Atomistic Dynamics of Acoustic Emission (AE) Generation in Ultra-Precision Machining (UPM) for Incipient Anomaly Detection Grant uri icon

abstract

  • This grant provides funding to explore the feasibility of an approach to relate the generation and propagation of acoustic emission waveforms from atomistic sources with the relevant plastic deformation mechanisms in ultra-precision machining processes, based on adapting recent advances in nanolithography, ray acoustics and material point theories, towards real-time monitoring of surface quality. Ultra-precision machining processes have a broad range of applications in automotive, aerospace, medical, and defense industries. Real-time monitoring of these processes is hampered by low signal-to-noise ratios of conventional sensors, and costs associated with advanced in-situ instruments. Currently, acoustic emission offers a viable means for real-time and high resolution monitoring of these processes. However, physical principles connecting the process microdynamics with acoustic emission waveforms, which are critical for real-time monitoring of anomalies in ultra-precision machining processes, remain elusive. The physical principles and models from this research can be vital for harnessing acoustic emission signals for real-time process monitoring to assure nano-scale precisions and surface quality. If successful, the investigations would lay foundations to advance sensing technologies for real-time quality assurance of ultra-precision manufacturing processes in the vital industries of today''s economy. Considerable short-term impact will accrue from active research partnerships with the industry, and involvement of graduate and undergraduate students. The affiliated educational activities will include enriching a graduate course by employing the new experimentation facilities and other research outcomes as part of the lab, as well as by incorporating the results from the proposed research as course modules in process modeling and mechanics tracks.

date/time interval

  • 2014 - 2018