VTechWorks Repository :: Browsing by Author "Komendera, Erik"

Browsing by Author "Komendera, Erik"

Now showing 1 - 17 of 17

Active Force Correction of Off-Nominal Structures Using Intelligent Scaffolding
Everson, Holly Kathleen (Virginia Tech, 2024-10-17)
The culmination of this research focuses on the area of structural support and stability as it relates to the field of large space structures. Fitting into the branch of in-space assembly, servicing, and manufacturing (ISAM), this topic covers essential subject matter areas of robotic manipulation, repair, state estimation, and structural health. As the next generation of space structures includes increased areas of modularity, the nature of structures built in-space lends itself significantly to repair efforts. With plans for these repair efforts in place, the lifetime of damaged structures can be greatly extended leading to a greater chance of mission success. By considering how repair efforts factor into the assembly scope, critical failures in large trusses, especially those involving single-point structural failures, can be mitigated. To do this, external forces are applied to the damaged structure utilizing an intelligent scaffolding formulation. This methodology employs robots to strategically apply loads to re-route abnormal stress and strain paths, correct for resulting deflections, and stabilize the structure itself. These tasks are vital to the safety of the structure and must take place before any repair efforts are considered in an effort to prevent cascading damage. The following research explores this damage simulation and correction paradigm through a variety of truss initial conditions, which allow for a suite of deflection responses. Utilizing these deflection responses a safe path for applying loads incrementally through generated waypoints is created with the help of the finite element modeler Ansys and a Python script. The ability for this system to successfully realign the wide scope of truss cases showcases that it is a truly adaptive system. Although this work is primarily proven within a simulation space, efforts to contextualize in a physical system and explore the elements needed to implement this method are also described. Finally, although this research is presented within the scope of damage repair, the final chapter looks to apply this method to other similarly unsupported structures by examining how critical it can be during assembly scenarios.
Analyzing Attitude Correction of a Spacecraft Due to the Motion of a Robotic Arm Payload
Molitor, Rowan Larson (Virginia Tech, 2024-06-06)
There are millions of pieces of space debris in orbit around Earth that pose threats to operating spacecraft. Some of these debris can be attributed to satellite failure, or end-of-life protocols. With a continual increase in commercial satellite launches per year, decommissioned spacecraft act as more debris polluting the space environment. Not only can robotic arms assist with active orbital debris removal to be more sustainable, they also support robotic on-orbit servicing (OOS). Additionally, using a robotic manipulator enables different servicing operations to take place, allowing for life extension capabilities for expired spacecraft. These life extension services allow for a broader application for robotic arms, which includes rendezvous proximity operations and docking. Robotic arms can also be used for assembly and manufacturing cases, establishing a more sustained presence and creating permanent structures in space. When considering any robotic rendezvous maneuvers or servicing, assembly, and manufacturing tasks aboard a spacecraft, it is important for the parent satellite to maintain attitude throughout robot motion, as in a zero gravity setting, any forces created by the robot act as equal and opposite forces applied to the parent spacecraft. The research performed in this thesis aims to create a model to describe changes in attitude throughout planned robot motion, as well as introduce methods for compensating for potential disturbances. Additionally, methods for describing the kinematics of a robot manipulator are presented and the forces and torques experienced by each joint are calculated using Newton-Euler inverse dynamics. Based on a calculated trajectory of the end effector, these torques are propagated to the parent spacecraft to determine the change in angular velocity. The results of this analysis are used to determine the required angular velocity to apply to the parent spacecraft in order to maintain attitude.
An Autonomous Task Assignment Paradigm for Autonomous Robotic In-Space Assembly
Hildebrand, Robert; Komendera, Erik; Moser, Joshua; Hoffman, Julia (Frontiers, 2022-02-25)
The development of autonomous robotic systems is a key component in the expansion of space exploration and the development of infrastructures for in-space applications. An important capability for these robotic systems is the ability to maintain and repair structures in the absence of human input by autonomously generating valid task sequences and task to robot allocations. To this end, a novel stochastic problem formulation paired with a mixed integer programming assembly schedule generator has been developed to articulate the elements, constraints, and state of an assembly project and solve for an optimal assembly schedule. The developed formulations were tested with a set of hardware experiments that included generating an optimal schedule for an assembly and rescheduling during an assembly to plan a repair. This formulation and validation work provides a path forward for future research in the development of an autonomous system capable of building and maintaining in-space infrastructures.
A Bayesian Framework for Multi-Stage Robot, Map and Target Localization
Papakis, Ioannis (Virginia Tech, 2019)
This thesis presents a generalized Bayesian framework for a mobile robot to localize itself and a target, while building a map of the environment. The proposed technique builds upon the Bayesian Simultaneous Robot Localization and Mapping (SLAM) method, to allow the robot to localize itself and the environment using map features or landmarks in close proximity. The target feature is distinguished from the rest of features since the robot has to navigate to its location and thus needs to be observed from a long distance. The contribution of the proposed approach is on enabling the robot to track a target object or region, using a multi-stage technique. In the first stage, the target state is corrected sequentially to the robot correction in the Recursive Bayesian Estimation. In the second stage, with the target being closer, the target state is corrected simultaneously with the robot and the landmarks. The process allows the robot's state uncertainty to be propagated into the estimated target's state, bridging the gap between tracking only methods where the target is estimated assuming known observer state and SLAM methods where only landmarks are considered. When the robot is located far, the sequential stage is efficient in tracking the target position while maintaining an accurate robot state using close only features. Also, target belief is always maintained in comparison to temporary tracking methods such as image-tracking. When the robot is closer to the target and most of its field of view is covered by the target, it is shown that simultaneous correction needs to be used in order to minimize robot, target and map entropies in the absence of other landmarks.
Built On-Orbit Robotically Assembled Gigatruss (BORG): Ground Robotic Demonstration
Chapin, Samantha; Everson, Holly; Chapin, William; Komendera, Erik (MDPI, 2024-05-31)
The next generation of large space infrastructure will require crucial advancements in current technology. Current methodologies focus on large deployable structures folded into cramped payload fairings or revolutionary assembly techniques requiring many moving components. Utilizing both in-space assembly and deployable concepts, a hybrid mixed assembly scheme was posed using smaller deployable units interspersed with rigid connecting elements to assemble these large architectures. The Built On-Orbit Robotically Assembled Gigatruss (BORG) structure allows for modularity in assembly and repair with the number of separate elements comprising the structure to be reduced, compared to strut-by-strut assembly. The following documents the process of constructing and running physical trials on a prototype BORG architecture. Additionally, a Semantic and Fiducial Aided Graph Simultaneous Localization and Mapping (SF-GraphSLAM) approach is taken to verify the relation of assembled and deployed truss elements to aid in error evaluation and state estimation. This technology demonstration stands as a proof of concept in verifying the viability of the BORG architecture as a method for large structure assembly.
Design of Time-Varying Hybrid Zero Dynamics Controllers for Exponential Stabilization of Agile Quadrupedal Locomotion
Martin, Joseph Bacon V (Virginia Tech, 2020-10-23)
This thesis explores the development of time-varying virtual constraint controllers that allow stable and agile gaits for full-order hybrid dynamical models of quadrupedal locomotion. Unlike time-invariant nonlinear controllers, time-varying controllers do not rely on sensor data for gait phasing and can initiate locomotion from zero velocity. Motivated by these properties, we investigate the stability guarantees that can be provided by the time-varying approach. More specifically, we systematically establish necessary and sufficient conditions that guarantee exponential stability of periodic orbits for time-varying hybrid dynamical systems utilizing the Poincar� return map. Leveraging the results of the presented proof, we develop time-varying virtual constraint controllers to stabilize bounding, trotting, and walking gaits of a 14 degree of freedom quadrupedal robot, Minitaur. A framework for selecting the parameters of virtual constraint controllers to achieve exponential stability is shown, and the feasibility of the analytical results is numerically validated in full-order model simulations of Minitaur.
Force-Controlled Pose Optimization and Trajectory Planning for Chained Stewart Platforms
Beach, Benjamin; Chapin, William; Glassner, Samantha; Hildebrand, Robert; Komendera, Erik (Frontiers, 2023-11-24)
Introduction: We study optimization methods for poses and movements of chained Stewart platforms (SPs) that we call an “Assembler” Robot. These chained SPs are parallel mechanisms that are stronger, stiffer, and more precise, on average, than their serial counterparts at the cost of a smaller range of motion. By linking these units in a series, their individual limitations are overcome while maintaining truss-like rigidity. This opens up potential uses in various applications, especially in complex space missions in conjunction with other robots. Methods: To enhance the efficiency and longevity of the Assembler Robot, we developed algorithms and optimization models. The main goal of these methodologies is to efficiently decide on favorable positions and movements that reduce force loads on the robot, consequently minimizing wear. Results: The optimized maneuvers of the interior plates of the Assembler result in more evenly distributed load forces through the legs of each constituent SP. This optimization allows for a larger workspace and a greater overall payload capacity. Our computations primarily focus on assemblers with four chained SPs. Discussion: Although our study primarily revolves around assemblers with four chained SPs, our methods are versatile and can be applied to an arbitrary number of SPs. Furthermore, these methodologies can be extended to general over-actuated truss-like robot architectures. The Assembler, designed to function collaboratively with several other robots, holds promise for a variety of space missions.
Incident Response Enhancements using Streamlined UAV Mission Planning, Imaging, and Object Detection
Link, Eric Matthew (Virginia Tech, 2023-06-29)
Systems composed of simple, reliable tools are needed to facilitate adoption of Uncrewed Aerial Vehicles (UAVs) into incident response teams. Existing systems require operators to have highly skilled level of knowledge of UAV operations, including mission planning, low-level system operation, and data analysis. In this paper, a system is introduced to reduce required operator knowledge level via streamlined mission planning, in-flight object detection, and data presentation. For mission planning, two software programs are introduced that utilize geographic data to: (1) update existing missions to a constant above ground level altitude; and (2) auto-generate missions along waterways. To test system performance, a UAV platform based on the Tarot 960 was equipped with an Nvidia Jetson TX2 computing device and a FLIR GigE camera. For demonstration of on-board object detection, the You Only Look Once v8 model was trained on mock propane tanks. A Robot Operating System package was developed to manage communication between the flight controller, camera, and object detection model. Finally, software was developed to present collected data in easy to understand interactive maps containing both detected object locations and surveyed area imagery. Several flight demonstrations were conducted to validate both the performance and usability of the system. The mission planning programs accurately adjust altitude and generate missions along waterways. While in flight, the system demonstrated the capability to take images, perform object detection, and return estimated object locations with an average accuracy of 3.5 meters. The calculated object location data was successfully formatted into interactive maps, providing incident responders with a simple visualization of target locations and surrounding environment. Overall, the system presented meets the specified objectives by reducing the required operator skill level for successful deployment of UAVs into incident response scenarios.
Inferring the Human's Objective in Human Robot Interaction
Hoegerman, Joshua Thomas (Virginia Tech, 2024-05-03)
This thesis discusses the use of Bayesian Inference in inferring over the human's objective for Human-Robot Interaction, more specifically, it focuses upon the adaptation of methods to better utilize the information for inferring upon the human's objective for Reward Learning and Communicative Shared Autonomy settings. To accomplish this, we first examine state-of-the-art methods for approaching Bayesian Inverse Reinforcement learning where we explore the strengths and weaknesses of current approaches. After which we explore alternative methods for approaching the problem, borrowing similar approaches to those of the statistics community to apply alternative methods to improve the sampling process over the human's belief. After this, I then move to a discussion on the setting of Shared Autonomy in the presence and absence of communication. These differences are then explored in our method for inferring upon an environment where the human is aware of the robot's intention and how this can be used to dramatically improve the robot's ability to cooperate and infer upon the human's objective. In total, I conclude that the use of these methods to better infer upon the human's objective significantly improves the performance and cohesion between the human and robot agents within these settings.
Localization Performance Improvement of a Low-Resolution Robotic System using an Electro-Permanent Magnetic Interface and an Ensemble Kalman Filter
Martin, Jacob Ryan (Virginia Tech, 2022-10-17)
As the United States is on the cusp of returning astronauts to the Moon, it becomes increasingly apparent that the assembly of structures in space will have to rely upon robots to perform the construction process. With a focus on sustaining a presence on the Moon's surface in such a harsh and unforgiving environment, demonstrating the robustness of autonomous assembly and capabilities of robotic manipulators is necessary. Current robotic assembly on Earth consists mainly of inspection or highly controlled environments, and always with a human in the loop to step in and fix issues if a problem occurs. To remove the human element, the robot system then must account for safety as well. Thus, system risk can easily overwhelm project costs. This thesis proposes a combination of hardware and state estimation solutions to improve the feasibility of low-fidelity and low-resolution robots for precision assembly tasks. Doing so reduces the risk to mission success, as the hardware becomes easier to replace or repair. The hardware modifications implement an electro-permanent magnet interface with alignment features to reduce the fidelity needed for the robot end effector. On the state estimation side, an Ensemble Kalman Filter is implemented, along with a scaling system to prevent FASER Lab hardware from becoming stuck due to hardware limitations. Overall, the three modifications improved the test robot's autonomous convergence error by 98.5%, bettering the system sufficiently to make an autonomous assembly process feasible.
Methods for Kinematic Analysis and Optimization of Overactuated Serial and Parallel Structures
Chapin, William Douglas (Virginia Tech, 2023-01-17)
This body of work presents methods for the optimization, analysis, and control of mixed serial-parallel structures known as SP-Stacks. A SP-Stack is a series of Stewart Platforms (SPs) linked via their top and bottom plates to create a serial chain of parallel mechanisms. SP-Stacks are unique in their bridging of the benefits of parallel architectures (high rigidity, strength, and precision) and serial architectures (reach and manipulability), at the cost of being extremely overactuated. SP-Stacks are also difficult to provide kinematic solutions for, as neither forward nor inverse kinematics of a system are closed form. The first work presented focuses on presenting algorithms and optimization functions pertaining to the kinematic configuration of a SP-Stack. It first presents two methods of fast inverse kinematics (IK) for the SP-Stack which do not take forces into account. The outputs of those more simplistic solvers as used as initial conditions for a Nonlinear Program (NLP) algorithm which optimizes the internal configuration of a SP-Stack such that the end effector (EE) plate remains at the desired location, and the maximal force experienced on any actuator is minimized. The second work presented focuses on hardware testing some of the constituent algorithms and conclusions drawn from the first paper and determining methods of compensating for, in software, detected defects in hardware and hardware measurement systems. This work also demonstrates a different form of force-optimization - compliance control (CC), which is executed on both a single SP responding to external forces, and a 2 SP-Stack responding to regular internal perturbation. Conclusions drawn from these works are useful for stacks of an arbitrary number of SPs, can be extended to other mixed-kinematic systems, and advance the capabilities of these systems to be useful contributors in field robotics.
Mobile Robot Obstacle Avoidance based on Deep Reinforcement Learning
Feng, Shumin (Virginia Tech, 2018)
Obstacle avoidance is one of the core problems in the field of autonomous navigation. An obstacle avoidance approach is developed for the navigation task of a reconfigurable multi-robot system named STORM, which stands for Self-configurable and Transformable Omni-Directional Robotic Modules. Various mathematical models have been developed in previous work in this field to avoid collision for such robots. In this work, the proposed collision avoidance algorithm is trained via Deep Reinforcement Learning, which enables the robot to learn by itself from its experiences, and then fit a mathematical model by updating the parameters of a neural network. The trained neural network architecture is capable of choosing an action directly based on the input sensor data using the trained neural network architecture. A virtual STORM locomotion module was trained to explore a Gazebo simulation environment without collision, using the proposed collision avoidance strategies based on DRL. The mathematical model of the avoidance algorithm was derived from the simulation and then applied to the prototype of the locomotion module and validated via experiments. Universal software architecture was also designed for the STORM modules. The software architecture has extensible and reusable features that improve the design efficiency and enable parallel development.
Multi-Axis Material Extrusion Additive Manufacturing of Continuous Carbon Fiber Composites
Beaumont, Kieran Deane (Virginia Tech, 2023-07-06)
Semantic and Fiducial Aided Graph Simultaneous Localization and Mapping for Robotic In-Space Assembly and Servicing of Large Truss Structures
Chapin, Samantha Helen Glassner (Virginia Tech, 2024-05-22)
This research focuses on the development of the semantic and fiducial aided graph simultaneous localization and mapping (SF-GraphSLAM) method that is tailored for robotic assembly and servicing of large truss structures. SF-GraphSLAM contributes to the state of the art by creating a novel way to add associations between map landmarks, in this scenario fiducials, by pre-generating a semantic map of expected relations based on the truss module known models, kinematic information about deployable modules, and hardware constraints for assembled modules. This additional information about the expected fiducial relations, and therefore truss module relative poses, can be used to add robustness to camera pose and measurement error. In parallel, the concept of a mixed assembly truss structure paradigm was created and analyzed. This mixed assembly method focuses on reducing the number of modules required to construct large truss structures by using a mixture of deployable and assembled truss modules to create a checkerboard array that is scalable to various dimensions and shapes while still minimizing the number of modules compared to a strut-by-strut method. Leveraging this paradigm SF-GraphSLAM is able to start at an advantage in terms of minimizing the state vector for the assembly testing. In addition, due to the added knowledge of the structure and the choice to utilize fiducial markers, SF-GraphSLAM is able to minimize the number of fiducials used to define the structure and therefore have the minimum state space to solve the assembly scenario, greatly improving the real-time estimation process between assembly steps. These optimizations will have a larger effect as the size of the scaled end structure increases. SF-GraphSLAM is derived in mathematical form following the same core process used for the pose and measurement components used in the base GraphSLAM. SF-GraphSLAM is evaluated against the state of the art example of GraphSLAM through simulation using an example 3x3x3 mixed assembly truss structure, known as the Built On-orbit Robotically-assembled Gigatruss (BORG). A physical BORG test truss was constructed to enable hardware trials of the SF-GraphSLAM algorithm. Although this ground hardware is not ideal for the high precision application of space structures it allows for rapid experimental robotic testing. This tailored SF-GraphSLAM approach will contribute to the state of the art of robotic in-space servicing, assembly, and manufacturing (ISAM) by providing progress on a method for dealing with the autonomous robotic assembly of movable modules to create larger structures. This will be critical for missions such as robotically assembling a large antenna structure or space telescope. Furthermore, the core methodology will study into how to best utilize information in a large-scale structure environment, including non-static flexible or deployable modules, to adequately map it which is also applicable to the larger field of robotic operations dealing with structures such as bridge surveying.
Task Modeling, Sequencing, and Allocation for In-Space Autonomous Assembly by Robotic Systems
Moser, Joshua Nickolas (Virginia Tech, 2022-07-18)
As exploration in space increases through the use of larger telescopes, more sophisticated structures, and physical exploration, the use of autonomous robots will become instrumental to build and maintain the infrastructures required for this exploration. These systems must be autonomous to deal with the infeasibility of teleoperation due signal delay and task complexity. The reality of using robots in the real world without direct human input will require the autonomous systems to have the capability of responding to errors that occur in an assembly scenario on their own. As such, a system must be in place to allow for the sequencing and allocation of tasks to the robotic workforce autonomously, giving the ability to re-plan in real world stochastic environments. This work presents four contributions towards a system allowing for the autonomous sequencing and allocation of tasks for in-space assembly problems. The first contribution is the development of the Stochastic Assembly Problem Definition (SAPD) to articulate all of the features in an assembly problem that are applicable to the task sequencing and allocation. The second contribution is the formulation of a mixed integer program to solve for assembly schedules that are optimal or a quantifiable measurement from optimal. This contribution is expanded through the development of a genetic algorithm formulation to utilize the stochastic information present in the assembly problem. This formulation extends the state-of-the-art techniques in genetic algorithms to allow for the inclusion of new constraints required for the in-space assembly domain. The third contribution addresses how to estimate a robot's ability to complete a task if the robot must be assigned to a task it was previously not expected to work on. This is accomplished through the development of four metrics and analyzed through the use of screw theory kinematics. The final contribution focuses on a set of metrics to guide the selection of a good scheduling method for different assembly situations. The experiments in this work demonstrate how the developed theory can be utilized and shows the scheduling systems producing the best or close to the best schedules for assemblies. It also shows how the metrics used to quantify and estimate robot ability are applied. The theory developed in this work provides another step towards autonomous systems that are capable of assembling structures in-space without the need for human input.
Topology and Toolpath Optimization via Layer-Less Multi-Axis Material Extrusion
Kubalak, Joseph Riley (Virginia Tech, 2021-01-28)
Although additive manufacturing technologies are often referred to as "3D printing," the family of technologies typically deposit material on a layer-by-layer basis. For material extrusion (ME) in particular, the deposition process results in weak inter- and intra-layer bonds that reduce mechanical performance in those directions. Despite this shortcoming, ME offers the opportunity to specifically and preferentially align the reinforcement of a composite material throughout a part by customizing the toolpath. Recent developments in multi-axis deposition have demonstrated the ability to place material outside of the XY-plane, enabling depositions to align to any 3D (i.e., non-planar) vector. Although mechanical property improvements have been demonstrated, toolpath planning capabilities are limited; the geometries and load paths are restricted to surface-based structures, rather than fully 3D load paths. By specifically planning deposition paths (roads) where the composite reinforcement is aligned to the load paths within a structure, there is an opportunity for a step-change in the mechanical properties of ME parts. To achieve this goal for arbitrary geometries and load paths, the author presents a design and process planning workflow that concurrently optimizes the topology of the part and the toolpath used to fabricate it. The workflow i) identifies the optimal structure and road directions using topology optimization (TO), ii) plans roads aligned to those optimal directions, iii) orders those roads for collision-free deposition, and iv) translates that ordered set of roads to a robot-interpretable toolpath. A TO algorithm, capable of optimizing 3D material orientations, is presented and demonstrated in the context of 2D and 3D load cases. The algorithm achieved a 38% improvement in final solution compliance for a 3D Wheel problem relative to existing TO algorithms with planar orientation optimization considerations. Optimized geometries and their associated orientation fields were then propagated with the presented alignment-focused deposition path planner and conventional toolpath planners. The presented method resulted in a 97% correlation between the road directions and the orientation field, while the conventional methods only achieved 77%. A planar multi-load case was then fabricated using each of these methods and tested in both tension and bending; the presented alignment-focused method resulted in a 108.24% and 29.25% improvement in each load case, respectively. To evaluate the workflow in a multi-axis context, an inverted Wheel problem was optimized and processed by the workflow. The resulting toolpaths were then fabricated on a multi-axis deposition platform and mechanically evaluated relative to geometrically similar structures using a conventional toolpath planner. While the alignment in the multi-axis specimen was improved from the conventional method, the mechanical properties were reduced due to limitations of the multi-axis deposition platform.
Towards Cyber-Physical Security for Additively Manufactured Parts via In Situ Monitoring and Electromechanical Impedance
Raeker-Jordan, Nathan Alexander (Virginia Tech, 2025-01-22)
The layer-by-layer nature of additive manufacturing (AM) allows for toolless fabrication of highly complex geometries that cannot be made via traditional processes. AM is unique in its ability to precisely define both the material properties and geometric shape throughout the volume of a part, giving designers unmatched freedom in the creation of new components. However, this freedom of design also creates numerous challenges in the qualification of these parts. As AM processes primitive material in real time to produce each voxel of part volume, manufacturing defects may be dispersed anywhere throughout the part. Many part designs may have complex geometries or material parameters that are challenging for traditional qualification and inspection techniques to inspect for such embedded errors. Even more troubling, this freedom of design also extends to malicious actors, who would then be able to embed intentional targeted defects within the volume of the part. As the AM process is driven almost entirely by computer controlled machines and cyber-domain data, the AM process is uniquely at risk of nearly undetectable cyber-physical attacks, or cyber attacks that can cause physical damage. Additionally, as much of the valuable intellectual property associated with the design and material parameters of parts are stored in digital form, theft of these design files could result in mass replication of lower quality counterfeit parts, putting the supply chain of these AM parts at risk. In order to mitigate these vulnerabilities in the AM process, prior works have focused on in situ monitoring of the manufacturing process in order to ensure the part is constructed as expected. Typically for in situ monitoring, the constructed geometry is compared to the design files associated with the part in question using a monitoring system connected to either the AM machine or the larger network. However, such methods trust the validity of both the design files and monitoring systems used for verification, when either or both may have also been attacked. Therefore, a valid in situ monitoring method needs secure access to a provable set of validation data, while also isolating or air-gapping itself from the network to prevent cyber attacks on the monitoring system itself. Similarly, other works have focused on mitigating the risk of counterfeiting by novel means of part identification tailored for the AM process. Many of these identification methods leverage stochastic or prescribed features, such as surface patterns measured via visible or ultraviolet scanning, or internal porosity features measured via x-ray computed tomography (CT) scanning. However, these surface features are not impacted by alterations or damage to the part in areas away from the specific features being measured, possibly preventing the detection of attacks or damage to other areas of the part in transit. CT scanning can be used to detect damage or alterations to more areas of the part and incorporate this measurement into the identification mechanism, but may be prohibitively expensive while also possibly failing to properly penetrate and measure a sufficiently complex AM part. In this work, efforts to expand the cyber-physical security of the AM process are explored, including (1) a novel method of in situ process validation by means of covertly transmit- ting process quality information to an otherwise air-gapped monitoring system, (2) a novel method of metal AM part identification via a low-cost piezoelectric sensor-actuator able to record a part frequency response that is dependent on the geometry and material properties of the part as a whole, (3) an exploration of part-to-part variation across AM processes, again measured via a piezoelectric sensor-actuator, and (4) a novel means of using the same piezoelectric sensor-actuator for detecting the presence of remaining powder in metal AM parts.

Browsing by Author "Komendera, Erik"

Results Per Page

Sort Options