scholarly journals Influence of the Porosity of Polymer Foams on the Performances of Capacitive Flexible Pressure Sensors

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 1968 ◽  
Author(s):  
Sylvie Bilent ◽  
Thi Hong Nhung Dinh ◽  
Emile Martincic ◽  
Pierre-Yves Joubert
Keyword(s):  
Experimental Data ◽  
Linear Model ◽  
Dielectric Material ◽  
Pressure Sensors ◽  
Volume Ratio ◽  
Measurement Range ◽  
Polymer Foams ◽  
First Order ◽  
Non Linear ◽  
Flexible Pressure Sensors

This paper reports on the study of microporous polydimethylsiloxane (PDMS) foams as a highly deformable dielectric material used in the composition of flexible capacitive pressure sensors dedicated to wearable use. A fabrication process allowing the porosity of the foams to be adjusted was proposed and the fabricated foams were characterized. Then, elementary capacitive pressure sensors (15 × 15 mm2 square shaped electrodes) were elaborated with fabricated foams (5 mm or 10 mm thick) and were electromechanically characterized. Since the sensor responses under load are strongly non-linear, a behavioral non-linear model (first order exponential) was proposed, adjusted to the experimental data, and used to objectively estimate the sensor performances in terms of sensitivity and measurement range. The main conclusions of this study are that the porosity of the PDMS foams can be adjusted through the sugar:PDMS volume ratio and the size of sugar crystals used to fabricate the foams. Additionally, the porosity of the foams significantly modified the sensor performances. Indeed, compared to bulk PDMS sensors of the same size, the sensitivity of porous PDMS sensors could be multiplied by a factor up to 100 (the sensitivity is 0.14 %.kPa−1 for a bulk PDMS sensor and up to 13.7 %.kPa−1 for a porous PDMS sensor of the same dimensions), while the measurement range was reduced from a factor of 2 to 3 (from 594 kPa for a bulk PDMS sensor down to between 255 and 177 kPa for a PDMS foam sensor of the same dimensions, according to the porosity). This study opens the way to the design and fabrication of wearable flexible pressure sensors with adjustable performances through the control of the porosity of the fabricated PDMS foams.

A Method of Computation for Application to Nonlinear Problems

1980 ◽  
Vol 17 (1) ◽  
pp. 77-84
Author(s):  
K. F. Browne ◽  
I. Kirkland
Keyword(s):  
Experimental Data ◽  
Differential Equations ◽  
Nonlinear Problems ◽  
The Self ◽  
Computer Programme ◽  
First Order ◽  
Non Linear ◽  
First Order Differential Equations

The solution of first-order differential equations with non-linear coefficients is assisted by a computer programme which generates sets of curves and their slopes from experimental data. An example predicts the self-excitation curves of a d.c. shunt-generator.

Identification of Non-Linear Force Coefficients for the Radial Motion of a Squeeze Film Damper

1994 ◽  
Vol 208 (4) ◽  
pp. 235-245 ◽  
Author(s):  
J X Zhang ◽  
J B Roberts
Keyword(s):  
Experimental Data ◽  
Linear Model ◽  
Radial Motion ◽  
Squeeze Film ◽  
Static Force ◽  
Fluid Force ◽  
Squeeze Film Damper ◽  
Force Coefficients ◽  
Supply Pressure ◽  
Non Linear

The fluid force generated in a squeeze film damper undergoing large amplitude radial motion is described in terms of non-linear hydrodynamic inertial and damping coefficients, together with afluid static force. Linear-in-the-parameter polynomial forms are introduced to represent the variation of these contributions with radial position. A generalized state variable filter identification method is developed which enables all the parameters in the non-linear model to be estimated from experimental data. The method is validated by processing simulated data and then applied to some new experimental data. Experimental results, relating to the influence of the supply pressure and the operating frequency on the coefficients, are presented and discussed. Comparisons are made with corresponding predictions derived from existing lubrication theory. The parametric non-linear model is found to give a good fit to experimental data over a significant region within the vicinity of the initial static equilibrium position. Through a combination of results, the variation of the fluid force coefficients and the fluid static force with eccentricity, over nearly the whole range of the radial clearance, is obtained. Temporal inertia is found to be more important than convective inertia for motion near the centre of the clearance circle. The existence of a fluid static force, suggested by previous work is confirmed. It is found that this force is linearly proportional to the oil supply pressure.

Distribution of Movement Time in a Target-Aiming Task

1976 ◽  
Vol 43 (2) ◽  
pp. 507-513 ◽  
Author(s):  
Tarald O. Kvålseth
Keyword(s):  
Experimental Data ◽  
Standard Deviation ◽  
Normal Distribution ◽  
Linear Model ◽  
Movement Time ◽  
Arm Movements ◽  
Order Linear ◽  
First Order ◽  
Index Of Difficulty ◽  
The Mean

Various characteristics of the distribution of movement time were analyzed for a task involving serial and rotary arm movements aimed at a target. For experimental data generated from five Ss, (a) the distribution tended to be unimodal and more peaked than a normal distribution, (b) the skewness of the distribution was predominantly positive and (c) the standard deviation, in addition to the mean, of movement time was significantly affected by the complexity of the task as measured by Fitts' index of difficulty, while the skewness and the kurtosis were not. For the average results for the Ss, a first-order linear model with the standard deviation of movement time as the dependent variable and Fitts' index as the independent one explained 67% of the variation in standard deviation as compared to 98% of the variation in mean movement time accounted for by the Fitts' index.

scholarly journals Porous Polymer Based Flexible Pressure Sensors for Medical Applications

Proceedings ◽  
2018 ◽  
Vol 2 (13) ◽  
pp. 849 ◽  
Author(s):  
Sylvie Bilent ◽  
Thi Hong Nhung Dinh ◽  
Emile Martincic ◽  
Pierre-Yves Joubert
Keyword(s):  
Mechanical Properties ◽  
Pressure Sensors ◽  
Behavioral Model ◽  
Measurement Range ◽  
Porous Polymer ◽  
Medical Applications ◽  
Capacitive Pressure Sensor ◽  
Pdms Film ◽  
Flexible Pressure Sensors ◽  
Film Porosity

This paper focuses on the use of microporous PDMS foams as a highly deformable film to improve the sensitivity of flexible capacitive pressure sensor dedicated to wearable use. A fabrication process allowing the mechanical properties of foams to be adjusted is proposed together with a non-linear behavioral model used to objectively estimate the sensor performances in terms of sensitivity and measurement range. Sensors fabricated and characterized in this study show that the sensitivity and the measurement range can be adjusted from 0.14%/kPa up to 13.07%/kPa, and from 594 kPa to 183 kPa, respectively, while the PDMS film porosity ranges from 0% up to 85%.

Statistical method of steepest improvement of response

2018 ◽  
Vol 84 (11) ◽  
pp. 74-87
Author(s):  
V. B. Bokov
Keyword(s):  
Statistical Method ◽  
Linear Model ◽  
Response Prediction ◽  
Initial Experiment ◽  
First Order ◽  
Prediction Response ◽  
Conditional Extremum ◽  
Order Polynomial ◽  
Limiting Values

A new statistical method for response steepest improvement is proposed. This method is based on an initial experiment performed on two-level factorial design and first-order statistical linear model with coded numerical factors and response variables. The factors for the runs of response steepest improvement are estimated from the data of initial experiment and determination of the conditional extremum. Confidence intervals are determined for those factors. The first-order polynomial response function fitted to the data of the initial experiment makes it possible to predict the response of the runs for response steepest improvement. The linear model of the response prediction, as well as the results of the estimation of the parameters of the linear model for the initial experiment and factors for the experiments of the steepest improvement of the response, are used when finding prediction response intervals in these experiments. Kknowledge of the prediction response intervals in the runs of steepest improvement of the response makes it possible to detect the results beyond their limits and to find the limiting values of the factors for which further runs of response steepest improvement become ineffective and a new initial experiment must be carried out.

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