Piezoelectric coefficients are constrained by the intrinsic crystal structure of the constituent material. Here we describe design and manufacturing routes to previously inaccessible classes of piezoelectric materials that have arbitrary piezoelectric coefficient tensors. Our scheme is based on the manipulation of electric displacement maps from families of structural cell patterns. We implement our designs by additively manufacturing free-form, perovskite-based piezoelectric nanocomposites with complex three-dimensional architectures. The resulting voltage response of the activated piezoelectric metamaterials at a given mode can be selectively suppressed, reversed or enhanced with applied stress. Additionally, these electromechanical metamaterials achieve high specific piezoelectric constants and tailorable flexibility using only a fraction of their parent materials. This strategy may be applied to create the next generation of intelligent infrastructure, able to perform a variety of structural and functional tasks, including simultaneous impact absorption and monitoring, three-dimensional pressure mapping and directionality detection. Piezoelectrics convert force into electrical charge, and vice versa, but the coefficients that determine piezoelectric behaviour are constrained by crystal structure. Here, metamaterials are 3D printed that show arbitrary piezoelectric coefficients.

Three-dimensional printing of piezoelectric materials with designed anisotropy and directional response.

Whole field evaluation of stress components in digital photoelasticity—Issues, implementation and application

Digital photoelasticity has rapidly progressed in the last few years and has matured into an industry-friendly technique. This review thematically classifies all the developments in digital photoelasticity and highlights the relative merits and drawbacks of the various techniques. The overall objective is to provide enough information and guidance to allow an end-user to make an informed choice on the type of technique to be used in a particular situation.

Digital photoelasticity – A comprehensive review

Road traffic noise is the most prevalent form of environmental noise pollution, and it can either be measured in the field or predicted through verified mathematical models. While many road traffic noise models are available around the world, these models cannot be simply generalized because local conditions affecting such noise (e.g., vehicle type and weather) vary from one locality to another. In this paper, the Artificial Neural Network (ANN) technique was employed to model road traffic noise in a city with known hot climate, namely Sharjah City in United Arab Emirates. Toward this end, field data were collected from three different road sites which resulted in more than 420 hourly measurements of noise level, traffic volume and classification, average speed, and roadway temperature. Overall, a total of 16 feed-forward back-propagation ANN models with one and two hidden layers were undertaken in this research. The best-performing models were compared with two conventional models: The Basic Statistical Traffic Noise model (BSTN) and the Ontario Ministry of Transport Road Traffic Noise model (ORNAMENT). In general, results showed that ANN models outperformed the conventional models. Furthermore, the performance of both ANN and conventional road noise models was further improved by including roadway temperature. While the reported work emphasizes the importance of adapting models to different local conditions, it also encourages others to consider the roadway temperature when modeling traffic noise, especially in areas of hot climate. Given that ANN models are often designated as “black-box,” a few techniques were utilized to expand the capabilities of ANN models to offer explanatory insights in modeling the inter-relations between the variables considered in this study. Generally, it was found that the ANN model was capable of identifying these relations as expected. In-depth analysis showed that the distance from the edge of road was found to be the most significant contributing factor whereas heavy-vehicle volume was surprisingly found to be the least.

Modeling roadway traffic noise in a hot climate using artificial neural networks

In this paper, a magnetorheological (MR) damper is optimally designed for use in smart prosthetic knees. The objective of optimization is to minimize the total energy consumption during one gait cycle and weight of the MR damper. Firstly, a smart prosthetic knee employing a DC motor, MR damper and springs is developed based on the kinetics characteristics of human knee during walking. Then the function of the MR damper is analyzed. In the initial stance phase and swing phase, the MR damper is powered off (off-state). While during the late stance phase, the MR damper is powered on to work as a clutch (on-state). Based on the MR damper model as well as the prosthetic knee model, the instantaneous energy consumption of the MR damper is derived in the two working states. Then by integrating in one gait cycle, the total energy consumption is obtained. Particle swarm optimization algorithm is used to optimize the geometric dimensions of MR damper. Finally, a prototype of the optimized MR damper is fabricated and tested with comparison to simulation.

https://iopscience.iop.org/article/10.1088/1361-665X/aa5494/pdf

Optimal design of a magnetorheological damper used in smart prosthetic knees

Data acquisition techniques in digital photoelasticity: a review

This paper demonstrates the applications of artificial neural networks to predict the equivalent continuous sound level $$(L_\mathrm{Aeq})$$
 and 10 Percentile exceeded sound level $$(L_\mathrm{10})$$
 generated due to traffic noise for various locations in Delhi. A Model based on back-propagation neural network was trained, validated, and tested using the measured data. The work shows that the model is able to produce accurate predictions of hourly traffic noise levels. A comparative study shows that neural networks out-perform the multiple linear regression models developed in terms of total traffic flow and equivalent traffic flow. The prediction model proposed in the study may serve as a vital tool for traffic noise forecasting and noise abatement measures to be undertaken for Delhi city.

Comparison of ANN and Analytical Models in Traffic Noise Modeling and Predictions

Magneto-rheological (MR) damper is one of the most promising semi-active devices. The MR dampers offer a reliability of a passive system yet maintain the versatility and adaptability of the fully active control devices. In this paper, an optimization process is developed to optimize the geometrical parameters of an MR damper using finite element method (FEM) coupled with Taguchi approach which is rarely available in the literature. The damping force of the MR damper is selected as an objective function. To achieve this objective, 18 FEM models, based on Taguchi orthogonal array, are developed on ANSYS platform. These results have been analyzed by using the design of experiment (DoE) methodology and an optimized solution is then arrived. The optimal solution is validated experimentally as well as through FEM for 95% confidence level. These results are found to be in good agreement with each other. This paper establishes that numerical technique results, e.g., FEM, can be used over the real experimental results for the geometric parameter optimization of an MR damper. The proposed methodology will save time and resources for designing an optimized MR damper for automotive and other applications.

/pdf/geometric-parameter-optimization-of-magneto-rheological-cw0909evnz.pdf

Geometric parameter optimization of magneto-rheological damper using design of experiment technique

Determination of characteristic parameters in integrated photoelasticity by phase-shifting technique

Magnetorheological fluids belong to a class of smart materials, whose rheological characteristics such as yield stress, viscosity, etc. change in the presence of applied magnetic field. In this paper, multi-response optimization of MR fluid constituents is obtained. For this, 18 samples of MR fluids are prepared using L-18 Orthogonal Array. These samples are experimentally tested on the in-house developed and fabricated electromagnet setup. The setup has been validated using a reference fluid. Yield stress of MR fluid mainly depends on the volume fraction of the iron particles and type of carrier fluid used in its preparation. An optimal combination of these input parameters with mineral oil as a carrier fluid and Fe 300 mesh (32% by volume) as an iron particle, oleic acid (0.5% by volume), and tetra-methyl-ammonium-hydroxide (0.7% by volume) has given the largest numerical values of on-state yield stress and viscosity of the MR fluid sample as 48.197 kPa and 573.0944 kPa-s, respectively, within the range of the input parameters. The yield stress of optimized MR fluid is higher than Lord MRF-122-EG fluid. Furthermore, a confirmation test on the optimized MR fluid sample has been carried out and the response parameters thus obtained are found to be matching quite well (with error less than 1%) with the statistically obtained values. This high value of the yield stress and viscosity can be used more effectively in the formulation and designing of MR devices requiring larger variation in the damping.

S. K. Mangal

Papers

Data acquisition techniques in digital photoelasticity: a review

Comparison of ANN and Analytical Models in Traffic Noise Modeling and Predictions

Geometric parameter optimization of magneto-rheological damper using design of experiment technique

Determination of characteristic parameters in integrated photoelasticity by phase-shifting technique

Multi-parameter optimization of magnetorheological fluid with high on-state yield stress and viscosity