 wt% EuCl₃. They found that those with the highest Ni content were the most resistant, suggesting a sacrificial mechanism where Mn dissolved to avoid Fe from being depleted. On the other hand, Raiman and Lee [64] compiled experimental data—in fluorides and chlorides—from several publications from the period between 1960 and 2016. Their correlation analysis yielded that salt purification played a significant factor in the corrosion rate, followed by experiment setup and salt type.

There a few publications in the scientific literature where selected groups of alloys have been tested in identical experimental conditions, which were later considered for the present study. Since a standardized procedure for corrosion experiments under molten salts does not exist, the data points in the database are highly scattered. A total of 130 different publications have been taken into account, representing a total of 135 different alloys: 38 austenitic stainless steels, 33 ferritic-martensitic steels, 3 HEAs, 2 Co-based, and 59 Ni-based alloys. Some of the experiments were performed in fluorides [65–67], nitrates [68–71], chlorides [72–80], carbonates [81–83], sulfates [84–86], and even mixtures of several of them [87–89]. Some researchers performed their experiments by controlling the purity of the salt [90–92] or choosing a particular cover gas such as Ar [93–95], air [96–98], CO₂ [99], or N [100–102]. The experiments reported in the scientific literature were performed at various temperatures, ranging from 250°C [103–105] up to around 900°C [106–108], whereas some were tested in a non-isothermal fashion [109–111]. The times of exposure were also very diverse, where some alloys were studied for a few hours [112–114] up to several weeks [115–117]. Even the chosen crucibles were distinct, being as different as oxides [118–121], glassy carbon [122–124], graphite [125–127], or stainless steels [128–130], among others. The assessment of corrosion also involved various methods, like measuring a weight change [131–133], the thickness of a corroded layer [134–136], or the corrosion current during an electrochemically monitored experiment [137–139].
### 3.1.2. Application of the Method: From SpringRank to Machine Learning Prediction of Corrosion Resistance

As corrosion is an electrochemical phenomenon that can take place via different attack modes—with each one having its own mechanisms [140]—the choice of a metric that faithfully reflects the alloy's resistance is not an obvious one. For the following, two criteria were used to express the metal's performance against molten salt corrosion:

* The absolute value of the mass change (both gain and loss) after a given exposure time to reflect the fact that a good alloy would either dissolve slowly [141], grow gradually a stable oxide scale [81], or even passivate [142]. On the contrary, an important weight loss [143] would indicate a fast dissolution and poor behavior, whereas a large gain in mass would suggest a rapid oxide growth [69], associated with a high risk of scale spallation [107] and eventually to an accelerated metal loss. The possibility of an important weight gain balanced by a subsequent weight loss is not considered in this approach.

* The corrosion rate measured by electrochemistry (a corrosion current density [144]) or attack depth [136].

Consequently, the comparative index $s_{ij}$ was given by the absolute value of the ratio between the alloy *i* that had the highest mass change (or corrosion rate) divided by that of its competitor *j*. A total of 4306 pairwise comparisons were used as inputs for the ranking.

Figure 1 shows the network generated from all the pairwise comparisons. Each node is an alloy, and the edges correspond to pairs of alloys that have been compared in the scientific literature. From its general structure, it can be seen that it is a sparse graph since not all nodes are connected to all others; in fact, some of them are just compared against only one alloy. This comes from the usual practice where an experimental study focuses on examining a particular commercial alloy against homemade ones in order to analyze the impact of minor changes in their chemical composition, as in the case of N1, N2, and Inconel 713LC [56]. A clear setback from the presented strategy is that all the considered alloys (i.e., nodes) must be connected: the resulting graph has to be a closed network without the presence of isolated islands, as shown in Figure 1. Nevertheless, some HEAs [145–147], Ni-based [148–153], Fe-based [154–157], and other [158–160] interesting alloys were found in the scientific literature, but unfortunately, they were not included in the current study simply because they were not also compared to other alloys present in the database in identical experimental conditions.

It is possible to study the structure of the network (Figure 1) in terms of graph theory. Even though there are several ways to identify the most important nodes inside a network [161], the centrality degree provides some insight, if assumed that the network is undirected as a first approximation. This parameter is defined as the total number of nodes (alloys) connected to a particular one. In other words, it serves as an idea of which alloys were used as major references for molten salt corrosion tests (Table 2) and the number of alloys against which they had already been compared in the scientific literature.

Figure 2 shows the overall ranking determined by SR. The lower the score, the more performant the alloy. (The best one is at the bottom of the chart). It is possible to see that alloys with a face-centered cubic (FCC) structure (SS and Ni-based alloys) are almost always ranked better than ferritic-martensitic (FM) steels and an HEA with a body-centered cubic (BCC) structure: the top 20% alloys have an FCC structure.

Table 3 shows the best ten alloys according to SR. (The complete list is presented in Appendix A). Even though almost all of them have a high content of NI, with the notable exception of an FCC HEA, it is possible to identify different clusters according to their chemical composition. The first group corresponds to Ni-rich alloys with a very high content in Mo (15–18 wt.%), with also Cr (6–23 wt.%) (MoNiCr [16], VDM 59 [88,162], Hastelloy C4 [162,163], and Hastelloy N [16,17,65,82,90,108,123,126,127,137,164–172]. The second one corresponds to Ni-rich alloys with a high content in Al (~5–6 wt.%), Si and Nb with a smaller amount of Cr and no Mo (N101 [39,173], N1 [56], N102 [39,173], and N2 [56]). Other alloys are the FCC HEA Al<sub>0.1</sub>CoCrFeNi [174] and Kubota UCX [133], which
might be considered as a third group in that they have in common a high Cr content (like VDM 59, which is also rich in Mo). According to the literature, Mo would slow down dissolution [18,175] and/or favor passivation [76], whereas Al would promote the slow growth of a stable and protective layer of alumina (Al₂O₃) [55,176]. Cr would induce the formation of a defensive oxide scale as well (chromia—Cr₂O₃), which seems somewhat efficient in fluorides [177] but not in chlorides [74,178]. It is noticeable that even though SR was not fed with any information regarding chemical composition, it seems able to identify and deduce indirectly which elements should contribute the most to the alloy's molten salt corrosion performance starting from macroscopic measurements (i.e., the mass change or corrosion rate).

<figure><img src="image_6.png" /><figcaption>**Figure 2.** Overall alloy rank analyzed in molten salt corrosion experiments. (The complete list of alloys with their chemical composition and individual score $S_i$ can be found in Appendix A of this manuscript). “Austenitic” stands for “austenitic stainless steels”.</figcaption></figure>

The next step was to learn the score inferred by SR as a function of alloy composition using GP regression. In Table 4, the spectrum of the possible values for each chemical species is displayed, characterized by its considerable breadth. The maxima indicate that all alloys relying on a principal element are based on either Fe, Ni, or Co.
<table><caption>**Table 3.** SR's prediction of the top-ten alloys with their average chemical composition (in wt.%).</caption><thead><tr><th>Alloy</th><th>Cr</th><th>Ni</th><th>Fe</th><th>Mo</th><th>Al</th><th>C</th><th>Cu</th><th>Si</th><th>Mn</th><th>W</th><th>Co</th><th>Ti</th><th>Nb</th><th>V</th><th>S<sub>i</sub></th></tr></thead><tbody><tr><td>MoNiCr</td><td>6.77</td><td>74.81</td><td>0.7</td><td>17.41</td><td>0.005</td><td>0.001</td><td>0.01</td><td>0.162</td><td>0.059</td><td>0.0075</td><td>0.036</td><td>0.001</td><td>0.011</td><td>0.022</td><td>0</td></tr><tr><td>N101</td><td>12</td><td>76.92</td><td>0.11</td><td>-</td><td>5.8</td><td>0.06</td><td>-</td><td>2.04</td><td>-</td><td>-</td><td>0.06</td><td>1.05</td><td>1.96</td><td>-</td><td>0.54</td></tr><tr><td>N1</td><td>12</td><td>77.38</td><td>0.05</td><td>-</td><td>6</td><td>0.05</td><td>-</td><td>2.03</td><td>-</td><td>-</td><td>-</td><td>0.49</td><td>2</td><td>-</td><td>0.65</td></tr><tr><td>N102</td><td>12.5</td><td>73.95</td><td>0.15</td><td>-</td><td>6.1</td><td>0.04</td><td>-</td><td>4.67</td><td>-</td><td>-</td><td>0.06</td><td>0.53</td><td>2</td><td>-</td><td>0.94</td></tr><tr><td>N2</td><td>11.98</td><td>74.6</td><td>0.05</td><td>-</td><td>5.95</td><td>0.05</td><td>-</td><td>4.95</td><td>-</td><td>-</td><td>-</td><td>0.49</td><td>1.93</td><td>-</td><td>0.97</td></tr><tr><td>Al<sub>0.1</sub>CoCrFeNi</td><td>22.84</td><td>25.73</td><td>24.48</td><td>-</td><td>1.17</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>25.78</td><td>-</td><td>-</td><td>-</td><td>1.11</td></tr><tr><td>Hastelloy C4</td><td>16</td><td>65.105</td><td>1.5</td><td>15.5</td><td>-</td><td>0.005</td><td>-</td><td>0.04</td><td>0.5</td><td>-</td><td>1</td><td>0.35</td><td>-</td><td>-</td><td>1.32</td></tr><tr><td>VDM 59</td><td>23</td><td>59.795</td><td>0.75</td><td>15.75</td><td>0.25</td><td>0.005</td><td>-</td><td>0.05</td><td>0.25</td><td>-</td><td>0.15</td><td>-</td><td>-</td><td>-</td><td>1.32</td></tr><tr><td>Kubota UCX</td><td>41.1</td><td>47.81</td><td>5.5</td><td>1.43</td><td>-</td><td>0.48</td><td>-</td><td>2.16</td><td>-</td><td>-</td><td>-</td><td>-</td><td>1.52</td><td>-</td><td>1.33</td></tr><tr><td>Hastelloy N</td><td>7</td><td>73.105</td><td>2</td><td>16</td><td>0.125</td><td>0.06</td><td>0.175</td><td>0.5</td><td>0.4</td><td>0.25</td><td>0.01</td><td>0.125</td><td>-</td><td>0.25</td><td>1.35</td></tr></tbody></table>

<table><caption>**Table 4.** Minimum and maximum values for each chemical element in the database.</caption><thead><tr><th>Element</th><th>Cr</th><th>Ni</th><th>Fe</th><th>Mo</th><th>Al</th><th>C</th><th>Cu</th><th>Si</th><th>Mn</th><th>W</th><th>Co</th><th>Ti</th><th>Nb</th><th>V</th></tr></thead><tbody><tr><td>Min (wt.%)</td><td>0.125</td><td>0.125</td><td>0.05</td><td>0.175</td><td>0.005</td><td>0.001</td><td>0.01</td><td>0.03</td><td>0.005</td><td>0.0075</td><td>0.01</td><td>0.001</td><td>0.011</td><td>0.022</td></tr><tr><td>Max (wt.%)</td><td>41.1</td><td>79.62</td><td>99.345</td><td>28.5</td><td>10.7</td><td>5.17</td><td>3.5</td><td>5</td><td>27</td><td>26.1</td><td>51.7</td><td>3.15</td><td>5.125</td><td>1.16</td></tr></tbody></table>

Figure 3 shows the comparison between the overall score inferred by SR and the one predicted by the GP, from which several distinct features can be observed. First, there is an associated uncertainty along the horizontal axis, where certain alloys have been attributed a different score by SR, although having somewhat close chemical compositions (e.g., Hastelloy N and MoNiCr from Table 3), implying that the GP model predicts similar values of $S_i$. Second, there is an uncertainty along the vertical axis, where those scores assigned by SR are very similar to one another but very different from those predicted by GP due to their different chemical composition. Third, there seems to be a cloud of data points around a score of 3.5, which are mostly those alloys with an FM microstructure. Fourth, it is interesting to see that according to SR, most austenitic steels and Ni-based alloys are grouped in scores between 1.5 and 3, although the GP predicts that all alloys up to a score of approximately 2.3 are purely Ni-based alloys, whereas the austenitic steels are mostly grouped between scores of 2.3 and 2.8. Nonetheless, it is possible to see a fair agreement between the scores by SR and GP.

However, the relatively high scatter indicates a potential error in predictions, but the good overall agreement shows that the trends are correctly identified. Due to their Bayesian nature, GPs provide an error estimate along with their predictions, inferred from the data's statistical analysis. This can be exploited to design alloys in a robust manner by taking the predictive error into account. (This will be shown in the second case study.) In any case, when designing alloys with complex characteristics, like corrosion resistance, optimizing by following correct trends would still represent a potential improvement compared to a complete absence of predictive tools. Works are currently ongoing toward this objective of designing alloys for future molten salt nuclear reactors. In the meantime, as exposed in the following section, the approach has also been applied to model the wear resistance of hard-facing alloys, through the analysis of a smaller dataset with a different network structure. The model has then been used to design new alloys by combinatorial optimization, followed by experimental validation, to illustrate the ability of the method to extract correct trends from a reduced and scattered pairwise comparative dataset and to make reliable predictions.
<figure><img src="image_7.png" /><figcaption>**Figure 3.** Comparison between the scores computed by SR and GP. "Austenitic" stands for "austenitic stainless steels".</figcaption></figure>

## 3.2. Case Study 2: Design of Wear-Resistant Hard-Facing Alloys

### 3.2.1. Current State of Wear-Resistance Data and Application of the Proposed Method

The prevention of wear on structural components can be achieved by depositing coatings of so-called hard-facing alloys. Consequently, the part can globally keep its bulk mechanical properties, including a good combination of strength, ductility, and toughness, while the hard coating faces external contact stresses, avoiding surface abrasion or erosion. Although the wear resistance of pure metals or solid solution alloys has been found to correlate mainly to hardness, this is not the case when a superior wear performance is to be obtained through complex microstructures [179]. In such cases, specific testing must be undertaken. However, the material's response to wear is, like in the corrosion case, highly sensitive to the testing conditions. It has been shown that hard-facing alloy performance is dependent on the experimental conditions, such as differences in the chemical composition [180,181] and microstructure [182] of the alloy, the deposition technique used to produce the coatings [183], the temperature [184], the atmospheric humidity [185], and the experimental setup itself [186]. The latter can involve, for instance, pin-on-disc, ball-on-disc, plate-on-plate, disc-on-disc geometries, etc. The counter material can be identical to the tested one or be a very hard material, e.g., a ceramic or cermet. The relative movement of both parts can be linear or rotary, with variable testing parameters like friction velocity, contact force or pressure, total length of friction, absence or presence of a lubricant, nature of the latter, etc. Thus, the measurement of wear itself is also different across studies. Indeed, a wear rate is usually expressed by the unit length of friction, but it can refer to a loss in mass or thickness, to the depth or the volume of the wear track, etc. Therefore, the problem is of the same nature as for molten salt corrosion: it is almost impossible to gather a significant dataset of materials tested in identical conditions. However, in several literature sources [180–184,187–192], two or more alloys have been characterized in the same manner, rendering possible a pairwise comparison strategy, provided that some alloys are present in different sources, to allow a proximity ranking. The so-obtained dataset is constituted of 15 Fe-based alloys, 14 Ni-based alloys, and 6 Co-based
alloys. The resulting network is presented in Figure 4 in a similar way as in the previous section. Several comments can be made: (i) with only 35 alloys, the size of the dataset remains rather small; (ii) there is also a limited number of edges; (iii) Stellite 6 is by far the most central alloy, i.e., the one most often used as a reference in the experimental studies, which seems normal since it is probably the most popular hard-facing alloy, followed by Norem 02, which is one of the most studied material as a potential Co-free replacement for Stellite alloys in the nuclear industry (this will be explained later); and (iv) besides such reference alloys, many materials have only been compared a few times. It is therefore interesting to see on the one hand if such a network structure can be exploited by a pairwise comparison algorithm and, on the other, if compositional trends can be learned by a GP to build a statistical regression model, which may eventually be used to design new highly performant alloys by combinatorial optimization.

<figure><img src="image_8.png" /><figcaption>**Figure 4.** Graphical representation of the pairwise comparisons between the alloys studied in the literature (blue: Ni-based alloys; black: Fe-based alloys; yellow: Co-based alloys). The size of nodes is proportional to their centrality degree. Nodes labeled A and B correspond to Stellite 6 and Norem 02, respectively.</figcaption></figure>

The method associating the pairwise comparison algorithm and a GP to produce a predictive model of alloy performance as a function of composition is similar to the one of the first case study. To illustrate a different possibility, the wear resistance rank is here fitted by the GP instead of the score $S_i$. The rank predicted by GP is plotted in Figure 5 against the one determined by SR.

Despite the small number of data points and a substantial dispersion, a fair agreement is obtained, indicating that the model seems capable to capture compositional trends governing the wear resistance of alloys. The originality here is to incorporate this model into an alloy design scheme by combinatorial multi-objective optimization, which is described hereafter.
<figure><img src="image_9.png" /><figcaption>**Figure 5.** Rank predicted by the GP regression model as a function of the rank attributed by the pairwise comparison algorithm (SR, see Appendix B) for the alloys of the database (being either Fe-based, Cot-based, or Ni-based). Stellite 6 and Norem 02 alloys are highlighted.</figcaption></figure>

### 3.2.2. Design of Superior Alloys: Objectives and Constraints in the Optimization Algorithm

Hard-facing alloys are, among others, used in the primary circuit of nuclear power plants, as wear-resistant coatings on parts of valves and pumps, as well as on rod control mechanisms. The most used material is Stellite 6, a Co-based alloy rich in Cr, hardened by carbides. It presents a very good combination of corrosion resistance, wear resistance, and ability to be deposited by welding-type processes. However, it suffers from a major drawback: following wear during service, alloy particles are driven in the primary circuit, and their Co is activated in the long-period $^{60}$Co radioactive isotope, which is at the origin of a significant part of the radiation dose received by maintenance workers in nuclear power plants and complicates future dismantling. Vast research has been made toward the development of Co-free alloys, like Norem 02 [23] or Nitromaxx [23], with mixed results, so the search for a better alternative is still ongoing. An approach is presented here to design new Fe-based, Co-free hard-facing alloys via a multi-objective combinatorial optimization. The latter was performed by a genetic algorithm [30], aiming among others at:

* Maximizing the wear resistance predicted by the new model described above, as-sociating a pairwise comparison algorithm and GP regression; this corresponds to minimizing the predicted rank. Nevertheless, to improve the robustness of the design process, minimization was not performed on the mean predicted rank but on the mean plus a standard deviation. Indeed, being statistical tools, GPs calculate the predictive distribution of the model output so that its standard deviation can be seen as a predictive error estimate. Doing so, a pessimistic rank is obtained, which should better guarantee that the designed alloys will actually match the objective.

* Targeting a specific type of as-solidified microstructure since alloys will be deposited by welding-type processes. It must be made of a metallic solid solution matrix reinforced by hard phases such as carbides and/or borides, as in most existing hard-facing alloys.

* Maximizing the chromium content in the solid solution matrix ("free Cr") to ensure corrosion resistance.
Microstructural features were predicted by computational thermodynamics, using the "calculation of phase diagrams" (Calphad) software Thermo-Calc (version 2018, Stockholm, Sweden) along with the TCFE9 database for Fe-based alloys. The Scheil model was employed, which is adapted to the simulation of as-solidified microstructures.

The genetic algorithm was configured to explore the compositional space of Table 5.

<table><caption>**Table 5.** Compositional space explored in alloy design (in wt.%). Iron is the balance element.</caption><thead><tr><th>Element</th><th>Cr</th><th>Mn</th><th>Mo</th><th>V</th><th>W</th><th>Ni</th><th>Nb</th><th>Si</th><th>C</th><th>B</th><th>N</th></tr></thead><tbody><tr><td>Min</td><td>15</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Max</td><td>40</td><td>5</td><td>3</td><td>3</td><td>10</td><td>5</td><td>3</td><td>3</td><td>5</td><td>2</td><td>1</td></tr><tr><td>Step</td><td>0.1</td><td>0.1</td><td>0.1</td><td>0.1</td><td>0.1</td><td>0.1</td><td>0.1</td><td>0.1</td><td>0.01</td><td>0.01</td><td>0.01</td></tr></tbody></table>

After different optimization runs, the algorithm produced hundreds of Pareto-optimal alloys, presenting different trade-offs between, among others, predicted wear resistance (rank) and corrosion resistance (free Cr). Among all designed materials, four alloys were selected (hereafter named AS1, AS2, AS3, and AS4), exhibiting different compromises between such characteristics. Their composition is given in Table 6.

<table><caption>**Table 6.** Composition of selected alloys (in wt.%). Iron is the balance element. Only a compositional range is given for AS4 due to confidentiality issues.</caption><thead><tr><th>Alloy</th><th>Cr</th><th>Mn</th><th>Mo</th><th>W</th><th>Ni</th><th>Nb</th><th>Si</th><th>C</th><th>B</th></tr></thead><tbody><tr><td>AS1</td><td>25</td><td>2</td><td>0.2</td><td>0</td><td>1</td><td>1.1</td><td>0</td><td>1.2</td><td>1</td></tr><tr><td>AS2</td><td>22</td><td>0</td><td>0.2</td><td>0</td><td>0.3</td><td>1.1</td><td>0.6</td><td>1.2</td><td>1</td></tr><tr><td>AS3</td><td>21</td><td>2.2</td><td>0</td><td>0</td><td>6.7</td><td>2</td><td>0</td><td>0.45</td><td>0.7</td></tr><tr><td>AS4</td><td>15–25</td><td>&lt;3</td><td>&lt;5</td><td>&lt;3</td><td>5–10</td><td>&lt;3</td><td>&lt;3</td><td>&lt;1.5</td><td>&lt;1.5</td></tr></tbody></table>

### 3.2.3. Experimental Validation of the Designed Alloys with Their Corresponding Rank

Selected alloys, along with two reference alloys (Norem 02 and the best alloy in terms of wear resistance identified by SR from the database [23,181], hereafter called "FeCrB"), were produced (300g ingots) by induction melting in a cold crucible. In agreement with the industrial process foreseen for this kind of materials, alloys were studied in the as-solidified state, with a microstructure similar to one of welded deposits. Specimens were investigated by a set of microstructural characterization techniques, such as X-ray diffraction, transmission electron microscopy (TEM), scanning electron microscopy (SEM) with energy dispersive spectroscopy (EDS), and electron backscatter diffraction (EBSD). W ear testing was undertaken at room temperature without lubricant using a rotary tribometer with a ball-on-disc geometry, with a 6 mm diameter tungsten carbide ball and a disc of the tested material polished with silicon carbide abrasive paper down to grade P1200. The contact force was 8 N, and the relative ball-track velocity was $1.5 \text{ mm.s}^{-1}$, for a total track length of 150 m, with a wear track diameter of 16 mm. The worn volume was then calculated as the product between the circumference and the cross-section area of the wear track, determined by profilometry after observing the track from above with an optical confocal microscope. (The cross-section area was averaged from eight locations around the track.) The worn volume was then divided by the contact force and the total track length to obtain the specific wear rate. In addition to in-house-produced alloys (AS1, AS2, AS3, AS4, Norem 02, and FeCrB), a disc of commercially available Stellite 6 was tested in identical conditions for comparison purposes. An in-depth analysis of the found microstructures and their general properties can be found elsewhere [23].
Wear resistance results are displayed in Figure 6, plotted as the measured specific wear rate versus the wear rank resistance predicted by the model.

<figure><img src="image_10.png" /><figcaption>**Figure 6.** Measured specific wear rate as a function of the robust wear rank predicted by the model for reference alloys (Norem 02, Stellite 6, and FeCrB) and the newly designed alloys (AS1, AS2, AS3, and AS4).</figcaption></figure>

Although there is no simple relation between both quantities, the predicted trend is satisfactory since the experimental measurements are in the same order as the predicted wear rank. This demonstrates the applicability of the new modeling method associating a pairwise comparison algorithm with a regression model. The best alloys are the already reported alloy FeCrB and our new alloys AS1 and AS2, with measured wear rates well below those of industrial alloys Stellite 6 and Norem 02. Alloy FeCrB performs slightly better than alloys AS1 and AS2, but its predicted free Cr concentration (12.8 wt.%) is lower than that of alloys AS1 (17.2 wt.%) and AS2 (15.9 wt.%), with an associated risk of lower corrosion resistance. All three alloys (FeCrB, AS1, and AS2) are rather brittle, with an experimental strain to failure around 3–4% in compression, rendering their application somewhat risky. Conversely, all other alloys could be strained to at least 10% in compression and appear more ductile, associated with a potentially safer use. Norem 02 is less resistant to wear, but among the Fe-based alloys, it is the one with the highest free Cr concentration: 19.4 wt.% vs. 17.7 wt.% for AS3 and 15.3 wt.% for AS4. (Not being Fe-based, Stellite 6 cannot be directly compared on this criterion.) Both alloys AS3 and AS4 could therefore be considered as interesting trade-offs compared to Norem 02, with a slightly reduced corrosion resistance but a substantial gain in wear resistance between ~20% and ~40%. Results are summarized in Figure 7 in terms of compromise between wear resistance and potential corrosion resistance of Fe-based alloys.

Aiming for a low wear rate and a high free Cr concentration, all alloys shown in Figure 7 would be Pareto-optimal except for AS4. Nevertheless, considering separately brittle and ductile alloys makes all of them Pareto-optimal within their own category.
<figure><img src="image_11.png" /><figcaption>Figure 7. Dependency of the specific wear rate with respect to the free Cr concentration (wt.%).</figcaption></figure>

4. Conclusions and Perspectives

In the present paper, a method is developed to exploit data on complex material
behaviors that are impossible to analyze with standard machine learning techniques. In
this respect, it cannot be compared to others and stands as a unique tool to tackle data in
which the comparative structure is the only exploitable feature. This method is here applied
to particular case studies: the corrosion of structural materials in molten salts and the wear
resistance of hard-facing alloys. A pairwise comparison algorithm—SR—is used to evaluate
the properties of pairs of alloys tested in identical conditions in a given study. By also
considering some common alloys but tested with different experimental setups by different
teams, an overall ranking between materials is deduced. At this first stage, no information
or data is needed on important characteristics, such as alloy composition, processing, or
microstructure: ranking is made on material performance only. As such, SR is able to infer
implicitly which elements contribute the most to the materials performance in each case
study. The obtained score—or rank—is then fitted as a function of alloy composition by a
GPs regression, as the data points are scarce and noisy.

For the first case—the resistance to corrosion by molten salts—a database is built from
alloys reported in the scientific literature. The dataset constitutes a rather dense network of
135 materials tested in 130 separate investigations (where at least two alloys are compared
in identical conditions), resulting in more than 4000 pairwise comparisons from which SR
is able to identify which constituents (e.g., Mo, Al, Cr, etc.) contribute the most to corrosion
resistance. Then, the score inferred by SR is fitted by a GP regression as a function of
14 compositional variables. There is a fair agreement between the actual and predicted
scores, and the model is able to reproduce some compositional trends that are expected
from a qualitative physical analysis (e.g., the role of the base element Fe or Ni and their
alloying constituents). Nonetheless, it is hoped that relevant interactions are also captured
and that a quantitative exploitation of such a model is possible.

This final point is demonstrated in the second case study, the wear resistance of
hard-facing alloys. The pairwise network is much smaller than in the previous case study
(35 materials tested in 11 separate investigations for a total of 106 pairwise comparisons)
and presents a smaller number of ramifications. However, it is shown that the obtained
model can be exploited to make reliable predictions and, as such, be used to design new
performant alloys by combinatorial optimization, which is here performed via a multi-
objective genetic algorithm. At this stage, in addition to the machine learning model built
using SR and GP, the microstructure resulting from both composition and processing is
taken into account through thermodynamics using the Calphad software Thermo-Calc, e.g.,
to aim for a certain type of solidification path and structure. Four new alloys have been selected, elaborated, and compared in identical testing conditions to concurrent materials. Some of the designed alloys display interesting trade-offs between several characteristics. In any case, the better the predicted wear resistance rank, the lower the actual wear rate, indicating that the new model is able to fit existing data and predict correct trends. This notion of trends is fundamental in alloy design and central to the present approach. Indeed, obtained models being based on comparative data that are, by nature, only comparable within a given study but incomparable across different studies, their interpretability can only be comparative itself. The inferred rank or score does not have any absolute physical meaning, but compositional trends are correct. At best, when a prediction is made for a new alloy, the user may try to find, among known alloys in the database, those that possess similar ranks or scores. Looking at the published works where the associated data came from, it could be possible to estimate the potential performance of the new alloy if it were to be tested in those particular conditions.

As a final point, it would be interesting to develop a more homogeneous database—e.g., having every single pairwise comparison tested in identical conditions—and to incorporate alloys in the analysis that present unique chemical compositions and microstructures (e.g., HEAs, intermetallics, and duplex steels, just to name a few). In fact, even if the method proved to be able to tackle successfully incomplete datasets on complex characteristics and although trends are exploitable, the level of scatter—hence uncertainty—remains substantial. This, in the end, also calls for more standardized testing protocols and cross-validation procedures between institutions [193–195]. Nonetheless, the method proposed in this paper should be capable to analyze other complex materials characteristics, i.e., irradiation resistance, formability, hydrogen embrittlement, etc., and to accelerate the search and design of alloys with superior properties.

**Author Contributions:** Conceptualization, R.H., L.R. and F.T.; methodology, R.H., L.R., A.F., G.R. and F.T.; software, G.R. and F.T.; validation, R.H. and L.R.; formal analysis, R.H. and L.R.; investigation, R.H. and L.R.; resources, J.D., A.F., G.R. and F.T.; data curation, R.H., L.R. and L.M.; writing—original draft preparation, R.H. and F.T.; writing—review and editing, L.M., F.B.-C., A.F. and G.R.; visualization, R.H. and L.R.; supervision, A.F. and F.T.; project administration, J.D., A.F. and F.T.; funding acquisition, F.B.-C. and J.D. All authors have read and agreed to the published version of the manuscript.

**Funding:** The work of L. Rateau (2019–2023) on wear-resistant alloys has notably benefited from the cofunding of Framatome Company and the Association Nationale de la Recherche et de la Technologie (ANRT), under agreement CIFRE-2019/1194. The work on molten salt corrosion (notably that of R. Herschberg in 2023–2024) has benefited from French government grants managed by the Agence Nationale de la Recherche (ANR) under the France 2030 program DIADEM; references ANR-22-PEXD-0003 and ANR-22-PEXD-0005.

**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

**Acknowledgments:** Authors thank the technical contribution of the staff of Centre Technique Framatome—Le Creusot to the measurement of wear resistance. The technical staff of MINES (M. Haering and C. Varillon) is acknowledged for the elaboration of model alloys.

**Conflicts of Interest:** Authors Lisa Rateau, Jean Dhers were employed by the company Framatome. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
<table><caption>Appendix A. Ranking of the Alloys Tested Under Molten Salts Experiments</caption><thead><tr><th>Alloy</th><th>Cr</th><th>Ni</th><th>Fe</th><th>Mo</th><th>Al</th><th>C</th><th>Cu</th><th>Si</th><th>Mn</th><th>W</th><th>Co</th><th>Ti</th><th>Nb</th><th>V</th><th>S<sub>i</sub></th></tr></thead><tbody><tr><td>MoNiCr</td><td>6.77</td><td>74.81</td><td>0.7</td><td>17.41</td><td>0.005</td><td>0.001</td><td>0.01</td><td>0.162</td><td>0.059</td><td>0.0075</td><td>0.036</td><td>0.001</td><td>0.011</td><td>0.022</td><td>0</td></tr><tr><td>N101</td><td>12</td><td>76.92</td><td>0.11</td><td>-</td><td>5.8</td><td>0.06</td><td>-</td><td>2.04</td><td>-</td><td>-</td><td>0.06</td><td>1.05</td><td>1.96</td><td>-</td><td>0.54</td></tr><tr><td>N1</td><td>12</td><td>77.38</td><td>0.05</td><td>-</td><td>6</td><td>0.05</td><td>-</td><td>2.03</td><td>-</td><td>-</td><td>-</td><td>0.49</td><td>2</td><td>-</td><td>0.65</td></tr><tr><td>N102</td><td>12.5</td><td>73.95</td><td>0.15</td><td>-</td><td>6.1</td><td>0.04</td><td>-</td><td>4.67</td><td>-</td><td>-</td><td>0.06</td><td>0.53</td><td>2</td><td>-</td><td>0.94</td></tr><tr><td>N2</td><td>11.98</td><td>74.6</td><td>0.05</td><td>-</td><td>5.95</td><td>0.05</td><td>-</td><td>4.95</td><td>-</td><td>-</td><td>-</td><td>0.49</td><td>1.93</td><td>-</td><td>0.97</td></tr><tr><td>Al<sub>0.1</sub>CoCrFeNi</td><td>22.84</td><td>25.73</td><td>24.48</td><td>-</td><td>1.17</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>25.78</td><td>-</td><td>-</td><td>-</td><td>1.11</td></tr><tr><td>Hastelloy C4</td><td>16</td><td>65.105</td><td>1.5</td><td>15.5</td><td>-</td><td>0.005</td><td>-</td><td>0.04</td><td>0.5</td><td>-</td><td>1</td><td>0.35</td><td>-</td><td>-</td><td>1.32</td></tr><tr><td>VDM 59</td><td>23</td><td>59.795</td><td>0.75</td><td>15.75</td><td>0.25</td><td>0.005</td><td>-</td><td>0.05</td><td>0.25</td><td>-</td><td>0.15</td><td>-</td><td>-</td><td>-</td><td>1.32</td></tr><tr><td>Kubota UCX</td><td>41.1</td><td>47.81</td><td>5.5</td><td>1.43</td><td>-</td><td>0.48</td><td>-</td><td>2.16</td><td>-</td><td>-</td><td>-</td><td>-</td><td>1.52</td><td>-</td><td>1.33</td></tr><tr><td>Hastelloy N</td><td>7</td><td>73.105</td><td>2</td><td>16</td><td>0.125</td><td>0.06</td><td>0.175</td><td>0.5</td><td>0.4</td><td>0.25</td><td>0.01</td><td>0.125</td><td>-</td><td>0.25</td><td>1.35</td></tr><tr><td>SS 310HCbN</td><td>25</td><td>20.5</td><td>52.28</td><td>-</td><td>-</td><td>0.07</td><td>-</td><td>0.75</td><td>1</td><td>-</td><td>-</td><td>-</td><td>0.4</td><td>-</td><td>1.37</td></tr><tr><td>GH 3535</td><td>6.94</td><td>70.95</td><td>3.93</td><td>16.6</td><td>0.18</td><td>0.53</td><td>-</td><td>0.32</td><td>0.5</td><td>0.05</td><td>-</td><td>-</td><td>-</td><td>-</td><td>1.4</td></tr><tr><td>Incoloy 800HT</td><td>21</td><td>32.5</td><td>45.395</td><td>-</td><td>0.5125</td><td>0.08</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0.5125</td><td>-</td><td>-</td><td>1.41</td></tr><tr><td>Incoloy 825 (3)</td><td>22.69</td><td>42.574</td><td>26.84</td><td>3.28</td><td>-</td><td>-</td><td>2.79</td><td>-</td><td>0.486</td><td>-</td><td>-</td><td>1.34</td><td>-</td><td>-</td><td>1.44</td></tr><tr><td>Hastelloy C2000</td><td>23</td><td>57.355</td><td>1.5</td><td>16</td><td>0.25</td><td>0.005</td><td>1.6</td><td>0.04</td><td>0.25</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>1.45</td></tr><tr><td>N103</td><td>20</td><td>66.355</td><td>0.05</td><td>-</td><td>6</td><td>0.05</td><td>-</td><td>5</td><td>0.005</td><td>-</td><td>0.04</td><td>0.5</td><td>2</td><td>-</td><td>1.47</td></tr><tr><td>Ni-26W-6Cr</td><td>5.86</td><td>66.123</td><td>0.54</td><td>1</td><td>-</td><td>0.037</td><td>-</td><td>0.14</td><td>-</td><td>26.1</td><td>-</td><td>0.2</td><td>-</td><td>-</td><td>1.52</td></tr><tr><td>Hastelloy C22</td><td>22</td><td>57.28</td><td>3</td><td>13</td><td>-</td><td>0.005</td><td>-</td><td>0.04</td><td>0.25</td><td>3</td><td>1.25</td><td>-</td><td>-</td><td>0.175</td><td>1.53</td></tr><tr><td>KhN62M</td><td>23.2</td><td>63.02</td><td>0.47</td><td>13</td><td>0.11</td><td>-</td><td>0.01</td><td>0.03</td><td>0.03</td><td>0.05</td><td>-</td><td>0.08</td><td>-</td><td>-</td><td>1.54</td></tr><tr><td>Haynes 263</td><td>20</td><td>51.49</td><td>0.35</td><td>6</td><td>0.3</td><td>0.06</td><td>-</td><td>0.2</td><td>0.4</td><td>-</td><td>20</td><td>1.2</td><td>-</td><td>-</td><td>1.55</td></tr><tr><td>SS S35140</td><td>21</td><td>26</td><td>48.575</td><td>1.5</td><td>-</td><td>0.05</td><td>-</td><td>0.375</td><td>2</td><td>-</td><td>-</td><td>-</td><td>0.5</td><td>-</td><td>1.57</td></tr><tr><td>Haynes 75</td><td>20</td><td>73.525</td><td>5</td><td>-</td><td>-</td><td>0.075</td><td>-</td><td>0.5</td><td>0.5</td><td>-</td><td>-</td><td>0.4</td><td>-</td><td>-</td><td>1.58</td></tr><tr><td>Inconel 713LC</td><td>12</td><td>79.45</td><td>-</td><td>-</td><td>5.9</td><td>0.05</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0.6</td><td>2</td><td>-</td><td>1.64</td></tr><tr><td>Haynes 214</td><td>16</td><td>76.1</td><td>3</td><td>-</td><td>4.5</td><td>0.05</td><td>-</td><td>0.1</td><td>0.25</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>1.65</td></tr></tbody></table>
<table><thead><tr><th>Alloy</th><th>Cr</th><th>Ni</th><th>Fe</th><th>Mo</th><th>Al</th><th>C</th><th>Cu</th><th>Si</th><th>Mn</th><th>W</th><th>Co</th><th>Ti</th><th>Nb</th><th>V</th><th>S<sub>i</sub></th></tr></thead><tbody><tr><td>SS 309</td><td>23</td><td>14</td><td>59.8</td><td>-</td><td>-</td><td>0.2</td><td>-</td><td>1</td><td>2</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>1.66</td></tr><tr><td>Inconel MA754</td><td>20</td><td>78.15</td><td>1</td><td>-</td><td>0.3</td><td>0.05</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0.5</td><td>-</td><td>-</td><td>1.68</td></tr><tr><td>Haynes 25</td><td>20</td><td>10</td><td>1.5</td><td>0.5</td><td>-</td><td>0.1</td><td>-</td><td>0.2</td><td>1.5</td><td>15</td><td>51.2</td><td>-</td><td>-</td><td>-</td><td>1.68</td></tr><tr><td>Hastelloy G</td><td>22.25</td><td>45.1</td><td>19.5</td><td>6.5</td><td>-</td><td>0.025</td><td>0.75</td><td>0.5</td><td>1.5</td><td>0.5</td><td>1.25</td><td>-</td><td>2.125</td><td>-</td><td>1.72</td></tr><tr><td>Kubota KHR45</td><td>36.2</td><td>39.98</td><td>19.1</td><td>1.5</td><td>-</td><td>0.4</td><td>-</td><td>1.31</td><td>-</td><td>-</td><td>-</td><td>-</td><td>1.51</td><td>-</td><td>1.75</td></tr><tr><td>N3</td><td>20.05</td><td>66.34</td><td>0.05</td><td>-</td><td>6.05</td><td>0.05</td><td>-</td><td>4.98</td><td>-</td><td>-</td><td>-</td><td>0.5</td><td>1.98</td><td>-</td><td>1.75</td></tr><tr><td>Multimet</td><td>21.25</td><td>20</td><td>30.38</td><td>3</td><td>-</td><td>0.12</td><td>-</td><td>0.5</td><td>1.5</td><td>2.5</td><td>19.75</td><td>-</td><td>1</td><td>-</td><td>1.80</td></tr><tr><td>Haynes 244</td><td>8</td><td>61.835</td><td>1</td><td>22.5</td><td>0.25</td><td>0.015</td><td>-</td><td>-</td><td>0.4</td><td>6</td><td>-</td><td>-</td><td>-</td><td>-</td><td>1.84</td></tr><tr><td>Haynes HR224</td><td>20</td><td>48.7</td><td>27.5</td><td>-</td><td>3.8</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>1.89</td></tr><tr><td>SS 310S</td><td>25</td><td>20.5</td><td>52.71</td><td>-</td><td>-</td><td>0.04</td><td>-</td><td>0.75</td><td>1</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>1.93</td></tr><tr><td>Inconel 718</td><td>19</td><td>53.385</td><td>17</td><td>3.05</td><td>0.5</td><td>0.04</td><td>0.15</td><td>0.175</td><td>0.175</td><td>-</td><td>0.5</td><td>0.9</td><td>5.125</td><td>-</td><td>1.93</td></tr><tr><td>Hastelloy G35</td><td>33</td><td>57.225</td><td>1</td><td>8</td><td>0.2</td><td>0.025</td><td>-</td><td>0.3</td><td>0.25</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>1.96</td></tr><tr><td>Rene 41</td><td>19</td><td>52.39</td><td>2.5</td><td>9.75</td><td>1.6</td><td>0.06</td><td>0.25</td><td>0.25</td><td>0.05</td><td>-</td><td>11</td><td>3.15</td><td>-</td><td>-</td><td>1.97</td></tr><tr><td>Haynes 282</td><td>20</td><td>56.865</td><td>0.75</td><td>8.5</td><td>1.5</td><td>0.06</td><td>-</td><td>0.075</td><td>0.15</td><td>-</td><td>10</td><td>2.1</td><td>-</td><td>-</td><td>1.98</td></tr><tr><td>Hastelloy C</td><td>16</td><td>73.16</td><td>5.5</td><td>-</td><td>-</td><td>0.04</td><td>-</td><td>0.05</td><td>0.5</td><td>4.75</td><td>-</td><td>-</td><td>-</td><td>-</td><td>2.00</td></tr><tr><td>Inconel 601</td><td>23</td><td>61.35</td><td>13</td><td>-</td><td>1.35</td><td>0.05</td><td>0.5</td><td>0.25</td><td>0.5</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>2.02</td></tr><tr><td>Inconel 702</td><td>15.5</td><td>79.62</td><td>1</td><td>0</td><td>3.25</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Kaeri Super alloy 4</td><td>8</td><td>36</td><td>55.485</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0.25</td><td>0.25</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>2.07</td></tr><tr><td>ATI 332Mo</td><td>21.5</td><td>33</td><td>41.025</td><td>2.5</td><td>-</td><td>0.05</td><td>-</td><td>0.25</td><td>1.25</td><td>-</td><td>-</td><td>-</td><td>0.425</td><td>-</td><td>2.15</td></tr><tr><td>Kubota KHR35</td><td>22.8</td><td>35.96</td><td>36.6</td><td>1.47</td><td>-</td><td>0.39</td><td>-</td><td>1.31</td><td>-</td><td>-</td><td>-</td><td>-</td><td>1.47</td><td>-</td><td>2.23</td></tr><tr><td>RA 333</td><td>25.5</td><td>45.5</td><td>17.085</td><td>3.25</td><td>-</td><td>0.04</td><td>-</td><td>1.125</td><td>1</td><td>3.25</td><td>3.25</td><td>-</td><td>-</td><td>-</td><td>2.23</td></tr><tr><td>Incoloy 825</td><td>21.5</td><td>42</td><td>29.475</td><td>3</td><td>0.1</td><td>0.025</td><td>2.25</td><td>0.25</td><td>0.5</td><td>-</td><td>-</td><td>0.9</td><td>-</td><td>-</td><td>2.23</td></tr><tr><td>Sanicro 25</td><td>22.5</td><td>25</td><td>43.15</td><td>-</td><td>-</td><td>0.05</td><td>3</td><td>0.2</td><td>0.5</td><td>3.6</td><td>1.5</td><td>-</td><td>0.5</td><td>-</td><td>2.24</td></tr><tr><td>RA 330</td><td>19</td><td>35.5</td><td>42.71</td><td>-</td><td>-</td><td>0.04</td><td>0.5</td><td>1.25</td><td>1</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>2.24</td></tr><tr><td>SS 304H</td><td>19</td><td>9.5</td><td>70.055</td><td>-</td><td>-</td><td>0.07</td><td>-</td><td>0.375</td><td>1</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>2.25</td></tr></tbody></table>
<table><thead><tr><th>Alloy</th><th>Cr</th><th>Ni</th><th>Fe</th><th>Mo</th><th>Al</th><th>C</th><th>Cu</th><th>Si</th><th>Mn</th><th>W</th><th>Co</th><th>Ti</th><th>Nb</th><th>V</th><th>S<sub>i</sub></th></tr></thead><tbody><tr><td>Hastelloy C276</td><td>15.5</td><td>57.28</td><td>5.5</td><td>16</td><td>-</td><td>0.005</td><td>-</td><td>0.04</td><td>0.5</td><td>3.75</td><td>1.25</td><td>-</td><td>-</td><td>0.175</td><td>2.25</td></tr><tr><td>06KhN28MDT</td><td>23.5</td><td>27</td><td>42.22</td><td>2.75</td><td>-</td><td>0.03</td><td>3</td><td>0.4</td><td>0.4</td><td>-</td><td>-</td><td>0.7</td><td>-</td><td>-</td><td>2.31</td></tr><tr><td>Incoloy 20</td><td>20</td><td>33.75</td><td>37.22</td><td>3.5</td><td>-</td><td>0.03</td><td>3.5</td><td>0.5</td><td>1</td><td>-</td><td>-</td><td>-</td><td>0.5</td><td>-</td><td>2.32</td></tr><tr><td>Hastelloy S</td><td>15.75</td><td>64.39</td><td>1.5</td><td>15.25</td><td>0.3</td><td>0.01</td><td>0.175</td><td>0.475</td><td>0.65</td><td>0.5</td><td>1</td><td>-</td><td>-</td><td>-</td><td>2.33</td></tr><tr><td>Inconel 600</td><td>15.5</td><td>75.425</td><td>8</td><td>-</td><td>-</td><td>0.075</td><td>0.25</td><td>0.25</td><td>0.5</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>2.34</td></tr><tr><td>Hastelloy B3</td><td>1.5</td><td>63.595</td><td>1.5</td><td>28.5</td><td>0.25</td><td>0.005</td><td>-</td><td>0.05</td><td>1.5</td><td>1.5</td><td>1.5</td><td>0.1</td><td>-</td><td>-</td><td>2.35</td></tr><tr><td>Haynes 242</td><td>8</td><td>63.435</td><td>1</td><td>25</td><td>0.25</td><td>0.015</td><td>0.25</td><td>0.4</td><td>0.4</td><td>-</td><td>1.25</td><td>-</td><td>-</td><td>-</td><td>2.35</td></tr><tr><td>Hastelloy X</td><td>22</td><td>47.8</td><td>18</td><td>9</td><td>-</td><td>0.1</td><td>-</td><td>0.5</td><td>0.5</td><td>0.6</td><td>1.5</td><td>-</td><td>-</td><td>-</td><td>2.35</td></tr><tr><td>Haynes 188</td><td>22</td><td>22</td><td>1.5</td><td>-</td><td>-</td><td>0.1</td><td>-</td><td>0.35</td><td>0.625</td><td>14</td><td>39.425</td><td>-</td><td>-</td><td>-</td><td>2.35</td></tr><tr><td>Inconel 617</td><td>22</td><td>52.2</td><td>1.5</td><td>9</td><td>1.15</td><td>0.1</td><td>0.25</td><td>0.5</td><td>0.5</td><td>-</td><td>12.5</td><td>0.3</td><td>-</td><td>-</td><td>2.35</td></tr><tr><td>Haynes 230</td><td>22</td><td>56.7</td><td>1.5</td><td>2</td><td>0.3</td><td>0.1</td><td>-</td><td>0.4</td><td>0.5</td><td>14</td><td>2.5</td><td>-</td><td>-</td><td>-</td><td>2.35</td></tr><tr><td>Sanicro 28</td><td>27</td><td>32.03</td><td>34.705</td><td>3.5</td><td>-</td><td>0.015</td><td>1</td><td>0.5</td><td>1.25</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>2.35</td></tr><tr><td>Inconel 625</td><td>21.5</td><td>61.9</td><td>2.5</td><td>9</td><td>0.2</td><td>0.05</td><td>-</td><td>0.25</td><td>0.25</td><td>-</td><td>0.5</td><td>0.2</td><td>3.65</td><td>-</td><td>2.35</td></tr><tr><td>Incoloy 825 (2)</td><td>24.63</td><td>39.47</td><td>32.1</td><td>3.19</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0.498</td><td>-</td><td>-</td><td>0.112</td><td>-</td><td>-</td><td>2.35</td></tr><tr><td>SS 310N</td><td>26</td><td>23</td><td>47.55</td><td>-</td><td>-</td><td>0.1</td><td>-</td><td>0.75</td><td>2</td><td>-</td><td>-</td><td>-</td><td>0.6</td><td>-</td><td>2.37</td></tr><tr><td>SS 316H</td><td>17</td><td>12</td><td>65.68</td><td>2.5</td><td>-</td><td>0.07</td><td>-</td><td>0.75</td><td>2</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>2.39</td></tr><tr><td>SS 304L</td><td>19</td><td>10</td><td>69.485</td><td>-</td><td>-</td><td>0.015</td><td>-</td><td>0.5</td><td>1</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>2.43</td></tr><tr><td>SS 316</td><td>17</td><td>12</td><td>66.96</td><td>2.5</td><td>-</td><td>0.04</td><td>-</td><td>0.5</td><td>1</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>2.43</td></tr><tr><td>Nimonic 90</td><td>19.5</td><td>56.585</td><td>0.75</td><td>-</td><td>1.5</td><td>0.065</td><td>0.1</td><td>0.5</td><td>0.5</td><td>-</td><td>18</td><td>2.5</td><td>-</td><td>-</td><td>2.44</td></tr><tr><td>SS 347H</td><td>18.5</td><td>11</td><td>68.27</td><td>-</td><td>-</td><td>0.07</td><td>-</td><td>0.5</td><td>1</td><td>-</td><td>-</td><td>-</td><td>0.66</td><td>-</td><td>2.45</td></tr><tr><td>Hastelloy B</td><td>0.5</td><td>63.925</td><td>5</td><td>28</td><td>-</td><td>0.025</td><td>-</td><td>0.5</td><td>0.5</td><td>-</td><td>1.25</td><td>-</td><td>-</td><td>0.3</td><td>2.46</td></tr><tr><td>Inconel 690</td><td>29</td><td>60.975</td><td>9</td><td>-</td><td>-</td><td>0.025</td><td>0.5</td><td>0.25</td><td>0.25</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>2.48</td></tr><tr><td>Haynes HR160</td><td>28</td><td>34.95</td><td>1.75</td><td>0.5</td><td>-</td><td>0.05</td><td>-</td><td>2.75</td><td>0.5</td><td>0.5</td><td>30</td><td>0.5</td><td>0.5</td><td>-</td><td>2.48</td></tr><tr><td>SS 317L</td><td>19</td><td>13</td><td>62.985</td><td>3.5</td><td>-</td><td>0.015</td><td>-</td><td>0.5</td><td>1</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>2.49</td></tr><tr><td>OC4</td><td>13.96</td><td>25.03</td><td>51.759</td><td>1.98</td><td>3.55</td><td>0.101</td><td>0.51</td><td>0.14</td><td>1.97</td><td>0.95</td><td>-</td><td>0.05</td><td>-</td><td>-</td><td>2.51</td></tr></tbody></table>
<table><thead><tr><th>Alloy</th><th>Cr</th><th>Ni</th><th>Fe</th><th>Mo</th><th>Al</th><th>C</th><th>Cu</th><th>Si</th><th>Mn</th><th>W</th><th>Co</th><th>Ti</th><th>Nb</th><th>V</th><th>S<sub>i</sub></th></tr></thead><tbody><tr><td>SS 353MA</td><td>25</td><td>35</td><td>38.565</td><td>-</td><td>-</td><td>0.035</td><td>-</td><td>0.65</td><td>0.75</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>2.51</td></tr><tr><td>Incoloy 800H</td><td>21</td><td>32.5</td><td>45.675</td><td>-</td><td>0.375</td><td>0.075</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0.375</td><td>-</td><td>-</td><td>2.57</td></tr><tr><td>Inconel X750</td><td>15.5</td><td>71.81</td><td>7</td><td>-</td><td>0.7</td><td>0.04</td><td>0.25</td><td>0.25</td><td>0.5</td><td>-</td><td>0.5</td><td>2.5</td><td>0.95</td><td>-</td><td>2.59</td></tr><tr><td>Incoloy 800</td><td>21</td><td>32.5</td><td>45.7</td><td>-</td><td>0.375</td><td>0.05</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0.375</td><td>-</td><td>-</td><td>2.67</td></tr><tr><td>SS 310</td><td>25</td><td>20.5</td><td>52.625</td><td>-</td><td>-</td><td>0.125</td><td>-</td><td>0.75</td><td>1</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>2.71</td></tr><tr><td>SS 347</td><td>17</td><td>11</td><td>69.66</td><td>-</td><td>-</td><td>0.04</td><td>-</td><td>0.5</td><td>1</td><td>-</td><td>-</td><td>-</td><td>0.8</td><td>-</td><td>2.73</td></tr><tr><td>SS 446</td><td>25</td><td>0.125</td><td>73.525</td><td>-</td><td>-</td><td>0.1</td><td>-</td><td>0.5</td><td>0.75</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>2.73</td></tr><tr><td>Inconel 686</td><td>21</td><td>57.745</td><td>1</td><td>16</td><td>-</td><td>0.005</td><td>-</td><td>0.04</td><td>0.375</td><td>3.7</td><td>-</td><td>0.135</td><td>-</td><td>-</td><td>2.75</td></tr><tr><td>SS HP40</td><td>26</td><td>35</td><td>33.45</td><td>0.5</td><td>-</td><td>0.55</td><td>-</td><td>2.5</td><td>2</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>2.76</td></tr><tr><td>SS 316L</td><td>17</td><td>12</td><td>66.985</td><td>2.5</td><td>-</td><td>0.015</td><td>-</td><td>0.5</td><td>1</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>2.77</td></tr><tr><td>SS 304</td><td>19</td><td>9.25</td><td>70.21</td><td>-</td><td>-</td><td>0.04</td><td>-</td><td>0.5</td><td>1</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>2.79</td></tr><tr><td>20#steel</td><td>0.125</td><td>0.125</td><td>98.65</td><td>-</td><td>-</td><td>0.205</td><td>0.125</td><td>0.27</td><td>0.5</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>2.81</td></tr><tr><td>Haynes HR120</td><td>25</td><td>35.85</td><td>33</td><td>1.25</td><td>0.1</td><td>0.05</td><td>-</td><td>0.6</td><td>0.7</td><td>1.25</td><td>1.5</td><td>-</td><td>0.7</td><td>-</td><td>2.87</td></tr><tr><td>SS 316Ti</td><td>17</td><td>12</td><td>66.735</td><td>2.5</td><td>-</td><td>0.04</td><td>-</td><td>0.375</td><td>1</td><td>-</td><td>-</td><td>0.35</td><td>-</td><td>-</td><td>2.87</td></tr><tr><td>Kaeri Super alloy 5</td><td>29</td><td>32</td><td>38.485</td><td>-</td><td>-</td><td>0.015</td><td>-</td><td>0.25</td><td>0.25</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>2.9</td></tr><tr><td>SS 405</td><td>13</td><td>-</td><td>85.76</td><td>-</td><td>0.2</td><td>0.04</td><td>-</td><td>0.5</td><td>0.5</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>2.91</td></tr><tr><td>SS 410</td><td>12.5</td><td>-</td><td>86.425</td><td>-</td><td>-</td><td>0.075</td><td>-</td><td>0.5</td><td>0.5</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>2.93</td></tr><tr><td>SS 1020</td><td>-</td><td>-</td><td>99.345</td><td>-</td><td>-</td><td>0.205</td><td>-</td><td>-</td><td>0.45</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>2.94</td></tr><tr><td>AlCoCrFeNi</td><td>20.6</td><td>23.3</td><td>22.1</td><td>-</td><td>10.7</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>23.3</td><td>-</td><td>-</td><td>-</td><td>2.95</td></tr><tr><td>SS 321</td><td>18</td><td>10.5</td><td>69.56</td><td>-</td><td>-</td><td>0.04</td><td>-</td><td>0.5</td><td>1</td><td>-</td><td>-</td><td>0.4</td><td>-</td><td>-</td><td>2.97</td></tr><tr><td>L 605</td><td>20</td><td>10</td><td>1.5</td><td>-</td><td>-</td><td>0.1</td><td>-</td><td>0.2</td><td>1.5</td><td>15</td><td>51.7</td><td>-</td><td>-</td><td>-</td><td>2.98</td></tr><tr><td>Nippon HR6W</td><td>24.5</td><td>34.35</td><td>30</td><td>-</td><td>-</td><td>0.1</td><td>-</td><td>1</td><td>1.5</td><td>8</td><td>-</td><td>0.2</td><td>0.35</td><td>-</td><td>3.02</td></tr><tr><td>SS 709</td><td>19.93</td><td>24.98</td><td>51.864</td><td>1.51</td><td>-</td><td>0.066</td><td>-</td><td>0.44</td><td>0.91</td><td>-</td><td>-</td><td>0.04</td><td>0.26</td><td>-</td><td>3.02</td></tr><tr><td>Haynes 556</td><td>22</td><td>20</td><td>32.8</td><td>3</td><td>0.2</td><td>0.1</td><td>-</td><td>0.4</td><td>1</td><td>2.5</td><td>18</td><td>-</td><td>-</td><td>-</td><td>3.07</td></tr><tr><td>SS 317</td><td>19</td><td>13</td><td>62.96</td><td>3.5</td><td>-</td><td>0.04</td><td>-</td><td>0.5</td><td>1</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>3.08</td></tr></tbody></table>
<table><thead><tr><th>Alloy</th><th>Cr</th><th>Ni</th><th>Fe</th><th>Mo</th><th>Al</th><th>C</th><th>Cu</th><th>Si</th><th>Mn</th><th>W</th><th>Co</th><th>Ti</th><th>Nb</th><th>V</th><th>S<sub>i</sub></th></tr></thead><tbody><tr><td>SS 15 15Ti</td><td>15.5</td><td>15.5</td><td>64.23</td><td>1.4</td><td>0.02</td><td>0.12</td><td>0.05</td><td>0.6</td><td>2</td><td>-</td><td>0.03</td><td>0.55</td><td>-</td><td>-</td><td>3.11</td></tr><tr><td>Nitronic 50</td><td>22</td><td>12.5</td><td>57.32</td><td>2.25</td><td>-</td><td>0.03</td><td>-</td><td>0.5</td><td>5</td><td>-</td><td>-</td><td>-</td><td>0.2</td><td>0.2</td><td>3.12</td></tr><tr><td>OCT</td><td>14</td><td>35</td><td>46</td><td>-</td><td>3</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>2</td><td>-</td><td>-</td><td>3.14</td></tr><tr><td>SS 321H</td><td>17</td><td>10.5</td><td>70.63</td><td>-</td><td>-</td><td>0.07</td><td>-</td><td>0.5</td><td>1</td><td>-</td><td>-</td><td>0.3</td><td>-</td><td>-</td><td>3.16</td></tr><tr><td>SS 330</td><td>19</td><td>36</td><td>43.395</td><td>-</td><td>-</td><td>0.04</td><td>-</td><td>0.565</td><td>1</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>3.24</td></tr><tr><td>Vuelvas 7</td><td>28.7</td><td>1.81</td><td>57.17</td><td>1.04</td><td>-</td><td>3.93</td><td>-</td><td>0.28</td><td>6.5</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0.57</td><td>3.25</td></tr><tr><td>SS 430</td><td>14.94</td><td>-</td><td>84</td><td>-</td><td>-</td><td>0.06</td><td>-</td><td>0.5</td><td>0.5</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>3.33</td></tr><tr><td>SS 1045</td><td>-</td><td>-</td><td>98.79</td><td>-</td><td>-</td><td>0.46</td><td>-</td><td>-</td><td>0.75</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>3.37</td></tr><tr><td>X20</td><td>11.25</td><td>0.55</td><td>85.705</td><td>1</td><td>0.02</td><td>0.2</td><td>0.15</td><td>0.2</td><td>0.65</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0.275</td><td>3.38</td></tr><tr><td>P91</td><td>8.75</td><td>0.2</td><td>88.89</td><td>0.95</td><td>0.01</td><td>0.1</td><td>-</td><td>0.35</td><td>0.45</td><td>-</td><td>-</td><td>0.005</td><td>0.08</td><td>0.215</td><td>3.39</td></tr><tr><td>Inconel 740H</td><td>24.5</td><td>49.1075</td><td>1.5</td><td>1</td><td>1.1</td><td>0.0425</td><td>0.25</td><td>0.5</td><td>0.5</td><td>-</td><td>18.5</td><td>1.5</td><td>1.5</td><td>-</td><td>3.40</td></tr><tr><td>Cr<sub>18</sub>Mn<sub>27</sub>Fe<sub>27.5</sub>Ni<sub>27.5</sub></td><td>18</td><td>27.5</td><td>27.5</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>27</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>3.41</td></tr><tr><td>16Kh12MVSFBR</td><td>11</td><td>0.65</td><td>84.39</td><td>0.75</td><td>-</td><td>0.16</td><td>-</td><td>1.15</td><td>0.65</td><td>0.65</td><td>-</td><td>-</td><td>0.3</td><td>0.3</td><td>3.43</td></tr><tr><td>Vuelvas 1</td><td>21</td><td>1.58</td><td>62.59</td><td>2.2</td><td>-</td><td>5.17</td><td>-</td><td>0.34</td><td>6.27</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0.85</td><td>3.44</td></tr><tr><td>T22</td><td>2.25</td><td>-</td><td>95.7</td><td>1</td><td>-</td><td>0.1</td><td>-</td><td>0.5</td><td>0.45</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>3.44</td></tr><tr><td>T23</td><td>2.25</td><td>-</td><td>94.99</td><td>0.175</td><td>0.015</td><td>0.07</td><td>-</td><td>0.25</td><td>0.35</td><td>1.6</td><td>-</td><td>-</td><td>0.05</td><td>0.25</td><td>3.47</td></tr><tr><td>SS 253MA</td><td>21</td><td>0.17</td><td>65.655</td><td>11</td><td>-</td><td>0.075</td><td>-</td><td>1.7</td><td>0.4</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>3.48</td></tr><tr><td>T9</td><td>9</td><td>-</td><td>88.85</td><td>1</td><td>-</td><td>0.075</td><td>-</td><td>0.625</td><td>0.45</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>3.5</td></tr><tr><td>SS4130</td><td>0.95</td><td>-</td><td>97.82</td><td>0.2</td><td>-</td><td>0.305</td><td>-</td><td>0.225</td><td>0.5</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>3.5</td></tr><tr><td>VM12</td><td>11.5</td><td>0.25</td><td>83.95</td><td>0.3</td><td>-</td><td>0.1</td><td>-</td><td>0.5</td><td>0.3</td><td>1.5</td><td>1.6</td><td>-</td><td>-</td><td>-</td><td>3.53</td></tr><tr><td>Vuelvas 6</td><td>27.7</td><td>1.86</td><td>56.65</td><td>0.98</td><td>-</td><td>3.93</td><td>-</td><td>0.32</td><td>7.42</td><td>-</td><td>-</td><td>-</td><td>-</td><td>1.14</td><td>3.59</td></tr><tr><td>T 11</td><td>1.2</td><td>-</td><td>96.97</td><td>0.5</td><td>-</td><td>0.1</td><td>-</td><td>0.79</td><td>0.44</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>3.61</td></tr><tr><td>Vuelvas 2</td><td>21.2</td><td>1.8</td><td>64.7</td><td>0.92</td><td>-</td><td>5.09</td><td>-</td><td>0.32</td><td>5.1</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0.87</td><td>3.64</td></tr><tr><td>SS 439</td><td>18</td><td>0.25</td><td>80.64</td><td>-</td><td>0.075</td><td>0.035</td><td>-</td><td>0.5</td><td>0.5</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>3.64</td></tr><tr><td>SS Q235A</td><td>-</td><td>-</td><td>99.015</td><td>-</td><td>-</td><td>0.11</td><td>-</td><td>0.175</td><td>0.7</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>3.73</td></tr></tbody></table>
<table><thead><tr><th>Alloy</th><th>Cr</th><th>Ni</th><th>Fe</th><th>Mo</th><th>Al</th><th>C</th><th>Cu</th><th>Si</th><th>Mn</th><th>W</th><th>Co</th><th>Ti</th><th>Nb</th><th>V</th><th>S<sub>i</sub></th></tr></thead><tbody><tr><td>T92</td><td>8.75</td><td>0.2</td><td>88.015</td><td>0.45</td><td>0.02</td><td>0.1</td><td>-</td><td>0.5</td><td>0.45</td><td>1.25</td><td>-</td><td>-</td><td>0.065</td><td>0.2</td><td>3.74</td></tr><tr><td>A516</td><td>-</td><td>-</td><td>98.39</td><td>-</td><td>-</td><td>0.31</td><td>-</td><td>0.275</td><td>1.025</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>3.74</td></tr><tr><td>OCI</td><td>14</td><td>12</td><td>63.5</td><td>-</td><td>2.5</td><td>-</td><td>3</td><td>-</td><td>5</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>3.9</td></tr><tr><td>T24</td><td>2.4</td><td>-</td><td>95.36</td><td>1</td><td>0.01</td><td>0.1</td><td>-</td><td>0.3</td><td>0.5</td><td>-</td><td>-</td><td>0.08</td><td>-</td><td>0.25</td><td>3.91</td></tr><tr><td>Vuelvas 5</td><td>29.7</td><td>1.94</td><td>53.85</td><td>3.25</td><td>-</td><td>4.1</td><td>-</td><td>0.4</td><td>5.6</td><td>-</td><td>-</td><td>-</td><td>-</td><td>1.16</td><td>3.92</td></tr><tr><td>A1</td><td>-</td><td>-</td><td>98.7</td><td>-</td><td>-</td><td>0.27</td><td>-</td><td>0.1</td><td>0.93</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>3.97</td></tr><tr><td>Vuelvas 3</td><td>22.52</td><td>1.68</td><td>62.945</td><td>2.47</td><td>-</td><td>4.65</td><td>-</td><td>0.32</td><td>4.8</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0.615</td><td>4.13</td></tr><tr><td>T5</td><td>5</td><td>-</td><td>93.85</td><td>0.55</td><td>-</td><td>0.075</td><td>-</td><td>0.075</td><td>0.45</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>4.16</td></tr><tr><td>T12</td><td>0.15</td><td>0.15</td><td>98.265</td><td>0.3</td><td>-</td><td>0.16</td><td>0.15</td><td>0.175</td><td>0.65</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>4.27</td></tr><tr><td>Vuelvas 4</td><td>30.48</td><td>1.96</td><td>53.12</td><td>3.3</td><td>-</td><td>4.29</td><td>-</td><td>0.42</td><td>5.9</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0.53</td><td>4.31</td></tr><tr><td>SB450</td><td>-</td><td>-</td><td>99.045</td><td>-</td><td>-</td><td>0.28</td><td>-</td><td>0.225</td><td>0.45</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>4.44</td></tr></tbody></table>

## Appendix B. Ranking of the Alloys Tested Under Wear Experiments

<table><thead><tr><th>Alloy</th><th>Fe</th><th>C</th><th>Cr</th><th>Mn</th><th>Mo</th><th>V</th><th>W</th><th>Co</th><th>Ni</th><th>B</th><th>Nb</th><th>Si</th><th>N</th><th>Rank</th></tr></thead><tbody><tr><td>Yoo-0,6B</td><td>76.6</td><td>1.73</td><td>19.94</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0.71</td><td>-</td><td>1.02</td><td>-</td><td>1</td></tr><tr><td>Yoo-0,3B</td><td>77.7</td><td>1.74</td><td>19.32</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0.34</td><td>-</td><td>0.9</td><td>-</td><td>2</td></tr><tr><td>Alloy S3/S21 (70/30)</td><td>0.93</td><td>1.76</td><td>29.45</td><td>0.52</td><td>1.65</td><td>-</td><td>8.75</td><td>53.24</td><td>2.08</td><td>0.7</td><td>-</td><td>0.93</td><td>-</td><td>3</td></tr><tr><td>Colmonoy 88</td><td>3.95</td><td>0.84</td><td>14.8</td><td>-</td><td>-</td><td>-</td><td>17.37</td><td>-</td><td>55.95</td><td>3.18</td><td>-</td><td>3.91</td><td>-</td><td>4</td></tr><tr><td>APM 2311</td><td>70.5</td><td>2</td><td>26</td><td>0.5</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>1</td><td>-</td><td>5</td></tr><tr><td>Stellite 12</td><td>2</td><td>1.8</td><td>29</td><td>-</td><td>-</td><td>-</td><td>8.5</td><td>54.20</td><td>3</td><td>-</td><td>-</td><td>1.5</td><td>-</td><td>6</td></tr><tr><td>Tribaloy T400</td><td>-</td><td>0.1</td><td>8.5</td><td>-</td><td>28.5</td><td>-</td><td>-</td><td>60.30</td><td>-</td><td>-</td><td>-</td><td>2.6</td><td>-</td><td>7</td></tr><tr><td>Yoo-1B</td><td>76.64</td><td>1.72</td><td>19.65</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>1.05</td><td>-</td><td>0.94</td><td>-</td><td>8</td></tr><tr><td>Colmonoy 6 Laser</td><td>3.8</td><td>0.73</td><td>14.56</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0.09</td><td>73.17</td><td>3.37</td><td>-</td><td>4.28</td><td>-</td><td>9</td></tr><tr><td>Yoo-2B</td><td>75.82</td><td>1.72</td><td>19.54</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>2</td><td>-</td><td>0.92</td><td>-</td><td>10</td></tr><tr><td>NOREM A</td><td>56.9</td><td>1.2</td><td>26</td><td>5.5</td><td>2</td><td>-</td><td>-</td><td>-</td><td>5</td><td>-</td><td>-</td><td>3.4</td><td>-</td><td>11</td></tr></tbody></table>
<table><thead><tr><th>Alloy</th><th>Fe</th><th>C</th><th>Cr</th><th>Mn</th><th>Mo</th><th>V</th><th>W</th><th>Co</th><th>Ni</th><th>B</th><th>Nb</th><th>Si</th><th>N</th><th>Rank</th></tr></thead><tbody><tr><td>Elmax</td><td>76.6</td><td>1.7</td><td>17</td><td>0.3</td><td>1</td><td>3</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0.4</td><td>-</td><td>12</td></tr><tr><td>Colmonoy 83</td><td>1.4</td><td>2</td><td>20</td><td>-</td><td>-</td><td>-</td><td>34</td><td>-</td><td>40.2</td><td>1</td><td>-</td><td>1.4</td><td>-</td><td>13</td></tr><tr><td>Tristelle TS-2</td><td>36</td><td>2</td><td>35</td><td>-</td><td>-</td><td>-</td><td>-</td><td>12</td><td>10</td><td>-</td><td>-</td><td>5</td><td>-</td><td>14</td></tr><tr><td>NOREM 02</td><td>59.7</td><td>1.23</td><td>25.1</td><td>4.4</td><td>2.03</td><td>-</td><td>-</td><td>-</td><td>4.13</td><td>-</td><td>-</td><td>3.23</td><td>0.18</td><td>15</td></tr><tr><td>Nelsit</td><td>59.97</td><td>0.03</td><td>18</td><td>2</td><td>3</td><td>-</td><td>-</td><td>-</td><td>10</td><td>-</td><td>-</td><td>7</td><td>-</td><td>16</td></tr><tr><td>Stellite 21</td><td>3</td><td>0.25</td><td>27</td><td>1</td><td>5.5</td><td>-</td><td>-</td><td>59.25</td><td>2.5</td><td>-</td><td>-</td><td>1.5</td><td>-</td><td>17</td></tr><tr><td>NOREM 01</td><td>55.28</td><td>1</td><td>25</td><td>9.3</td><td>2</td><td>-</td><td>-</td><td>-</td><td>4.02</td><td>-</td><td>-</td><td>3.3</td><td>0.1</td><td>18</td></tr><tr><td>Stellite 6</td><td>2.01</td><td>1.2</td><td>29</td><td>0.13</td><td>1.5</td><td>-</td><td>4.5</td><td>59.02</td><td>1.8</td><td>-</td><td>-</td><td>0.84</td><td>-</td><td>19</td></tr><tr><td>Yoo-0B</td><td>77.03</td><td>1.64</td><td>20.34</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0.01</td><td>-</td><td>0.98</td><td>-</td><td>20</td></tr><tr><td>NOREM 04</td><td>46.73</td><td>1.05</td><td>24.81</td><td>12</td><td>1.96</td><td>-</td><td>-</td><td>-</td><td>8.05</td><td>-</td><td>-</td><td>5.17</td><td>0.23</td><td>21</td></tr><tr><td>AI 1236</td><td>2.86</td><td>1.58</td><td>9.56</td><td>-</td><td>-</td><td>-</td><td>25.92</td><td>-</td><td>55.13</td><td>2.15</td><td>-</td><td>2.8</td><td>-</td><td>22</td></tr><tr><td>Tribaloy T200</td><td>-</td><td>-</td><td>11</td><td>-</td><td>-</td><td>-</td><td>-</td><td>61.75</td><td>20</td><td>-</td><td>6.5</td><td>0.75</td><td>-</td><td>23</td></tr><tr><td>NoCo-M2</td><td>55.95</td><td>0.9</td><td>25</td><td>5</td><td>2</td><td>-</td><td>-</td><td>-</td><td>8</td><td>-</td><td>-</td><td>3</td><td>0.15</td><td>24</td></tr><tr><td>Everit 50</td><td>67.7</td><td>2</td><td>25</td><td>0.9</td><td>3.5</td><td>0.5</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0.4</td><td>-</td><td>25</td></tr><tr><td>Colmonoy 5</td><td>4.6</td><td>0.5</td><td>12.6</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>75.8</td><td>2.5</td><td>-</td><td>4</td><td>-</td><td>26</td></tr><tr><td>Nucalloy 453</td><td>3</td><td>0.85</td><td>10</td><td>-</td><td>-</td><td>-</td><td>2</td><td>-</td><td>78.35</td><td>0.5</td><td>-</td><td>5.3</td><td>-</td><td>27</td></tr><tr><td>Tribaloy T700</td><td>-</td><td>0.08</td><td>16</td><td>-</td><td>32</td><td>-</td><td>-</td><td>1.5</td><td>47.02</td><td>-</td><td>-</td><td>3.4</td><td>-</td><td>28</td></tr><tr><td>Deloro 40</td><td>2.5</td><td>0.45</td><td>10</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>82.25</td><td>2.5</td><td>-</td><td>2.3</td><td>-</td><td>29</td></tr><tr><td>Deloro 50</td><td>4</td><td>0.6</td><td>13</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>75.4</td><td>3</td><td>-</td><td>4</td><td>-</td><td>30</td></tr><tr><td>Inconel 625</td><td>1</td><td>0.05</td><td>21</td><td>-</td><td>9</td><td>-</td><td>-</td><td>-</td><td>68.95</td><td>-</td><td>-</td><td>-</td><td>-</td><td>31</td></tr><tr><td>Nucalloy 488</td><td>5.5</td><td>0.3</td><td>17.5</td><td>-</td><td>-</td><td>-</td><td>1</td><td>-</td><td>67.9</td><td>1</td><td>-</td><td>6.8</td><td>-</td><td>32</td></tr><tr><td>Tribaloy T700 + 10% Fe</td><td>10</td><td>0.08</td><td>16</td><td>-</td><td>32</td><td>-</td><td>-</td><td>1.5</td><td>35.02</td><td>-</td><td>-</td><td>5.4</td><td>-</td><td>33</td></tr><tr><td>Tribaloy T700 + 5% Fe</td><td>5</td><td>0.08</td><td>16</td><td>-</td><td>32</td><td>-</td><td>-</td><td>1.5</td><td>41.02</td><td>-</td><td>-</td><td>4.4</td><td>-</td><td>34</td></tr><tr><td>Tribaloy T700 + 15% Fe</td><td>15</td><td>0.08</td><td>16</td><td>-</td><td>32</td><td>-</td><td>-</td><td>1.5</td><td>29.02</td><td>-</td><td>-</td><td>6.4</td><td>-</td><td>35</td></tr></tbody></table>
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