form 3+ low force stereolithography (lfs)tm 3d printer (Formlabs Inc)
90
Structured Review
Formlabs Inc
form 3+ low force stereolithography (lfs)tm 3d printer
Form 3+ Low Force Stereolithography (Lfs)tm 3d Printer, supplied by Formlabs Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/form+3+low+force+stereolithography+(lfs)+3d+printer/3d+printer+form+2/10__1080_slash_17452759__2025__2512977-142-70-77
Average 90 stars, based on 1 article reviews
Form 3+ Low Force Stereolithography (Lfs)tm 3d Printer, supplied by Formlabs Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/form+3+low+force+stereolithography+(lfs)+3d+printer/3d+printer+form+2/10__1080_slash_17452759__2025__2512977-142-70-77
Average 90 stars, based on 1 article reviews
form 3+ low force stereolithography (lfs)tm 3d printer - by Bioz Stars,
2026-10
90/100 stars
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other:Article Title: Multi-plane acoustic hologram generation with a physics-enhanced neural network for micro-particle manipulation Article Snippet: Applied Acoustics 214 (2023) 109714 Contents lists available at ScienceDirect Applied Acoustics journal homepage: www.elsevier.com/locate/apacoust Multi-plane acoustic hologram generation with a physics-enhanced neural network for micro-particle manipulation Rujun Zhang a,b, Feiyan Cai a,∗, Qin Lin c, Yiying Mo c, Hairong Zheng a a Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China b University of Chinese Academy of Sciences, Beijing, 100049, China c School of Biomedical Engineering, Guangdong Medical University, Dongguan, 523808, China A R T I C L E I N F O A B S T R A C T Keywords: Acoustic hologram Multi-plane Deep learning Acoustic field reconstruction A physics-enhanced multi-plane acoustic hologram deep neural network (PhysenNet_MPAH) approach is proposed for generating multi-plane acoustic hologram.. By combining a convolutional neural network with a physical model, the PhysenNet_MPAH approach can generate high-quality acoustic holograms for holographic rendering of targeted acoustic intensity fields at multiple planes.. This approach is capable of reconstructing both strong and weak-targeted multi-plane fields, with superior quality compared to traditional iterative angular spectrum approach. Article Title: Tuning the mechanical behaviour of additively manufactured meta-materials with twinning and meta-harmonics Article Snippet: Relative property Scaling law Ideal exponent Behaviour Modulus, E̅ = E∗ Es E̅ = Ar̅a a = 2 Bending- dominated a = 1 Stretch- dominated Yield strength, s̅y = s∗ ss s̅y = Br̅b 3 2 ≤ b ≤ 2 Bending- dominated b = 1 Stretch- dominated Elastic buckling strength, s̅el = s∗el Es s̅el = Cr̅c c = 2 Bucklingdominated using Rigid 4K resin by SLA in a Form Article Title: Instructional dermatology surface models and methods of use Article Snippet: Various suitable 3D printers are known in the art, including but not limited to, Article Title: The generation of acoustic multi-vortex beams using a phase-only holographic lens Article Snippet: After the thickness map is computed, it can be printed using white resin material via a Article Title: PACS: Projection-driven with Adaptive CADs X-ray Scatter compensation for additive manufacturing inspection Article Snippet: The scanned object was an AM component, specifically a bevel gear (as shown in Fig. 1), which was fabricated using a Form |