Piezoelectric energy transducers offer great potential for converting the vibrations of pedestrian footsteps or cars moving on a bridge or road into electricity. However, existing piezoelectric energy-harvesting transducers are limited by their poor durability. In this paper, to enhance this durability, a piezoelectric energy transducer with a flexible piezoelectric sensor is fabricated in a tile protype with indirect touch points and a protective spring. The electrical output of the proposed transducer is examined as a function of pressure, frequency, displacement, and load resistance. The maximum output voltage and maximum output power obtained were 6.8 V and 4.5 mW, respectively, at a pressure of 70 kPa, a displacement of 2.5 mm, and a load resistance of 15 kΩ. The designed structure limits the risk of destroying the piezoelectric sensor during operation. The harvesting tile transducer can work properly even after 1000 cycles. Furthermore, to demonstrate its practical applications, the tile was placed on the floor of an overpass and a walking tunnel. Consequently, it was observed that the electrical energy harvested from the pedestrian footsteps could power an LED light fixture. The findings suggest that the proposed tile offers promise with respect to harvesting energy produced during transportation.

This paper presents an optimization of dressing conditions for SKD-11 steel grinding using HaiDuong grinding wheel made in Vietnam. Taguchi method was used to design experiment and calculate the optimized dressing conditions. Effects of the six input parameters including feed rate (S), depth of rough dressing cut (aedr), rough dressing times (nr), depth of finish dressing cut (aedf), finish dressing times (nf) and non-feeding dressing (nnon) with 4 levels on the machined surface roughness were investigated for optimization process. To find out the influence degree of each input parameter on output results, S/N ratio was analysized. Experimental results show that the average surface roughness after 3 times of the repeated experiments was 0.208 μm and deviation was 11.23% comparing with the predicted values.

This study is to determine effects of the dressing parameters to the flatness tolerance (Fl) when grinding SKD11 steel using HaiDuong grinding wheel and also propose the suitable dressing parameters to obtain the smallest flatness tolerance. In this paper, the effects of the six input parameters including feed rate (S), depth of rough dressing cut (ar), rough dressing times (nr), depth of finish dressing cut (af), finish dressing times (nf) and non-feeding dressing (nnon) to the flatness tolerance were investigated. To find out the influence of each input parameter on output results, the S/N ratio was analysized. Evaluated experimental results show that, the average flatness tolerance was 4.05μm and deviation of this value was 11.38% compared with the predicted value.

Nowadays, surface grinding is one of the most common of metal finishing methods. The efficiency of this process is affected by the so-called process parameters such as dressing feed rate (S), rough dressing depth (ar), rough dressing times (nr), fine dressing depth (af), fine dressing times (nf), and non-feeding dressing (nnon). etc. In this paper, the optimization of dressing parameters in surface grinding SKD11 tool steel is studied. The aim of the study is to find the most appropriate value set of dressing parameters to maximize the material removal rate (MRR). In order to solve the problem, the Taguchi method is used. Based on an orthogonal array L16(44x22), sixteen experiments have been conducted. By analyzing the experimental results, an optimal solution of such optimization problem has been solved, presenting the most appropriate dressing parameters as follows: ar = 0.015 mm, nr = 2 times, af = 0.005 mm, nf = 0 times, nnon = 0 times, S = 1.6 m/min. The discovered technology mode has been applied to the real machining process and the outcome shows out a much better result in comparison with default setting modes, that the difference between the model values and the real values of the roughness average is controlled within 3.87% of the ranges.