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Research

Overview: Controlled systems for human health and advanced manufacturing

The research team in the Hoelzle Research Lab (HRL) investigates problems in dynamics and control for application to advanced manufacturing and microsystem development.  Broadly classified, we are interested in high-value added microscale manufacturing applications with impacts in both human health and sensor technology.  The key applications are engineered synthetic tissues, microfluidic devices for studying mechanobiology, and products made by additive manufacturing (AM, also call 3D printing).  Our key expertise lies in learning-based control algorithms, sensor development, and system design.

Small disclaimer: Some of our on-going research projects have publications in progress and we unfortunately have to describe the research in vague terms on this public venue.  If you are interested in learing more about our latest developments, please set up a meeting with Prof. Hoelzle and the members working on the project.


Manufacturing system control and analysis

Learning based control for microscale additive manufacturing (μ-AM)

Spatial iterative learning control (SILC) is a novel paradigm of the ILC methodology. Similar to standard temporal ILC, SILC learns from repetitive processes to achieve precise signal tracking. However, SILC is advantageous for applications whose spatial dynamics are dominant and the temporal dynamics can be ignored. One representative application is the micro-additive manufacturing process. Research work takes place in HRL (with collaboration with University of Michigan) has demonstrated high-resolution (down to 5 micron) complex object AM regulated with SILC. SILC will be a promising control methodology not only for the micro-AM, but for an extended class of AM applications.

Key team members: Zhi Wang, Prof. Hoelzle, and our collaborators in the Barton Research Group at the University of Michigan.

Funding source: the National Science Foundation

Computationally efficient thermo-mechanical modeling of metal powder bed fusion (PBF) additive manufacturing (AM)

To optimize part orientation and support structure for minimum thermal distortion in metal powder bed fusion (PBF) additive manufacturing, fast part-scale thermal and thermo-mechanical models need to be developed. Many existing models formulate the thermal prediction using finite element (FE) models of the complex partial differential equation with a moving heat source that defines heat flow, often taking days to compute a solution.  We have taken a different approach, using a novel thermal circuit network (TCN) model and breaking parts and support into a network of thermal capacitances and resistances. For predicting the thermal distortion, a quasi-static thermo-mechanical (QTM) FE model is developed by using the temperature history from the TCN model. Both the on- and off-substrate stresses and displacements are predicted with orders of magnitude increased computational efficiency.

Key team members: Hao Peng, Prof. Hoelzle, and collaborators in the Small Scale Transport Lab at Notre Dame, the Shankar Lab at the University of Pittsburg, and Johnson & Johnson

Funding Source: The National Centers for Defense Manufacturing and Machining and America Makes.

Next generation AM tools for tissue engineering

This research leverages improvements in two areas of medicine that have occurred in the last two decades. These areas are endoscopic surgical robotics and tissue engineering (TE) via additive manufacturing (AM). Together these advancements facilitate a significant breakthrough in medicine and engineering: an endoscopic AM TE surgical robotic device that can print custom, synthetic tissues in the patient through standard ‘keyhole’ surgical ports. Such a tool will not only significantly advance regenerative medicine, but also many minimally invasive surgical procedures. However, the very nature of an intracorporeal – inside the body – application of AM TE presents significant challenges. Thus, we are working to understand the material delivery in such tools.  Key engineering fields engaged include robotics, fluid mechanics, and kinematics.

Key team members: Andrej Simeunovic and Prof. Hoelzle.

Funding source: the National Science Foundation

Fabrication of advanced architecture synthetic tissue scaffolds

(a) We are interested in building manufacturing systems that can build scaffolds with design complexity on the three levels of macroscale shape, mesoscale macroporosity, and microscale material composition. (b and c) Our previous work has demonstrated that multi-material direct-write AM tools enable such advanced architecture scaffold fabrication. Calcium phosphate (CaP) materials have been proven to be efficacious as bone scaffold materials, but are difficult to fabricate into complex architectures because of the high processing temperatures required.  In contrast, polymeric materials are facilely formed into scaffolds with near net shape forms of patient specific defects and with domains of different materials; however, with reduced load-bearing capacity compared to CaPs.  To preserve the merits of CaP scaffolds and to enable advanced scaffold manufacturing, we have investigated multi-material direct write AM tools that enable the fabrication of CaP scaffolds that have both complex, near net shape contours and also distinct domains with different microstructures.  This tool has been used to fabricate a case scaffold for a 5 cm orbital socket defect.  This scaffold has complex external contours, interconnected porosity on the order of 300 µm throughout, and two distinct domains of different material microstructures.    

Key team members: Prof. Hoelzle and collaborators at the Alleyne and Wagoner Johnson groups at the University of Illinois.

Funding source: the National Science Foundation, completed in 2013.


Microsystems for mechanobiology characterization

High throughput mechanotyping of a large populations of cells

MAPS microsystem and quasi-static performance.Researchers have shown that a cell's pathology can be obtained by measuring cell mechanical properties such as Young’s modulus. However, current mechanical phenotyping or ‘mechanotype’ tools are slow and/or cannot process cell measurement data in real-time. To address this engineering challenge, we are designing a microelectromechanical systems (MEMS) based tool the Mechanically Activated Phenotyping and Sorting (MAPS) device to quantify the Young’s modulus of single cells and sort them on that basis at a high-throughput goal of ~100 cells/s. High-throughput is achieved by quickly moving cells in a microfluidic channel past a high-speed electromechanical force probe comprising of an electrostatic actuator and sensor.  The sorting component of the device is downstream of the sensor to sort cells based on mechanotype. Currently, we are in the transducer design phase, investigating the interesting nonlinear dynamics of transducers specifically designed for the underwater environment.  Once fully realized, the MAPS device will be used to test our hypothesis that the individual cells in a cancer cell population have a heterogeneous distribution of mechanical properties and that this heterogeneity is an indication of cancer invasiveness.

Key team members: Preetham Burugupally, Mindy Lake, and Prof. Hoelzle.

Funding source: the American Cancer Society, the Walther Cancer Foundation, and the Advanced Diagnositics and Theurapeutics Initiative at Notre Dame.

Regulated environment for micro-organs (REM-Chip)

Schematic of the REMChip demonstrating the chip size, fluidic architecture, and location of a specimen in the chamber. We have used this controlled microdevice to better understand calcium waves in the presence of a mechanical stress in developing model tissues.Tissues and organs develop in mechanically dynamic environments, and mechanical forces are critical but poorly understood inputs during organ development. We have developed a chip-based regulated environment for micro-organs (REM-Chip) that allows systematic investigations of the impact of mechanical compression on developing tissues. The key elements of the REM-Chip are integrated fluidic channels to deliver growth media or other chemical constituents, deformable diaphragms to apply a compressive stress to an organ culture, and compatibility with small working distance objectives for real-time fluorescence imaging. Working with the Zartman lab (University of Notre Dame), we used the REM-Chip to measure the effects of mechanical compression on intercellular calcium signaling (an important biochemical process for coordinating an organism’s development) in fruit fly (Drosophila) wing discs (the progenitor organs of adult fly wings). For the first time, we discovered that the release of mechanical compression causes calcium waves rather than the onset of compression. This knowledge could not be obtained using other existing experimental approaches. The REM-Chip significantly advances existing methods by combining precise concurrent mechanical and chemical perturbations with live imaging.

Key team members: Nick Contento, Prof. Hoelzle, and our collaborators in the Zartman Lab at the University of Notre Dame.

Funding source: the National Science Foundation


Funding

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