TO IMPROVE LEARNING EFFECT IN MECHANICAL COMPONENT DESIGN BY 3D PRINTING— ASSISTIVE DEVICE DESIGN AS AN EXAMPLE

Author(s):  
Cheng-Jung Yan ◽  
◽  
Shing-Jye Chen ◽  
Ru-Mei Hsieh ◽  
◽  
...  
2013 ◽  
Vol 365-366 ◽  
pp. 28-31
Author(s):  
Li Yang Xie ◽  
Wen Xue Qian ◽  
Ning Xiang Wu

Taking into account the uncertainty in material property and component quality, a complex mechanical component such as a gear should be treated as a series system instead of a component when evaluating its reliability, since there exist many sites of equal likelihood to fail. Besides, conventional system reliability model is not applicable to such a system because of the statistical dependence among the failures of the every element (damage site). The present paper presents a model to estimate complex mechanical component reliability by incorporating order statistic of element strength into load-strength interference analysis, which can deal with multiple failure mechanisms, reflect statistical dependence among element failure events and that among different failure modes.


Author(s):  
Andrea Kratz ◽  
Marc Schoeneich ◽  
Valentin Zobel ◽  
Bernhard Burgeth ◽  
Gerik Scheuermann ◽  
...  

2020 ◽  
Author(s):  
Colter S. Reed ◽  
Donald A. Smith

BioTechniques ◽  
2021 ◽  
Author(s):  
Vedika J Shenoy ◽  
Chelsea ER Edwards ◽  
Matthew E Helgeson ◽  
Megan T Valentine

3D printing holds potential as a faster, cheaper alternative compared with traditional photolithography for the fabrication of microfluidic devices by replica molding. However, the influence of printing resolution and quality on device design and performance has yet to receive detailed study. Here, we investigate the use of 3D-printed molds to create staggered herringbone mixers (SHMs) with feature sizes ranging from ∼100 to 500 μm. We provide guidelines for printer calibration to ensure accurate printing at these length scales and quantify the impacts of print variability on SHM performance. We show that SHMs produced by 3D printing generate well-mixed output streams across devices with variable heights and defects, demonstrating that 3D printing is suitable and advantageous for low-cost, high-throughput SHM manufacturing.


IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
I-Jan Wang ◽  
Bor-Shing Lin ◽  
Chi-Ming Wu ◽  
Chia-Hung Yeh ◽  
Shu-Ting Hsu
Keyword(s):  

2019 ◽  
Vol 13 (4) ◽  
Author(s):  
Emerson Paul Grabke ◽  
Kei Masani ◽  
Jan Andrysek

Abstract Many individuals with lower limb amputations or neuromuscular impairments face mobility challenges attributable to suboptimal assistive device design. Forward dynamic modeling and simulation of human walking using conventional biomechanical gait models offer an alternative to intuition-based assistive device design, providing insight into the biomechanics underlying pathological gait. Musculoskeletal models enable better understanding of prosthesis and/or exoskeleton contributions to the human musculoskeletal system, and device and user contributions to both body support and propulsion during gait. This paper reviews current literature that have used forward dynamic simulation of clinical population musculoskeletal models to perform assistive device design optimization using optimal control, optimal tracking, computed muscle control (CMC) and reflex-based control. Musculoskeletal model complexity and assumptions inhibit forward dynamic musculoskeletal modeling in its current state, hindering computational assistive device design optimization. Future recommendations include validating musculoskeletal models and resultant assistive device designs, developing less computationally expensive forward dynamic musculoskeletal modeling methods, and developing more efficient patient-specific musculoskeletal model generation methods to enable personalized assistive device optimization.


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