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The inner structure of a material is called microstructure. It stores the genesis of a material and determines all its physical and chemical properties. While microstructural characterization is widely spread and well known, the microstructural classification is mostly done manually by human experts, which gives rise to uncertainties due to subjectivity. Since the microstructure could be a combination of different phases or constituents with complex substructures its automatic classification is very challenging and only a few prior studies exist. Prior works focused on designed and engineered features by experts and classified microstructures separately from the feature extraction step. Recently, Deep Learning methods have shown strong performance in vision applications by learning the features from data together with the classification step. In this work, we propose a Deep Learning method for microstructural classification in the examples of certain microstructural constituents of low carbon steel. This novel method employs pixel-wise segmentation via Fully Convolutional Neural Network (FCNN) accompanied by a max-voting scheme. Our system achieves 93.94% classification accuracy, drastically outperforming the state-of-the-art method of 48.89% accuracy. Beyond the strong performance of our method, this line of research offers a more robust and first of all objective way for the difficult task of steel quality appreciation.

Automated, reliable, and objective microstructure inference from micrographs is essential for a comprehensive understanding of process-microstructure-property relations and tailored materials development. However, such inference, with the increasing complexity of microstructures, requires advanced segmentation methodologies. While deep learning offers new opportunities, an intuition about the required data quality/quantity and a methodological guideline for microstructure quantification is still missing. This, along with deep learning’s seemingly intransparent decision-making process, hampers its breakthrough in this field. We apply a multidisciplinary deep learning approach, devoting equal attention to specimen preparation and imaging, and train distinct U-Net architectures with 30–50 micrographs of different imaging modalities and electron backscatter diffraction-informed annotations. On the challenging task of lath-bainite segmentation in complex-phase steel, we achieve accuracies of 90% rivaling expert segmentations. Further, we discuss the impact of image context, pre-training with domain-extrinsic data, and data augmentation. Network visualization techniques demonstrate plausible model decisions based on grain boundary morphology. Segmentation and classification of microstructures are required by quality control and materials development. The authors apply deep learning for the segmentation of complex phase steel microstructures, providing a bridge between experimental and computational methods for materials analysis.

The piezoresistive effect of materials can be adopted for a plethora of sensing applications, including force sensors, structural health monitoring, motion detection in fabrics and wearable, etc. Although metals are the most widely adopted material for sensors due to their reliability and affordability, they are significantly affected by temperature. This work examines the piezoresistive performance of carbon nanoparticle (CNP) bulk powders and discusses their potential applications based on strain-induced changes in their resistance and displacement. The experimental results are correlated with the characteristics of the nanoparticles, namely, dimensionality and structure. This report comprehensively characterizes the piezoresistive behavior of carbon black (CB), onion-like carbon (OLC), carbon nanohorns (CNH), carbon nanotubes (CNT), dispersed carbon nanotubes (CNT-D), graphite flakes (GF), and graphene nanoplatelets (GNP). The characterization includes assessment of the ohmic range, load-dependent electrical resistance and displacement tracking, a modified gauge factor for bulk powders, and morphological evaluation of the CNP. Two-dimensional nanostructures exhibit promising results for low loads due to their constant compression-to-displacement relationship. Additionally, GF could also be used for high load applications. OLC’s compression-to-displacement relationship fluctuates, however, for high load it tends to stabilize. CNH could be applicable for both low and high loading conditions since its compression-to-displacement relationship fluctuates in the mid-load range. CB and CNT show the most promising results, as demonstrated by their linear load-resistance curves (logarithmic scale) and constant compression-to-displacement relationship. The dispersion process for CNT is unnecessary, as smaller agglomerates cause fluctuations in their compression-to-displacement relationship with negligible influence on its electrical performance.

Surface structures in the micro- and nanometre length scale exert a major influence on performance and functionality for many specialized applications in surface engineering. However, they are often limited to certain pattern scales and materials, depending on which processing technique is used. Likewise, the morphology of the topography is in complex relation to the utilized processing methodology. In this study, the generation of hierarchical surface structures in the micro- as well as the sub-micrometre scale was achieved on ceramic, polymer and metallic materials by utilizing Ultrashort Pulsed Direct Laser Interference Patterning (USP-DLIP). The morphologies of the generated patterns where examined in relation to the unique physical interaction of each material with ultrashort pulsed laser irradiation. In this context, the pattern formation on copper, CuZn37 brass and AISI 304 stainless steel was investigated in detail by means of a combination of experiment and simulation to understand the individual thermal interactions involved in USP-DLIP processing. Thereby, the pattern’s hierarchical topography could be tailored besides achieving higher process control in the production of patterns in the sub-µm range by USP-DLIP.

Static contact angle measurements are one of the most popular methods to analyze the wetting behavior of materials of any kind. Although this method is readily applicable without the need of sophisticated machinery, the results obtained for the very same material may vary strongly. The sensitivity of the measurement against environmental conditions, sample preparation and measurement conduction is a main factor for inconsistent results. Since often no detailed measurement protocols exist alongside published data, contact angle values as well as elaborated wetting studies do not allow for any comparison. This paper therefore aims to discuss possible influences on static contact angle measurements and to experimentally demonstrate the extent of these effects. Sample storage conditions, cleaning procedures, droplet volume, water grade and droplet application as well as the influence of evaporation on the static contact angle are investigated in detail. Especially sample storage led to differences in the contact angle up to 60%. Depending on the wetting state, evaporation can reduce the contact angle by 30–50% within 10 min in dry atmospheres. Therefore, this paper reviews an existing approach for a climate chamber and introduces a new measuring setup based on these results. It allows for the observation of the wetting behavior for several minutes by successfully suppressing evaporation without negatively affecting the surface prior to measurement by exposure to high humidity environments.

Diamond is an attractive material for photonic quantum technologies because its colour centres have a number of outstanding properties, including bright single photon emission and long spin coherence times. To take advantage of these properties it is favourable to directly fabricate optical microcavities in high-quality diamond samples. Such microcavities could be used to control the photons emitted by the colour centres or to couple widely separated spins. Here, we present a method for the fabrication of one- and two-dimensional photonic crystal microcavities with quality factors of up to 700 in single crystal diamond. Using a post-processing etching technique, we tune the cavity modes into resonance with the zero phonon line of an ensemble of silicon-vacancy colour centres, and we measure an intensity enhancement factor of 2.8. The controlled coupling of colour centres to photonic crystal microcavities could pave the way to larger-scale photonic quantum devices based on single crystal diamond. Optical microcavities have been fabricated in single-crystal diamond and tuned into resonance with the zero phonon line of an ensemble of silicon-vacancy colour centres, which results in an enhancement of spontaneous emission.

Microstructural modification of high chromium cast irons (HCCI) through the precipitation of secondary carbides (SC) during destabilization treatments is essential for improving their tribological response. However, there is not a clear consensus about the first stages of the SC precipitation and how both the heating rate (HR) and destabilization temperature can affect the nucleation and growth of SC. The present work shows the microstructural evolution, with a special focus on the SC precipitation, in a HCCI (26 wt% Cr) during heating up to 800, 900, and 980 °C. It was seen that the HR is the most dominant factor influencing the SC precipitation as well as the matrix transformation in the studied experimental conditions. Finally, this work reports for first time in a systematic manner, the precipitation of SC during heating of the HCCI, providing a further understanding on the early stages of the SC precipitation and the associated microstructural modifications.

Carbon nanotube reinforced nickel matrix composites (Ni/CNT) with different CNT compositions were fabricated by solid state processing and subjected to severe plastic deformation (SPD) by means of high pressure torsion (HPT). A thorough study on the microstructural changes during heating and on the thermal stability was performed using differential scanning calorimetry (DSC), high temperature X-ray diffraction (HT-XRD) and electron backscattered diffraction (EBSD). Furthermore, the formation and dissolution of the metastable nickel carbide Ni 3 C phase was evidenced by DSC and HT-XRD in composites, where sufficient carbon atoms are available, as a consequence of irreversible damage on the CNT introduced by HPT. Finally, it was shown that the composites exhibited an improved thermal stability with respect to nickel samples processed under the same conditions, with a final grain size dependent on the CNT volume fraction according to a V CNT −1/3 relationship and that lied within the ultrafine grained range.

The adoption of artificial intelligence (AI) in industrial manufacturing lags behind research progress, partly due to smaller, imbalanced datasets derived from real processes. In non-destructive aerospace testing, this challenge is amplified by the low defect rates of high-quality manufacturing. This study evaluates the use of synthetic data, generated via multiresolution stochastic texture synthesis, to mitigate class imbalance in material defect classification for the superalloy Inconel 718. Multiple datasets with increasing imbalance were sampled, and an image classification model was tested under three conditions: native data, data augmentation, and synthetic data inclusion. Additionally, round robin tests with experts assessed the realism and quality of synthetic samples. Results show that synthetic data significantly improved model performance on highly imbalanced datasets. Expert evaluations provided insights into identifiable artificial properties and class-specific accuracy. Finally, a quality assessment model was implemented to filter low-quality synthetic samples, further boosting classification performance to near the balanced reference level. These findings demonstrate that synthetic data generation, combined with quality control, is an effective strategy for addressing class imbalance in industrial AI applications.

Understanding contact mechanics is essential for optimizing electrical and mechanical interfaces, particularly in systems where surface structuring influences performance. This study investigates the mechanical contact behavior of hot-dip tinned copper surfaces modified via Direct Laser Interference Patterning (DLIP). Asymmetric, line-like microstructures with varying periodicities (2–10 µm) and tilt angles (0°, 15°, 30°) were fabricated on both as-received and aged hot-dip tinned copper substrates. The resulting surfaces were characterized using confocal laser scanning microscopy and subjected to indentation testing under controlled loads. Contact mechanical calculations and finite element simulations were employed to determine critical values for plastic deformation onset and to access the real contact area. Results show that structural periodicity, tilt angle, and material condition significantly affect load-bearing capacity and deformation behavior. Notably, intermediate periodicities (e.g., 7.5 µm) on as-received material at 0° tilt exhibited the highest susceptibility to plastic deformation, while aged samples demonstrated improved mechanical stability due to the harder Cu6Sn5 surface layer, which forms directly after coating and grows during aging until it reaches the surface and no residual tin is left. These findings provide valuable insights into the design of structured contact surfaces for electrical applications, highlighting the importance of tailored surface morphology and material selection.