<p>Anti-counterfeiting and provenance determination are serious
concerns in many industries, including automotive, aerospace, and defense. These
concerns are addressed by ensuring traceability during manufacturing,
transport, and use of goods. In increasingly globalized manufacturing contexts,
one-size-fits-all traceability solutions are not always appropriate. Manufacturers
may not have the means to re-tool production to meet marking, tagging, or other
traceability requirements. This is especially true when manufacturers require
high processing flexibility to produce specialized parts, as is increasingly
the case in modern supply chains. Counterfeiters and saboteurs, meanwhile, have
a growing attack surface over which to interfere with existing supply chains,
and have a leg up when implementation details of traceability methods are
widely known. There is a growing need to provide solutions to traceability that
i) are particularized to specific industrial contexts with heterogeneous
security and robustness requirements, and ii) reliably transmit information
needed for traceability throughout the product life cycle. </p>
<p><br></p><p>This dissertation presents investigations into tailorable
traceability schemes for modern manufacturing, with a focus on applications in
additive manufacturing. The primary contributions of this dissertation are
frameworks for designing traceability schemes that i) achieve traceability
through recovery of manufacturer-specified signals, from simple identity
information to more detailed strings of provenance data, and ii) are tuned to
maximize information carrying capacity subject to the available data and
intended use cases faced by the manufacturer.</p>
<p><br></p><p>In the vein of physically unclonable function (PUF)
literature, these frameworks leverage the intrinsic information present in
material structure, such as phase or grain statistics. These structures, being
functions of largely random and uncontrollable physical and chemical processes,
are by their nature uncontrollable by a manufacturer. According to the frameworks proposed in this
dissertation, anti-counterfeiting and traceability schemes are designed by
extracting large libraries of features from these properties, and designing
methods for identifying parts based on a subset of the extracted features that
demonstrate good utility for the present use case. Such schemes are customized
to handle specific material systems, metrology, expected part damage, and other
concerns raised by a manufacturer or other supply chain stakeholders.</p>
<p><br></p><p>First, this dissertation presents a framework that leverages
this intrinsic information, and models for damage that may occur during use,
for designing schemes for genuinity determination. Such schemes are useful in
contexts like anti-counterfeiting and part tracing. Once this framework is
established, it is then extended to design schemes for dynamically and securely
embedding manufacturer-specified messages during the manufacturing process,
with a focus on implementation in additive manufacturing. Such schemes leverage
both the intrinsic information inherent to the material / manufacturing process
and extrinsically introduced information. This extrinsic information may
include cryptographic keys, message information, and specifications regarding
how an authorized user may read the embedded message. The resulting embedding
schemes are formalized as "malleable PUFs.'' </p>
<p><br></p><p>The outcomes of these investigations are frameworks for
designing, evaluating, and implementing traceability schemes that can be used
by manufacturers, academics, and other stakeholders seeking to implement secure
and informative traceability schemes subject to their own unique constraints. Importantly, these frameworks can be adapted
for a range of industrial contexts, and can be readily extended as new methods
for in-situ measurement and control in additive manufacturing are developed.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/12735854 |
| Date | 29 July 2020 |
| Creators | Adam Dachowicz (9139691) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/Tailored_Traceability_and_Provenance_Determination_in_Manufacturing/12735854 |
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