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Activity Number:
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556
- Essentials of Statistics for Advanced Manufacturing Quality
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Type:
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Invited
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Date/Time:
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Wednesday, August 2, 2017 : 2:00 PM to 3:50 PM
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Sponsor:
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Quality and Productivity Section
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Abstract #321984
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Title:
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Statistical Methods for Processing Dynamic Nano Material Characterization Data
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Author(s):
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Yu Ding* and Yanjun Qian and Jianhua Huang
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Companies:
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Texas A&M University and Texas A&M University and Texas A&M University
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Keywords:
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Nano manufacturing ;
Dynamic video data ;
Change point detection ;
System informatics ;
In-situ measurements ;
In-process quality improvement
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Abstract:
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In-situ transmission electron microscopy technique has caught a lot of recent attention in material and manufacturing research, because the in-situ technology makes possible discoveries that ex-situ instruments cannot otherwise enable and provides the capability of looking into the mechanism underlying nanocrystal growth. As more and more dynamic TEM video data become available, one of the bottlenecks appears to be the lack of automated and quantitative analytic tools that can process the video data efficiently. The current processing is largely manual in nature and laborious. The absence of the automated processing of TEM videos does not come as a surprise, as the growth of nanocrystals is highly stochastic and goes through multiple stages. Our research team develops statistical modeling and processing methods for analyzing in-situ, dynamic material characterization data. It learns and tracks the normalized particle size distribution and identifies the phase change points delineating the stages in nanocrystal growth. This research undertaking enables the in-process quality improvement capability in nano-manufacturing in the near future.
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Authors who are presenting talks have a * after their name.
