the Creative Commons Attribution 4.0 License.

the Creative Commons Attribution 4.0 License.

# Dynamic characterization of multi-component sensors for force and moment

### Rolf Kumme

### Rainer Tutsch

An improved set-up for the characterization of multi-component sensors for force and moment is presented. It aims at calibrating such sensors under continuous sinusoidal excitation. Special focus is put on the design of load masses and adapting elements to activate uniaxial force and moment components where possible. To identify the motion and acceleration of the load mass with 6 degrees of freedom, a photogrammetric measurement system is implemented in the existing set-up. Using the set-up described, different experiments are performed to analyse a commercial multi-component sensor and perform a parameter identification for its force components.

The calibration and characterization of sensors for force and moment are typically performed in a static manner (DIN 51309, 2005; ISO 376, 2011). The dynamic behaviour of these sensors may differ significantly from the static behaviour. Especially the sensitivity coefficient, which is necessary to transform the sensor output signal into a force or moment signal, is subject to changes with higher frequencies of the dynamic excitation (Schlegel et al., 2012; Vlajic and Chijioke, 2016). Therefore, if such sensors are to be used in a dynamic environment, their dynamic behaviour needs to be analysed (Bartoli et al., 2012). For force and acceleration sensors, typical characterization methods are sinusoidal excitation (Schlegel et al., 2012) and shock excitation (Bruns et al., 2002). Torque sensors are currently evaluated under sinusoidal excitation (Klaus, 2016).

The aforementioned calibration approaches are limited to uniaxial sensors or calibrations of only one axis of multiaxial or multi-component sensors (MCSs). Such MCSs have become more popular in the last few years, which has resulted in the need for new calibration procedures (Baumgarten et al., 2016; Kim, 2000; Nitsche et al., 2017a). Challenges in the calibration of MCSs are, among other things, the generation of forces and moments with defined directions, the identification of the force and moment vector in a reference system (Röske et al., 2001) and the alignment of sensor and reference coordinate systems (Nitsche et al., 2017b).

For the dynamic calibration of MCSs, very few approaches exist. Park et al. (2008) deployed an electrodynamic shaker set-up with a vertical excitation direction at the Physikalisch-Technische Bundesanstalt (PTB) using different adapting elements to activate sinusoidal excitations in different directions. Schleichert et al. (2016) designed a fixed sensor frame with different adapting directions for an electrodynamic plunger actuator. In both set-ups, the reference force can only be calculated in the direction of excitation. Additional components like rocking modes cannot be detected.

In the following sections, an improved set-up for the dynamic analysis of MCSs is described. The force generation is based on the periodic acceleration of a load mass connected to an MCS. The theory behind sinusoidal calibration is explained using the example of uniaxial force calibration in Sect. 2. The improved set-up includes a specially designed load mass and a three-dimensional acceleration reference based on photogrammetry. Section 3 summarizes the new set-up. A dynamic model of an MCS is described in Sect. 4. Finally, the parameters of the model are identified from experimental data as described in Sect. 5.

The aim of the dynamic calibration of force and moment
sensors is the identification of the parameters needed to describe the
dynamic behaviour and the sensitivity coefficient as a function of the
excitation frequency (Schlegel et al., 2012). The typical model used for the
description of such a sensor is a mass-spring-damper system. The parameters
needed to describe this model are the spring constant *k*_{z}, the damping
parameter *b*_{z} and the mass *m* (Fig. 1).
The mass *m* is composed of the external load mass *m*_{e}, the mass of
adapting elements *m*_{a} and the internal moving mass of the sensor *m*_{1}.

From the given parameters, the dynamic sensitivity *S* can be calculated
according to Schlegel et al. (2012):

with *c* as a factor for unit conversion with the unit U m^{−1} (U
as a sensor signal unit, e.g. mV V^{−1} for strain gauge sensors). In
addition to the sensitivity *S*, the phase shift *φ*_{s} between the
excitation and the sensor output can be calculated as follows:

Both Eqs. (1) and (2) are approximations for a stiff coupling of the top mass to the sensor, which allows the influence of an additional spring-damper system describing the coupling to be neglected.

As an alternative to identifying the model parameters *k* and *b*, the
dynamic sensitivity can be calculated as the ratio of the sensor signal
output *U* and the acting dynamic force *F* (Park et al., 2008):

In this case, only the moving mass *m* and its acceleration *a* are needed to
calculate the acting force:

The measurement set-up for the periodic excitation of MCSs used in this work is based on the shaker set-up located in the dynamic force calibration laboratory at the PTB (Schlegel et al., 2012). It consists of an LDS 10 kN electrodynamic shaker for frequencies up to 2 kHz and a laser scanning vibrometer as an acceleration reference. Different accelerometers can be used as additional acceleration references.

The electrodynamic shaker is only capable of generating accelerations in one direction. To activate force and moment components in all six directions, different adapting elements are needed. The design of such elements is described in Sect. 3.1.

The scanning vibrometer measures accelerations in the direction of the excitation of the shaker system. Accelerations perpendicular to the excitation direction, as well as twisting motions of the load mass, cannot be detected. To rectify this, a photogrammetric measurement system is used to detect accelerations that are not visible to the scanning vibrometer. Details of the photogrammetric system, synchronization and data analysis are described in Sect. 3.2.

## 3.1 Load masses and adapting elements

In past works (Park et al., 2002a, b, 2008), different MCSs were
investigated on the previously described shaker in a similar set-up. This
set-up consisted of a cylindrical load mass in combination with an
air-bearing guide for axial force. Transverse force components were generated
using 90^{∘} angular adapters and a cubic load mass. Bending moments and
torque were generated using a lever arm in combination with the
aforementioned cubic load mass.

Due to its design, this set-up was only capable of generating uniaxial force
components in the axial force direction *F*_{z}. Transverse force components
*F*_{x} and *F*_{y} were superimposed by moment components *M*_{y} and *M*_{x} as a
result of the distance between the sensor coordinate system and the centre of
gravity of the external load mass. Torque components *M*_{z} were superimposed
by bending moments *M*_{x} or *M*_{y} and transverse forces *F*_{y} or *F*_{x}.

To overcome these disadvantages, a special load mass was designed in order to
move the centre of gravity of the load mass to the origin of the coordinate
system of the sensor under test (Nitsche et al., 2018a). Using this load mass,
rocking modes for the axial force component *F*_{z} are reduced and transverse
force components *F*_{x} and *F*_{y} can be generated without superimposed moment
components. The mass of the load mass is ${m}_{\mathrm{L}}=(\mathrm{6.9605}\pm \mathrm{0.0010})$ kg. The angular adapter for activating transverse forces is
designed in order to move the centre of gravity of the whole set-up in the
axis of the shaker table to avoid moment loads on the shaker bearings. The
set-up including load mass, sensor and the adapting element for transverse
force components is shown in Fig. 2.

For the generation of moment components, a beam cross is used. The cross is
designed in a way to move the centre of gravity of the moment generating mass
components to the *x*–*y* plane of the sensor coordinate system. This
results in a superposition of bending moments *M*_{x} or *M*_{y} with only one
axial force *F*_{z} and the superposition of torque *M*_{z} with only one
transverse force *F*_{x} or *F*_{y}. The distance between the load mass and the
sensor can be changed to generate different amplitudes of the moment
component. To reduce moment loads on the shaker bearing, a counter mass is
attached to the base of the angular adapter. The beam cross set-up is shown
in Fig. 3.

## 3.2 Three-dimensional acceleration reference

To evaluate whether signal outputs of inactive
force or moment components of the sensor are a result of signal crosstalk
from the active component or of bending or twisting moments, a complete
three-dimensional movement of the set-up needs to be identified. The scanning
vibrometer is capable of measuring the acceleration in the direction of the
shaker axis at different points of the set-up. From the acceleration
distribution, rocking modes around the *x* and *y* axes can be identified.
Three-dimensional accelerometers can be added to specific points in the
system to obtain three-dimensional acceleration information at that point.
However, the mass of the accelerometer and its cable has an influence on the
dynamic behaviour of the whole set-up.

To extend the acceleration measurement provided by the laser vibrometer and the accelerometers, a photogrammetric measurement set-up is installed. It consists of two stereo camera systems: one camera system observing the load mass on top of the sensor, the other one directed at the surface of the shaker. All four cameras are triggered simultaneously using a signal generator. One master camera is used to trigger two LED strobe lights to illuminate the observed area. This trigger signal is recorded by the analogue to digital converter (ADC) in the junction box of the data acquisition PC of the scanning vibrometer for synchronization purposes. The trigger signal also serves as the time stamp of the image acquisition. The six force and moment signals of the sensor under test are amplified by a 6-channel bridge amplifier (Dewetron, 2012), digitized by a simultaneously sampling 16-channel ADC card (National Instruments, 2012) and saved on a separate data acquisition PC. The camera trigger signal is also recorded by this PC for synchronization. A block diagram of the extended shaker set-up is shown in Fig. 4. A photograph of the sensor with the attached load mass, the shaker and the stereo camera systems is shown in Fig. 5.

Random greyscale patterns are attached to the surfaces observed by the cameras. The images of these patterns are analysed using digital image correlation (DIC) algorithms (Pan et al., 2009). In DIC, the camera images are split into small areas and the correlation between the areas in different camera images is calculated. In this way, one area of the observed surface can be identified in the images of both cameras and at different times. Each stereo set-up is able to calculate the position of multiple areas on the observed surface in space. Those three-dimensional points can be used to calculate a three-dimensional rigid body movement as well as a deformation of the load mass in the observed area.

The rotation and translation of the rigid body transformation are calculated using the singular value decomposition (SVD) method presented by Arun et al. (1987) (Eggert et al., 1997). From the resulting rotation matrix, the rotation angles are calculated in a fixed axis system. This assumption can be used as the rotations around any axis are expected to be very small and therefore do not influence each other.

The cameras used for DIC are industrial CMOS cameras with frame rates of up to 166 images per second for 8-bit greyscale images. At 12-bit colour depth, the frame rate is reduced to a maximum of 82 images per second. This frame rate is too low to satisfy the Nyquist–Shannon sampling theorem for frequencies higher than 40 Hz. However, using the additional knowledge of the excitation frequency of the shaker and the precise adjustment of the sampling rate of the cameras, both frequencies can be adjusted to achieve a beat frequency.

The known frequency distribution resulting from the vibrometer measurement is
used to define a fitting function for the displacement of the load mass
*S*(*t*):

with *f*_{i} as the identified frequencies of the frequency distribution,
${\widehat{s}}_{i}$ the amplitude, *φ*_{i} the phase shift of each frequency and
*o* the offset of the mean. The parameters $\widehat{s}$, *φ* and *o* can
be estimated by a least squares fitting algorithm. The resulting function
*S*(*t*) represents the axial or angular displacement of the load mass over
time.

## 3.3 Experimental analysis of the set-up

An experimental evaluation of the set-up described
is performed using the axial force components. The excitation frequency is
set to *f*_{ex}=106.25 Hz. The amplitude of the acceleration
signal is 0.5 V. The laser vibrometer sampling frequency is 51.2 kHz
and the cameras are triggered with 52.874 Hz. Figure 6
shows the acceleration signal recorded from the laser vibrometer, a fitted
sine function and the camera trigger signal as a synchronization reference.
The parameters of the sine fitting are listed in Table 1. The
camera trigger is started manually after starting the vibrometer recording.
The trigger signal at 0.97 s represents the first image acquired by
the cameras.

The difference between the camera frequency and the excitation frequency results in a beat of 0.502 Hz. The first camera image is taken as a reference image against which all displacements are calculated. Each camera acquires 211 images, which results in 210 displacement measurements within 3.97 s.

For each displacement measurement, the rigid body transformation is calculated as described in Sect. 3.2. As a result, three axial translations and three rotation angles along the coordinate system of the camera set-up are obtained. For each displacement measurement, the mean of the exposure time, which is identified from the strobe trigger signal, is used as the time of acquisition. The data sets of acquisition time and axial and angular displacement are used to fit a sine function according to the excitation frequency of the shaker.

Sine fitting is performed using a nonlinear least squares fitting function,
*curve fit*, of the Python scipy optimize package. Starting values for
the fitting frequencies are taken from the frequency spectrum of the
vibrometer reference. Figure 7 shows the frequencies of the
fast Fourier transform (FFT) up to
1000 Hz.

The main contributions can be seen at the excitation frequency *f*_{ex}=106.25 Hz and its multiples 425, 212.5 and
318.75 Hz. The amplitude at the second frequency (425 Hz)
reaches a value of 0.5 % of the amplitude of the excitation
frequency.

Sine fitting of only the excitation frequency *f*_{ex}=106.25 Hz results in a standard deviation of the difference between
the fitting function and the measured displacements of 0.0036 mm.
Using a fitting function of the two main frequencies 106.25 and
425 Hz reduces the standard deviation of the residuum to
0.0023 mm. Additional fitting frequencies show no further improvement
of the results. The displacement in *z*, measured with the photogrammetry
set-up and the fitted sine function, is shown in Fig. 8.
Figure 9 shows the axial and angular displacement
along the *x* axis. The resulting parameters from the sine fitting are listed
in Table 1.

To calculate the resulting force and moment components on the sensor, the axial and angular acceleration of the load mass is needed. Both values can be calculated from the fitting function of the displacement as the second derivative with respect to time. For a sine function, the second derivative can easily be calculated by multiplying the function by the negative square of the angular frequency ${\mathit{\omega}}_{i}=\mathrm{2}\cdot \mathit{\pi}\cdot {f}_{\mathrm{i}}$. The acceleration of the fitting function of Eq. (5) results in

From the values of the fitting function listed in Table 1,
the amplitude of the acceleration ${\widehat{a}}_{i}=-{\mathit{\omega}}^{\mathrm{2}}\cdot {\widehat{s}}_{i}$ from
the photogrammetric measurement is calculated to be ${\widehat{a}}_{\mathrm{1}}=-\mathrm{190.6131}$ m s^{−2} and ${\widehat{a}}_{\mathrm{2}}=\mathrm{28.5161}$ m s^{−2}. While
the acceleration at the excitation frequency shows a small deviation of
0.68 % to the reference value of the vibrometer, the acceleration
at 425 Hz differs significantly. According to the reference
acceleration, the amplitude of the second frequency has a minor influence on
the overall acceleration and will be neglected in the subsequent analysis. It
must however be included in the pending uncertainty budget.

For uniaxial force sensors, a dual-mass oscillator as shown in Fig. 1 can be used as an acceptable description of the dynamic behaviour (Kumme, 1996; Schlegel et al., 2012). MCSs show a more complex structure which results in a more complex physical model. To reduce the complexity of the model, the observed six-component MCS is split into two independent models, one for the three force components, one for the moment components.

## 4.1 Dynamic MC force model

The dynamic model for a three-component force
sensor is based on a superposition of three orthogonally aligned
spring-damper systems connected to two masses *m*_{1} and *m*_{2}. A simplified
illustration of a two-dimensional case of this model is shown in Fig. 10. Mutual influences between the spring-damper systems
are neglected. To describe this model, eight different parameters are needed:
three spring constants *k*_{x}, *k*_{y}, *k*_{z}, three damping parameters *b*_{x},
*b*_{y}, *b*_{z} and the two masses *m*_{1} and *m*_{2}. The masses are assumed to be
equal for excitation in the *x*, *y* and *z* directions.

## 4.2 Dynamic MC moment model

A dynamic model for a three-component moment sensor is more complex than the model for a three-component force sensor. Basically, the model from Fig. 10 can be adapted by changing the springs and dampers of the model from linear to rotational ones. The exchange of the masses is more difficult, as the mass moment of inertia is needed for dynamic moment measurements. This parameter depends on a rotational axis and therefore it cannot be assumed to be equal for the different excitation directions.

## 5.1 Determination of the internal mass

To determine the internal mass *m*_{1} of the sensor,
the sensor is mounted onto an indexing head with a horizontal rotation axis.
The *z* axis of the sensor is aligned with the rotation axis of the indexing
head. Sensor readings are recorded without an attached load mass. The sensor
is rotated in steps of 22.5^{∘}. The sensor readings are
transformed into force and moment values using the calibration matrix
provided by the sensor manufacturer. A sine function is fitted to the force
values for *F*_{x} and *F*_{y}. The amplitude of the sine function represents the
maximum force on each channel resulting from the internal mass of the sensor.
The mass itself is calculated from the amplitude, divided by the local
acceleration of gravity *g*_{loc}:

Force readings and the sine fitting for *F*_{x} and *F*_{y} are shown in
Fig. 11. The resulting amplitude of the sine function is
7.558 N for *F*_{x} and 7.532 N for *F*_{y}, resulting in
internal masses of *m*_{1x}=0.770 kg and *m*_{1y}=0.768 kg.

The described experiment is repeated with the load mass shown in
Fig. 2 attached to the sensor. Including the attached load
mass, the amplitude of the sine function is 75.877 N for *F*_{x} and
75.736 N for *F*_{y}. The mass of the attached load mass is calculated
according to Eq. (7) to *m*_{Lx}=6.962 and
*m*_{Ly}=6.951 kg, which differ by 0.028 % and
−0.139 % respectively from the weighted mass
*m*_{L}=6.9605 kg.

## 5.2 Determination of the spring constant and damping coefficient

The spring constant is calculated according to
the method presented by Schlegel et al. (2012) from the transfer function
*H*(*f*) as the ratio of the acceleration on the load mass *a*_{t} and the
acceleration of the shaker surface *a*_{b}:

The frequency of the maximum of the transfer function represents the
resonance frequency *f*_{0}, from which the spring constant can be calculated:

The transfer function *H*(*f*) for *F*_{z}, resulting from a periodic chirp
excitation with two different load masses *m*_{1}=4.236 kg and
*m*_{2}=8.798 kg, is shown in Fig. 12. Resonance
frequencies are identified at ${f}_{\mathrm{0},\mathrm{1}}=\mathrm{1477.35}$ Hz and
${f}_{\mathrm{0},\mathrm{2}}=\mathrm{1092.97}$ Hz. With the additional internal mass
*m*_{i}=0.769 kg, the spring constant according to
Eq. (9) is calculated to be ${k}_{\mathrm{1}}=\mathrm{4.313}\times {\mathrm{10}}^{\mathrm{8}}$ N m^{−1} and ${k}_{\mathrm{2}}=\mathrm{4.512}\times {\mathrm{10}}^{\mathrm{8}}$ N m^{−1}.

Figure 13 shows the transfer function *H*(*f*) for
excitation in *F*_{x} and *F*_{y}. Because of the horizontal installation of the
sensor (Fig. 2), the transfer function is calculated as the
ratio of the acceleration of the load mass and the acceleration of the
mounting adapter connected to the sensor. The figure shows two resonance
peaks for each load mass, which can be explained by the mounting set-up. The
connection between the horizontal mounting adapter connected to the sensor
and the angular adapter introduces a second spring-damper system into the
model. It can be seen that the resonance frequencies are identical for
excitation in *F*_{x} and *F*_{y}, while the amplitudes of the resonance peaks
differ for the higher resonance frequencies. The identified resonance
frequencies and the resulting spring coefficients are listed in
Table 2.

In Schlegel et al. (2012), the damping coefficient of the spring-damper model is calculated using the ratio of the resonance frequency and the width of the resonance peak of the transfer function at half the amplitude:

For the given transfer function in Fig. 12, the damping
coefficients are calculated to be *b*_{1}=699.18 kg s^{−1} and
*b*_{2}=1566.01 kg s^{−1}. The damping coefficients for excitation in
*x* and *y* are listed in Table 2.

## 5.3 Determination of the dynamic sensitivity and phase shift

To calculate the dynamic sensitivity of the
sensor, sinusoidal excitations at different frequencies are used.
Measurements are performed at 12 different frequencies from 53.7 to
1020.3 Hz. The sensitivity *S* is calculated from the ratio of the
amplitude of the sensor signal $\widehat{s}$, the top mass *m*_{t} and the
amplitude of the top mass acceleration ${\widehat{a}}_{\mathrm{t}}$:

The acceleration ${\widehat{a}}_{\mathrm{t}}$ is measured at eight different positions on the top mass using the laser vibrometer.

The phase shift between the acceleration of the top mass *a*_{t} and the sensor
signal *s* is calculated from the phase of the sine fitting functions of the
sensor and the vibrometer signal. The synchronization between the signal
sources is performed using the camera trigger signal as described in Sect. 3.2.

Figure 14 shows the mean value of the dynamic sensitivity and the phase shift over the eight acceleration measurements and the resulting standard deviation. The static sensitivity from the sensor calibration data is given as a reference. A rocking mode occurs at a frequency of 375.9 Hz, which leads to higher deviations for the acceleration measurements in the outer areas of the load mass. The rocking motion can also be seen in the higher standard deviation of the phase shift values. Up to the excitation frequency of 1000 Hz, the sensitivity drops to 95.3 % of the static value. The phase shift drops to −0.25 rad at 800 Hz. For higher frequencies up to 1000 Hz, the phase shift does not change significantly.

An extended set-up for the dynamic calibration of multi-component sensors for force and moment measurement has been described. It is based on the periodic acceleration of a sensor and an attached load mass on an electrodynamic shaker. In comparison to earlier works, the design of the load mass and adapting elements was focused on activating single force and moment components where possible. Force components can be activated using a load mass with its centre of gravity in the origin of the sensor coordinate system. For the moment components, a beam cross with a movable load mass was designed.

To identify the movement of the load mass with 6 degrees of freedom, a
photogrammetric set-up was installed in the existing laboratory set-up. Two
stereo camera systems observe the load mass and the shaker surface. From the
displacement of the observed surfaces and the time stamp of the camera
images, accelerations can be calculated. With the additional information of
the excitation frequency and reference measurements using a laser
interferometer, the displacement of the set-up can be calculated even with
camera frame rates lower than the excitation frequency. From an experimental
evaluation, a deviation of 0.68 % of the acceleration was achieved
for an excitation frequency of *f*_{ex}=106.25 Hz.

The dynamic parameters of the sensor are identified based on a
three-dimensional mass-spring-damper system. The internal mass of the sensor
was calculated in a static manner from measurements at different rotation
angles around the *z* axis. The resulting internal masses of *x*=0.77 and
*y*=0.768 kg differ by 0.3 %. The spring constant was
calculated from a transfer function as the ratio of the top mass acceleration
and the acceleration of the shaker surface. For two different load masses,
the resulting spring constant differs by 4.4 % for *F*_{z}. The
spring constant for excitation in the *x* and *y* directions shows small
deviations of less than 0.5 % for the different orientations with
the same load mass, while the difference at different load masses for the
same orientation rises to up to 15 %. It has to be pointed out
that the adapting elements for excitation in the *x* and *y* directions are
still subject to optimization. At frequencies from 350 to 500 Hz,
pitching motions occur which show an influence on the measurements.

The dynamic sensitivity of the sensor was analysed for the *F*_{z} component.
In this set-up, a rocking motion is visible at 375.9 Hz, which
increases the standard deviation of the measured accelerations on the top
mass. Up to a frequency of 1000 Hz, the dynamic sensitivity drops
to 95.3 % of the static value. The phase shift between the sensor
signal and the top mass acceleration drops to −0.25 rad at
frequencies of 800 Hz.

As a next step, the analysis performed will be extended to the moment
excitation. The previously described beam cross and moment excitation mass
are currently being manufactured. Further optimization of the horizontal
set-up to reduce pitching motions is suggested to reduce signal crosstalk.
Alternatively, a three-dimensional shaker set-up can be used in combination
with the adapting element for *F*_{z} excitation. Finally, the measurement
uncertainty for the different set-ups will be analysed.

Datasets used in Sects. 3.3 and 5.2 are available at the PTB's public repository https://doi.org/10.7795/720.20181105 (Nitsche et al., 2018).

JN designed and installed the setup, performed measurements and evaluation and wrote the manuscript. RK and RT raised the research problem, supervised the research, and discussed and proofread the paper.

Author Rainer Tutsch is a member of the editorial board of the journal.

The authors gratefully acknowledge the funding of this work by the Deutsche
Forschungsgemeinschaft (DFG) under grants Tu 135/24 and Ku 3367/1. The
authors thank Thomas Bruns and Leonard Klaus of the PTB for assistance with
the dynamic moment model, sine fitting and hardware.

Edited by: Ulrich Schmid

Reviewed by: two anonymous referees

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