scholarly journals Thin-Walled Compliant Mechanism Component Design Assisted by Machine Learning and Multiple Surrogates

2015 ◽  
Author(s):  
Kai Liu ◽  
Andres Tovar ◽  
Emily Nutwell ◽  
Duane Detwiler
Keyword(s):  
Machine Learning ◽  
Compliant Mechanism ◽  
Component Design ◽  
Thin Walled ◽  
Mechanism Component

scholarly journals Design of progressively folding thin-walled tubular components using compliant mechanism synthesis

2015 ◽  
Vol 95 ◽  
pp. 208-220 ◽  
Author(s):  
Punit Bandi ◽  
Duane Detwiler ◽  
James P. Schmiedeler ◽  
Andrés Tovar
Keyword(s):  
Compliant Mechanism ◽  
Mechanism Synthesis ◽  
Thin Walled ◽  
Tubular Components

Overview and Kinetostatic Characterization of Compliant Shell Mechanism Building Blocks

2020 ◽  
Vol 12 (6) ◽  
Author(s):  
Joep P. A. Nijssen ◽  
Giuseppe Radaelli ◽  
Charles J. Kim ◽  
Just L. Herder
Keyword(s):  
Mechanism Design ◽  
Compliant Mechanism ◽  
Building Blocks ◽  
Concept Design ◽  
Spatial Mechanism ◽  
Thin Walled Structures ◽  
Spatial Geometry ◽  
Thin Walled ◽  
Compliant Mechanism Design

Abstract Compliant shell mechanisms utilize thin-walled structures to achieve motion and force generation. Shell mechanisms, because of their thin-walled nature and spatial geometry, are building blocks for spatial mechanism applications. In spatial compliant mechanism design, the ratio of compliance is the representation of the kinetostatics involved. Using shell mechanisms in concept design, however, can prove difficult without a uniform characterization method. In this article, we make use of compliance ellipsoids to achieve characterization of the ratio of compliance for shell mechanisms. Ten promising shells are presented with the kinetostatic characteristics, combined with a uniform method of determining the kinetostatic characteristics for other unknown shells. Finally, we show how shells are indeed a valid alternative in the spatial mechanism design, compared to conventional flexure mechanisms.

Reaping the benefits of machine learning pattern recognition in nanophotonic component design

2019 ◽  
Author(s):  
Yuri Grinberg ◽  
Daniele Melati ◽  
Mohsen Kamandar Dezfouli ◽  
Siegfried Janz ◽  
Jens Schmid ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Learning Pattern ◽  
Component Design

Proposal of Rationalized Assessment Procedure for Buckling of Thin-Walled Cylindrical Tanks

2007 ◽  
Vol 120 ◽  
pp. 199-206
Author(s):  
Hitohsi Kaguchi ◽  
Koji Hamada ◽  
Akihisa Sugiyama ◽  
Hideyuki Morita ◽  
Koji Setta ◽  
...  
Keyword(s):  
Nuclear Power ◽  
Power Plants ◽  
Design Procedure ◽  
Absorption Capacity ◽  
Assessment Procedure ◽  
Storage Tanks ◽  
Design Guideline ◽  
Liquid Storage ◽  
Component Design ◽  
Thin Walled

As for thin walled cylindrical liquid storage tanks in nuclear power plants, the current elastic design guideline against seismic loading might result in too conservative component design. Therefore, it is thought possible to make the design procedure more reasonable by taking dynamic response reduction into account. Experiments using scaled models as well as numerical analyses were carried out, and seismic behavior of thin walled cylindrical liquid storage tanks was simulated to investigate energy absorption capacity and seismic resistance of those tanks. Based on the test and analysis results, assessment procedure for buckling considering post-buckling behavior has been proposed.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining
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