Force Sensing Resistor data for enhancing part-to-part repeatability using a Six Sigma approach

Citation Author(s):
Leonel
Paredes-Madrid
Andres
Garzon Posada
Victor M.
Fontalvo
Angela
Peña Puerto
Submitted by:
Leonel Paredes-...
Last updated:
Tue, 11/16/2021 - 16:05
DOI:
10.21227/jqt0-r832
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Abstract 

These datasets report data of 64 Force Sensing Resistors at multiple voltages. It was foun that the input voltage can be used to trim sensors' sensitivity and ultimately to reduce dispersion. The DMAIC cycle was used to reduce process variability on the basis of the Six Sigma Methodology. The zip folder contains:

1) a Matlab file for loading the data

2) four .txt files with the experimental data of Force Sensing resistors

Instructions: 

Unzip the folder and open the file: "loading_data.m" using matlab, the instructions for the datasets are next described:

The following script loads sensor data for 16 specimens of FSRs at 20 different voltages. The voltage legend is located at the end of this script. The step increment of the force profile is 1 N. 

Please change the name of the "txt" file to load data from more a different set of sensors.  With these data we can assess individual sensor sensitivity at any input voltage.

 

The column format of the txt files is next described:

% Column 1: control data, always zero

% Column 2: control data, incremental numbering

% Column 3: control data, either 1 or -1

% Column 4: nominal force to close the force loop

% Column 5: amplifier output voltage for sensor 1

% Column 6: force applied to sensor 1

% Column 7: amplifier output voltage for sensor 2

% Column 8: force applied to sensor 2

% the same pattern repeats up to sensor 16

% Column 37: timestamp

% Column 38: Input voltage applied to the sensor.

 

Voltage legend

 

% Figura nro    Voltaje

% 1             0

% 2             0.25

% 3             0.5

% 4             0.75

% 5             1

% 6             1.5

% 7             2

% 8             2.5

% 9             3

% 10            3.5

% 11            4

% 12            4.5

% 13            5

% 14            5.5

% 15            6

% 16            6.5

% 17            7

% 18            7.5

% 19            8

% 20            8.5