Layer Separation Mechanisms in DLP 3D Printing

Patil, Yogesh; Richa, Patil; Chandrashekhar, N. S.; Karunakaran, K. P.

doi:10.1007/978-981-32-9433-2_15

Cited by 5 publications

(1 citation statement)

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“…However, it has a lower cost than introducing oxygen as an inhibitor [33] or using a water pumping mechanism [18]. Figure 5 shows that the curve of the spring-assisted mechanism's minimum separation force or separation time was between those of the pulling-up and tilting mechanisms.…”

Section: Discussionmentioning

confidence: 99%

Comprehensive performance of a low-cost spring-assisted mechanism for digital light processing

Yang

Wu²

2023

Int J Adv Manuf Technol

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Highlights1. Comprehensively evaluated the performance of a low-cost spring-assisted mechanism for the separation process.2. The Taguchi method was used to obtain the parameters that minimize the separation force and time.3. The spring-assisted mechanism demonstrated better manufacturing stability than the pulling-up or tilting mechanism. 4. A linear regression equation was established to predict the separation force of specific geometric shapes and areas to greatly reduce the calculation costs and time.

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Section: Discussionmentioning

confidence: 99%

Comprehensive performance of a low-cost spring-assisted mechanism for digital light processing

Yang

Wu²

2023

Int J Adv Manuf Technol

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Artificial Intelligence‐Augmented Additive Manufacturing: Insights on Closed‐Loop 3D Printing

Sani,

Zolfagharian,

Kouzani

2024

Advanced Intelligent Systems

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The advent of 3D printing has transformed manufacturing. However, extending the library of materials to improve 3D printing quality remains a challenge. Defects can occur when printing parameters like print speed and temperature are chosen incorrectly. These can cause structural or dimensional issues in the final product. This review investigates closed‐loop artificial intelligence‐augmented additive manufacturing (AI2AM) technology that integrates AI‐based monitoring, automation, and optimization of printing parameters and processes. AI2AM uses AI to improve defect detection and prevention, improving additive manufacturing quality and efficiency. This article explores generic 3D printing processes and issues using existing research and developments. Next, it focuses on fused deposition modeling (FDM) printers and reviews their parameters and issues. The current remedies developed for defect detection and monitoring in FDM 3D printers are presented. Then, the article investigates AI‐based 3D printing monitoring, closed‐loop feedback systems, and parameter optimization development. Finally, closed‐loop 3D printing challenges and future directions are discussed. AI‐based systems detect and correct 3D printing failures, enabling current printers to operate within optimal conditions and minimizing the risk of defects or failures, which in turn leads to more sustainable manufacturing with minimum waste and extending the library of materials.

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