Plasma Sprayed Ceramic Coatings on Metallic Substrates

Plasma spraying stands out as one of the most versatile and technologically sophisticated thermal technique in the field of surface engineering. Plasma sprayed ceramic coated components are used as wear resistant and as ther-mal barrier materials. The objective of the present work is to study the tribo-logical and thermal behaviour of different types of cera-mic coatings. Zircon and aluminazircon coatings are tried out on mild steel substrates with (Ni -Al and high carbon iron) bond coat and also without bond coat. The coatings have been characterized by XRD, SEM, Wear, Grindability and Thermal Fatigue successfully. The presence of mullite in the top coat has been found to be reponsible for the superior high temperature properties of the coating

Skyrmion Based Spin-Torque Nano-Oscillator

Using micromagnetic simulation, we investigate the self-sustained oscillation of magnetic skyrmion in a ferromagnetic circular nanodot, driven by spin-torque which is generated from a reference layer of a circular nanopillar device. We demonstrate, by lowering the value of uniaxial anisotropy constant ($K_u$), the velocity of the skyrmion can be increased and using this property, gyration frequency of the skyrmion oscillator can be enhanced. Annihilation of the skyrmion at higher current densities, limit the gyration frequency of the oscillator, whereas by modifying the $K_u$ value at the edge of nanodot, we are able to protect the skyrmion from being annihilated at higher current densities which in turn, increases the gyration frequency of the skyrmion based oscillator. By linear fitting the velocity value, obtained from the motion of the skyrmion in a nanostrip, we also predict the gyration frequency of the skyrmion in the nanodot which proves the validity of our idea in an intuitive way. We have also varied the radius of the nanodisk to see its effect on skyrmion

Modelling and characterisation of ultrasonic joints for Li-ion batteries to evaluate the impact on electrical resistance and temperature raise

In automotive and stationary Li-ion battery packs, a large number of individual cells, typically hundreds to thousands of cells, are electrically connected to achieve pack specification. These large number of interconnections are mainly achieved by welding cell tab to bus-bar using a welding technique of choice. Ultrasonic metal welding (UMW) is one of the common joining technique employed to join pouch cell’s tabs to bus-bar. Although commonly employed, there is little research currently exist in literature reporting the joint characteristics in terms of electrical resistance and temperature raise due to charge-discharge current. Li-ion batteries reaching sub-milliohm internal resistance, risks the temperature raise at the joint could be even higher than the cell itself which raise a serious safety concern and they are to be addressed. This research investigates the electrical and thermal characteristics of ultrasonic joints of 0.3 mm aluminium/nickel coated copper tabs to 1.0 mm copper bus-bar. This article reports the dynamic behaviour of electrical resistance and corresponding temperature increase as a result of current flow. To capture the electrical and thermal behaviour of the joint, a numerical model has been developed and validated with experimental results, which can be employed to analyse battery pack performance

Learning Expressive Prompting With Residuals for Vision Transformers

Prompt learning is an efficient approach to adapt transformers by inserting learnable set of parameters into the input and intermediate representations of a pre-trained model. In this work, we present Expressive Prompts with Residuals (EXPRES) which modifies the prompt learning paradigm specifically for effective adaptation of vision transformers (ViT). Out method constructs downstream representations via learnable ``output'' tokens, that are akin to the learned class tokens of the ViT. Further for better steering of the downstream representation processed by the frozen transformer, we introduce residual learnable tokens that are added to the output of various computations. We apply EXPRES for image classification, few shot learning, and semantic segmentation, and show our method is capable of achieving state of the art prompt tuning on 3/3 categories of the VTAB benchmark. In addition to strong performance, we observe that our approach is an order of magnitude more prompt efficient than existing visual prompting baselines. We analytically show the computational benefits of our approach over weight space adaptation techniques like finetuning. Lastly we systematically corroborate the architectural design of our method via a series of ablation experiments.Comment: Accepted at CVPR (2023