Browsing by Author "Mishra, Rajiv S."

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    Additive friction stir deposition: a deformation processing route to metal additive manufacturing
    Yu, Hang Z.; Mishra, Rajiv S. (2021-02-01)
    As the forging counterpart of fusion-based additive processes, additive friction stir deposition offers a solid-state deformation processing route to metal additive manufacturing, in which every voxel of the feed material undergoes severe plastic deformation at elevated temperatures. In this perspective article, we outline its key advantages, e.g. rendering fully-dense material in the as-printed state with fine, equiaxed microstructures, identify its niche engineering uses, and point out future research needs in process physics and materials innovation. We argue that additive friction stir deposition will evolve into a major additive manufacturing solution for industries that require high load-bearing capacity with minimal post-processing.
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    Numerically Trained Ultrasound AI for Monitoring Tool Degradation
    Jin, Yuqi; Wang, Xinyue; Fox, Edward A.; Xie, Zhiwu; Neogi, Arup; Mishra, Rajiv S.; Wang, Tianhao (Wiley, 2022-01-13)
    Monitoring tool degradation during manufacturing can ensure product accuracy and reliability. However, due to variations in degradation conditions and complexity in signal analysis, effective and broadly applicable monitoring is still challenging to achieve. Herein, a novel monitoring method using ultrasound signals augmented with a numerically trained machine learning technique is reported to monitor the wear condition of friction stir welding and processing tools. Ultrasonic signals travel axially inside the tools, and even minor tool wear will change the time and amplitude of the reflected signal. An artificial intelligence (AI) algorithm is selected as a suitable referee to identify the small variations in the tool conditions based on the reflected ultrasound signals. To properly train the AI referee, a human‐error‐free data bank using numerical simulation is generated. The simulation models the experimental conditions with high fidelity and can provide comparable ultrasound signals. As a result, the trained AI model can recognize the tool wear from real experiments with subwavelength accuracy prediction of the worn amount on the tool pins.