Mining and analysis of massive population-based shape data can
result in knowledge of shape variability of the population. such
knowledge can lead to the construction of faithful
subject-specific 3D shape models from sparse measurements,
predict shape-specific functional performance
and
population-wide structural performance variation. Such an ability
brings about unprecedented capabilities and tantalizing
opportunities for mass customization, part-specific failure
prediction and just-in-time part maintenance, and patient-specific
biomedical intervention and treatment. This research proposes a
statistical atlas based approach that incorporates statistical shape
modeling in subject-specific shape reconstruction, finite element
(FE) modeling and analysis. The statistical atlas contains three
parts: the mean shape and the variation modes of the shape
population which span a linear shape space, the FE mesh of the
mean shape (template mesh), and the selected feature points and
sizing dimensions which are obtained by maximizing the total
variance they capture of the shape population. Given a subject
(e.g. a person), the corresponding dimensions are measured and
the3D shape model is synthesized. The template mesh can be
morphed to the subject shape to conduct subject-specific fe
analysis. The FE solution on the template mesh can also be
extrapolated to the subject shape through Taylor expansion. The
shape variances along the variation modes are obtained by the
principal component analysis. These variances tell the amount of
shape variabilities along the variation modes and are combined
with the Taylor expansion of the fE solution to obtain the
structural performance variation across the population. The 2D/3D
numerical examples demonstrate the efficiency and effectiveness
of the proposed approach

Additive manufacturing (AM) enables seamless integration of product
design and manufacturing phases, and thus offers significant
advantages over conventional manufacturing. Despite the enormous
progress in recent AM technologies, certain intractable quality issues
persist. These product defects lead to considerable rework and scrap
rates, and thus pose significant impediments for sustainability of am.
Consequently, there is a vital need to advance online methods for
defect detection in AM processes. so that incipient process anomalies
can be identified and possibly prevented at an early stage of
manufacturing. With the above focus, this talk will introduce some of
the ongoing research related to real-time in situ process monitoring
for AM performed in the Sensing and Analytics of Smart Manufacturing
Laboratory at Virginia Tech. The topics cover: applications of machine
learning for real-time process monitoring in AM; 3D point cloud based
dimensional integrity assessment for AM; and real-time sensing based
process monitoring for AM cybersecurity.