Machine Learning-Based Investigation of the 3D Printer Cooling Effect on Print Quality in Fused Filament Fabrication: A Cybersecurity Perspective

Autor:	Haijun Si, Zhicheng Zhang, Orkhan Huseynov, Ismail Fidan, Syed Rafay Hasan, Mohamed Mahmoud
Jazyk:	angličtina
Rok vydání:	2023
Předmět:	additive manufacturing machine learning cybersecurity fan speed Engineering machinery tools and implements TA213-215 Technological innovations. Automation HD45-45.2
Zdroj:	Inventions, Vol 8, Iss 1, p 24 (2023)
Druh dokumentu:	article
ISSN:	2411-5134 48708402
DOI:	10.3390/inventions8010024
Popis:	Additive manufacturing (AM), also known as three-dimensional (3D) printing, is the process of building a solid object in a layer-wise manner. Cybersecurity is a prevalent issue that appears more and more frequently as AM becomes popular. This paper focuses on the effect of fan speed on the printing quality and presents a plugin called Fan Speed Attack Detection (FSAD) that predicts and monitors fan speeds throughout the printing process. The goal of the plugin is to prevent cybersecurity attacks, specifically targeting fan speed. Using the proposed FSAD, any fan speed changes during the printing process are evaluated to see whether the printer can sustain the abnormal fan speed change and still maintain good print quality.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/0c0cc336da1a48708402ee43191f74e7 Zobrazit plný text záznamu View record in DOAJ