Titanium and nickel alloys represent a significant metal portion of the aircraft structural and engine components. When these critical structural components in aerospace industry are manufactured with the objective to reach high reliability levels, surface integrity is one of the most relevant parameters used for evaluating the quality of finish machined surfaces. The residual stresses and surface alteration (white etch layer and depth of work hardening) induced by machining of titanium alloys and nickel-based alloys are very critical due to safety and sustainability concerns. This review paper provides an overview of machining induced surface integrity in titanium and nickel alloys. There are many different types of surface integrity problems reported in literature, and among these, residual stresses, white layer and work hardening layers, as well as microstructural alterations can be studied in order to improve surface qualities of end products. Many parameters affect the surface quality of workpieces, and cutting speed, feed rate, depth of cut, tool geometry and preparation, tool wear, and workpiece properties are among the most important ones worth to investigate. Experimental and empirical studies as well as analytical and Finite Element modeling based approaches are offered in order to better understand machining induced surface integrity. In the current state-of-the-art however, a comprehensive and systematic modeling approach based on the process physics and applicable to the industrial processes is still missing. It is concluded that further modeling studies are needed to create predictive physics-based models that is in good agreement with reliable experiments, while explaining the effects of many parameters, for machining of titanium alloys and nickel-based alloys.

Machining induced surface integrity in titanium and nickel alloys: A review

In recent years, environmental considerations have prompted the use of wind power as a renewable energy resource. However, the biggest challenge in integrating wind power into the electric grid is its intermittency. One approach to deal with wind intermittency is forecasting future values of wind power production. Thus, several wind power or wind speed forecasting methods have been reported in the literature over the past few years. This paper provides insight on the foremost forecasting techniques, associated with wind power and speed, based on numeric weather prediction (NWP), statistical approaches, artificial neural network (ANN) and hybrid techniques over different time-scales. An overview of comparative analysis of various available forecasting techniques is discussed as well. In addition, this paper further gives emphasis on the major challenges and problems associated with wind power prediction.

A review of wind power and wind speed forecasting methods with different time horizons

Steels for bearings

ARMA based approaches for forecasting the tuple of wind speed and direction

This paper presents an overview of existing research on wind speed and power forecasting. It first discusses state-of-the-art wind speed and power forecasting approaches. Then, forecasting accuracy is presented based on variable factors. Finally, potential techniques to improve the accuracy of forecasting models are reviewed. A full survey on all existing models is not presented, but attempts to highlight the most promising body of knowledge concerning wind speed and power forecasting.

Current status and future advances for wind speed and power forecasting

On comparing three artificial neural networks for wind speed forecasting

Fine tuning support vector machines for short-term wind speed forecasting

Object localization based on radio frequency identification (RFID) technology has promising potentials. By combining localization with its identification capability, existing applications can be enhanced and new applications can be developed for this technology. This paper starts with an overview introducing the available technologies for localization with a focus on radio frequency based technologies. The existing and potential applications of RFID localization in various industries are then summarized. Moreover, RFID localization algorithms are reviewed, which can be categorized into multilateration, Bayesian inference, nearest-neighbor, proximity, and kernel-based learning methods. Also, we present a localization case study using passive RFID technology, and it shows that objects can be successfully localized using either multilateration or Bayesian inference methods. The survey also discusses the challenges and future research on RFID localization.

RFID localization algorithms and applications—a review

Forecasting of wind speed and wind power generation is indispensible for the effective operation of a wind farm, and the optimal management of its revenue and risks. Hybrid forecasting of time series data is considered to be a potentially viable alternative compared with the conventional single forecasting modeling approaches such as autoregressive integrated moving average (ARIMA), artificial neural network (ANN), and support vector machine (SVM). Hybrid forecasting typically consists of an ARIMA prediction model for the linear component of a time series and a nonlinear prediction model for the nonlinear component. In this paper, we systematically and comprehensively investigate the applicability of this methodology based on two case studies on wind speed and wind power generation, respectively. Two hybrid models, namely, ARIMA–ANN and ARIMA–SVM, are selected to compare with the single ARIMA, ANN, and SVM forecasting models. The results show that the hybrid approaches are viable options for forecasting both wind speed and wind power generation time series, but they do not always produce superior forecasting performance for all the forecasting time horizons investigated.

Jing Shi

Papers

ARMA based approaches for forecasting the tuple of wind speed and direction

On comparing three artificial neural networks for wind speed forecasting

Fine tuning support vector machines for short-term wind speed forecasting

RFID localization algorithms and applications—a review

Evaluation of hybrid forecasting approaches for wind speed and power generation time series