scholarly journals Design optimization of piezoresistive cantilevers for force sensing in air and water

2009 ◽  
Vol 106 (6) ◽  
pp. 064310 ◽  
Author(s):  
Joseph C. Doll ◽  
Sung-Jin Park ◽  
Beth L. Pruitt
Keyword(s):  
Design Optimization ◽  
Force Sensing ◽  
Piezoresistive Cantilevers

Direct Measurement of the Binding Force between Microfabricated Particles and a Planar Surface in Aqueous Solution by Force-Sensing Piezoresistive Cantilevers

Langmuir ◽  
2005 ◽  
Vol 21 (24) ◽  
pp. 11251-11261 ◽  
Author(s):  
Hiroaki Onoe ◽  
Murat Gel ◽  
Kazunori Hoshino ◽  
Kiyoshi Matsumoto ◽  
Isao Shimoyama
Keyword(s):  
Aqueous Solution ◽  
Direct Measurement ◽  
Force Sensing ◽  
Planar Surface ◽  
Binding Force ◽  
Piezoresistive Cantilevers

Piezoresistive Cantilever Optimization and Applications

2009 ◽  
Vol 1222 ◽  
Author(s):  
Joseph C. Doll ◽  
Sung-Jin Park ◽  
Nahid Harjee ◽  
Ali J. Rastegar ◽  
Joseph R. Mallon ◽  
...  
Keyword(s):  
Ion Implantation ◽  
Design Optimization ◽  
Low Cost ◽  
Analytical Models ◽  
Design Parameters ◽  
Scanning Gate Microscopy ◽  
Scanning Gate ◽  
Piezoresistive Cantilevers ◽  
Force Displacement

AbstractPiezoresistors are commonly used in microsystems for transducing force, displacement, pressure and acceleration. Silicon piezoresistors can be fabricated using ion implantation, diffusion or epitaxy and are widely used for their low cost and electronic readout. However, the design of piezoresistive cantilevers is complicated by coupling between design parameters as well as fabrication and application constraints. Here we discuss analytical models and design optimization for piezoresistive cantilevers, and describe several applications ranging from studying electron movement using scanning gate microscopy to measuring the biomechanics of whole organisms.

Rubber Composition–Properties Relationships during Tire Numerical Simulation and Design Optimization

2017 ◽  
Vol 45 (1) ◽  
pp. 71-84 ◽  
Author(s):  
Alexey Mazin ◽  
Alexander Kapustin ◽  
Mikhail Soloviev ◽  
Alexander Karanets
Keyword(s):  
Numerical Simulation ◽  
Design Optimization ◽  
Strain Tensor ◽  
General Purpose ◽  
Orthogonal Functions ◽  
Element Analysis ◽  
Time Investment ◽  
Footprint Analysis ◽  
Composition Properties ◽  
Nonlinear Elastic

ABSTRACT Numerical simulation based on finite element analysis is now widely used during the design optimization of tires, thereby drastically reducing the time investment in the design process and improving tire performance because it is obtained from the optimized solution. Rubber material models that are used in numerical calculations of stress–strain distributions are nonlinear and may include several parameters. The relations of these parameters with rubber formulations are usually unknown, so the designer has no information on whether the optimal set of parameters is reachable by the rubber technological possibilities. The aim of this work was to develop such relations. The most common approach to derive the equation of the state of rubber is based on the expansion of the strain energy in a series of invariants of the strain tensor. Here, we show that this approach has several drawbacks, one of which is problems that arise when trying to build on its basis the quantitative relations between the rubber composition and its properties. An alternative is to use a series expansion in orthogonal functions, thereby ensuring the linear independence of the coefficients of elasticity in evaluation of the experimental data and the possibility of constructing continuous maps of “the composition to the property.” In the case of orthogonal Legendre polynomials, the technique for constructing such maps is considered, and a set of empirical functions is proposed to adequately describe the dependence of the parameters of nonlinear elastic properties of general-purpose rubbers on the content of the main ingredients. The calculated sets of parameters were used in numerical tire simulations including static loading, footprint analysis, braking/acceleration, and cornering and also in design optimization procedures.

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