In infrastructure monitoring, manual inspections and conventional computer vision techniques have long been the standard for detecting structural damage. Nevertheless, these approaches are frequently constrained by their reliance on human knowledge and susceptibility to effectively handle complex or large-scale data sets. Although early machine learning techniques brought automation, they lacked the accuracy and scalability required to manage many kinds and quantities of damage, especially in dynamic contexts. While certain deep learning methods, such as early Mask region-based convolutional neural networks (R-CNN) models and Faster R-CNN, partially solved these issues, they frequently had to choose between computational viability, speed, and accuracy. This study presents a methodology for quantifying structural damage by calculating the percentage area of damage and pixel-based metrics for various types, including cracks, corrosion, and spalling. The damages are then classified as low, medium, high, and critical, depending on their severity. The different types of damages were detected using a state-of-the-art computer vision instance segmentation approach that employed Mask R-CNN and You Only Look Once (YOLO) models like YOLOv5, v7, and v8. A data set of 6,000 images was incorporated for this purpose, where the sizes ranged from 416x416 to 640x640 pixels to achieve optimal balance between speed, accuracy, and resource utilization for instance segmentation models. The data sets were gathered from damaged sites and online data sets and were annotated in polygon annotation format with damages categorized into the different damage categories: cracks, spall, and corrosion. The best model achieved an accuracy of 85%. This demonstrates an effective, convenient, and affordable approach for structural damage assessment. Future work can be extended by integrating with Internet of Things technology like closed-circuit television (CCTV) cameras, street cameras, drones, and dash cameras or adapt it for real-time monitoring applications in diverse environments.
Delaminated nickel hydroxide – a better catalyst for OER, stabilized by specifically chosen multi-ion intercalation between the layers, leads to layer expansion via alleviating the charge transfer resistance between the nickel hydroxide layers.
In some IC engines, fuel injection pump is driven by camshaft; thus, these camshafts are designed for bending and torsional loads. Conventionally, camshafts are built-to-specification. Typically, durability assessment of camshaft happens at engine level, this calls for proto or calibration engine to be made and available for testing. As there are limited number of engine level proto testing, the overall scatter in camshafts due to manufacturing/process variations is not possible to be covered. This poses a risk of camshaft failures in the final stages of product development. To mitigate this risk, a component level standard test method is needed for quickly validating design and manufacturing process of camshafts for second source suppliers. The current paper discusses the process followed for arriving at a standard test setup and overcoming the challenges in terms of capturing the appropriate physics for camshaft failure during the engine level testing. Camshaft rear end experiences bending load due to FIP operation. The component level testing method is established by ensuring load and bending moment, and it is used for validating improvements done on manufacturing process and design quickly with confidence for design implementation approval for a test concern. To gain confidence on test outcomes, strain measurement is performed on camshaft with the proposed test setup and found to have more than 98% correlation with CAE results. This newly developed test methodology is added as a DVP requirement for all upcoming projects. It has benefited from time savings of around 120 days per project for camshaft testing.
In this work, a novel gallium nitride/aluminium gallium nitride (GaN/AlGaN) high electron mobility transistor (HEMT) structures, such as TiN as Schottky contact, HEMT with TiN as Schottky contact, and AlInGaN barrier layer are propounded and have analyzed its DC, RF performance parameters in comparison with SiO 2 ‐based metal‐oxide‐semiconductor high electron mobility transistor (MOSHEMT) utilizing Synopsys Sentaurus technology computer‐aided design (TCAD) simulator. HEMT with TiN as Schottky contact and AlInGaN barrier layer is showing peak transconductance (G m ) of 135 mS mm −1 , which is higher than HEMT with TiN as Schottky contact, that is, 117 mS mm −1 . The device with TiN Schottky gate contact for the HEMT exhibits high cutoff frequency ( f T = 20 GHz) and maximum oscillation frequency ( f max = 94.6 GHz) when compared with SiO2‐based MOSHEMT ( f T = 12.8 GHz, f max = 27.5 GHz). Introducing the AlInGaN barrier layer for the TiN Schottky contact‐based HEMT further increased the cutoff frequency ( f T = 59.6 GHz) and maximum oscillation frequency ( f max = 324 GHz), indicating high frequency operation range for communication applications.
For the diesel engines first designed & developed before 2000s, push-rod type valvetrains with mechanical valve lash adjustment were common. For one such legacy diesel engine, first developed for tractors and now applicated for on road vehicles, having push-rod valvetrain architecture & mechanical valve lash adjustment (Type-5 valvetrain system) with flat follower tappet, integrating HLAs for enhancing the NVH & serviceability presented certain challenges. This paper delves into the challenges faced in the design & development phase of HLA integration project on a four-cylinder diesel engine. For integration of HLA, first, the packaging evaluation of valvetrain assembly was done followed by oil flow assessment and necessary changes in the oil pump and circuit. Then, valve lift profile optimizations were done since the ramp rate & seating velocity requirements are different for valvetrains with mechanical lash and HLAs. Numerous iterations were performed for cam-profile design to balance the air flow & volumetric efficiency requirements with the kinematic limitations of higher inertia valvetrain. In parallel, spring force margin was checked for each cam-profile proposal to prevent loss of contact during high speed engine operation and springs with higher preloads & stiffness were evaluated while maintaining the contact stresses at cam nose under material limits. Analytical excel-based calculators were developed for quick first-level assessment of valvetrain kinematics, spring force margin, spring design, cam-profile curve generation from valve lift profile & cam-lobe peak contact stress calculation. For combinations that passed the analytical assessment, 1D simulations were done for checking the engine performance & efficiency while CAE simulation was performed for the valvetrain dynamics. Physical DVP was performed with the finalized valvetrain configuration which included Overloading, High-speed and Cyclic loading tests on engine-level to confirm the performance, functionality & durability with HLA integration.
