This paper reviews the flow behavior and mathematical modeling of various metals and alloys at a wide range of temperatures and strain rates. Furthermore, it discusses the effects of strain rate and temperature on flow behavior. Johnson–Cook is a strong phenomenological model that has been used extensively for predictions of the flow behaviors of metals and alloys. It has been implemented in finite element software packages to optimize strain, strain rate, and temperature as well as to simulate real behaviors in severe conditions. Thus, this work will discuss and critically review the well-proven Johnson–Cook and modified Johnson–Cook-based models. The latest model modifications, along with their strengths and limitations, are introduced and compared. The coupling effect between flow parameters is also presented and discussed. The various methods and techniques used for the determination of model constants are highlighted and discussed. Finally, future research directions for the mathematical modeling of flow behavior are provided.

The aim of this paper is to establish a reliable model that provides the best fit to the specific behavior of the flow stresses of the 10%Cr steel alloy at the time of hot deformation. Modified Johnson–Cook and strain-compensated Arrhenius-type (phenomenological models), in addition to two Artificial Neural Network (ANN) models were established with the view toward investigating their stress prediction performances. The ANN models were trained using Scaled Conjugate Gradient (SCG) and Levenberg–Marquardt (LM) algorithms. The prediction accuracy of the established models was evaluated using the following well-known statistical parameters: (a) correlation coefficient (R), (b) Average Absolute Relative Error (AARE), (c) Root Mean Squared Error (RMSE), and Relative Error (RE). The results showed that both of the modified Johnson–Cook and strain-compensated Arrhenius models could not competently predict the flow behavior. On the contrary, the results indicated that the two proposed ANN models precisely predicted the flow stress values and that the LM-trained ANN provided a superior performance over the SCG-trained model, as it yielded an RMSE of as low as 0.441 MPa.

Conventionally, the methods used for the sorting of tomatoes are manual. These methods are costly, non-productive, and their reliability is uncertain. With advancing technology, deep-learning, and artificial intelligence techniques are being utilized to develop fully automated system controllers. The primary reason behind using these techniques is their competitive performance in solving high nonlinear classification problems. Therefore, this paper investigates the performance and combination scenarios of a number of effective artificial intelligence techniques and strategies. Improving the classification accuracy of automated tomato-sorting controllers shall also be explored. Convolution neural network (CNN), artificial neural network (ANN), self-organizing map (SOM), learning vector quantization (LVQ), and support vector machine (SVM) are developed, optimized, assessed, and compared. In this paper, three main categories are considered, namely, unripe, ripe, and defective (overripe and rotten). Moreover, an experimental setup is designed, manufactured, and tested to verify the computational results obtained from the neural networks as well as to assess the real-time performance of the proposed algorithm. As per the research findings, the utilization of a hybrid CNN-ANN-based algorithm is favored, as it demonstrated a superior performance during validation and experimental testing. The CNN-ANN-based control algorithm yielded a theoretical classification performance of 100% for all classes while the experimental results produced 100% for unripe and ripe classifications and 90% for the ripe and defective (overripe and rotten) classifications. The results of this paper have the potential to improve the classification accuracy of similar fruit and vegetable sorting machines.

In rotating machinery, the second most common fault after imbalance is misalignment. Misalignment can have a severe impact on equipment and may reduce the machine’s lifetime considerably. In this paper, the simultaneous effect of imbalance and misalignment (parallel or angular) on the vibration spectra of rotating machinery will be discussed. A numerical model is developed and used to obtain the time and frequency responses of the rotor-coupling-bearing system to the simultaneous effect of these faults. The numerical model shows that the imbalance was mainly related to the peak located around 1X, whereas misalignment was linked to the peak around 2X. In addition, the parallel misalignment fault magnifies the 2X amplitude of the displacement response, whereas the response of angular misalignment is captured at the 2X and 4X amplitudes. This study also examines the effects of changing the model’s rotational speed, misalignment level, and coupling type for angular and parallel misalignments.

This is an Open Access Article. It is published by Elsevier under the Creative Commons Attribution 4.0 NonCommercial-NoDerivatives License(CC BY-NC-ND). Full details of this licence are available at: http://creativecommons.org/licenses/by-nc-nd/4.0/The efficiency and profitability of Floating, Production, Storage and Offloading platform (FPSO) terminals depends on various factors such as LNG liquefaction process type, system reliability and maintenance approach. This review is organized along the following research questions: (i) what are the economic benefit of FPSO and how does the liquefaction process type affect its profitability profile?, (ii) how to improve the reliability of the liquefaction system as key section? and finally (iii) what are the major CBM techniques applied on FPSO. The paper concluded the literature and identified the research shortcomings in order to improve profitability, efficiency and availability of FPSOs