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"Salman, Mohammad"
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Feature-based volumetric defect classification in metal additive manufacturing
Volumetric defect types commonly observed in the additively manufactured parts differ in their morphologies ascribed to their formation mechanisms. Using high-resolution X-ray computed tomography, this study analyzes the morphological features of volumetric defects, and their statistical distribution, in laser powder bed fused Ti-6Al-4V. The geometries of three common types of volumetric defects; i.e., lack of fusions, gas-entrapped pores, and keyholes, are quantified by nine parameters including maximum dimension, roundness, sparseness, aspect ratio, and more. It is shown that the three defect types share overlaps of different degrees in the ranges of their morphological parameters; thus, employing only one or two parameters cannot uniquely determine a defect’s type. To overcome this challenge, a defect classification methodology incorporating multiple morphological parameters has been proposed. In this work, by employing the most discriminating parameters, this methodology has been shown effective when implemented into decision tree (>98% accuracy) and artificial neural network (>99% accuracy).
Additively manufactured materials contain different types of volumetric defects. Here, the authors utilize the most distinguishing morphological features among different defect types to propose a defect classification methodology.
Journal Article
Performance evaluation of logarithmic spiral search and selective mechanism based arithmetic optimizer for parameter extraction of different photovoltaic cell models
The imperative shift towards renewable energy sources, driven by environmental concerns and climate change, has cast a spotlight on solar energy as a clean, abundant, and cost-effective solution. To harness its potential, accurate modeling of photovoltaic (PV) systems is crucial. However, this relies on estimating elusive parameters concealed within PV models. This study addresses these challenges through innovative parameter estimation by introducing the logarithmic spiral search and selective mechanism-based arithmetic optimization algorithm (Ls-AOA). Ls-AOA is an improved version of the arithmetic optimization algorithm (AOA). It combines logarithmic search behavior and a selective mechanism to improve exploration capabilities. This makes it easier to obtain accurate parameter extraction. The RTC France solar cell is employed as a benchmark case study in order to ensure consistency and impartiality. A standardized experimental framework integrates Ls-AOA into the parameter tuning process for three PV models: single-diode, double-diode, and three-diode models. The choice of RTC France solar cell underscores its significance in the field, providing a robust evaluation platform for Ls-AOA. Statistical and convergence analyses enable rigorous assessment. Ls-AOA consistently attains low RMSE values, indicating accurate current-voltage characteristic estimation. Smooth convergence behavior reinforces its efficacy. Comparing Ls-AOA to other methods strengthens its superiority in optimizing solar PV model parameters, showing that it has the potential to improve the use of solar energy.
Journal Article
Atomistic Study for the Tantalum and Tantalum–Tungsten Alloy Threshold Displacement Energy under Local Strain
The threshold displacement energy (TDE) is an important measure of the extent of a material’s radiation damage. In this study, we investigate the influence of hydrostatic strains on the TDE of pure tantalum (Ta) and Ta–tungsten (W) alloy with a W content ranging from 5% to 30% in 5% intervals. Ta–W alloy is commonly used in high-temperature nuclear applications. We found that the TDE decreased under tensile strain and increased under compressive strain. When Ta was alloyed with 20 at% W, the TDE increased by approximately 15 eV compared to pure Ta. The directional-strained TDE (Ed,i) appears to be more influenced by complex ⟨i j k⟩ directions rather than soft directions, and this effect is more prominent in the alloyed structure than in the pure one. Our results suggest that radiation defect formation is enhanced by tensile strain and suppressed by compressive strain, in addition to the effects of alloying.
Journal Article
A Novel 2-DOF PIDA control strategy with GCRA-based parameter optimization for electric furnace temperature control
Accurate and energy-efficient temperature regulation in electric furnace systems remains a challenging control problem due to nonlinear dynamics, significant thermal inertia, and inevitable time delays. Conventional proportional–integral–derivative (PID) and PID–acceleration (PIDA) controllers, though widely used, often exhibit degraded performance under such conditions, particularly when implemented in a single-degree-of-freedom. To address these limitations, this study proposes, for the first time, a two-degree-of-freedom (2-DOF) PIDA controller tailored for electric furnace temperature control. The controller structure allows independent tuning of set-point tracking and disturbance rejection by introducing separate feedforward paths in the proportional and derivative channels while maintaining integral and acceleration actions on the error signal. To optimize the controller parameters, the recently developed greater cane rat algorithm (GCRA) is employed for the first time in this context. A novel adaptive objective function (combining normalized overshoot, normalized settling time, and cumulative tracking error) guides the tuning process to achieve a balanced improvement in both transient and steady-state performance. The proposed GCRA-based 2-DOF PIDA controller is evaluated through extensive simulations and compared against state-of-the-art metaheuristic tuning approaches, including polar fox optimization (PFA), hiking optimization (HOA), success-history based adaptive differential evolution with linear population size reduction (L-SHADE), and particle swarm optimization (PSO), as well as several benchmark furnace control methods. Results demonstrate that the proposed method consistently achieves faster settling times, reduced overshoot, and near-zero steady-state error, while maintaining robustness under external disturbances and measurement noise. For instance, in the nominal case, the method yields an overshoot of 1.8382% and a settling time of 3.4542 s, outperforming PFA, HOA, L-SHADE, and PSO. Robustness tests under load disturbances and measurement noise confirm stable operation with minimal performance degradation, achieving less than 2.5% overshoot and under 4 s settling time across all evaluated scenarios. These findings highlight the potential of the GCRA-based 2-DOF PIDA controller as a high-precision and energy-efficient solution for temperature regulation in industrial time-delay systems.
Journal Article
A novel hybridization of birds of prey-based optimization with differential evolution mutation and crossover for chaotic dynamics identification
Parameter identification of chaotic systems such as Lorenz, Chen, and Rössler has long been recognized as a challenging inverse problem, since even slight perturbations in system coefficients can yield qualitatively different trajectories. Conventional time-domain error formulations are often ill-conditioned under these conditions, which has motivated the design of more robust objective functions and the adoption of metaheuristic optimization strategies. In this study, a hybrid birds of prey-based optimization with differential evolution (h-BPBODE) is introduced to address these challenges. The method enriches the four canonical behavioral phases of BPBO (individual hunting, group hunting, attacking the weakest, and relocation) by embedding DE mutation and crossover operators after each candidate update. This design injects recombinative diversity while retaining BPBO’s adaptive and collective search mechanisms, thereby improving the balance between exploration and exploitation. The algorithm is validated on Lorenz, Chen, and Rössler systems, where the task is to recover unknown parameters by minimizing trajectory mismatches between true and simulated models. Comparative simulations against standard BPBO, starfish optimization, hippopotamus optimization, particle swarm optimization (PSO), and DE confirm that h-BPBODE consistently achieves exact parameter recovery with negligible residuals, faster convergence, and markedly lower run-to-run variance. Statistical analyses, convergence traces, and parameter evolution curves further demonstrate its robustness and precision. These findings establish h-BPBODE as a reliable and efficient framework for chaotic system identification and suggest its potential for broader nonlinear estimation tasks.
Journal Article
Cybercrime and Harassment: The Impact of Blackmailing on Jordanian Society as a Case Study
This study aims to uncover the relationship between two cybercrimes, harassment and blackmailing, as well as their impact on Jordanian society. The study population included 90 prosecutors working in Jordanian courts. The researchers used the relational method in the second half of the academic year 2020. According to Jordanian prosecutors, the rate of cybercrime harassment is average, whereas that of blackmail is high. The findings also show a statistically significant relationship between harassment and blackmail crimes among the sample members, which is a statistically significant rate. The study also showed that the spread of harassment had six consequences: threatening and defaming the victim, family breakup, social decay, loss of values, instilling skcepticism and loss of self-confidence, and security instability. The authors recommend increasing citizens' awareness of the concept of electronic governance to combat cybercrime. They also recommend that governments conclude agreements and treaties that criminalize all types of crimes, pinpoint their locations when they are committed, and explain how cybercriminals should be delivered.
Journal Article
Optimizing AVR system performance via a novel cascaded RPIDD2-FOPI controller and QWGBO approach
Maintaining stable voltage levels is essential for power systems’ efficiency and reliability. Voltage fluctuations during load changes can lead to equipment damage and costly disruptions. Automatic voltage regulators (AVRs) are traditionally used to address this issue, regulating generator terminal voltage. Despite progress in control methodologies, challenges persist, including robustness and response time limitations. Therefore, this study introduces a novel approach to AVR control, aiming to enhance robustness and efficiency. A custom optimizer, the quadratic wavelet-enhanced gradient-based optimization (QWGBO) algorithm, is developed. QWGBO refines the gradient-based optimization (GBO) by introducing exploration and exploitation improvements. The algorithm integrates quadratic interpolation mutation and wavelet mutation strategy to enhance search efficiency. Extensive tests using benchmark functions demonstrate the QWGBO’s effectiveness in optimization. Comparative assessments against existing optimization algorithms and recent techniques confirm QWGBO’s superior performance. In AVR control, QWGBO is coupled with a cascaded real proportional-integral-derivative with second order derivative (RPIDD 2 ) and fractional-order proportional-integral (FOPI) controller, aiming for precision, stability, and quick response. The algorithm’s performance is verified through rigorous simulations, emphasizing its effectiveness in optimizing complex engineering problems. Comparative analyses highlight QWGBO’s superiority over existing algorithms, positioning it as a promising solution for optimizing power system control and contributing to the advancement of robust and efficient power systems.
Journal Article
A Critical Review of Sustainable Vanillin-modified Vitrimers: Synthesis, Challenge and Prospects
Nearly 90% of thermosets are produced from petroleum resources, they have remarkable mechanical characteristics, are chemically durable, and dimensionally stable. However, they can contribute to global warming, depletion of petroleum reserves, and environmental contamination during manufacture, use, and disposal. Using renewable resources to form thermosetting materials is one of the most crucial aspects of addressing the aforementioned issues. Vanillin-based raw materials have been used in the industrial manufacturing of polymer materials because they are simple to modify structurally. Conversely, traditional thermosetting materials as a broad class of high-molecular-weight molecules are challenging to heal, decompose and recover owing to their permanent 3-D crosslinking network. Once the products are damaged, recycling issues could arise, causing resource loss and environmental impact. It could be solved by inserting dynamic covalent adaptable networks (DCANs) into the polymer chains, increasing product longevity, and minimizing waste. It also improves the attractiveness of these products in the prospective field. Moreover, it is essential to underline that increasing product lifespan and reducing waste is equivalent to reducing the expense of consuming resources. The detailed synthesis, reprocessing, thermal, and mechanical characteristics of partly and entirely biomass thermosetting polymers made from vanillin-modified monomers are covered in the current work. Finally, the review highlights the benefits, difficulties, and application of these emerging vanillin-modified vitrimers as a potential replacement for conventional non-recyclable thermosets.
Journal Article
Application of optimization algorithms for classification problem
The work presented in this paper investigates the use of metaheuristic optimization algorithms for the face recognition problem. In the first setup, a face recognition system is implemented using particle swarm optimization (PSO) and firefly optimization algorithms, separately. PSO and firefly are used for forming the feature vectors in the feature selection stage. These feature vectors serve as the new representation for the face images that will be fed to the classifier. In the second setup, selected features from both PSO and firefly algorithms are fused to form one single feature vector for each face image before the classification stage. Extensive simulations are conducted using Poznan University of Technology (PUT) and face recognition technology (FERET) face databases. Optimal values for population size and maximum iterations number were selected before conducting the experiments. The effect of using different numbers of selected features on the performance is investigated for feature selection using PSO, firefly, and feature fusion of both.
Journal Article