Hoelzle Research Lab: Research
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
Accordions
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
Towards Intracorporeal Tissue Engieering
Intracorporeal TE via Endoscopic AM enables bioprinting soft TE scaffolds deep in the abdominal space with all the degrees of freedom of a bench-top bioprinter, but in a minimally invasive manner, through a mm scale keyhole incision. Intracorporeal TE via Endoscopic AM aims to bridge the gap between lab success and clinical success by eliminating the need for invasive surgery, in vitro maturation, complications associated with delivery to the clinic, and defect/scaffold mismatch. In order to perform Intracorporeal TE:
• Control over material flowrate is required to avoid excess or lack of material deposition inside the patient’s body.
• A printable biomaterial at intracorporeal conditions and methods to integrate the biomaterial into soft tissue are required.
• Demonstration of the feasibility and understanding the effects of Endoscopic AM on cell function are required.
Key team members: Ali Asghari Adib, Prof. Hoelzle, and our collaborators.
Funding source: the National Science Foundation
State estimator-based process monitoring of the metal powder bed fusion process
This research uses an Ensemble Kalman Filter (EnKF) to estimate internal temperatures during a powder bed fusion (PBF) build. Estimates are generated by using measurements of the exposed build surface, collected via infrared cameras, to correct simplistic physics-based model predictions of the temperature field. These resulting estimates are approximately 2-norm optimally accurate. Accurate temperature field estimates imply accurate estimations of many types of defects and engineering features of interest, all of which are functions of the evolving temperature field. The filter is linear, and thus does not provoke the excessive computational cost of advanced physics-based models. Furthermore, the filter is self-tuning, and does not require a library of training data to be implemented like machine learning-based algorithms. Therefore, this research constitutes an advancement of the state of the art in PBF process monitoring and quality control, by developing a process monitoring algorithm that combines the benefits of physics-based methods and machine learning methods without the need for large quantities of training data.
Key team members: Nathaniel Wood, Prof. Hoelzle, and our collaborators
Funding sources: Air Force Research Laboratory and Ohio State Smart Vehicle Concepts Center
Autonomous AM systems
This track is at the confluence of artificial intelligence (AI)/machine learning and controls with application to real-world autonomous systems. This research involves developing data-efficient learning algorithms for making manufacturing systems autonomous. Specifically, reinforcement learning algorithms (a type of AI that is used for sequential decision-making under uncertainty) are developed, that can be translated to real-world applications where data is expensive to collect. A state-of-the-art autonomous manufacturing research bot to deploy these learning algorithms on a physical testbed is also being developed in this research track.
Key team members: Ferdous Alam, Prof. Hoelzle, and our collaborators in the Barton Research Group at the University of Michigan.
Funding source: the National Science Foundation
Fabrication of advanced architecture synthetic tissue scaffolds
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.
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.
Accordions
High throughput mechanotyping of a large populations of cells
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)
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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