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The hands of intelligent robots perceive external stimuli and respond effectively according to tactile or pressure sensors. However, the traditional tactile and pressure sensors cannot perform human-skin-like intelligent properties of high sensitivity, large measurement range, multi-function and flexibility simultaneously. The purpose of this paper is to present a flexible tactile-pressure sensor based on hyper-elastics polydimethylsiloxane and plate capacitance.

With regard to this problem, this paper presents a flexible tactile-pressure sensor based on hyper-elastics PDMS and plate capacitance. The sensor has a size of 10 mm × 10 mm × 1.3 mm and is composed of four upper electrodes, one middle driving electrode and one lower electrode. The authors first analyzed the structure and the tactile-pressure sensing principle of human skin to obtain the design parameters of the sensor. Then they presented the working principle, material selection and mechanical structure design and fabrication process of the sensor. The authors also fabricated several sample devices of the sensor and carried out experiments to establish the relationship between the sensor output and the pressure.

The results show that the tactile part of the sensor can measure a range of 0.05-1N/mm² micro pressure with a sensitivity of 2.93 per cent/N and a linearity of 0.03 per cent. The pressure part of the sensor can measure a range of 1-30N/mm² pressure with a sensitivity of 0.08 per cent/N and a linearity of 0.07 per cent.

This paper analyzes the tactile and pressure sensing principles of human skin and develop an intelligent sensitive human-skin-like tactile-pressure sensor for intelligent robot perception systems. The sensor can achieve to imitate the tactile and pressure function simultaneously with a measurement resolution of 0.01 N and a spatial resolution of 2 mm.

For the difference of the change law of material memory performance and the influence of damage state on memory performance, this paper aims to establish a general model of fatigue damage accumulation based on dynamic residual S–N curve and material memory characteristics.

This paper introduces the material memory characteristics, combined with the residual S–N curve method, and uses the exponential decay function of the load cycle to construct the material memory performance function. While considering the damage state, the loading order can be fully considered. The parameter d in the function not only represents the variation of the material's memory property, but also considers the influence of the damage state.

According to the test data of welding joints of common materials, alloy materials and other materials, the validity and feasibility of the fatigue cumulative damage model constructed were verified. The numerical results show that under the grading load, the fatigue cumulative damage model can be used to predict the fatigue life of welded structures and has high prediction accuracy and more approximate to the actual experiment results. It can be directly applied to the fatigue life prediction and design of actual engineering welded structures.

The model not only considers the effect of damage state and loading order on damage accumulation, but also contains only one material parameter, which is easy to obtain. The prediction accuracy and engineering practicability of fatigue were significantly improved.