Spatiotemporal G-code modeling for secure FDM-based 3D printing

Rais, Muhammad H.; Li, Ye; Ahmed, Irfan

doi:10.1145/3450267.3450545

Cited by 14 publications

(5 citation statements)

References 19 publications

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“…We have summarized 41 articles that do not specify specific industries in the discussion. While the rest of the articles defined specific industries, and this review categorized them into 14 types of industries, such as aero-engine [43], automotive [32], [33], electronics [23], [39], [77], fast-moving consumer goods [69], garment [30], machining [62], [67], [68], [76], [80], [82], metallurgies [78], [79], [63], milling [26], [59] and gas [50], semiconductor [49], water treatment plant [73], tobacco [86], transportation [54], small-medium enterprises [52]. The correlation between IoT and AI technologies with the categorized industries type is shown in Figure 5.…”

Section: Answering Rq4: Type Of Manufacturing System Decision-making ...mentioning

confidence: 99%

“…Simulation [82] The existing research on secure 3D printing mostly focuses on nozzle kinetics, while attacks on filament kinetics and thermodynamics can also damage the printed object.…”

Section: Ai-3 Iot-4mentioning

confidence: 99%

“…ML techniques are categorized into several classes depending on how they are learned and what they are used for. UL, a branch of ML where a model learns patterns and structures in data without any explicit labeling or guidance from a predefined set of output labels, is considered in [27], [33], [46], [57], [59], [62], [78], [82]. UL in hardware/sensor networks and cloud/fog/edge architecture of IoT domain improves manufacturing analytics in operations management processes, especially to obtain better detection for condition monitoring of sensor-node [27], [57], machine anomalies [33], and alteration of transmitted manufacturing data [82].…”

mentioning

confidence: 99%

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Artificial intelligence and internet of things in manufacturing decision processes

Wijaya,

Hermanto Rudy,

Debora

et al. 2024

IJ-AI

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This paper explores the influence of the internet of things (IoT) and artificial intelligence (AI) on the decision-making processes of modern manufacturing systems. With the proliferation of IoT devices and the development of AI technologies, manufacturing companies increasingly leverage these technologies to improve their decision-making abilities. This study aims to investigate the potential benefits, difficulties, and ramifications of integrating IoT and AI in manufacturing systems. The review employs the preferred reporting items for systematic reviews and meta-analyses (PRISMA) method with a systematic mapping process with four research questions. A total of 1282 articles were collected between 2017 and 2023, reviewed in accordance with the inclusion and exclusion criteria, and 66 articles were chosen. The research on IoT and AI technologies influentially affects other research in the production control layer manufacturing area based on the top-ten cited articles. In contrast, the research in this area focused on the operations management layer, specifically manufacturing analytics processes. This paper’s findings contribute to a greater understanding of the impact of IoT and AI on decision-making in modern multi-domain manufacturing systems and provide direction for future research in this field.

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Section: Answering Rq4: Type Of Manufacturing System Decision-making ...mentioning

confidence: 99%

“…Simulation [82] The existing research on secure 3D printing mostly focuses on nozzle kinetics, while attacks on filament kinetics and thermodynamics can also damage the printed object.…”

Section: Ai-3 Iot-4mentioning

confidence: 99%

mentioning

confidence: 99%

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Artificial intelligence and internet of things in manufacturing decision processes

Wijaya,

Hermanto Rudy,

Debora

et al. 2024

IJ-AI

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“…However, none of these contributions gives a straightforward, repeatable and low-budget method for planning the trajectories of a gantry-based system equipped with a multitude of extrusion heads, being able to avoid collisions among the extrusion head and using today's slicing tools (such as Ultimaker Cura), whose output is the G-code file. The G-code tells a machine controller where the motors should go, how quickly they should move and what path they should take (Di Angelo et al , 2020; Rais et al , 2021).…”

Section: Introductionmentioning

confidence: 99%

Efficient toolpath planning for collaborative material extrusion machines

Bacciaglia

Ceruti

2023

RPJ

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Purpose Timing constraints affect the manufacturing of traditional large-scale components through the material extrusion technique. Thus, researchers are exploring using many independent and collaborative heads that may work on the same part simultaneously while still producing an appealing final product. The purpose of this paper is to propose a simple and repeatable approach for toolpath planning for gantry-based n independent extrusion heads with effective collision avoidance management. Design/methodology/approach This research presents an original toolpath planner based on existing slicing software and the traditional structure of G-code files. While the computationally demanding component subdivision task is assigned to computer-aided design and slicing software to build a standard G-code, the proposed algorithm scans the conventional toolpath data file, quickly isolates the instructions of a single extruder and inserts brief pauses between the instructions if the non-priority extruder conflicts with the priority one. Findings The methodology is validated on two real-life industrial large-scale components using architectures with two and four extruders. The case studies demonstrate the method's effectiveness, reducing printing time considerably without affecting the part quality. A static priority strategy is implemented, where one extruder gets priority over the other using a cascade process. The results of this paper demonstrate that different priority strategies reflect on the printing efficiency by a factor equal to the number of extrusion heads. Originality/value To the best of the authors’ knowledge, this is the first study to produce an original methodology to efficiently plan the extrusion heads' trajectories for a collaborative material extrusion architecture.

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“…The attacks can be detected by comparing the results obtained by the monitoring system and the original printing settings. More recently, Rais et al [13] proposed a framework for checking the integrity of 3D printing process, utilizing the G-code as the ground truth to verify the acquired sensors' data, in order to determine if the printing process has been attacked. It is obvious that this framework is based on the assumption that the security of G-code is not compromised by an attacker.…”

Section: Introductionmentioning

confidence: 99%

Self-embedding watermarking method for G-code used in 3D printing

Li¹,

Gong²,

Tan³

et al. 2021

2021 IEEE International Workshop on Information Forensics and Security (WIFS)

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3D printing is faced with a lot of security issues, such as malicious tampering, intellectual property theft and so on. This work aims to protect the G-code file which controls the 3D printing process by proposing a self-embedding watermarking method for G-code file. This method groups the G-code lines into code blocks and achieves a random mapping relationship for each code block. Each code block is divided into two parts, carrying the authentication and recovery bits, respectively. The tampered regions are detected by leveraging the authentication bits in each code block. Meanwhile, the G-code files are restored based on the recovery bits and the geometric information of the neighboring code blocks. Experimental results indicate that the proposed method can effectively detect the tampered region and restore the G-code file to a large extent, while limiting the distortion caused to the 3D printed object by the watermarking.

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Spatiotemporal G-code modeling for secure FDM-based 3D printing

Cited by 14 publications

References 19 publications

Artificial intelligence and internet of things in manufacturing decision processes

Artificial intelligence and internet of things in manufacturing decision processes

Efficient toolpath planning for collaborative material extrusion machines

Self-embedding watermarking method for G-code used in 3D printing

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