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The purpose of this study is to develop an output model to extract surface microstructure characteristics of different objects, so as to predict the response of the output voltage obtained from tactile texture sensor.

The model is based on the consideration of the inverse-magnetostrictive effect, the flexure mode, the linear constitutive equations and the strain principle.

This research predicts and investigates the effect of the texture properties on the tactile texture sensor output characteristics.

The surface texture characteristic is regarded to be important information to evaluate and recognize the object.

Magnetic permeability variations of ferromagnetic materials under elastic stress offer the potential to monitor tension based on the inverse magnetostrictive effect. The purpose of this paper is to propose an innovative self-inductance tension eddy current sensor to detect tension.

The effectiveness of conventional elasto-magnetic (EM) sensor is limited during signal detection, due to its complex sensor structure, which includes excitation and induction coils. In this paper, a novel self-inductance tension eddy current sensor using a single coil is presented.

The output signal was analyzed through oscilloscope in the frequency domain and via self-developed data logger in the time domain. Experimental results show the existence of a linear relationship between voltage across the sensor and tension. The sensor sensitivity is dependent on operating conditions, such as current and frequency of the input signal.

The self-inductance sensor has great potential for replacing conventional EM sensor due to its low cost, simple structure, high precision and good repeatability in tension detection.

A spilt sleeve structure provides a higher permeability path to magnetic field lines than a non-sleeve structure, thus reducing the loss of magnetic field. The self-developed data logger improves sensitivity and signal-to-noise ratio of sensor. The novel sensor, as a replacement of the EM sensor, can easily and accurately monitor the tension force.

Owing to the excellent performance, giant magnetostrictive materials (GMMs) are widely used in many engineering fields. The dynamic Jiles–Atherton (J-A) model, derived from physical mechanism, is often used to describe the hysteresis characteristics of GMM. However, this model, despite cited by many different literature studies, seems not to possess unique expressions, which may cause great trouble to the subsequent application. This paper aims to provide the rational expressions of the dynamic J-A model and propose a numerical computation scheme to obtain the model results with high accuracy and fast speed.

This paper analyzes different published papers and provides a reasonable form of the dynamic J-A model based on functional properties and physical explanations. Then, a numerical computation scheme, combining the Newton method and the explicit Adams method, is designed to solve the modified model. In addition, the error source and transmission path of the numerical solution are investigated, and the influence of model parameters on the calculation error is explored. Finally, some attempts are made to study the influence of numerical scheme parameters on the accuracy and time of the computation process. Subsequently, an optimization procedure is proposed.

A rational form of the dynamic J-A model is concluded in this paper. Using the proposed numerical calculation scheme, the maximum calculation error, while computing the modified model, can remain below 2 A/m under different model parameter combinations, and the computation time is always less than 0.5 s. After optimization, the calculation speed can be enhanced with the computation accuracy guaranteed.

To the best of the authors’ knowledge, this paper is the first one trying to provide a rational form of the dynamic J-A model among different citations. No other research studies focus on designing a detailed computation scheme targeting the fast and accurate calculation of this model as well. And the performance of the proposed calculation method is validated in different conditions.

This paper aims to design a magnetostrictive tactile sensor for surface depth detection. Unlike the human finger, although most tactile sensors have high sensitivity to pressure, they cannot detect millimeter-level depth information on the surface of objects precisely. To enhance the ability to detect surface depth information, a piezomagnetic sensor combining inverse magnetostrictive effect and bionic structure is developed in this paper.

A magnetostrictive tactile sensor based on Galfenol [(Fe₈₃Ga₁₇)_99.4B_0.6] is designed and studied for surface depth measurement. The optimal structure of the sensor is determined by experiment and theory. The test platforms for static and dynamic characteristics are set up. The static and the dynamic sensing performance of the sensor are studied experimentally.

The sensor can detect 0–2 mm depth change with a sensitivity of 91.5 mV/mm. A resolution of 50 µm can be achieved in the depth direction. In 50 cycles of loading and unloading tests, the maximum error of the sensor output voltage amplitude is only 2.23%.

The sensor can measure the depth information of object surface precisely with good repeatability through sliding motion and provide reference for object surface topography detection.

Gesture recognition plays an important role in many fields such as human–computer interaction, medical rehabilitation, virtual and augmented reality. Gesture recognition using wearable devices is a common and effective recognition method. This study aims to combine the inverse magnetostrictive effect and tunneling magnetoresistance effect and proposes a novel wearable sensing glove applied in the field of gesture recognition.

A magnetostrictive sensing glove with function of gesture recognition is proposed based on Fe-Ni alloy, tunneling magnetoresistive elements, Agilus30 base and square permanent magnets. The sensing glove consists of five sensing units to measure the bending angle of each finger joint. The optimal structure of the sensing units is determined through experimentation and simulation. The output voltage model of the sensing units is established, and the output characteristics of the sensing units are tested by the experimental platform. Fifteen gestures are selected for recognition, and the corresponding output voltages are collected to construct the data set and the data is processed using Back Propagation Neural Network.

The sensing units can detect the change in the bending angle of finger joints from 0 to 105 degrees and a maximum error of 4.69% between the experimental and theoretical values. The average recognition accuracy of Back Propagation Neural Network is 97.53% for 15 gestures.

The sensing glove can only recognize static gestures at present, and further research is still needed to recognize dynamic gestures.

A new approach to gesture recognition using wearable devices.

This study has a broad application prospect in the field of human–computer interaction.

The sensing glove can collect voltage signals under different gestures to realize the recognition of different gestures with good repeatability, which has a broad application prospect in the field of human–computer interaction.

The paper aims to analyze the behavior of the Galfenol rods under bending conditions that are employed in a vibration energy harvester by illustrating the spatial variations in stress and magnetic field.

This paper describes a 3‐D static finite element model of magnetostrictive materials, considering magnetic and elastic boundary value problems that are bidirectionally coupled through stress and field dependent variables. The finite element method is applied to a small vibration‐driven generator of magnetostrictive type employing Iron‐Gallium alloy (Galfenol).

The 3‐D static finite element modeling presented here highlights the spatial variations in magnetic field and relative permeability due to the corresponding stress distribution in the Galfenol rods subjected to transverse load. The numerical calculations show that about 1.1 T change in magnetic flux density is achieved which demonstrates the effectiveness of the inspected vibration‐driven generator in voltage generation and energy harvesting. The model predictions agree with the experimental results and are coherent with the magnetostriction phenomenon.

This paper fulfils the behavior analysis of Galfenol rods under transverse load that includes both compression and tension. The compressive and tensile stresses contributions to change in magnetic flux densities in the Galfenol rods were calculated by which the effectiveness of the inspected vibration‐driven generator in voltage generation and energy harvesting is demonstrated.