# Segmentation of phases in low carbon steels by weak-supervised deep learning: a round robin test on different microscopes and EBSD systems

M. Lavrskyi, Nathalie Gey, B. Gueraud, Audrey Couturier, M. Salib, L. Germain

► To cite this version:

M. Lavrskyi, Nathalie Gey, B. Gueraud, Audrey Couturier, M. Salib, et al. Segmentation of phases in low carbon steels by weak-supervised deep learning: a round robin test on different microscopes and EBSD systems. Materials Characterization, 2025, 230 (A), pp.115718. 10.1016/j.matchar.2025.115718. hal-05388017

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# Segmentation of phases in low carbon steels by weak-supervised deep learning: a round robin test on different microscopes and EBSD systems

M. Lavrskyi¹,², N. Gey²,³, B. Gueraud⁴, A. Couturier⁵, M. Salib⁶, L. Germain²,³

¹Institut de Recherche Technologique Matériaux, Métallurgie et Procédés, 4 rue Augustin Fresnel F-57078, Metz, France

²Université de Lorraine, CNRS, Arts et Métiers Paris Tech, LEM3, F-57000 Metz, France

³Laboratory of Excellence on Design of Alloy Metals for Low-mAss Structures (DAMAS), Université de Lorraine, France

⁴Framatome, Technical Center, Le Creusot, France

⁵INDUSTEEL (ArcelorMittal), Centre de Recherche des Matériaux du Creusot (CRMC), Le Creusot, France

⁶ArcelorMittal Maizieres, Research and Development, Maizières-les-Metz, France

## Abstract

Characterizing the microstructure of multiphase steels is essential in process control and alloy design. This highly complex and time-consuming task can be automated using Convolutional Neural Networks (CNN). The successful application of artificial neural networks requires sufficient labeled data that are representative of the steel under analysis. Labeling significantly complicates obtaining data for training. Weak supervision is a promising approach to reduce the need for labeled data and to improve the efficiency of CNNs. This study demonstrates the application of weak supervision learning to discriminate martensite, upper bainite, and lower bainite on EBSD maps. Additionally, it discussed the robustness of CNNs to EBSD acquisition conditions from round robin measurements. The developed U-Net models show the ability to automatically distinguish upper and lower bainite in low-carbon industrial steel with an accuracy of over 90% but reveal also some sensitivity to acquisition conditions and some weakness when working exclusively within a weakly supervised framework.

Keywords: Deep Learning, Steel, Martensite, Bainite, EBSD

## 1 Introduction

Characterizing the fraction, distribution, and topology of transformation products in steel microstructures is critical for properties control and for the design of new steels. However, segmenting those different phases is a complex and time-consuming task. Using EBSD data for phase segmentation provides certain advantages over conventional microscopy: access to crystallographic orientations and the possibility to define grains automatically based on a misorientation threshold, while its main drawback, its slow acquisition time, has become very reasonable with CMOS cameras (~3000 Hz).

In some microstructures, the phases can be segmented from EBSD data using conventional image analysis tools. Most published studies addressed two-phase microstructures (i.e., Martensite/Ferrite, Martensite/Bainite ...) using Band Slope averaged in grains [1–3], KAM/GAM [4], or grain size or aspect ratio [2]. However, for multiphase steels, only handcrafted multicriteria methods with manually chosen thresholds have been published [5,6]. Therefore, using EBSD alone for this task remains complex and subjective or requires multimodal or correlative microscopy, i.e., coupling EBSD with EDS [7–12] or EBSD with BSE-imaging [13,14].

Recently, Deep Learning has been applied for the semantic segmentation of the phases in steels with remarkable results [15–19]. It presents several advantages over more classical image analysis. It is fast (a trained model makes predictions within seconds) and avoids the subjective choice of features and thresholds, since the model automatically learns the most relevant features from the training data. However, it suffers from two main drawbacks. First, the limited experience available with these methods is not sufficient to ensure that the models generalize on acquisition conditions
other than those seen during training. To our knowledge, only one study addressed the robustness of CNNs with respect to EBSD acquisition parameters, which, however, focused exclusively on Kikuchi pattern analysis [20]. Second, supervised learning requires a large database with labeled phases, which is a tedious and time-consuming task; moreover, for complex steels, it is often subjective (operator-dependent) to distinguish the occurring phases.

This paper aims to address those two issues. We compared three different acquisition conditions using two microscopes and EBSD set-ups and evaluated how a model trained on data from one condition performed on the others. Also, a weak-supervised framework was designed. Weak supervision refers to supervised machine learning in which the labels can be inferred directly from the data, limiting the labelling effort. In this work, the microstructures used for training were designed to present a single phase, reducing the labelling task to a minimum while still relying on supervised learning. In addition, some synthetic multiphase microstructures were produced by data augmentation. A similar weak supervision scheme was applied to segment organs in 3D medical images with great success [21], and therefore, we wanted to evaluate its performance on steel microstructures.

The two-fold goal of the paper is to provide an assessment of this weak supervised framework and to evaluate the robustness of the models to acquisition conditions.

# 2 Materials & Methods

## 2.1 Materials

Three low-carbon steels with various compositions were provided by Framatome (the nominal composition is given in Table 1). Two dilatometry samples have been cut out of each steel and heated in the austenitic range at 900 °C and cooled to obtain a single transformation product:

- Two samples have been Helium quenched to produce a pure martensite microstructure,

- Two samples have been held for 20 min at 424 °C and 454 °C to produce two different pure upper bainite microstructures,

- Two samples have been held for 2 min and 20 min at 374 °C to produce two different lower bainite microstructures.

The presence of a very small fraction of another phase could not be excluded, but the quasi-single-phase nature of the microstructures was confirmed by dilatometry curves, by displaying Kernel Average Misorientation maps (KAM), Image Quality maps (IQ), and by looking at the misorientation distribution [22]. Heat treatments and sample names are given in Table 2.

<table><caption>Table 1 Nominal compositions of samples (weight %).</caption><thead><tr><th>Alloy</th><th>C</th><th>Mn</th><th>Ni</th><th>Mo</th><th>Si</th><th>Cr</th><th>Cu</th><th>Al</th><th>P</th><th>S</th><th>N</th></tr></thead><tbody><tr><td>CS-A</td><td>0.189</td><td>1.386</td><td>0.764</td><td>0.484</td><td>0.188</td><td>0.122</td><td>-</td><td>0.020</td><td>0.004</td><td>0.0013</td><td>0.0026</td></tr><tr><td>VB-K</td><td>0.204</td><td>1.388</td><td>0.85</td><td>0.508</td><td>0.17</td><td>0.142</td><td>0.068</td><td>0.012</td><td>&lt;0.005</td><td>&lt;0.001</td><td>-</td></tr></tbody></table>

<table><caption>Table 2: List of studied samples – UB=Upper Bainite, LB=Lower Bainite, M=Martensite, Q = Quench</caption><thead><tr><th>Alloys</th><th>Microstructure name</th><th>Post-austenitization cooling</th><th>Dominant phase</th></tr></thead><tbody><tr><td>CS-A</td><td>M4</td><td>Quench He</td><td>M</td></tr><tr><td>VB-K</td><td>M3</td><td>Quench He</td><td>M</td></tr><tr><td>VB-K</td><td>LB1</td><td>Hold at 374 °C for 120 s + Q</td><td>LB</td></tr><tr><td>VB-K</td><td>LB2</td><td>Hold at 374 °C for 1200 s + Q</td><td>LB</td></tr><tr><td>VB-K</td><td>UB2</td><td>Hold at 424 °C for 1200 s + Q</td><td>UB</td></tr><tr><td>VB-K</td><td>UB3</td><td>Hold at 454 °C for 1200 s + Q</td><td>UB</td></tr></tbody></table>
## 2.2 EBSD acquisitions and cleaning

Three different EBSD datasets were acquired:

The first two datasets marked “H1” and “H2” were obtained on a FEG SEM GEMINISEM 450 (ZEISS) equipped with EDAX camera HIKARI Plus and APEX acquisition software. The maps were acquired one the same sample at 6 months interval, and the samples were metallographically prepared before each acquisition. Sample preparation was done by mechanical grinding down to 4000 followed by 3 µm diamond paste and a final OPS polishing. The EBSD maps have been acquired with a horizontal step of 0.06 µm on a hexagonal grid, an accelerating voltage of 20 kV, currents between 2 and 6 nA, and working distances from 15 to 20 mm. The camera resolution was set to 320×240 pixels (i.e., 2×2 binning), and data collection was performed with the exposure time at 8.11 ms and 110 frames/s. The maps were indexed exclusively as ferrite, using a confidence index threshold of 0.1 as a filtering criterion. The main difference between the two acquisitions lies in sample preparation, “H1” was acquired on a sample prepared “twice” for EBSD and therefore had slightly higher IQ and angular resolution.

The third dataset marked “S” was obtained on a ZEISS Auriga 40 dual beam FEG-SEM equipped with the Oxford-instruments camera Symmetry and AZtec acquisition software. The maps were acquired with a step of 0.1 µm on a square grid, an accelerating voltage of 20 kV, at a working distance 13.4 mm. The camera resolution was set to 156×128 pixels (i.e., 8×8 binning), the exposure time at 1 ms and the ‘Refined accuracy’ option for indexing, resulting in a speed of acquisition around 970 Hz. Solutions with a Mean Angle Deviation (MAD) > 2° or with less than 7 bands detected were classified as Zero Solution. The maps of this dataset were indexed with ferrite and austenite phases. In this condition, the M4 sample was missing.

The resulting map sizes are 95.88 x 86.04 µm² in the “H2” dataset, 105.48 x 94.62 µm² in the “H1” dataset, and 114.1 x 85.6 µm² in the “S” dataset.

Before being used in the study, all Hikari’s maps were transformed into a square grid. In hexagonal grids, every even row contains one pixel fewer than the odd rows. The conversion was done by padding (padding “same”) every two rows of the hexagonal grid and directly reading the data into a rectangular grid, which was then assumed to be square. Compared to nearest-neighbor assignment, this method has the advantage of being reversible, but it stretches the maps vertically by a factor of $\frac{1}{\cos(30^\circ)}$. All the EBSD maps were cleaned up from wild spikes and from pseudo symmetry misindexed points of [111] 30° and [100] 45°.

# 3 DEEP LEARNING STRATEGY

## 3.1 Preprocessing of the Input data

Unlike RGB images, EBSD maps cannot readily be given to a convolutional neural network: The different channels of EBSD maps are different in nature and not all of them are relevant for phase segmentation purposes. In previous work, we have shown that the inputs producing the most accurate segmentations in steels are the Band Slope, Band Contrast, and the KAM (Kernel Average Misorientation) or any combination of them [15]. In this study, the Band Slope was ignored since it only exists in Oxford Instrument systems. We also included the non-indexed points as an input, this is referred to Zero Solutions (ZS) in the following. Representative crops for the different microstructures are shown in Figure 1. At first glance, one can notice some differences between the considered phases, including differences in misorientations as observed by Takayama et al. [23]. All features have been pre-processed before being fed to the network. The characteristic of each feature is described in the following.
<figure><img src="image_3.png" /><figcaption>Figure 1: Representative EBSD maps of martensite, lower bainite, and upper bainite. First row: Band Contrast (BC). Second row: KAM overlapped with ZS and RA in black and white, respectively. Third row: specific misorientations, with a color scheme shown on the right.</figcaption></figure>

Band Contrast (BC)

Band Contrast (BC, in Oxford systems) and Image Quality (IQ, in Edax systems) correspond to approximately the average height of the peaks in the Hough transform [24]. Often, it is darker for martensite and brighter for upper bainite or ferrite. But it is also brighter inside the grains than at the boundaries. Thus, it is an interesting input to recognize higher-order features like grain shapes or grey level textures. The BC is an 8-bit integer, while the IQ is a 32-bit float resulting from the butterfly mask convolution [25]. Since there is no standardized way to map the IQ to the BC, we normalized the IQ according to the maximal value observed in our Hikari maps leading to this conversion formula:

$$BC = \frac{255 \times IQ}{167000} \qquad (1)$$

The BC distributions for each map are given in Figure 2. It shows a strong overlap, preventing the possibility of segmenting the phases by thresholding. BC is very sensitive to sample preparation and to acquisition conditions. At fixed acquisition conditions, the average BC is the highest for Upper Bainite and the lowest for Martensite. Also, there is a difference in the shape of the distributions. The maps H1 & H2 always present a Gaussian-like distribution, whereas the distribution tails are asymmetric in the “S” maps. We can qualitatively rank the conditions with increasing BC values as “H2” < “H1” < “S”. The relative ranking of the Hikari’s acquisition conditions can be attributed to sample preparation, but their position relative to “S” depends on the chosen conversion rule given in equation (1).
<figure><img src="image_4.png" /><figcaption>Figure 2. BC distributions of samples obtained with different cameras. Different colors mark microstructures: martensite is blue, lower bainite is green, and upper bainite is yellow.</figcaption></figure>

## Kernel Average Misorientation (KAM)

The KAM evaluates the local misorientation and correlates with the GND density [26]. The KAM was calculated within a 3x3 kernel by averaging the misorientation angles to the central pixel while all misorientations higher than 3° were ignored [27]. The distributions of KAM are given in Figure 3. KAM values tend to be the highest in Martensite and the lowest in Upper Bainite, but again, the distributions overlap largely. KAM also tends to be more reproducible across the datasets. The position of the tails can be used to rank the datasets in increasing order of KAM: “H1” < “S” < “H2”. One drawback of the KAM is its dependence on the acquisition step size [28]. Larger step sizes lead to higher KAM values [28,29]. One of the solutions to this problem is to use an additional input parameter – acquisition step size – during training neural networks to provide more context about the data [16].

<figure><img src="image_5.png" /><figcaption>Figure 3: KAM distributions of the samples obtained with different cameras. Different colours mark microstructures: martensite is blue, lower bainite is green, and upper bainite is yellow.</figcaption></figure>

## Zero Solutions

Non-indexed pixels are often related to high dislocation density regions and are mainly located at grain boundaries. Features for ZS were generated so that a value of 1 was given to ZS pixels and 0 to others. Introducing the positions of ZS pixels as input to the deep learning model may help to distinguish martensite from lower bainite microstructures. For the Hikari datasets, ZS were allocated to pixels with a confidence index (CI) lower than 0.1 [30].

## 3.2 Datasets and data augmentation

Six different datasets were used: “S”, “H1”, “H2”, “S & H1”, “S & H2”, and all three datasets together. By construction, those datasets are balanced, which means that they contain the same amount of data for each phase.
Additionally, another dataset was created with synthetic multiphase maps using the scheme in Figure 4: In a first step, parent austenite grains were reconstructed from EBSD monophase maps [31,32]. In the example in Figure 4, the map with Lower Bainite from the “S” dataset was chosen. Then, a subset of randomly selected parent austenite grains covering approximately 50% of the map was selected. The subset contains information about pixel orientations and corresponding Band Contrast. The rest of the map forms an anti- subset, or (subset)-¹ in Figure 4. Finally, the subset replaces corresponding parts of the EBSD map with Upper Bainite, and (subset)-¹ replaces parts of the map with Martensite microstructure. As a result, there are two maps with mixed microstructures: Upper Bainite + Lower Bainite and Martensite + Lower Bainite. The same procedure was applied to maps with Upper Bainite and Martensite microstructures leading to six mixed maps using the “S” dataset. This new dataset is marked as “Mixed dataset”.

Sampling the data using reconstructions allowed the intra-grain crystallographic relationships of transformation products to be preserved. In Bainite, variants belonging to the same Bain domains are grouped together whereas in martensite, it is those belonging to the same packets. Sampling data at the scale of the austenite grains allowed us to fulfil both conditions while keeping the procedure simple enough. As a result, synthetic maps present realistic misorientation angle distributions.

<figure><img src="image_6.png" /><figcaption>Figure 4 Scheme of creating a map with two phases from two single-phase maps.</figcaption></figure>

Each dataset was split into training, validation, and testing datasets. The training dataset is used to optimize the model's parameters, while the validation dataset is used to evaluate the model's performance during training. The test dataset is never seen during training and is used to evaluate the model's performance after training. All EBSD maps were cut into two equal pieces vertically so that the left part was used for training and validation, and the right one for testing. The maps for training and validation were first divided into 224 x 224 pixels patches, and 80% of the patches were used for training and 20% for validation.

Data augmentation was performed on the training dataset by applying discrete rotations of 180° around the x, y, and z axes and 90° and 270° around the z-axis, therefore multiplying the size of the dataset by 5.

3.3 Deep learning model

The schematic representation of the U-Net architecture used here is shown in Figure 5. Our U-Net consists of convolutional layers with a ReLu activation function, with a 20% dropout, and a 3x3 pixels kernel, max pooling, and
upsampling layers with a 2x2 pixels kernel. Convolutional layers initially have 32 filters, which are doubled after each max pooling layer. The model comprises five max pooling layers, which means that it can take as input all images with dimensions proportional to 2⁵ pixels. A “Softmax” function performs the final classification. A “Categorical Cross Entropy” for the multi-phase classification was used as a loss function. Backpropagation learning was performed with the stochastic gradient descent with the optimizer “adam” and a learning rate of 1. A detailed description of the model can be found in [15].

<figure><img src="image_7.png" /><figcaption>Figure 5: Scheme of the U-Net architecture developed and implemented to segment microstructures</figcaption></figure>

## 3.4 Training

24 U-Net models have been trained. For each of the six datasets, four different input combinations were considered: BC alone, KAM alone, BC and KAM together, and BC, KAM, and ZS together. Additionally, the acquisition step size was used as an input parameter in each model training.

Before each training, all the inputs were normalized, so their distributions were centered at 0 with a standard deviation of 1. Therefore, the normalization parameters are dataset-dependent. Normalizing each EBSD map individually by its maximum value was also tested but consistently resulted in lower model performance. A possible explanation is that the acquisition conditions of our datasets do not differ strongly enough to benefit from per-map scaling. At the same time, the common normalization allows the model to exploit better the phase-dependance of BC values across the whole dataset.

An early stopping mechanism during training was used on minimal validation loss with a patience of 10 epochs. All the models have been trained on the Cassiopee Arts et Métiers Institute of Technology HPC Center using NVIDIA Tesla V100S GPU. All raw results are given in the appendix.
The results are discussed in terms of accuracy which is a very intuitive indicator (it is fraction of correctly predicted pixels). One can note that, in this study, it is equivalent also to IoU (an indicator increasingly used in segmentation) because of balanced dataset and the monophase maps used in this study.

# 4 Sensitivity to acquisition conditions

In this part of the work, the performances of the trained models are evaluated on the test dataset, which was unseen during training. The complete test maps were given to the model directly to avoid stitching the patches together, which would introduce border bias.

## 4.1 Models trained with a single acquisition condition

The results in Table 3 show the testing accuracies averaged for the concerned maps (for example, M3 and M4 for Martensite) for the 12 models combining the 4 features and the 3 training datasets.

The performances of the models are usually good when exposed to microstructure acquired in the same conditions as those seen during training. Those results are highlighted in bold in Table 3 and are always greater than 85% and often greater than 95%. This is a good indicator that the models have converged.

<table><caption>Table 3. Models' test accuracies on the different EBSD maps. Bold accuracies highlight performances when the test and training datasets were under the same acquisition conditions.</caption><thead><tr><th rowspan="3">Used inputs</th><th rowspan="3">Training dataset</th><th colspan="3">Testing accuracy (%)</th><th colspan="3">Testing accuracy (%)</th><th colspan="3">Testing accuracy (%)</th></tr><tr><th colspan="3">Martensite</th><th colspan="3">Lower Bainite</th><th colspan="3">Upper Bainite</th></tr><tr><th>S</th><th>H1</th><th>H2</th><th>S</th><th>H1</th><th>H2</th><th>S</th><th>H1</th><th>H2</th></tr></thead><tbody><tr><td rowspan="3">BC</td><td>S</td><td>95</td><td>99.8</td><td>100</td><td>98.3</td><td>0</td><td>0</td><td>97.9</td><td>0</td><td>0</td></tr><tr><td>H1</td><td>100</td><td>99.9</td><td>99.3</td><td>0</td><td>96.9</td><td>78.8</td><td>0</td><td>98.3</td><td>0</td></tr><tr><td>H2</td><td>3.5</td><td>99.9</td><td>100</td><td>100</td><td>3.3</td><td>85.9</td><td>0</td><td>0</td><td>99.7</td></tr><tr><td rowspan="3">KAM</td><td>S</td><td>97.5</td><td>95.1</td><td>98.5</td><td>96.8</td><td>84.4</td><td>59.2</td><td>99.9</td><td>83.7</td><td>9.5</td></tr><tr><td>H1</td><td>96.6</td><td>99.7</td><td>99.9</td><td>99.4</td><td>85.1</td><td>56</td><td>99.8</td><td>98.9</td><td>16.3</td></tr><tr><td>H2</td><td>2.7</td><td>92.1</td><td>98.3</td><td>62.1</td><td>51.4</td><td>90</td><td>100</td><td>98.9</td><td>86.2</td></tr><tr><td rowspan="3">BC & KAM</td><td>S</td><td>99.5</td><td>99.7</td><td>99.9</td><td>93.2</td><td>43.7</td><td>29.2</td><td>99.7</td><td>0.2</td><td>0</td></tr><tr><td>H1</td><td>100</td><td>99.8</td><td>99.9</td><td>0</td><td>95.4</td><td>60.4</td><td>0.1</td><td>97.1</td><td>1.8</td></tr><tr><td>H2</td><td>100</td><td>87.5</td><td>99</td><td>44.3</td><td>95.7</td><td>87.9</td><td>0</td><td>3.6</td><td>98.7</td></tr><tr><td rowspan="3">BC & KAM & ZS</td><td>S</td><td>99.6</td><td>95.7</td><td>99</td><td>95.9</td><td>92.7</td><td>80.4</td><td>100</td><td>57</td><td>0.3</td></tr><tr><td>H1</td><td>100</td><td>99.4</td><td>99.8</td><td>0</td><td>95.9</td><td>82.5</td><td>0</td><td>96.9</td><td>3.6</td></tr><tr><td>H2</td><td>100</td><td>94.9</td><td>99.7</td><td>0</td><td>33</td><td>89.5</td><td>0</td><td>86.7</td><td>99.3</td></tr></tbody></table>

When exposed to acquisition conditions not seen during training, the performances of the models depend in the first order on the microstructure, then on the acquisition conditions, and finally on the inputs selected to train the model.

Martensitic microstructures are well segmented with all Models. The reason for that is that martensite has the darkest BC, the highest KAM, and the highest number of non-indexed pixels for all acquisition conditions. The two bainites are more difficult to distinguish, and performance is influenced by both the selected features and the training dataset.
When performances are low, the model recognizes another microstructure than the real one. For example, the model trained with BC and the dataset "H2" recognizes both lower and upper bainite maps from the "H1" dataset as martensite. Models trained with KAM show similar issues in recognizing microstructures from different datasets. Figure 6 shows the segmentation results of the LB1 microstructure for two models. The first was trained on the "H2" dataset, and the second was on the "S" dataset. The first model recognizes lower bainite correctly for the map from the "H2" dataset (accuracy of 93.5%), but confuses it with upper bainite when segmenting the "S" dataset (accuracy of 83.2%), probably because the "S" dataset has lower KAM values compared to the "H2" dataset. Thereby, regions in LB1 from "S" with lower KAM values are like the upper bainite in the "H2" dataset for the model. And vice versa, the second model shows high efficiency on the map from the "S" dataset (accuracy of 98.3%), see the bottom row in Figure 5, but often failed with maps from the "H2" dataset (accuracy of 55.5%). The reason for the confusion between lower bainite and martensite is the same, the regions with low values of KAM from "H2" have a similar distribution to those of martensite in the "S" dataset. Notice for later that the shape of the 2nd phase islands does not follow grain boundaries. This is addressed in greater detail in §4.2.

<figure><img src="image_8.png" /><figcaption>Figure 6 Examples of segmentation of the LB1 microstructure from the "H2" (left column) and "S" (right column) datasets. The models used KAM and acquisition step size as input features.</figcaption></figure>

The performances also depend on the chosen inputs. Using the Band Contrast alone produces models that are more sensitive to acquisition conditions. Those models are only able to segment the acquisition conditions seen during training. On the other hand, the KAM models are better at generalizing on other acquisition conditions. This can be explained by the differences between the datasets. As shown in Figure 2, the BC distribution for Upper bainite presents the highest scatter among the datasets and, as a result, it is the most difficult phase to segment on datasets not seen during training.

Unlike the BC, the distributions of KAM present a better repeatability among datasets (Figure 3), which induce a better robustness of the U-Net models against acquisition conditions. Still some small differences can be noticed: The "H2" has a lower angular resolution, and the "S" has similar angular resolution as the "H1" but spans across a larger field, whereas the "H1" KAM peaks much more.

Combining features does not really improve the results. One can just notice that the BC & KAM & ZS model improves slightly the results over those with BC & KAM alone. Then a KAM & ZS would be an interesting combination to evaluate in the future.

Models trained with the H1 dataset have better generalizing abilities because the features on these maps are intermediate between "H2" and "S". The distribution of BC lies between that of the "S" and "H2". For the KAM, the similarity of step size with "H2" on one hand and the similarity in angular resolution with the "S" dataset may be the determining factor.
## 4.2 Models trained on all datasets together

The models' performances are very good when all acquisition conditions are used during training (Table 4). The model with BC only as an input provides the best results, this model has 99.3% average accuracy over all maps. This is counterintuitive because this input was leading to the worst performances when trained with one acquisition condition. The second-best model is the one coupling BC and KAM, with 98.8% followed by the KAM alone. Here, the "worst" model is the one coupling all features, but the difference is low, and one must keep in mind that the models are initialized randomly, which induces variability between different training [33].

All models are sensitive to acquisition conditions. Their performance degrades drastically when exposed to conditions not seen during training. The models trained with KAM are better at generalizing because it is more reproducible across datasets. On the other hand, models trained with BC are worse in generalization, but when exposed to all datasets, they show the best performance. This may be because more meaningful information (what the model evaluates in the hidden layers) can be extracted from the BC, but the input is more sensitive to acquisition conditions. While a metallographer would identify phases primarily by morphology and contrast (e.g., lath shape, packet/block arrangement), the BC channel instead provides a defect-sensitive contrast linked to crystallographic quality, which indirectly correlates with phase identity, offering a complementary perspective for phase segmentation.

<table><caption>Table 4. Models' test accuracies on the different EBSD maps.</caption><thead><tr><th rowspan="3">Used inputs</th><th colspan="12">Testing accuracy (%)</th></tr><tr><th colspan="3">Martensite</th><th colspan="3">Lower Bainite</th><th colspan="3">Upper Bainite</th><th rowspan="2">Average</th></tr><tr><th>S</th><th>H1</th><th>H2</th><th>S</th><th>H1</th><th>H2</th><th>S</th><th>H1</th><th>H2</th></tr></thead><tbody><tr><td>BC & step</td><td>100</td><td>99.9</td><td>100</td><td>99.6</td><td>98.9</td><td>98.7</td><td>99.1</td><td>97.6</td><td>99.5</td><td>99.3</td></tr><tr><td>KAM & step</td><td>99.6</td><td>99.7</td><td>99.9</td><td>99.6</td><td>90.1</td><td>89.3</td><td>99.9</td><td>99.4</td><td>95.4</td><td>97.0</td></tr><tr><td>BC & KAM & step</td><td>99.7</td><td>99.7</td><td>100</td><td>99.9</td><td>99.7</td><td>95.2</td><td>99.9</td><td>97.4</td><td>97.6</td><td>98.8</td></tr><tr><td>BC & KAM & ZS & step</td><td>82.7</td><td>99.3</td><td>100</td><td>97.3</td><td>95.5</td><td>93</td><td>100</td><td>99.4</td><td>98</td><td>96.1</td></tr></tbody></table>

## 4.3 Models trained with two datasets

Training with all 3 datasets gives the best results, but producing many datasets is costly. As an alternative, models trained on two datasets may provide an effective compromise between performance and acquisition effort. We therefore evaluate whether such models can generalize to the remaining unseen dataset. The results are presented in Table 5. The observations are very similar to those of the previous paragraphs. The models show very good performances on the dataset they have been exposed to during training, but the performances drop for the unseen dataset, especially for models trained with BC. Interestingly, the model trained on the most different datasets ("S" and "H2") gives the best performances on the intermediate unseen dataset "H1". Therefore, having the most diverse dataset would induce more robustness in the models.
<table><caption>Table 5. Models' test accuracy for models trained with two mixed datasets. In bold, results on the unseen dataset but for which the corresponding training dataset was used.</caption><thead><tr><th rowspan="3">Used inputs</th><th rowspan="3">Training dataset</th><th colspan="3">Testing accuracy (%)</th><th colspan="3">Testing accuracy (%)</th><th colspan="3">Testing accuracy (%)</th></tr><tr><th colspan="3">Martensite</th><th colspan="3">Lower Bainite</th><th colspan="3">Upper Bainite</th></tr><tr><th colspan="3">Test dataset:</th><th colspan="3">Test dataset:</th><th colspan="3">Test dataset:</th></tr><tr><th></th><th></th><th>S</th><th>H1</th><th>H2</th><th>S</th><th>H1</th><th>H2</th><th>S</th><th>H1</th><th>H2</th></tr></thead><tbody><tr><td rowspan="2">BC + step</td><td>S & H1</td><td><strong>97.9</strong></td><td><strong>100</strong></td><td>99.9</td><td><strong>99.5</strong></td><td><strong>97.2</strong></td><td>70.5</td><td><strong>99.7</strong></td><td><strong>98.7</strong></td><td>0</td></tr><tr><td>S & H2</td><td><strong>90.1</strong></td><td>95.8</td><td><strong>100</strong></td><td><strong>99.8</strong></td><td>24.5</td><td><strong>93.8</strong></td><td><strong>97.9</strong></td><td>20.5</td><td><strong>99.4</strong></td></tr><tr><td rowspan="2">KAM + step</td><td>S & H1</td><td><strong>98.4</strong></td><td><strong>99.3</strong></td><td>99.8</td><td><strong>99.3</strong></td><td><strong>85.5</strong></td><td>60.9</td><td><strong>99.6</strong></td><td><strong>96.9</strong></td><td>6.4</td></tr><tr><td>S & H2</td><td><strong>100</strong></td><td>96.4</td><td><strong>99</strong></td><td><strong>99.8</strong></td><td>78.9</td><td><strong>85.3</strong></td><td><strong>99.7</strong></td><td>93.2</td><td><strong>94.4</strong></td></tr><tr><td rowspan="2">KAM + BC + step</td><td>S & H1</td><td><strong>95.5</strong></td><td><strong>99.8</strong></td><td>100</td><td><strong>97.7</strong></td><td><strong>94.9</strong></td><td>28</td><td><strong>99.8</strong></td><td><strong>96.8</strong></td><td>2</td></tr><tr><td>S & H2</td><td><strong>99.2</strong></td><td>97.8</td><td><strong>99.9</strong></td><td><strong>98.8</strong></td><td>44.9</td><td><strong>86</strong></td><td><strong>99.9</strong></td><td>94.1</td><td><strong>99.8</strong></td></tr><tr><td rowspan="2">KAM + BC + ZS + step</td><td>S & H1</td><td><strong>97.6</strong></td><td><strong>99.1</strong></td><td>100</td><td><strong>99.5</strong></td><td><strong>99.2</strong></td><td>62.3</td><td><strong>100</strong></td><td><strong>98.9</strong></td><td>3.6</td></tr><tr><td>S & H2</td><td><strong>99.7</strong></td><td>86.1</td><td><strong>97.3</strong></td><td><strong>98.2</strong></td><td>62.1</td><td><strong>91.4</strong></td><td><strong>99.9</strong></td><td>62.5</td><td><strong>96.5</strong></td></tr></tbody></table>

## 4.4 Simulating new acquisition conditions by data augmentation

Combining two datasets leads to interesting performances when the considered feature does not vary too much across datasets and when the model is exposed to the most “extreme” cases.

Since it is not always possible to obtain such a dataset, we tried to simulate this by data augmentation. Here, we can use the “S” dataset and add noise to it to simulate something close to the “H2” dataset. In this approach, the EBSD data input is degraded by adding noise to it. Then the model is trained with original data and noisy data. Noise was added to the orientations and to the BC inputs. Orientation noise can be added by rotating each orientation by a small angle. Using quaternions, this is done by:

$$q_{\text{new}} = q \times q_{\text{noise}},$$

here $q_{\text{new}}$ is a quaternion describing the orientation with added noise, $\times$ is the quaternion product, and $q_{\text{noise}}$ is defined as

$$q_{\text{noise}} = \left( \cos \frac{\theta_{\text{gauss}}}{2}, \vec{u}_{\text{uniform}} \sin \frac{\theta_{\text{gauss}}}{2} \right).$$

Here, $\vec{u}_{\text{uniform}}$ is a unit random vector drawn from a uniform distribution on the unit sphere; $\theta_{\text{gauss}}$ is a random angle which follows a normal distribution with an average $\mu = 0$ and a standard deviation $\sigma = 0.3^\circ$. This noise was chosen because it allows the distribution of KAM in the noisy “S” dataset to approach that of the “H2”.

The resulting KAM maps were used as a training/validation dataset for the U-Net model. The results of testing are presented in the Table 6.

<table><caption>Table 6. Models' test accuracies on the different EBSD maps. In bold are the best scores per column.</caption><thead><tr><th rowspan="3">Used inputs:<br>KAM & step</th><th colspan="9">Testing accuracy (%)</th></tr><tr><th colspan="3">Martensite</th><th colspan="3">Lower Bainite</th><th colspan="3">Upper Bainite</th><th rowspan="2">Average</th></tr><tr><th>S</th><th>H1</th><th>H2</th><th>S</th><th>H1</th><th>H2</th><th>S</th><th>H1</th><th>H2</th></tr></thead><tbody><tr><td>S</td><td>97.5</td><td>95.1</td><td>98.5</td><td>96.8</td><td>84.4</td><td>59.2</td><td>99.9</td><td><strong>83.7</strong></td><td><strong>9.5</strong></td><td>79.4</td></tr><tr><td>S with noise<br>augmentation</td><td><strong>97.0</strong></td><td><strong>95.6</strong></td><td><strong>98.9</strong></td><td><strong>99.0</strong></td><td><strong>88.9</strong></td><td><strong>71.7</strong></td><td><strong>100</strong></td><td>74.9</td><td>1.1</td><td><strong>80.8</strong></td></tr></tbody></table>
The first point to note is that the new model does not degrade the performance on the “S” dataset, it even improves it slightly. For other datasets, it shows significant improvement of accuracy, especially on the maps with Lower Bainite, but tends to degrade the results on the maps with Upper Bainite microstructure. This means that using the same noise for all phases was probably not optimal. Improving the results further would require a better characterization of the data we want to mimic while adding noise.

Data augmentation can be performed on the BC maps in a similar way:

$$BC_{new} = BC - BC_{noise},$$

here BC is a value of Band Contrast of the pixel, $BC_{noise}$ is a random value with normal distribution with $\mu = 30$ and $\sigma = 5$.

Then the training “S” dataset was augmented with noisy BC data. The testing results are presented in Table 7.

<table><caption>Table 7. Models' test accuracies on the different EBSD maps.</caption><thead><tr><th rowspan="3">Used inputs:<br>BC & step</th><th colspan="9">Testing accuracy (%)</th></tr><tr><th colspan="3">Martensite</th><th colspan="3">Lower Bainite</th><th colspan="3">Upper Bainite</th><th rowspan="2">Average</th></tr><tr><th>S</th><th>H1</th><th>H2</th><th>S</th><th>H1</th><th>H2</th><th>S</th><th>H1</th><th>H2</th></tr></thead><tbody><tr><td>S</td><td>95</td><td>99.8</td><td>100</td><td>98.3</td><td>0</td><td>0</td><td>97.9</td><td>0</td><td>0</td><td>54.55</td></tr><tr><td>S + noise<br>augmentation</td><td>84.3</td><td>53.3</td><td>63.1</td><td>98.6</td><td>59.3</td><td>37.5</td><td>96.8</td><td>81.2</td><td>5.6</td><td>64.4</td></tr></tbody></table>

The testing results show improvements for maps with Lower and Upper bainite microstructures, but a significant degradation on maps with Martensitic microstructures. Here, the parameter for the noise was independent of the phase and was adjusted so that the noisy “S” BC-distribution corresponds to that of the “H2” dataset on average. Here again, a better choice of noise may improve the results further.

## 5 Efficiency of weak supervision

All results discussed in the previous section were obtained by weak-supervised learning. The weak supervision strategy presents the enormous advantage of reducing the labelling task to the minimum and gives very good results on the used datasets. It should be emphasized that the quasi-single-phase assumption does not imply a complete absence of minor constituents; small fractions of secondary phases may still be present after heat treatment. These residual fractions act as bounded label noise rather than as a systematic labeling error, since the dominant phase of each map remains correctly assigned. The robustness of deep learning models to such small-scale mislabeling has been demonstrated in our previous work [15]. This suggests that the limited errors introduced by quasi-single-phase maps do not drastically affect the training outcome. Despite the fairly high accuracy of segmentation during testing on the monophase maps, the models trained on single phase maps seem not to have an internal representation of grain boundaries or interphase boundaries (see in Figure 6). Importantly, the model did not see any example of interphase boundaries during training. For the model to better recognize microstructures on EBSD maps, multiphase maps should be included in the training/validation dataset, which means abandoning weak supervision. As an alternative, we explored a data augmentation strategy by generating synthetic multiphase maps from existing quasi-single-phase datasets.

The Mixed maps were tested by the models trained on the “S” dataset. The segmentation results confirm the poor ability of the weak supervised models to separate microstructures on the multiphase maps, see Table 8, Figure 7 & Figure 8. Figure 7 b), e) and Figure 8 b), e) show segmentation results provided by two models with the BC and the KAM as inputs, respectively. The ability of models to segment multiphase maps needs to be improved.
<table><caption>Table 8 Models' test accuracy on the EBSD maps with artificially mixed microstructures. Test maps contain a combination of two microstructures mentioned in the table.</caption><thead><tr><th rowspan="2">Used inputs</th><th rowspan="2">Training dataset</th><th colspan="3">Testing accuracy (%)</th></tr><tr><th>Martensite +<br>Lower Bainite</th><th>Lower Bainite +<br>Upper Bainite</th><th>Upper Bainite +<br>Martensite</th></tr></thead><tbody><tr><td>BC & step</td><td>S</td><td>76.6</td><td>73.4</td><td>48.0</td></tr><tr><td>BC & step</td><td>S + Mixed maps</td><td>75.5</td><td>82.1</td><td>77.8</td></tr><tr><td>KAM & step</td><td>S</td><td>79.1</td><td>78.8</td><td>60.5</td></tr><tr><td>KAM & step</td><td>S + Mixed maps</td><td>78.6</td><td>87.7</td><td>86.2</td></tr></tbody></table>

<figure><img src="image_9.png" /><figcaption>Figure 7 EBSD map of mixed LB1 and M3 maps. The first column shows a) the BC, d) the KAM of the map, and g) ground truth overlapped BC. The segmentation results: with "S" training dataset and b) BC, e) KAM as inputs; with "S + Mixed maps" training dataset and c) BC, f) KAM as inputs.</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>Figure 8 EBSD map of mixed UB2 and M3 maps. The first column shows a) the BC, d) the KAM of the map, and g) ground truth overlapped BC. The segmentation results: with "S" training dataset and b) BC, e) KAM as inputs; with "S + Mixed maps" training dataset and c) BC, f) KAM as inputs.</figcaption></figure>

In the next step, two models that use BC or KAM as input features were trained. The training/validation dataset contains the "S" dataset and the Mixed dataset with augmented data. The segmentation results are presented in Table 8 and Figure 9, which shows a significant improvement in accuracy for the mixed maps. The performance is increased by more than 10% for all microstructures except for a 1% decrease on the lower bainite mixed with martensite, for which the results were already at an acceptable level. This strategy improves the performance while keeping weak supervision.

To evaluate the practical relevance of the synthetic mixing strategy, we compared the performance of the model trained exclusively on a single-phase dataset with that trained on a synthetic multiphase dataset on a real EBSD map with upper bainite and martensite, see Figure 10. This map was obtained using the same acquisition conditions as for the "H2" dataset. The first model was trained on a dataset containing "S", "H1", and "H2" single-phase datasets. The training dataset of the second model was augmented with synthetic mixed maps. Both models provide reasonable segmentation of the multiphase EBSD map. However, a closer inspection of the BC and KAM maps reveals that the model trained only on a single-phase dataset ("S + H1 + H2") performs less accurately in distinguishing martensite from upper bainite. The model trained with the inclusion of synthetic mixed maps shows a more precise separation of these phases, particularly at interphase boundaries. Nevertheless, both models struggle with correctly identifying small martensite regions, which we attribute to the limited number of such examples in the training data. These observations highlight that while single-phase training can capture the dominant microstructural features, synthetic mixing provides the model with critical examples of phase coexistence, thereby improving its practical applicability for multiphase steels.
<figure><img src="image_11.png" /><figcaption>Figure 9 Models' test accuracies on the EBSD maps with artificially mixed microstructures. The input features of models are indicated in parentheses.</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>Figure 10 EBSD map of a multiphase microstructure. From left to right: (i) BC map, (ii) KAM map, (iii) segmentation result using the "S + H1 + H2" training dataset with KAM as input, and (iv) segmentation result using the "S + H1 + H2 + Mixed maps" training dataset with KAM as input.</figcaption></figure>

Weak supervision provided a cost-effective way to obtain a large database and to train effective models if the models are to be used on similar microstructures. The correct detection of interphase boundaries can be improved by data augmentation while mixing data at the scale of the austenite grains. Additionally, data for weak supervision can be easily integrated into classical supervised learning.

# 6 Conclusions

The objective of this work was two-fold. First, it assesses the potential of weak supervision for phase discrimination, and second, it assesses the robustness of those models to acquisition conditions. The following conclusions can be drawn from here:

- Weak supervision is a cost-effective method to produce a large database and train models with very good performance using single-phase microstructures. However, those models are not very good at segmenting interphase boundaries. Data augmentation performed by adding synthetic mixed-phase microstructures improves the performance readily.

- Contrary to what is said in the literature, the deep learning models were proven to be sensitive to acquisition conditions. In general, it is recommended to train the model on a dataset representative of the maps it will be applied to in the future.

- Having the most diverse database (microstructures, alloys, phases, acquisition conditions) should produce more robust models.
It must also be emphasized that the generalizability of the proposed approach is inherently dependent on the diversity and representativeness of the training dataset. While combining several acquisition conditions improved robustness in this study, the model cannot be expected to perform reliably on EBSD maps of steels with substantially different chemistries (e.g., higher carbon contents) or on microstructures outside the scope of the present work. Expanding the database to encompass a broader variety of alloys, microstructures, and acquisition conditions is therefore a necessary step to ensure transferability to new cases.

- Data augmentation by adding noise to orientation and BC can be used to produce more robust models in degraded acquisition conditions without degrading the performance on the actual dataset. This would work efficiently if one made sure that the added noise mimics the degraded data accurately.

# 7 Acknowledgments

The present work is part of a study led by LEM3 and IRT-M2P through the EXPAANS project. It was supported by the French government's Plan d'Investissement d'Avenir (PIA), ARCELORMITTAL, INDUSTEEL ARCELORMITTAL and FRAMATOME.

The authors acknowledge the use of the Cassiopee Arts et Métiers Institute of Technology HPC Center made available for conducting the research reported in this paper.

# 8 Data availability

The authors do not have permission to share the Data at the time of publication.

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# 10 Appendix

<table><caption>Table 9. Models' test accuracies on the different EBSD maps.</caption><thead><tr><th rowspan="3">Used inputs</th><th rowspan="3">Training dataset</th><th colspan="3">Testing accuracy (%)</th><th colspan="3">Testing accuracy (%)</th><th colspan="3">Testing accuracy (%)</th></tr><tr><th colspan="3">Martensite</th><th colspan="3">Lower Bainite</th><th colspan="3">Upper Bainite</th></tr><tr><th>S</th><th>H1</th><th>H2</th><th>S</th><th>H1</th><th>H2</th><th>S</th><th>H1</th><th>H2</th></tr></thead><tbody><tr><td rowspan="5">BC + step</td><td>S</td><td>95</td><td>99.8</td><td>100</td><td>98.3</td><td>0</td><td>0</td><td>97.9</td><td>0</td><td>0</td></tr><tr><td>H1</td><td>100</td><td>99.9</td><td>99.3</td><td>0</td><td>96.9</td><td>78.8</td><td>0</td><td>98.3</td><td>0</td></tr><tr><td>H2</td><td>3.5</td><td>99.9</td><td>100</td><td>100</td><td>3.3</td><td>85.9</td><td>0</td><td>0</td><td>99.7</td></tr><tr><td>S & H1</td><td>97.9</td><td>100</td><td>99.9</td><td>99.5</td><td>97.2</td><td>70.5</td><td>99.7</td><td>98.7</td><td>0</td></tr><tr><td>S & H2</td><td>90.1</td><td>95.8</td><td>100</td><td>99.8</td><td>24.5</td><td>93.8</td><td>97.9</td><td>20.5</td><td>99.4</td></tr><tr><td rowspan="5">Used inputs</td><td rowspan="5">Training dataset</td><th colspan="3">Test accuracy (%)</th><th colspan="3">Test accuracy (%)</th><th colspan="3">Test accuracy (%)</th></tr><tr><th colspan="3">Martensite</th><th colspan="3">Lower Bainite</th><th colspan="3">Upper Bainite</th></tr><tr><th>S</th><th>H1</th><th>H2</th><th>S</th><th>H1</th><th>H2</th><th>S</th><th>H1</th><th>H2</th></tr><tr><td>S</td><td>97.5</td><td>95.1</td><td>98.5</td><td>96.8</td><td>84.4</td><td>59.2</td><td>99.9</td><td>83.7</td><td>9.5</td></tr><tr><td>H1</td><td>96.6</td><td>99.7</td><td>99.9</td><td>99.4</td><td>85.1</td><td>56</td><td>99.8</td><td>98.9</td><td>16.3</td></tr><tr><td rowspan="5">KAM + step</td><td>S</td><td>2.7</td><td>92.1</td><td>98.3</td><td>62.1</td><td>51.4</td><td>90</td><td>100</td><td>98.9</td><td>86.2</td></tr><tr><td>H1</td><td>96.6</td><td>99.7</td><td>99.9</td><td>99.4</td><td>85.1</td><td>56</td><td>99.8</td><td>98.9</td><td>16.3</td></tr><tr><td>H2</td><td>2.7</td><td>92.1</td><td>98.3</td><td>62.1</td><td>51.4</td><td>90</td><td>100</td><td>98.9</td><td>86.2</td></tr><tr><td>S & H1</td><td>98.4</td><td>99.3</td><td>99.8</td><td>99.3</td><td>85.5</td><td>60.9</td><td>99.6</td><td>96.9</td><td>6.4</td></tr><tr><td>S & H2</td><td>100</td><td>96.4</td><td>99</td><td>99.8</td><td>78.9</td><td>85.3</td><td>99.7</td><td>93.2</td><td>94.4</td></tr><tr><td rowspan="5">Used inputs</td><td rowspan="5">Training dataset</td><th colspan="3">Test accuracy (%)</th><th colspan="3">Test accuracy (%)</th><th colspan="3">Test accuracy (%)</th></tr><tr><th colspan="3">Martensite</th><th colspan="3">Lower Bainite</th><th colspan="3">Upper Bainite</th></tr><tr><th>S</th><th>H1</th><th>H2</th><th>S</th><th>H1</th><th>H2</th><th>S</th><th>H1</th><th>H2</th></tr><tr><td>S</td><td>99.5</td><td>99.7</td><td>99.9</td><td>93.2</td><td>43.7</td><td>29.2</td><td>99.7</td><td>0.2</td><td>0</td></tr><tr><td>H1</td><td>100</td><td>99.8</td><td>99.9</td><td>0</td><td>95.4</td><td>60.4</td><td>0.1</td><td>97.1</td><td>1.8</td></tr><tr><td rowspan="5">BC + KAM+ step</td><td>S</td><td>100</td><td>99.8</td><td>99.9</td><td>0</td><td>95.4</td><td>60.4</td><td>0.1</td><td>97.1</td><td>1.8</td></tr><tr><td>H1</td><td>100</td><td>87.5</td><td>99</td><td>44.3</td><td>95.7</td><td>87.9</td><td>0</td><td>3.6</td><td>98.7</td></tr><tr><td>H2</td><td>100</td><td>87.5</td><td>99</td><td>44.3</td><td>95.7</td><td>87.9</td><td>0</td><td>3.6</td><td>98.7</td></tr><tr><td>S & H1</td><td>95.5</td><td>99.8</td><td>100</td><td>97.7</td><td>94.9</td><td>28</td><td>99.8</td><td>96.8</td><td>2</td></tr><tr><td>S & H2</td><td>99.2</td><td>97.8</td><td>99.9</td><td>98.8</td><td>44.9</td><td>86</td><td>99.9</td><td>94.1</td><td>99.8</td></tr><tr><td rowspan="5">Used inputs</td><td rowspan="5">Training dataset</td><th colspan="3">Test accuracy (%)</th><th colspan="3">Test accuracy (%)</th><th colspan="3">Test accuracy (%)</th></tr><tr><th colspan="3">Martensite</th><th colspan="3">Lower Bainite</th><th colspan="3">Upper Bainite</th></tr><tr><th>S</th><th>H1</th><th>H2</th><th>S</th><th>H1</th><th>H2</th><th>S</th><th>H1</th><th>H2</th></tr><tr><td>S</td><td>99.6</td><td>95.7</td><td>99</td><td>95.9</td><td>92.7</td><td>80.4</td><td>100</td><td>57</td><td>0.3</td></tr><tr><td>H1</td><td>100</td><td>99.4</td><td>99.8</td><td>0</td><td>95.9</td><td>82.5</td><td>0</td><td>96.9</td><td>3.6</td></tr><tr><td rowspan="5">BC + KAM + ZS + step</td><td>S</td><td>100</td><td>99.4</td><td>99.8</td><td>0</td><td>95.9</td><td>82.5</td><td>0</td><td>96.9</td><td>3.6</td></tr><tr><td>H1</td><td>100</td><td>94.9</td><td>99.7</td><td>0</td><td>33</td><td>89.5</td><td>0</td><td>86.7</td><td>99.3</td></tr><tr><td>S + H1</td><td>97.6</td><td>99.1</td><td>100</td><td>99.5</td><td>99.2</td><td>62.3</td><td>100</td><td>98.9</td><td>3.6</td></tr><tr><td>S + H2</td><td>99.7</td><td>86.1</td><td>97.3</td><td>98.2</td><td>62.1</td><td>91.4</td><td>99.9</td><td>62.5</td><td>96.5</td></tr><tr><td>All datasets</td><td>82.7</td><td>99.3</td><td>100</td><td>97.3</td><td>95.5</td><td>93</td><td>100</td><td>99.4</td><td>98</td></tr></tbody></table>