dc.contributor.author	Sturm, Logan Daniel	en
dc.contributor.committeechair	Williams, Christopher Bryant	en
dc.contributor.committeemember	Tarazaga, Pablo Alberto	en
dc.contributor.committeemember	Zheng, Xiaoyu	en
dc.contributor.committeemember	Camelio, Jaime A.	en
dc.contributor.department	Mechanical Engineering	en
dc.date.accessioned	2022-06-10T06:00:07Z	en
dc.date.available	2022-06-10T06:00:07Z	en
dc.date.issued	2020-12-16	en
dc.description.abstract	Additive manufacturing (AM) is a growing section of the advanced manufacturing field and is being used to fabricate an increasing number of critical components, from aerospace components to medical implants. At the same time, cyber-physical attacks targeting manufacturing systems have continued to rise. For this reason, there is a need to research new techniques and methods to ensure the integrity of parts fabricated on AM systems. This work seeks to address this need by first performing a detailed analysis of vulnerabilities in the AM process chain and how these attack vectors could be used to execute malicious part sabotage attacks. This work demonstrated the ability of an internal void attack on the .STL file to reduce the yield load of a tensile specimen by 14% while escaping detection by operators. To mitigate these vulnerabilities, a new impedance-based approach for in situ monitoring of AM systems was created. Two techniques for implementing this approach were investigated, direct embedding of sensors in AM parts, and the use of an instrumented fixture as a build plate. The ability to detect changes in material as small as 1.38% of the printed volume (53.8 mm3) on a material jetting system was demonstrated. For metal laser powder bed fusion systems, a new method was created for representing side-channel meltpool emissions. This method reduces the quantity of data while remaining sensitive enough to detect changes to the toolpath and process parameters caused by malicious attacks. To enable the SCMS to validate part quality during fabrication required a way to receive baseline part quality information across an air-gap. To accomplish this a new process noise tolerant method of cyber-physical hashing for continuous data sets was presented. This method was coupled with new techniques for the storage, transmission, and reconstructing of the baseline quality data was implemented using stacks of "ghost" QR codes stored in the toolpath to transmit information through the laser position. A technique for storing and transmitting quality information in the toolpath files of parts using acoustic emissions was investigated. The ATTACH (additive toolpath transmission of acoustic cyber-physical hash) method used speed modulation of infill roads in a material extrusion system to generate acoustic tones containing quality information about the part. These modulations were able to be inserted without affecting the build time or requiring additional material and did not affect the quality of the part that contained them. Finally, a framework for the design and implementation of a SCMS for protecting AM systems against malicious cyber-physical part sabotage attacks was created. The IDEAS (Identify, Define, Establish, Aggregate, Secure) framework provides a detailed reference for engineers to use to secure AM systems by leveraging the previous work in vulnerability assessment, creation of new side-channel monitoring techniques, concisely representing quality data, and securely transmitting information to air-gapped systems through physical emissions.	en
dc.description.abstractgeneral	Additive manufacturing (AM), more widely known as 3D printing, is a growing field of manufacturing where parts are fabricated by building layers of material on top of each other. This layer-based approach allows the production of parts with complex shapes that cannot be made using more traditional approaches such as machining. This capability allows for great freedom in designing parts, but also means that defects can be created inside of parts during fabrication. This work investigates ways that an adversary might seek to sabotage AM parts through a cyber-physical attack. To prevent attacks seeking to sabotage AM parts several new approaches for security are presented. The first approach uses tiny vibrations to detect changes to part shape or material by attaching a small sensor either directly to the parts or to the surface that they are built on. Because an attack that sabotages an AM system (3D printer) could also affect the systems used to detect part defects these systems should be digitally separated from each other. By using a series of QR codes fabricated by the AM system along with the parts, information can be sent from the AM system to the monitoring system through its sensors. This prevents a cyber-attack from jumping from the AM system to the monitoring system. By temporarily turning off the laser power and tracking the movements of the guiding mirrors the QR code information can be sent to the monitoring system without having to actually print the QR code. The information stored in the QR code is compared to the emission generated when fabricating the parts and is used to detect if an attack has occurred since that would change the emissions from the part, but not from the QR code. Another approach for sending information from the AM system using physical emissions is by using sounds generated during part fabrication. Using a desktop scale 3D printer, the speed of certain movements was increased or decreased. The change in speed causes the sound emitted from the printer to change, while not affecting the actual quality of the print. By using a series of tones, similar to Morse code, information can be sent from the printer. Research was performed on the best settings to use to transmit the information as well as how to automatically receive and decode the information using a microphone. The final step in this work is a framework that serves as a guide for designing and implementing monitoring systems that can detect sabotage attacks on AM parts. The framework covers how to evaluate a system for potential vulnerabilities and how to use this information to choose sensors and data processing techniques to reduce the risk of cyber-physical attacks.	en
dc.description.degree	Doctor of Philosophy	en
dc.format.medium	ETD	en
dc.identifier.other	vt_gsexam:27979	en
dc.identifier.uri	http://hdl.handle.net/10919/110586	en
dc.publisher	Virginia Tech	en
dc.rights	In Copyright	en
dc.rights.uri	http://rightsstatements.org/vocab/InC/1.0/	en
dc.subject	Additive Manufacturing	en
dc.subject	Cyber-physical Security	en
dc.subject	Side-channels	en
dc.subject	In Situ Monitoring	en
dc.title	Cyber-Physical Security for Additive Manufacturing Systems	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

Cyber-Physical Security for Additive Manufacturing Systems

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