Additive manufacturing of metal components through directed energy deposition or powder bed fusion is a complex undertaking, often involving hundreds or thousands of individual laser deposits. During processing, conditions may fluctuate, e.g. material feed rate, beam power, surrounding gas composition, local and global temperature, build geometry, etc., leading to unintended variations in final part geometry, microstructure and properties. To assess or control as-deposited quality, researchers have used a variety of methods, including those based on sensing of melt pool and plume emission characteristics, characteristics of powder application, and layer-wise imaging.
Here, a summary of ongoing process monitoring activities at Penn State is provided, along with a discussion of recent advancements in the area of layer-wise image acquisition and analysis during powder bed fusion processing. Specifically, methods that enable direct comparisons of CAD model, build images, and 3D micro-tomographic scan data will be covered, along with thoughts on how such analyses can be related to overall process quality.
This paper presents an approach to multi-modal detection of man-made objects from aerial imagery. Detections are
made in polarization imagery, hyperspectral imagery, and LIDAR point clouds then fused into a single confidence map.
The detections are based on reflective, spectral, and geometric features of man-made objects in airborne images. The
polarization imagery detector uses the Stokes parameters and the degree of linear polarization to find highly polarizing
objects. The hyperspectral detector matches scene spectra to a library of man-made materials using a combination of the
spectral gradient angle and the generalized likelihood ratio test. The LIDAR detector clusters 3D points into objects
using principle component analysis and prunes the detections by size and shape. Once the three channels are mapped
into detection images, the information can be fused without some of the problems of multi-modal fusion, such as edge
reversal. The imagery used in this system was simulated with a first-principles ray tracing image generator known as
This paper examines the performance of the local level set method on the surface reconstruction problem for
unorganized point clouds in three dimensions. Many laser-ranging, stereo, and structured light devices produce three
dimensional information in the form of unorganized point clouds. The point clouds are sampled from surfaces
embedded in R3 from the viewpoint of a camera focal plane or laser receiver. The reconstruction of these objects in the
form of a triangulated geometric surface is an important step in computer vision and image processing. The local level
set method uses a Hamilton-Jacobi partial differential equation to describe the motion of an implicit surface in threespace.
An initial surface which encloses the data is allowed to move until it becomes a smooth fit of the unorganized
point data. A 3D point cloud test suite was assembled from publicly available laser-scanned object databases. The test
suite exhibits nonuniform sampling rates and various noise characteristics to challenge the surface reconstruction
algorithm. Quantitative metrics are introduced to capture the accuracy and efficiency of surface reconstruction on the
degraded data. The results characterize the robustness of the level set method for surface reconstruction as applied to 3D
In this paper we explore image fusion methods for 3D LIDAR sensors, thermal sensors, and visible color sensors.
Traditional display methods are demonstrated in contrast to the proposed robust representations. The new fused
representations make full use of the display gamut of a color monitor. In addition, a data transformation on 3D LIDAR
points is demonstrated which ports hard sensor data into information space. The LIDAR data is classified and clustered
in a hierarchical fashion, which allows temporal and spatial coherent fusion with soft sensor data.
Extremely large, lightweight, in-space deployable active and passive microwave antennas are demanded by future
space missions. This paper investigates the development of PVDF based piezopolymer actuators for controlling the
surface accuracy of a membrane reflector. Uniaxially stretched PVDF films were poled using an electrodeless
method which yielded high quality poled piezofilms required for this applications. To further improve the
piezoperformance of piezopolymers, several PVDF based copolymers were examined. It was found that one of
them exhibits nearly three times improvement in the in-plane piezoresponse compared with PVDF and P(VDF-TrFE)
piezopolymers. Preliminary experimental results indicate that these flexible actuators are very promising in
controlling precisely the shape of the space reflectors. To evaluate quantitatively the effectiveness of these PVDF
based piezopolymer actuators for space reflector applications, an analytical approach has been established to study
the performance of the coupled actuator-reflector-control system. This approach includes the integration of a
membrane reflector model, PVDF piezopolymer actuator model, solution method, and shape control law. The reflective Newton method was employed to determine the optimal electric field for a given actuator configuration and loading/shape error.