[Iefac.list] IISE QCRE Webinar -- Dr. Hui Yang, Penn State University -- Sensor-based Modeling and Optimization of Additive Manufacturing

[Xiaolei Fang]

Dear Colleagues,

IISE Quality Control & Reliability Engineering (QCRE) division would 
like to invite you to attend our webinar on Wednesday, October 13, 3-4 
p.m., Eastern Time.

*Zoom 
Link:*https://ncsu.zoom.us/j/96908345006?pwd=eTZnR1EwcnRJWWtwSDNxc1JYbUVsZz09 
<https://ncsu.zoom.us/j/96908345006?pwd=eTZnR1EwcnRJWWtwSDNxc1JYbUVsZz09>

*Meeting ID:*969 0834 5006

*Time:*October 13, Wednesday, 3-4 p.m., Eastern Time.

Click to add this event to your***Google calendar* 
<https://calendar.google.com/event?action=TEMPLATE&tmeid=aHFyOWg1OTI4MXYxZzlzNWtnNG0yNXZzaHMgY185M2VlaTdkczMzNHZqcWdodTQwbGFpOWFpY0Bn&tmsrc=c_93eei7ds334vjqghu40lai9aic%40group.calendar.google.com>**

*Title: Sensor-based Modeling and Optimization of Additive Manufacturing
Presenter:* Dr. Hui Yang, Professorof Industrial and Manufacturing 
Engineering, Bioengineering, The Penn State University

*Abstract**:* Additive manufacturing (AM) provides a greater level of 
flexibility to produce a 3D part with complex geometries directly from 
the design. However, the widespread application of AM is currently 
hampered by technical challenges in process repeatability and quality 
control. To enhance the in-process information visibility, advanced 
sensing is increasingly invested for real-time AM process monitoring. 
The proliferation of in-situ sensing data calls for the development of 
analytical methods for the extraction of features sensitive to layerwise 
defects, and the exploitation of pertinent knowledge about defects for 
in-process quality control of AM builds. As a result, there are 
increasing interests and rapid development of sensor-based models for 
the characterization and estimation of layerwise defects in the past few 
years. However, there is a dearth of concentrated implementation of 
Six-Sigma quality management approaches for quality control of AM 
builds. In this talk, we present new data-driven analytical methods, 
including deep learning, machine learning, and network science, to 
characterize and model the interrelationships between engineering 
design, machine setting, process variability and final build quality. 
Further, this talk will demonstrate the methodologies of ontology 
analytics, design of experiments (DOE) and simulation analysis for AM 
system improvements. In closing, new process control approaches will be 
discussed to optimize the action plans, once an anomaly is detected, 
with specific consideration of lead time and energy consumption.

*Biography*: Dr. Hui Yang is a Professor of Industrial and Manufacturing 
Engineering, Bioengineering at Penn State, and is affiliated with Penn 
State Cancer Institute (PSCI), Clinical and Translational Science 
Institute (CTSI), Institute for Computational and Data Sciences (ICDS), 
CIMP-3D. Currently, he serves as the PI and site director of NSF Center 
for Health Organization Transformation (CHOT). Dr. Yang was the 
president (2017-2018) of IISE Data Analytics and Information Systems 
Society, the president (2015-2016) of INFORMS Quality, Statistics and 
Reliability (QSR) society, and the program chair of 2016 IISE Annual 
Conference. He is also a department editor for IISE Transactions 
Healthcare Systems Engineering, an associate editor for IISE 
Transactions, IEEE Journal of Biomedical and Health Informatics (JBHI), 
IEEE Transactions on Automation Science and Engineering (TASE), IEEE 
Robotics and Automation Letters (RA-L), Quality Technology & 
Quantitative Management, and an Associate Editor for Proceedings of IEEE 
CASE, IEEE EMBC, and IEEE BHI.

Na Zou, Texas A&M University
Xiaolei Fang, North Carolina State University

IISE Quality Control and Reliability Engineering

Follow us on Twitter <https://twitter.com/qcreiise>, LinkedIn 
<https://www.linkedin.com/in/iise-quality-control-and-reliability-engineering-qcre-628a631b5/>and 
Facebook <https://www.facebook.com/iise.qcre>

-------------- next part --------------
An HTML attachment was scrubbed...
URL: https://lists.clemson.edu/pipermail/iefac.list/attachments/20210825/b94f238e/attachment.html