A Comprehensive Design of Six-Axis Force/Moment Sensor.

Anton Royanto Ahmad,Terrence Wynn,Chyi-Yeu Lin

doi:10.3390/s21134498

Anton Royanto Ahmad, Terrence Wynn + Show 1 more

Open Access

https://doi.org/10.3390/s21134498

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Abstract

Strain gage type six-axis force/moment (F/M) sensors have been largely studied and implemented in industrial applications by using an external data acquisition board (DAQ). The use of external DAQs will ill-affect accuracy and crosstalk due to the possibility of voltage drop through the wire length. The most recent research incorporated DAQ within a relatively small F/M sensor, but only for sensors of the capacitance and optical types. This research establishes the integration of a high-efficiency DAQ on six-axis F/M sensor with a revolutionary arrangement of 32 strain gages. The updated structural design was optimized using the sequential quadratic programming method and validated using Finite Element Analysis (FEA). A new, integrated DAQ system was designed, tested, and compared to commercial DAQ systems. The proposed six-axis F/M sensor was examined with the calibrated jig. The results show that the measurement error and crosstalk have been significantly reduced to 1.15% and 0.68%, respectively, the best published combination at this moment.

Highlights

Until now, the six-axis force/moment (F/M) sensor has been extensively investigated and has been developed with many types
F/M sensors can be classified based on the decoupling method used for decoupling of the force sensor signal, such as linear regression [3], the least squares method (LSM) [4], least squares support vector machines (LS-SVMs) [5], neural networks [6], the shape–form–motion approach [7], and support vector regression (SVRs) [8,9]
We will demonstrate the performance of a six-axis force sensor with strain gage arrangements

Summary

Introduction

The six-axis force/moment (F/M) sensor has been extensively investigated and has been developed with many types. F/M sensors can be classified based on the transducer used or the structure type. F/M sensors can be classified based on the decoupling method used for decoupling of the force sensor signal, such as linear regression [3], the least squares method (LSM) [4], least squares support vector machines (LS-SVMs) [5], neural networks [6], the shape–form–motion approach [7], and support vector regression (SVRs) [8,9]. A commercial data acquisition board (DAQ) will be used for acquiring data from a six-axis F/M sensor producing analog output signals. A few researchers have constructed their own DAQ within a six-axis

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