5 research outputs found
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Stability of Microstructure in Al3003 Builds Made by Very High Power Ultrasonic Additive Manufacturing
Very High Power Ultrasonic Additive Manufacturing (VHP-UAM) system was used to
produce aluminum parts from 150 µm thick Al3003-H18 foils. The build was processed at 36 μm
vibration amplitude, 8 kN normal load, and 35.6 mm/s weld speed at 20 kHz frequency. Almost
100% linear weld density was achieved. A deformation-interaction volume of ~20 μm was
observed below the bonded interface. The microstructural stability including grain boundary
structures, and crystallographic orientations was evaluated after annealing these samples at
343oC for 2 hours and 450oC for 2 hours. After heat treatment, small grains persisted at the
interfaces with sluggish grain growth kinetics. In contrast, normal grain growth kinetics was
observed in the middle of the foils. Possible mechanisms for such phenomena are discussed.Mechanical Engineerin
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Quantitative Evaluation of Crystallographic Texture in Aluminum Alloy Builds Fabricated by Very High Power Ultrasonic Additive Manufacturing
Very high power ultrasonic additive manufacturing (VHPUAM) has shown good bond
quality over traditional ultrasonic consolidation processes. However, the stability of
microstructure in bulk and interface regions is unknown. Our earlier research showed a large
difference in grain growth kinetics between bulk and interface regions. Therefore, we have
performed in-situ studies of crystallographic texture evolution using a neutron beam line, before,
during, and after heat treatment at 343oC for 2 hours. Shear texture in the as-received condition
was found to be stronger with higher vibration amplitudes. We also observed rapid reduction of
rolling textures in the initial material and presence of shear textures even after heat treatment.Mechanical Engineerin
Using LSTM to translate Thai sign language to text in real time
Abstract Between 2019 and 2022, as the Covid-19 pandemic unfolded, numerous countries implemented lockdown policies, leading most corporate companies to permit employees to work from home. Communication and meetings transitioned to online platforms, replacing face-to-face interactions. This shift posed challenges for deaf or hearing-impaired individuals who rely on sign language, using hand gestures for communication. However, it also affected those who can hear clearly but lack knowledge of sign language. Unfortunately, many online meeting platforms lack sign language translation features. This study addresses this issue, focusing on Thai sign language. The objective is to develop a model capable of translating Thai sign language in real-time. The Long Short-Term Memory (LSTM) architecture is employed in conjunction with MediaPipe Holistic for data collection. MediaPipe Holistic captures keypoints of hand, pose, and head, while the LSTM model translates hand gestures into a sequence of words. The model’s efficiency is assessed based on accuracy, with real-time testing achieving an 86% accuracy, slightly lower than the performance on the test dataset. Nonetheless, there is room for improvement, such as expanding the dataset by collecting data from diverse individuals, employing data augmentation techniques, and incorporating an attention mechanism to enhance model accuracy