Process-Controlled Information Embedding for Enhanced Security in Manufacturing

The digitization of manufacturing has transformed the product realization process across many industries, from aerospace and automotive to medicine and healthcare. While this progress has accelerated product development cycles and enabled designers to create products with previously unachievable complexity and precision, it has also opened the door to a broad array of unique security concerns, from theft of intellectual property to supply chain attacks and counterfeiting. To address these concerns, information embedding has emerged as a promising solution to enhance product security and traceability, particularly in the additive manufacturing (AM) community.

Information embedding techniques involve storing unique and secure information within parts, making them easier to track and to verify for authenticity. However, a successful information embedding scheme requires information to be transmitted in physical parts both securely and in a way that is accessible to end users. Ensuring these qualities introduces unique computational and engineering challenges. Current embedding schemes often require modifications to parts’ geometry or manufacturing equipment, which can be prohibitive for many applications. Moreover, there is a lack of knowledge on maximizing the information content in a part, given an embedding scheme and use case, while maintaining the part’s functional performance.

To address these challenges, this dissertation presents a cohesive approach that combines process parameter control, information theory, and topology optimization to establish efficient and reliable information embedding schemes that maximize the amount of information that can be encoded into parts while maintaining the functional performance. By leveraging the unique capabilities of AM processes, we aim to maximize the amount of data encoded into parts without compromising their functionality.