Protection against Counterfeiting Attacks in 3D Printing by Streaming Signature-embedded Manufacturing Process Instructions

Tiwari, Akash; Villasenor, Eduardo Jose; Gupta, Nïkhil; Reddy, Narasimha; Karri, Ramesh; Bukkapatnam, Satish

doi:10.1145/3462223.3485620

Cited by 6 publications

(5 citation statements)

References 12 publications

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“…The adversaries' economic interests will then be satisfied as the customers would switch to other biochip companies in the marketplace. With the advent of manufacturing-as-aservice, 45 FMBs have become more vulnerable to materiallevel attacks. 20,45 Fig.…”

Section: Lab On a Chip Papermentioning

confidence: 99%

“…With the advent of manufacturing-as-aservice, 45 FMBs have become more vulnerable to materiallevel attacks. 20,45 Fig. 1 illustrates the adversarial model and highlights the vulnerable points corresponding to a material-level attack.…”

Section: Lab On a Chip Papermentioning

confidence: 99%

“…1 illustrates the adversarial model and highlights the vulnerable points corresponding to a material-level attack. 20,[45][46][47] The model has five parties: the customer, the FMB company, the designer, the manufacturer, and the quality control unit. The process flow of a typical FMB service is shown in Fig.…”

Section: Lab On a Chip Papermentioning

confidence: 99%

“…With the advent of manufacturing-as-a-service, 45 FMBs have become more vulnerable to material-level attacks. 20,45…”

Section: Adversarial Modelmentioning

confidence: 99%

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Material-level countermeasures for securing microfluidic biochips

Baban,

Saha,

Jancheska

et al. 2023

Lab Chip

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Section: Lab On a Chip Papermentioning

confidence: 99%

Section: Lab On a Chip Papermentioning

confidence: 99%

Section: Lab On a Chip Papermentioning

confidence: 99%

“…With the advent of manufacturing-as-a-service, 45 FMBs have become more vulnerable to material-level attacks. 20,45…”

Section: Adversarial Modelmentioning

confidence: 99%

See 2 more Smart Citations

Material-level countermeasures for securing microfluidic biochips

Baban,

Saha,

Jancheska

et al. 2023

Lab Chip

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“…With the emergence of Manufacturing-as-a-Service (MaaS) paradigm, it becomes imperative to secure essential products against counterfeiting and cyber-attacks (Tiwari et al, 2021). Anticounterfeit tags, such as QR codes introduced on the surface of physical products can be tampered with, thus destroying the ability to identify these parts.…”

Section: Product Defectsmentioning

confidence: 99%

Imaging systems and techniques for fusion-based metal additive manufacturing: a review

Balhara,

Karthikeyan,

Hanchate

et al. 2023

Front. Manuf. Technol.

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This study presents an overview and a few case studies to explicate the transformative power of diverse imaging techniques for smart manufacturing, focusing largely on various in-situ and ex-situ imaging methods for monitoring fusion-based metal additive manufacturing (AM) processes such as directed energy deposition (DED), selective laser melting (SLM), electron beam melting (EBM). In-situ imaging techniques, encompassing high-speed cameras, thermal cameras, and digital cameras, are becoming increasingly affordable, complementary, and are emerging as vital for real-time monitoring, enabling continuous assessment of build quality. For example, high-speed cameras capture dynamic laser-material interaction, swiftly detecting defects, while thermal cameras identify thermal distribution of the melt pool and potential anomalies. The data gathered from in-situ imaging are then utilized to extract pertinent features that facilitate effective control of process parameters, thereby optimizing the AM processes and minimizing defects. On the other hand, ex-situ imaging techniques play a critical role in comprehensive component analysis. Scanning electron microscopy (SEM), optical microscopy, and 3D-profilometry enable detailed characterization of microstructural features, surface roughness, porosity, and dimensional accuracy. Employing a battery of Artificial Intelligence (AI) algorithms, information from diverse imaging and other multi-modal data sources can be fused, and thereby achieve a more comprehensive understanding of a manufacturing process. This integration enables informed decision-making for process optimization and quality assurance, as AI algorithms analyze the combined data to extract relevant insights and patterns. Ultimately, the power of imaging in additive manufacturing lies in its ability to deliver real-time monitoring, precise control, and comprehensive analysis, empowering manufacturers to achieve supreme levels of precision, reliability, and productivity in the production of components.

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