Abstract The recently published calibration framework for areal surface texture measurement included in ISO 25178 describes a set of material measures, metrological characteristics, and routines for their determination. One approach is the application of the “Siemens star” which is standardized as type ASG (areal star-shaped grooves) material measure in ISO 25178-70 and can be used to determine the topographic spatial resolution (TSR) of optical surface texture measuring instruments. We describe a precise and reproducible evaluation routine for the type ASG material measure based on the Radon transform, which allows a significantly more efficient evaluation of measured data and eliminates unwanted human influences in the evaluation process. The algorithm is introduced and its robustness is demonstrated using a set of type ASG material measures with varying amplitudes and multiple optical measuring instruments. The influence of the manufactured test amplitude of the ASG material measures is examined in detail. It can be shown that the algorithm allows a robust evaluation of both the TSR and the corresponding transfer function with complete reproducibility. Ultimately, this results in a simplified and improved implementation of the ISO-compliant determination of the TSR.
Abstract Silicon carbide (SiC) ceramics possess excellent high temperature resistance, high frequency adaptability, high power load bearing capacity and radiation resistance, showing broad application prospects under extreme service conditions. However, machining SiC materials with conventional grinding processes tends to produce large grinding forces and induce severe subsurface damage defects.To address this machining challenge, this paper adopts molecular dynamics (MD) simulation to investigate the synergistic regulation mechanism of structured abrasives and ultrasonic energy field on the scratching behavior of SiC from five dimensions: stress field, subsurface defects, scratching force, surface morphology and temperature field. The results indicate that structured abrasives can disperse loads via groove structures, which effectively reduces hydrostatic stress and Von Mises stress inside the material, suppresses the generation of defective atoms and decreases the depth of subsurface damage layer. Meanwhile, structured abrasives lower scratching forces, improve surface morphology and mitigate machining temperature rise. After coupling with the ultrasonic energy field, the stress distribution in the scratching zone becomes more uniform with increasing ultrasonic amplitude and frequency, and both tangential and normal scratching forces decrease continuously. Nevertheless, ultrasonic excitation aggravates local surface undulations of the workpiece, accelerates the formation of defective atoms and elevates machining temperature. Moreover, ultrasonic frequency presents a nonlinear regulation effect on internal stress and subsurface damage. In this work, the optimal ultrasonic amplitude is identified as 20Å, and the optimal frequency range is 20~25GHz. This study reveals the synergistic regulation mechanism of structured abrasives and ultrasonic energy field. Proper matching of ultrasonic amplitude and frequency can balance the regulation effects of scratching force, stress, subsurface damage and temperature rise, providing a molecular scale theoretical basis for high efficiency and precision scratching of brittle silicon carbide ceramics.
Abstract Surface texturing has been widely used to improve the performance of friction pairs. However, existing studies have mainly focused on the role of texture geometry in wear-debris storage, whereas the mechanisms by which the internal flow field of textures regulates particle migration, retention, and friction reduction remain unclear.In this study, continuous micro-groove textures were fabricated by ball-end milling.Fluent numerical simulations and reciprocating friction tests were conducted to systematically investigate the capture behavior of micrometer-sized solid particles induced by vortex structures inside the micro-grooves and their effects on the frictionreducing performance of the textured surface. The results show that the local geometric expansion at the micro-groove inlet induces a stable vortex, which governs particle entry, migration, and retention through shear-layer entrainment and recirculating transport. The introduced particles do not merely act as passively accommodated objects; instead, they modify the spatial scale of the vortex and the velocity-gradient distribution near the groove opening through particle-fluid momentum exchange.When the particle diameter is 2 μm and the mass fraction is 0.7%, the vortex inside the groove is moderately weakened, internal recirculation dissipation is reduced, and the hydrodynamic wedge effect is enhanced, resulting in a 5.7% increase in the loadcarrying capacity of the oil film compared with the particle-free condition. The friction test results indicate that the coefficient of friction first decreases and then increases with increasing particle diameter, reaching its minimum at 2 μm. This study clarifies the vortex-dominated particle capture and friction-reduction mechanism in continuous micro-grooves, providing a theoretical basis for the design of textured surfaces and the optimization of particle-containing lubrication systems.
Abstract Wire arc additive manufacturing (WAAM) of aluminum alloys often produces components with complex surface morphologies that directly influence functional performance. In this study, the effect of surface condition on the wettability, surface energy and hardness of WAAM-fabricated AlSi5 was systematically investigated without relying on microstructural analysis. Three surface states; as-built, mechanically polished and NaOH-etched were evaluated. The as-built surface exhibited high roughness (R a = 38.6 ± 5.1 µ m), a peak-dominated morphology (R sk = + 0.85) and hydrophobic behavior (water contact angle ≈98°), resulting in low surface energy (∼28.5 mJ m −2 ). Mechanical polishing significantly reduced surface roughness ( R a = 1.2 ± 0.3 μ m ), which eliminated roughness-induced indentation artifacts and led to more reliable and higher apparent hardness values ( 61 ± 3 HV0 .1 ) with improved uniformity, without altering the intrinsic material hardness. In contrast, NaOH etching introduced controlled micro-texturing (R a = 4.7 ± 0.8 µ m) and yielded the highest wettability ( θ ≈64°) and surface energy (∼47.8 mJ m −2 ). A key finding is that wettability cannot be described by roughness amplitude (R a ) alone; higher-order parameters such as skewness (R sk ) and kurtosis (R ku ), together with surface chemistry, play a decisive role. Statistical analysis confirmed that surface condition significantly affects hardness ( p < 0.001). Unlike conventional WAAM studies that primarily focus on microstructural characterization, this study proposes a surface-functionality-oriented framework for WAAM AlSi5 where functional performance is directly evaluated through roughness statistics, wettability, surface energy and hardness. This approach demonstrates that application-relevant surface behavior can be effectively tailored and predicted without relying on extensive microstructural analysis.
Abstract Accurate wafer surface classification is essential for semiconductor process control, but its practical application is limited by the small number of labeled hyperspectral samples and the continuous emergence of previously unseen surface classes. To address these challenges, this study proposes a diffusion-model-enhanced incremental learning framework for hyperspectral wafer surface classification. A hyperspectral dataset containing eight representative wafer surface classes was constructed, covering normal material or structural states, process-related surface conditions, and potential defects. The diffusion model jointly learns the spatial structures and spectral correlations of hyperspectral data and generates supplementary samples for classifier training. During incremental updates, generated samples from previously learned classes are further used as rehearsal data to preserve historical knowledge, while the classification head is expanded to accommodate newly introduced classes. Experimental results show that diffusion-based augmentation improves the accuracy and Macro F1 score of the convolutional neural network by more than three percentage points compared with the non-augmented setting. In the continual-learning comparison, the proposed method achieves an accuracy of 95.98 percent and a Macro F1 score of 95.97 percent on the diffusion-enhanced dataset. The forgetting rate decreases from 6.79 percent to 4.64 percent, corresponding to a relative reduction of 31.66 percent. These results demonstrate that diffusion-generated rehearsal can improve hyperspectral wafer surface classification while effectively mitigating catastrophic forgetting during incremental updates.
