The present work presents evaluation of the sliding surface morphology of brake pads during stick-slip. A low-metallic (LM) and a Non Asbestos Organic (NAO) brake friction materials were subjected to slide against a brake disc under conditions favorable to produce stick-slip phenomenon. The experiments were conducted in a laboratory-scale tribometer, which was especially designed to test brake pads used in vehicle. Delta torque divided by slip time (dT/dtslip) was the parameter used to quantify stick-slip propensity. In addition, optical microscope images of the material's surface were obtained at different stages of the braking test. These images were post-processed in appropriate computational software and by means of the segmentation technique, the real contact area, size and amount of contact plateaus related to the brake pad surface were estimated. This technique was effective to quantify the differences in the sliding surface morphology during low speed braking test. Finally, some trends related to the surface morphology of the brake pads selected in this study are shown as well as the relationship between both the material stick-slip behavior and morphological parameters is established and discussed.

In recent years, additive manufacturing, especially 3D printing, has been gaining more space in the production of parts and components in various industries. Among the polymer materials, polylactic acid (PLA) stands out due to its renewable sources, such as corn starch or sugar cane. This study focused on the evaluation of the coefficient of friction (CoF) and wear resistance of pins of pure PLA, pure thermoplastic polyurethane (TPU), and bronze‐filled PLA (BRO), all produced by fused deposition modeling (FDM). The pins were rubbed against a SAE 1020 steel counterpart in a pin‐on‐disc apparatus. The wear tests also included combinations of BRO/PLA and TPU/PLA (with a pattern on the surface), combined at three different percentages (25%, 50%, and 75%). Pure BRO showed the lowest wear rate ( K ≈0.9 × 10 ⁻⁵ mm ³ /N m), while pure PLA exhibited the lowest CoF among the pins ( μ ≈0.24) but the highest wear ( K ≈16.7 × 10 ⁻⁴ mm ³ /N m). The combination of 75% of BRO with 25% of PLA demonstrated a favorable balance between friction and wear, with a wear resistance close to BRO ( K ≈0.8 × 10 ⁻⁵ mm ³ /N m) and a CoF similar to pure PLA ( μ ≈0.28). The paper extensively discusses the wear mechanisms observed on the worn surfaces, as well as the tribofilm formed on the steel disc counterpart.

This paper presents the development of a prototype system for in-situ wear monitoring of helical gears using vision-based condition monitoring (VCM). The research addresses the challenge of monitoring gearbox wear, particularly pitting, an inherent failure mechanism in high-power applications such as wind turbines. A VCM system was integrated into an FZG gear test rig to automatically capture images of gear surfaces during operation, using an oil splash mitigation strategy to ensure high image quality. These images were processed using an image processing pipeline, incorporating a deep learning Feature Pyramid Network for automated annotation of wear defects, trained based on an iterative approach to reduce annotation effort during the training phase. Hereafter, this paper explores metrics related to pitting evolution, including pit area, height, width, and location on the gear surface, as well as insights these metrics give on pitting evolution. This framework’s ability to track the spatiotemporal development of gear wear provides valuable insights for condition monitoring and predictive maintenance in industrial applications