scholarly journals Reducing the Structural Mass of Large Direct Drive Wind Turbine Generators through Triply Periodic Minimal Surfaces Enabled by Hybrid Additive Manufacturing

2021 ◽  
Vol 3 (1) ◽  
pp. 227-242
Author(s):  
Austin C. Hayes ◽  
Gregory L. Whiting
Keyword(s):  
Additive Manufacturing ◽  
Minimal Surfaces ◽  
Fused Deposition Modeling ◽  
Functionally Graded ◽  
Image Correlation ◽  
Direct Drive ◽  
Baseline Design ◽  
Fused Deposition ◽  
Triply Periodic Minimal Surfaces ◽  
Triply Periodic

As the power output of direct drive generators increases, they become prohibitively large with much of this material structural support. In this work, implicit modeling was coupled to finite element analysis through a genetic algorithm variant to automate lattice optimization for the rotor of a 5 MW permanent magnet direct drive generator for mass reduction. Three triply periodic minimal surfaces (TPMS) were chosen: Diamond, Schwartz Primitive, and Gyroid. Parameter and functionally graded lattice optimization were employed to reduce mass within deflection criteria. Inactive mass for the 5 MW Diamond, Schwartz Primitive, and Gyroid optimized designs was 10,043, 10,858, and 10,990 kg, respectively. The Schwartz Primitive rotor resulted in a 34% reduction in inactive mass compared to a 5 MW baseline design. Radial and axial deflections were below the critical limit of 0.65 and 32.17 mm, respectively. The lowest torsional deflection was seen in the Schwartz Primitive TPMS lattice at 3.89 mm. Based on these designs, hybrid additive manufacturing with investment casting was used to validate manufacturability in metal. A fused deposition modeling (FDM) TPMS topology was printed for validation of the FEA results. Comparison between digital image correlation of the FDM printed design and FEA design resulted in a 6.7% deformation difference for equivalent loading conditions.

Investigation of the Functional Properties of Additively-Fabricated Triply Periodic Minimal Surface-Based Bone Scaffolds for the Treatment of Osseous Fractures

2021 ◽  
Author(s):  
Abigail Chaffins ◽  
Mohan Yu ◽  
Pier Paolo Claudio ◽  
James B. Day ◽  
Roozbeh (Ross) Salary
Keyword(s):  
Mechanical Properties ◽  
Additive Manufacturing ◽  
Fused Deposition Modeling ◽  
Fused Deposition ◽  
Bone Scaffolds ◽  
Periodic Minimal Surface ◽  
Triply Periodic ◽  
Triply Periodic Minimal Surface ◽  
Deposition Modeling

Abstract Fused deposition modeling (FDM), is a direct-write material extrusion additive manufacturing process, which has emerged as a method of choice for the fabrication of a wide range of biological tissues and structures. FDM allows for non-contact, multi-material deposition of a broad spectrum of functional materials for tissue engineering applications. However, the FDM process is intrinsically complex, consisting of a multitude of parameters as well as material-machine interactions, which may adversely influence the mechanical properties, the surface morphology, and ultimately the functional integrity of fabricated bone scaffolds. Hence, process optimization in addition to physics-based characterization of the FDM process would be inevitably a need. The overarching goal of this research work is to fabricate biocompatible, porous bone scaffolds, incorporating autologous human bone marrow mesenchymal stem cells (hBMSCs), for the treatment of osseous fractures, defects, and eventually diseases. The objective of this work is to investigate the mechanical properties of several triply periodic minimal surface (TPMS) bone scaffolds, fabricated using fused deposition modeling (FDM) additive manufacturing process. In this study, biocompatible TPMS bone scaffolds were FDM-deposited, based on a medical-grade polymer composite, composed of polyamide, polyolefin, and cellulose fibers (named PAPC-II). In addition, the experimental characterization of the TPMS bone scaffolds was on the basis of a single factor experiment. The compression properties of the fabricated bone scaffolds were measured using a compression testing machine. Furthermore, a digital image processing program was developed in the MATLAB environment to characterize the morphological properties of the fabricated bone scaffolds.

Additive manufacturing of architected catalytic ceramic substrates based on triply periodic minimal surfaces

2019 ◽  
Vol 102 (10) ◽  
pp. 6176-6193 ◽  
Author(s):  
Oraib Al‐Ketan ◽  
Marco Pelanconi ◽  
Alberto Ortona ◽  
Rashid K. Abu Al‐Rub
Keyword(s):  
Additive Manufacturing ◽  
Minimal Surfaces ◽  
Ceramic Substrates ◽  
Triply Periodic Minimal Surfaces ◽  
Triply Periodic

Layered infill area generation from triply periodic minimal surfaces for additive manufacturing

2019 ◽  
Vol 107 ◽  
pp. 50-63 ◽  
Author(s):  
Jiawei Feng ◽  
Jianzhong Fu ◽  
Zhiwei Lin ◽  
Ce Shang ◽  
Xiaomiao Niu
Keyword(s):  
Additive Manufacturing ◽  
Minimal Surfaces ◽  
Triply Periodic Minimal Surfaces ◽  
Triply Periodic

scholarly journals Automated Testing and Characterization of Additive Manufacturing (ATCAM)

2021 ◽  
Author(s):  
Arash Alex Mazhari ◽  
Randall Ticknor ◽  
Sean Swei ◽  
Stanley Krzesniak ◽  
Mircea Teodorescu
Keyword(s):  
Additive Manufacturing ◽  
Fused Deposition Modeling ◽  
Cell System ◽  
Automated Testing ◽  
Fused Deposition ◽  
Deposition Modeling ◽  
Machine Calibration

AbstractThe sensitivity of additive manufacturing (AM) to the variability of feedstock quality, machine calibration, and accuracy drives the need for frequent characterization of fabricated objects for a robust material process. The constant testing is fiscally and logistically intensive, often requiring coupons that are manufactured and tested in independent facilities. As a step toward integrating testing and characterization into the AM process while reducing cost, we propose the automated testing and characterization of AM (ATCAM). ATCAM is configured for fused deposition modeling (FDM) and introduces the concept of dynamic coupons to generate large quantities of basic AM samples. An in situ actuator is printed on the build surface to deploy coupons through impact, which is sensed by a load cell system utilizing machine learning (ML) to correlate AM data. We test ATCAM’s ability to distinguish the quality of three PLA feedstock at differing price points by generating and comparing 3000 dynamic coupons in 10 repetitions of 100 coupon cycles per material. ATCAM correlated the quality of each feedstock and visualized fatigue of in situ actuators over each testing cycle. Three ML algorithms were then compared, with Gradient Boost regression demonstrating a 71% correlation of dynamic coupons to their parent feedstock and provided confidence for the quality of AM data ATCAM generates.

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