The reach of tribology has expanded in diverse fields and tribology related research activities have seen immense growth during the last decade. This review takes stock of the recent advances in research pertaining to different aspects of tribology within the last 2 to 3 years. Different aspects of tribology that have been reviewed including lubrication, wear and surface engineering, biotribology, high temperature tribology, and computational tribology. This review attempts to highlight recent research and also presents future outlook pertaining to these aspects. It may however be noted that there are limitations of this review. One of the most important of these is that tribology being a highly multidisciplinary field, the research results are widely spread across various disciplines and there can be omissions because of this. Secondly, the topics dealt with in the field of tribology include only some of the salient topics (such as lubrication, wear, surface engineering, biotribology, high temperature tribology, and computational tribology) but there are many more aspects of tribology that have not been covered in this review. Despite these limitations it is hoped that such a review will bring the most recent salient research in focus and will be beneficial for the growing community of tribology researchers.

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A review of recent advances in tribology

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 recent years, traffic congestion prediction has led to a growing research area, especially of machine learning of artificial intelligence (AI) With the introduction of big data by stationary sensors or probe vehicle data and the development of new AI models in the last few decades, this research area has expanded extensively Traffic congestion prediction, especially short-term traffic congestion prediction is made by evaluating different traffic parameters Most of the researches focus on historical data in forecasting traffic congestion However, a few articles made real-time traffic congestion prediction This paper systematically summarises the existing research conducted by applying the various methodologies of AI, notably different machine learning models The paper accumulates the models under respective branches of AI, and the strength and weaknesses of the models are summarised

/pdf/a-review-of-traffic-congestion-prediction-using-artificial-3guq1d3ymb.pdf

A Review of Traffic Congestion Prediction Using Artificial Intelligence

Nitinol, a shape-memory alloy (SMA), is gaining popularity for use in various applications. Machining of these SMAs poses a challenge during conventional machining. Henceforth, in the current study, the wire-electric discharge process has been attempted to machine nickel-titanium (Ni55.8Ti) super-elastic SMA. Furthermore, to render the process viable for industry, a systematic approach comprising response surface methodology (RSM) and a heat-transfer search (HTS) algorithm has been strategized for optimization of process parameters. Pulse-on time, pulse-off time and current were considered as input process parameters, whereas material removal rate (MRR), surface roughness, and micro-hardness were considered as output responses. Residual plots were generated to check the robustness of analysis of variance (ANOVA) results and generated mathematical models. A multi-objective HTS algorithm was executed for generating 2-D and 3-D Pareto optimal points indicating the non-dominant feasible solutions. The proposed combined approach proved to be highly effective in predicting and optimizing the wire electrical discharge machining (WEDM) process parameters. Validation trials were carried out and the error between measured and predicted values was negligible. To ensure the existence of a shape-memory effect even after machining, a differential scanning calorimetry (DSC) test was carried out. The optimized parameters were found to machine the alloy appropriately with the intact shape memory effect.

https://www.mdpi.com/1996-1944/12/8/1277/pdf

Multi-Response Optimization of WEDM Process Parameters for Machining of Superelastic Nitinol Shape-Memory Alloy Using a Heat-Transfer Search Algorithm.

Artificial intelligence based ensemble model for prediction of vehicular traffic noise.

In India, the transportation sector is growing rapidly and the number of vehicles on Indian roads is increasing at a very fast rate leading to overcrowded roads and noise pollution. The traffic scenario is typically different from other countries due to predominance of a variety of two-wheelers which has doubled in the last decade and forms a major chunk of heterogeneous volume of vehicles. Also tendency of not following the traffic norms and poor maintenance adds to the noise generation. In the present study, Multilayer feed forward back propagation (BP) neural network has been trained by Levenberg–Marquardt (L–M) algorithm to develop an Artificial Neural Network (ANN) model for predicting highway traffic noise. The developed ANN model is used to predict 10 Percentile exceeded sound level (L10) and Equivalent continuous sound level (Leq) in dB (A). The model input parameters are total vehicle volume/hour, percentage of heavy vehicles and average vehicle speed. The predicted highway noise descriptors, Leq and L10 from ANN approach and regression analysis have also been compared with the field measurement. The results show that the percentage difference is much less using ANN approach as compared to regression analysis. Further goodness-of-fit of the models against field data has been checked by statistical t-test at 5% significance level and proved the Artificial Neural Network (ANN) approach as a powerful technique for traffic noise modeling.

Vehicular traffic noise modeling using artificial neural network approach

This study investigates the effect of electric discharge machining (EDM) process parameters [current, pulse-on time (Ton), pulse-off time (Toff) and electrode material] on material removal rate (MRR), electrode wear rate (EWR) and surface roughness (SR) during machining of aluminum boron carbide (Al–B4C) composite. This article also summarizes a brief literature review related to aluminum metal matrix composites (Al-MMCs) based on different process and response parameters, work and tool material along with their sizes, dielectric fluid and different optimization techniques used. The MMC used in the present work is stir casted using 5% (wt) B4C particles of 50 micron size in Al 6061 metal matrix. Taguchi technique is used for the design of experiments (L9-orthogonal array), while the experimental results are analyzed using analysis of variance (ANOVA). Response table for average value of MRR, EWR and SR shows that current is the most significant factor for MRR and SR, while electrode material is mo...

Paras Kumar

Papers

Vehicular traffic noise modeling using artificial neural network approach

Experimental investigation and optimization of EDM process parameters for machining of aluminum boron carbide (Al–B4C) composite

Surface texturing techniques to enhance tribological performance: A review

Effect of gear misalignment on contact area: Theoretical and experimental studies

Fatigue failure prediction in spur gear pair using AGMA approach