Deep Learning-Enhanced Wear Analysis of Fe-Cr Composite Coatings for Automotive Brake
Applications under Euro 7 Requirements
(Tae-Jun Park)
12
(Gye-Won Lee)
12
(Jong-Il Kim)
1
(Sahn Nahm)
2
(Yoon-Suk Oh)
1*
Copyright © 2025 The Korean Institute of Metals and Materials
Key words(Korean)
Fe-Cr composite coating, HVOF, Wear mechanism, Deep learning, Tribology, Wear pattern classification, Euro 7 compliance
1. INTRODUCTION
The transition of the automotive industry toward electric vehicles (EVs) has introduced
new challenges in brake system design and particulate emission control [1]. Although EVs reduce tailpipe emissions, their increased mass intensifies the mechanical
demands on brake systems, potentially exacerbating non-exhaust particulate matter
(PM) emissions [2,3]. These brake-derived particles, which are typically in the respirable size range,
pose significant environmental and health concerns owing to their high reactivity
with biological tissues [4,5].
This challenge has been formally recognized in the new Euro 7 regulation (Regulation
(EU) 2024/1257), which introduced specific limits for brake particle emissions for
the first time [6]. The regulation stipulates that from November 2026, passenger cars and light commercial
vehicles must satisfy PM10 emission limits of 7 and 3 mg km−1 for conventional powertrains and battery EVs, respectively, with stricter limits
planned for implementation from January 2030 [1].
1.1. Background
In light of these stringent regulatory requirements, critical reassessment of current
brake system materials is required. Conventional brake systems predominantly employ
gray cast iron (GCI) discs because of their cost-effectiveness and adequate mechanical
properties. However, GCI exhibits several significant limitations that are problematic
in the context of modern applications and emerging emission standards [7,8]. This material exhibits a notable susceptibility to corrosion in humid environments
and experiences accelerated wear when exposed to road salt. These conditions can lead
to increased PM emission under corrosive conditions.
Furthermore, friction-induced temperature elevation during braking significantly affects
the wear characteristics and braking performance [9]. This thermal effect creates a need for materials that can maintain stable friction
coefficients across diverse operating conditions [10], particularly in demanding EV applications.
1.2. Current Solutions and Limitations
To address these challenges, various coating materials, including WC, Ni, and Cr,
have been developed to enhance the wear resistance and corrosion protection [11]. These materials demonstrate superior mechanical properties and thermal stability
compared to conventional GCI, making them promising candidates for brake-disc applications
[12].
For example, high-velocity oxygen fuel (HVOF) coating techniques have garnered attention
owing to their effectiveness when applied to brake discs [13,14]. The HVOF process involves heating and accelerating the coating particles through
an HVOF jet to create dense, high-bonding coatings characterized by enhanced hardness
and strong adhesion strength [15].
Fe-Cr-based composite materials have emerged as particularly promising candidates,
offering an attractive combination of performance and cost-effectiveness [16,17]. These materials demonstrate favorable phase transformation characteristics during
thermal spray processing, uniform microstructure development, and superior mechanical
properties in the Fe matrix, while utilizing more economically viable raw materials
[18,19].
1.3. Wear Analysis Challenges
Parallel to material development, the characterization of wear mechanisms presents
its own set of challenges. Traditional wear mechanism analysis relies heavily on the
expert interpretation of scanning electron microscopy (SEM) images, which poses significant
limitations. This process involves considerable subjectivity in pattern recognition
and is time-intensive, particularly when processing large datasets [20-22].
Although artificial intelligence approaches have shown promise for automated wear
classification, existing studies have shown notable constraints. To overcome these,
our study introduces a multimodal deep learning framework that integrates SEM image
analysis with friction coefficient-derived features, addressing class imbalance, morphological
variability, and subjectivity. This multimodal approach ensures higher robustness
compared with previous methods. These include limitations in binary classification
capabilities, restricted dataset sizes, and persistent challenges with class-imbalance
issues [21,22].
1.4. Research Objectives
Considering the above-mentioned challenges, this study aimed to advance the understanding
of Fe-Cr-based composite coatings and automated wear pattern analysis through an integrated
approach. Consequently, this study had three interconnected objectives.
1. This study focused on tribological performance evaluation through the application
of Fe-Cr-based composite coatings on GCI substrates using HVOF. This includes a systematic
assessment using ball-on-disk testing and a comparison of the wear behavior against
Si3N4 and WC counterfaces.
2. We developed advanced pattern recognition capabilities by implementing a deep ensemble
convolutional neural network (CNN) model for wear classification. This involves creating
a comprehensive, balanced dataset and implementing model averaging to enhance generalization.
3. The study addressed practical implementation by validating the method through classification
reliability across diverse wear patterns, assessment of model performance under various
tribological conditions, and establishment of an automated wear analysis framework.
The selection of Si3N4 and WC as counterface materials enabled a comprehensive evaluation of their contrasting
properties. Si3N4 provides high-temperature stability and chemical inertness, whereas WC provides superior
hardness and wear resistance. This strategic selection of materials directly supports
the first objective of tribological performance evaluation, whereas the implementation
of deep ensemble CNN modeling addresses the second objective of automated pattern
recognition. By integrating these approaches, this study establishes a robust methodology
for both coating performance optimization and wear mechanism classification in tribological
applications, particularly for automotive brake systems that are required to meet
Euro 7 standards.
2. Experimental
2.1. High-velocity Oxygen Fuel Spray Coating and Specimen Preparation
The substrate material selected for this study, GCI (FC200D, Myunghwa Ind. Co., Ltd.,
Republic of Korea), was machined into disk specimens with dimensions of 30 mm × 10
mm. Two novel Fe-Cr-based composite materials (Atometal Tech Korea Inc., Republic
of Korea), named Composites A and B, were coated onto the substrates to enhance their
glass-forming ability, which refers to the tendency of the powders to form amorphous-like
or fine crystalline structures during the rapid solidification process in HVOF coating,
potentially improving the coating's mechanical properties and wear resistance. These
coating layers were deposited onto the substrates using an HVOF spraying process,
performed by Shinhwa Metal Co., Ltd., Republic of Korea. The coating process was conducted
according to the standardized industrial procedures of the company using powder particle
size of 15–45 μm and target coating thickness >100 μm to ensure coating quality and
reliability.
The surface preparation of the coated specimens was prepared by mechanical grinding
and polishing processes. The final coating thickness was controlled to be greater
than 100 μm, as measured using a coating thickness gauge (OM-8811FN, Optech, China),
with an accuracy of ±2 μm. Measurement was obtained at five randomly selected points
per specimen to ensure data precision. Figure 1(a) shows the SEM micrographs of the coating cross sections, illustrating the uniformity
of the coating thickness and the quality of the interface between the coating and
substrate.
Surface roughness was characterized using a surface profilometer (SURFTEST SJ-410,
Mitutoyo Corporation, Japan) with a diamond stylus tip of 2 μm radius. Three measurements
were obtained for each specimen to achieve a final Ra value in the range of 0.15–0.2 μm, which ensures optimal wear resistance and frictional
performance while maintaining experimental consistency and cost efficiency.
Hardness measurements were performed (50× magnification) using a Vickers hardness
tester (HV-114, Mitutoyo Corporation, Japan). The measurements were performed under
an applied load of 1 kgf (9.81 N), with a dwell time of 10 s. Five measurements were
obtained per specimen at 500 μm intervals to ensure representative sampling of the
material.
2.2. Tribological Testing
Friction and wear behavior evaluations were performed using a ball-on-disk tribometer
(MVP 110, Neoplus, Republic of Korea) in accordance with the ISO 20808 standard. This
study employed two counterface materials: Si3N4 and WC. Both ball specimens were manufactured with a diameter of 12.7 mm and surface
roughness Ra < 0.3 μm.
Test conditions were carefully controlled to ensure reliability. The tests were performed
under a normal force of 20 N at a rotation speed of 100 RPM, corresponding to a line
speed of 0.120 m s-1. The track radius was set to 11.5 mm for a total line distance of 1000 m (Fig 2(b)). Environmental conditions were maintained at 33 ± 2 °C with relative humidity of
50% ± 10%, in accordance with the ISO 20808 standard.
Wear-volume measurements were conducted using a high-precision surface profilometer
(Alpha-Step ET3000, Kosaka Laboratory Ltd., Japan) (Fig 2(c)). The measurement parameters included a scanning speed of 0.1 mm s-1, stylus force of 300 μN, and horizontal resolution of 0.01 μm. The 0.01 μm digital
scale embedded in the X-coordinate reading enabled high-accuracy horizontal measurement
data sampling.
The wear-track profile was evaluated at four equidistant positions at 90° intervals
around the wear track. The total wear volume (Vdisk) was calculated based on the methodology described in ISO 20808:2016 [23] as follows:
where R represents the wear track radius (m) and S1–S4 denote the cross-sectional areas of the wear track (m2) at the four measured positions.
The specific wear rate (Ws) was subsequently determined using
where P represents the applied normal load (N), and L denotes the total sliding distance (m), as described in ISO 20808:2016 [23].
2.3. Classification of Wear Surfaces
2.3.1. Data Collection and Preprocessing
Microstructural analysis of the worn coating surfaces after tribological testing was
performed using SEM (JSM-6390, JEOL, Japan). Energy-dispersive X-ray spectroscopy
(EDS; Oxford Instruments, United Kingdom) was used for the elemental analysis of the
wear surfaces. To enable systematic wear pattern classification, we collected a comprehensive
dataset of 500 high-resolution SEM micrographs documenting various wear mechanisms
observed under various test conditions (Table 1).
This dataset forms the foundation for the deep learning-based classification approach
described in subsequent sections. The dataset comprised 500 SEM micrographs distributed
across five distinct categories (0–4), representing different tribological testing
conditions and surface morphologies. Class 0 represents untested surfaces, whereas
Classes 1–4 correspond to specific material pair combinations under standardized testing
conditions.
The dataset was systematically categorized into five distinct wear-surface types based
on their morphological characteristics and underlying wear mechanisms, as listed in
Table 1. Image quality consistency was maintained using standardized imaging parameters on
the secondary electron mode and 300× magnification.
2.3.2. Data Management and Augmentation
Dataset partitioning was implemented using the train test split function of the Scikit-learn
library with stratification to maintain balanced class distributions across all wear
categories. Initially, 20% of the dataset (100 images) was allocated to the test set,
and the remaining 400 images were split at an 80:20 ratio, resulting in 320 training
and 80 validation images. The training set was used to teach the model by updating
the weights through backpropagation, the validation set was used to tune hyperparameters
and monitor overfitting, and the test set was used to evaluate the final model performance
on unseen data. A consistent random seed number of 42 was applied throughout the splitting
process to ensure reliability.
After partitioning, data augmentation was performed using the PyTorch transform function
to improve the generalization ability of the model by artificially increasing the
diversity of the training data. The augmentation operations included the following
steps:
· Conversion to PyTorch tensor format: Transforming images into tensors for processing within PyTorch.
· Spatial resizing to uniform dimensions of 100 × 100 pixels: This ensures that all images are of consistent size to be input into the model.
· Normalization using ImageNet statistics: Standardizing the input data to match a common scale.
· Conversion to grayscale: Reducing image complexity by limiting color channels and focusing on surface wear
features.
· Random sharpness enhancement by a factor of 2: Increasing image sharpness to enhance edge features, making patterns more distinguishable.
· Automatic contrast adjustment: Balancing contrast to improve the visual differentiation of features.
Data processing was conducted in batches of 10 images using DataLoader function of
PyTorch library with two worker processes for efficient data handling. Subset random
samplers were employed to maintain a stratified distribution of wear categories across
all dataset splits during the training and evaluation phases. This ensured that the
distribution remained consistent, and each mini-batch was representative of the overall
dataset.
2.3.3. Model Architecture and Training
A VGG-style CNN architecture was implemented for wear surface classification, comprising
six convolutional layers with batch normalization divided in three distinct stages:
· Initial stage:
- Two convolutional layers with 32 filters (3 × 3 kernels each), followed by batch
normalization and rectified linear unit (ReLU) activation.
- A 2 × 2 max pooling (stride 2) layer to reduce spatial dimensions.
· Intermediate stage:
- Two convolutional layers with 64 filters (3 × 3 kernels each), followed by batch
normalization and ReLU activation.
- A 2 × 2 max pooling (stride 2) layer to further reduce dimensionality.
· Final stage:
- Two convolutional layers with 128 filters (3 × 3 kernels each), followed by global
average pooling to condense the feature maps.
- A fully connected layer (128 to 5 features) for final classification into five wear
surface categories.
The model was trained using cross-entropy loss to handle multiclass classification
and the Adam optimizer with default parameters (learning rate = 1 × 10-3). Training was conducted over 50 epochs with a batch size of 10, which balanced computational
efficiency and model convergence.
To improve the classification robustness, an ensemble of five independently trained
models was used, each initialized with a different random seed (seed = 2020). The
final predictions were determined by majority voting across the ensemble members.
The model checkpoints were preserved at the epoch with the highest validation accuracy,
helping to prevent overfitting and improve generalization.
All models were implemented using PyTorch and trained on an NVIDIA Tesla T4 GPU (Google
Colab) using CUDA 11.8. The complete dataset and source code are publicly available
at https://doi.org/10.17632/m7vmhjbktv.1 for further reference and reproducibility. This computational approach complements
the physical tribological testing previously described, creating an integrated workflow
where material characterization leads directly to quantitative analysis of wear patterns,
bridging the gap between traditional materials science and advanced computational
methods.
3. RESULTS AND DISCUSSION
3.1. Characterization of Mechanical Properties
3.1.1. Hardness Analysis
Vickers hardness measurements revealed significant differences between the test materials
(Table 2). The counterface materials demonstrated superior hardness characteristics, with
Si3N4 and WC measuring (15.33 ± 0.47) and (16.40 ± 0.35) GPa, respectively. These values
exceed those of the friction materials by approximately 2.3 times, ensuring geometric
stability throughout the wear testing process [24].
The Fe-Cr-based composite coatings exhibited substantially enhanced hardness compared
to that of the GCI substrate. The GCI substrate, Composite A, and Composite B achieved
hardness values of (1.98 ± 0.15), (7.51 ± 0.20), and (6.52 ± 0.13) GPa, respectively.
This significant improvement in the composites, which was approximately 3.2 times
greater than that of the substrate, suggests an enhanced potential for wear resistance
[25]. As demonstrated in subsequent tribological testing, this marked increase in hardness
directly influences friction behavior and wear mechanisms, establishing a fundamental
relationship between mechanical properties and tribological performance that forms
the basis for our subsequent analysis.
3.1.2. Surface Topography
Surface roughness measurements confirmed a consistent surface finish across all the
specimens, maintaining Ra values in the range of 0.15–0.2 μm (Table 2). This standardization enabled a direct comparison of the wear behavior based on
intrinsic material properties rather than surface-finish variations [26,27].
3.2. Tribological Performance Analysis
3.2.1. Friction Behavior
The friction coefficient (μ) measurements revealed distinct material-dependent patterns
across the test specimens (Fig 3). The GCI specimens exhibited a friction coefficient of 0.91 against Si3N4, which notably decreased to 0.36 against WC, representing a 60.4% reduction in friction
against the WC counterface (Table 3). This significant difference highlights the strong dependence of friction behavior
on counterface material selection.
Composite A demonstrated a different behavior, with a friction coefficient of 0.82
against Si3N4 and an unexpected increase to 0.90 against WC, representing a 9.8% increase in friction
with the WC counterface. Composite B showed the most distinctive behavior, maintaining
a friction coefficient of 1.03 against Si3N4. In contrast, it exhibited the highest observed friction coefficient of 2.00 against
the WC counterface. Detailed analysis of the friction data revealed that Composite
B with WC counterface demonstrated the highest variability (standard deviation: 0.19)
and peak-to-peak amplitude (0.69), indicating greater tribological instability in
this material combination.
3.2.2. Wear Rate Analysis
The specific wear rates exhibited clear material-dependent behavior patterns (Table 3). The GCI showed wear rates of 25.78 × 10-6m3N-1 m-1 against Si3N4 and 2.05 × 10-6m3N-1m -1 against WC. Composite A exhibited improved wear resistance with rates of 5.07
× 10-6 and 13.43 × 10-6 m3 N-1 m-1 against Si3N4 and WC, respectively. Composite B exhibited contrasting behavior, achieving a low
wear rate of 7.37 × 10-6 mm3 N-1 m-1 against Si3N4, but an exceptionally high wear rate of 387.26 × 10-6 mm3 N-1 m-1 against WC, representing a dramatic 52-fold increase. This extreme tribological behavior
can be attributed to the significant hardness mismatch between Composite B (~6.5 GPa)
and WC ball (~16.4 GPa), creating a hardness ratio of approximately 2.5:1. The combination
of high contact pressure and material incompatibility likely promoted severe adhesive
wear mechanisms, characterized by material junction growth and subsequent fracture.
Additionally, the friction coefficient data revealed exceptional instability for this
pairing (μ = 2.00 ± 0.19), with the highest standard deviation and peak-to-peak amplitude
(0.69) observed across all test combinations, indicating severe stick-slip behavior
that would accelerate wear processes.
The relationship between the friction coefficient and wear rate reveals notable material-dependent
characteristics. The GCI exhibited conventional behavior with a proportional relationship
between these parameters. Composite A exhibited a moderate deviation from this proportionality,
maintaining lower wear rates than its friction coefficients. Composite B demonstrated
a unique counterface-dependent behavior, maintaining low wear rates against Si3N4 but exhibiting exceptionally high wear rates against WC.
These findings indicate that counterface material selection significantly influences
tribological behavior, whereas composite coatings exhibit complex friction-wear relationships
that deviate from traditional proportional patterns. More importantly, the results
demonstrate that higher friction coefficients are not always correlated with increased
wear rates, except in cases similar to those of Composite B with WC counterfaces,
where both parameters increase. This non-linear relationship between friction and
wear forms a critical foundation for understanding the tribochemical interactions
and morphological changes observed in the subsequent wear surface characterization,
in which specific wear mechanisms are identified and correlated with the observed
tribological behaviors.
3.3. Wear Surface Characterization
3.3.1. Morphological Analysis
SEM examination revealed distinct wear mechanisms across different material combinations
(Fig 4). Testing with Si3N4 counterfaces predominantly resulted in abrasive wear patterns, whereas testing with
WC counterface conditions showed the dominance of adhesive wear mechanisms. These
observations were further supported by quantitative analysis of the friction behavior,
where Si3N4 counterfaces demonstrated lower variability in friction coefficients (standard deviation:
0.07–0.09) than WC counterfaces (standard deviation: 0.11–0.19), particularly for
Composite B. Transfer films were consistently observed across all the composite coating
combinations, indicating complex material interactions during the wear process.
3.3.2. Chemical Analysis
EDS mapping was performed to identify the specific tribochemical interactions in each
counterface material. The results revealed distinct oxidation patterns that varied
depending on the counterface material used during the wear tests. Thus, the results
provide insights into the tribochemical mechanisms at play.
In the tests involving Si3N4 as a counterface material, the EDS analysis revealed the formation of SiO2 through oxidation, as described by the following equation:
This oxidation reaction occurred uniformly across the wear track, which is evident
from the consistent distribution of O-rich regions in correlation with the Si signals.
Figure 5 illustrates the EDS elemental maps of the wear tracks, showing the elemental distribution
of Fe, Cr, Si, and O. Uniform coverage of SiO2 was observed across the wear track, indicating an effective tribochemical interaction
between the counterface and the composite material.
When WC was used as the counterface, EDS analysis showed a distinct tribochemical
inter-action characterized by the formation of WO2. This was confirmed by the following equation, which describes the oxidation process
leading to WO2 formation alongside CO2:
The mapping in Fig 6 shows the localized W transfer and oxidation patterns, which are distinct in the
wear tracks. In contrast to the Si3N4 counterface, the WC counterface led to the transfer of W to the surface, resulting
in areas with high concentrations of W and O. These distinct chemical signatures and
morphological features create the visual patterns that form the basis of the proposed
deep learning-based classification approach, which are presented in the following
section. By training a CNN model on these unique wear patterns, we can establish a
direct link between tribochemical mechanisms and automated pattern recognition capabilities.
3.4. Deep Learning-based Classification of Wear Surface Patterns
Although conventional SEM and EDS analyses provide valuable insights into wear mechanisms,
the automated classification of complex wear patterns requires a more systematic approach.
Consequently, a deep learning-based classification framework was developed and evaluated
using a comprehensive dataset of 500 SEM micrographs (Fig 7).
3.4.1. Dataset Organization and Model Training
The dataset was systematically partitioned while maintaining balanced class distributions
across all wear categories. The final distribution is as follows:
· Training set: 320 images (64%, 64 images per class)
· Validation set: 80 images (16%, 16 images per class)
· Test set: 100 images (20%, 20 images per class)
The CNN model demonstrated consistent learning behavior throughout the 50-epoch training
period (Fig 8), indicating effective knowledge acquisition from the wear pattern images. The training
process exhibited three distinct phases of development, each representing a critical
stage in the learning progression of the model.
1. Initial rapid learning phase (epochs 1–10): Training loss decreased substantially
from approximately 1.3 to 0.6
2. Progressive refinement phase (epochs 11–30): Loss further reduced to approximately
0.3 with increasing stability
3. Fine-tuning phase (epochs 31–50): Optimization of model performance, with loss
stabilizing below 0.2
3.4.2. Ensemble Model Performance
This study implemented an ensemble of five independently trained models to enhance
classification reliability, particularly for challenging wear patterns. This ensemble
approach demonstrated exceptional performance across all evaluation metrics. On the
training set, the ensemble achieved accuracy of 0.947, precision of 0.952, recall
of 0.947, and F1-score of 0.946. The evaluation metrics were defined as follows: Accuracy
represents the proportion of correctly classified samples. Precision measures the
accuracy of positive predictions (TP/(TP+FP)). Recall represents the model's ability
to identify all positive cases (TP/(TP+FN)). F1-score provides the harmonic mean of
precision and recall. All metrics were calculated using macro-averaging to ensure
equal treatment across all classes. The validation set exhibited perfect performance
across all metrics with values of 1.000, while the test set maintained strong performance
with consistent values of 0.960 across all metrics (Table 4).
The ensemble model demonstrated significantly superior performance compared to individual
models in the test set evaluation. While individual model performances varied, Model
1 achieved an accuracy of 0.830 and an F1-score of 0.811, Model 2 achieved 0.930 for
both metrics, Model 3 attained an accuracy of 0.940 and an F1-score of 0.939, Model
4 achieved 0.920 for both accuracy and F1-score, and Model 5 achieved the highest
individual accuracy and F1-score of 0.990. These results highlight the consistency
and reliability obtained of the ensemble approach.
3.4.3. Classification Analysis and Error Patterns
Confusion matrix analysis revealed highly accurate classification patterns across
most wear categories (Fig 9). Classes 0, 1, and 2 were perfectly classified, with the accurate identification
of all 20 test samples. The classification of Class 3 showed strong performance, with
the accurate classification of 18 out of 20 samples, exhibiting minimal confusion
with Class 4. Similarly, in the identification of Class 4, robust performance was
achieved with the accurate classification of 18 out of 20 samples, showing a marginal
confusion with Class 3.
The observed misclassifications primarily occurred in cases where the wear patterns
shared similar morphological features, particularly between the wear surfaces of the
samples under Classes 3 and 4 (Fig 9). This pattern aligns with our tribological findings, where both classes represent
WC counterface combinations that produced comparable wear mechanisms, albeit with
different intensities due to material-specific properties. Analysis of the feature
activations from the CNN model revealed that Class 3 (WC & Composite A) and Class
4 (WC & Composite B) exhibited relatively similar average activation values (0.478
and 0.477, respectively) with comparable standard deviations (0.435 and 0.491), explaining
the classification challenge. This pattern suggests that, although the model distinguishes
different wear mechanisms, more subtle variations in the surface morphology can present
classification challenges (Fig 10).
In our evaluation, the ensemble approach demonstrated strong performance particularly
in mitigating classification errors, exhibiting solid performance across all wear
pattern categories analyzed. The high level of accuracy (96.0% on the test set) verifies
the potential of deep-learning-based approaches for automated wear surface analysis
in tribological applications.
4. CONCLUSIONS
This comprehensive study of Fe-Cr-based composite coatings provided significant insights
into both material performance and automated analysis capabilities. The study of the
mechanical properties revealed that Fe-Cr-based composite coatings achieved superior
hardness values ranging from 6.52–7.51 GPa, substantially surpassing that of the GCI
substrate of 1.98 GPa. Surface consistency was maintained across all specimens, with
roughness values (Ra) in the range of 0.15–0.20 μm, enabling reliable comparative
analysis.
The tribological performance analysis demonstrated particularly noteworthy results
for Composite B, which exhibited unique characteristics, including elevated friction
coefficients against Si3N4 (μ = 1.03), while maintaining significantly low specific wear rates (7.37 × 10-6 mm3 N-1 m-1). This represents a 3.5-fold improvement in the wear resistance compared to that
of the conventional GCI. Further analysis of friction behavior indicated that although
Composite B exhibited greater stick-slip amplitude and transient events, it maintained
superior wear resistance against Si3N4 counterfaces, suggesting a complex relationship between friction instability and
wear resistance in these material combinations. This study identified distinct counterface-dependent
wear mechanisms, with Si3N4 promoting combined abrasive and oxidative wear. In contrast, WC interfaces primarily
exhibited adhesive wear with oxide formation.
A detailed tribochemical analysis revealed specific oxidation patterns that are characteristic
of each counterface material. The test considering Si3N4 resulted in SiO2 formation through tribochemical reactions, whereas the WC interfaces produced WO2 via similar mechanisms. These distinct chemical interactions were thoroughly validated
by EDS analysis, which confirmed the material-transfer patterns and oxidation mechanisms
and provided critical insights into the fundamental wear processes underlying material
performance in automotive brake applications.
The implementation of deep learning classification achieved significant results, with
the ensemble model demonstrating superior performance across all evaluation metrics.
The training, validation, and test accuracies were 0.947, 1.000, and 0.960, respectively.
The classification system showed particular strength in distinguishing between most
wear patterns, with only minimal confusion between similar wear mechanisms, accounting
for 4 out of 100 misclassifications.
These findings show the significant potential of Fe-Cr-based composite coatings for
tribological applications and verify the effectiveness of machine learning approaches
in wear mechanism analysis. The integrated methodology developed in this study provides
a cohesive research framework in which mechanical properties directly influence tribological
performance, which in turn produces characteristic wear patterns that can be automatically
classified using deep learning techniques. This interconnected approach successfully
bridges the traditional tribological characterization with advanced pattern recognition,
establishing a comprehensive framework that follows the material life cycle from mechanical
properties to wear mechanism identification. Such an integrated approach provides
a robust foundation for future wear-related studies in automotive brake applications,
particularly those aimed at satisfying Euro 7 emission requirements.