Towards Cyber-Physical Security for Additively Manufactured Parts via In Situ Monitoring and Electromechanical Impedance
| dc.contributor.author | Raeker-Jordan, Nathan Alexander | en |
| dc.contributor.committeechair | Williams, Christopher Bryant | en |
| dc.contributor.committeemember | Kong, Zhenyu | en |
| dc.contributor.committeemember | Bartlett, Michael David | en |
| dc.contributor.committeemember | Komendera, Erik | en |
| dc.contributor.department | Mechanical Engineering | en |
| dc.date.accessioned | 2025-01-23T09:00:23Z | en |
| dc.date.available | 2025-01-23T09:00:23Z | en |
| dc.date.issued | 2025-01-22 | en |
| dc.description.abstract | 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. | en |
| dc.description.abstractgeneral | Additive manufacturing (AM), or 3D printing, allows for the creation of highly complex parts. AM machines do this by building parts layer-by-layer, processing (e.g., selectively melting metal powder) and placing each segment of a part from the bottom up, allowing it to make internal features which would be impossible with traditional manufacturing processes, such as machining.. However, because these parts can be so complicated, it is difficult to validate that a part is "good", i.e., is free from defects. As the entire volume of the part is built layer- by-layer, any layer anywhere in the part could be defective, with very few techniques being capable of detecting the defect from the outside. Worse, because the AM process is driven by digital design files and other data, cyber attacks have the ability to maliciously change the design of a part before it is made, resulting in physical damage. These cyber-physical attacks can similarly affect existing validation methods, allowing these attacked parts to slip through undetected. Alternatively, part designs can be stolen, allowing the thieves to produce unauthorized and possibly subpar counterfeits. These dangers require new means of validating the AM process and the parts it can produce. In order to detect a cyber-physical attack, previous studies have looked to recording and monitoring the physical actions of the AM process in order to ensure the part is built layer- by-layer as expected. Typically, the part design files are sent to the network-connected monitoring system, which then compares the files to the as-built geometry being recorded. However, in this case, the design files can themselves be attacked, as can the monitoring system recording and comparing the part geometry. In order to detect bad parts without exposing the system to cyber attacks, the monitoring system needs a way to validate the AM part without relying on the part design files directly or being connected to the network. To determine is a part is counterfeit or not, previous studies have tried to create "fingerprints" for parts, allowing a unique part to be identified. However, many of these techniques require changes to the part in question, or rely on features that could be duplicated (i.e. copying the fingerprint) by a skilled attacker. Certain methods using x-ray computed tomography (CT) scanning, while effective at fingerprinting small parts, can be very expensive, and may not work for parts which are too large or complex for x-rays to cleanly pass through. To be successful, a fingerprint needs to be simple to measure, and dependent on the entirety of the part itself, not just a handful of manufactured features. This can be done using the frequency response of the part, or how much the part vibrates over a range of frequencies. This response is dependent on the entire part, including the geometry and the material properties, and can be measured using low-cost equipment, allowing it to be used for a variety of different purposes. In this work, several methods to enhance the cyber-physical security of the AM process are explored. These include (1) a method of validating the AM process by covertly transmitting information to a network disconnected monitoring system, (2) a method of identifying metal AM parts identification using the parts frequency response as a fingerprint, (3) an exploration of part frequency response for fingerprinting across other AM processes, including both metal and polymer parts, and (4) a means of using the frequency response of a part for detecting the presence of residual powder from powder-based AM processes. | en |
| dc.description.degree | Doctor of Philosophy | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:42402 | en |
| dc.identifier.uri | https://hdl.handle.net/10919/124316 | en |
| dc.language.iso | en | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | Creative Commons Attribution 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en |
| dc.subject | Additive Manufacturing | en |
| dc.subject | Cyber-Physical Security | en |
| dc.subject | In Situ Sensing | en |
| dc.subject | Electromechanical Impedance | en |
| dc.subject | Identification | en |
| dc.subject | Anti-Counterfeiting | en |
| dc.title | Towards Cyber-Physical Security for Additively Manufactured Parts via In Situ Monitoring and Electromechanical Impedance | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Mechanical Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Doctor of Philosophy | en |