Hybrid Flow Shop Scheduling (HFS) has garnered significant interest in terms of problem formulation and solution approaches. This work introduces an optimization approach for a case study on a hybrid flow shop scheduling problem. The objective is to minimize the makespan, energy consumption, and idle machines in manufacturing shop. The HFS consists of many concurrent production lines, each containing several machines, operating in one or more stages. A case study was conducted using fourteen jobs across three stages, which utilized lathe, milling, and deburring machines. The EE-HFS was optimized using Multi-Objective Tiki Taka Optimization (MOTTA). The study considered machine idle time as a key factor influencing energy efficiency, incorporating it into the scheduling evaluation. The optimization result was compared to established algorithms, such as the Non-dominated Sorting Genetic Algorithm-II, the Multi Objectives Evolutionary Algorithm Based on Decomposition, the Multi Objectives Particle Swarm Optimization, and the recent algorithm Multi Objectives Grey Wolf Optimizer. The metrics used for comparison include Error Ratio (ER), Pareto Percentage (%), Spacing, Maximum Spread, computational speed, Hyper Volume, Inverted Generational Distance (IGD), and Generational Distance (GD). The results indicate that MOTTA exhibits superior performance with 78.42% best overall and 100% better in the convergence and domination of the case study solution (ER, ND, GD, and IGD). Overall, the findings have important implications for Hybrid flow shop scheduling in terms of the energy utilization model, reducing idle machine time, and the promising potential of MOTTA for application in other combinatorial scheduling challenges. This case study provides substantial advantages to the organization by effectively decreasing its daily energy consumption, equipment usage, and enhancing resource management.

Fibre Bragg Grating (FBG) sensors are preferred over traditional electrical sensors due to their high sensitivity, electromagnetic immunity, and multiplexing capability. However, the simultaneous measurement of humidity and temperature remains challenging due to cross-sensitivity issues. This study presents the development of a simple and efficient coated multiplexed FBG sensor to address this challenge. The sensor was fabricated using two separate single-mode fibre FBGs, each coated with polydimethylsiloxane (PDMS) and polyvinyl alcohol (PVA), respectively. Experimental results demonstrated that PDMS exhibited excellent temperature sensitivity, while PVA showed high moisture sensitivity. The measured sensitivities were 0.0989 pm/% RH (equivalent to 9.89 pm.φ-1) and 28.775 pm/°C (equivalent to 28.775 pm/°K) for PDMS-coated FBG, and 12.593 pm/% RH and 14.515 pm/°C for PVA-coated FBG, indicating that PDMS had minimal response to humidity, whereas PVA exhibited a degree of temperature sensitivity. To mitigate cross-sensitivity, a sensitivity matrix was employed, enabling accurate simultaneous measurement of humidity and temperature. The experimental validation confirmed that the sensor achieved a percentage error below 10%, demonstrating high accuracy and reliability. Given the simplicity of fabrication and calibration, the proposed coated multiplexed sensor exhibits strong potential for practical applications in environmental monitoring and industrial sensing.

Ankle rehabilitation is an important indicator of walking ability recovery because it is used as a marker of early recovery of mobility function in post-stroke patients. Robot-assisted ankle rehabilitation has been proven to be more optimal for restoring range of motion, balance, and gait proprioception in patients. This study aims to optimize the design of an ankle rehabilitation exoskeleton through structural simulation, biomechanical alignment, and efficiency based on several alternative actuator designs. Alternative exoskeleton designs are focused on the rehabilitation of dorsiflexion-plantar flexion and inversion-eversion movements. The analysis method for assessing the best exoskeleton design alternatives uses an engineering design methodology approach based on static and dynamic test parameters, namely kinematics and FEA. The results of the design engineering implementation show that the exoskeleton design with Concept B is more efficient based on several mechanical test parameters compared to Concept A. Simulation results show that Design B alternative is superior in all test parameters with a value of (4.22 versus 3.68) in the safety factor, a lower peak stress of (30.43 MPa versus 39.15 MPa), and produces energy efficiency with lower torque requirements. The mechanical stability of Concept B is characterized by using a more efficient actuator design with superior safety improvements for users. Based on the parameters and characteristics of the simulation test using design engineering, Design B is more feasible to be developed as a robotic mechanical system for the needs of post-stroke patient ankle rehabilitation.

The automotive industry requires materials that are light in weight to enhance fuel efficiency while optimizing safety standards alongside having a high impact resistance to improve the vehicle's overall safety features. This study focuses on evaluating the low velocity impact performance of fibre metal laminates (FMLs) with different configurations to identify the most suitable crash resistant structures for vehicles. Five FML configurations were fabricated using 2024-T3 aluminum with CFRP (B2), GFRP (B1), SRPP (B3) and hybrid combinations consist of CFRP-GFRP (B4) and SRPP-GFRP (B5) in 3/2 layered structures that were tested under low-velocity impact at 2.7-4.5 m/s using drop-weight testing with force, displacement and energy absorption measurements. B5 recorded the highest impact force of 13827.1 N due to the synergistic bonding of thermoplastic SRPP and the GFRP layer. B1 exhibited the best energy absorption of 86.4 J outperforming other configurations by 10% because of the glass fibre's strain-to-failure characteristics which allows for significant plastic deformation. B2 lagged in both energy absorption and force at 78.5 J and 11476.2 N respectively due to the brittleness of the carbon fibre. The ranking for energy absorption was B1 > B4 > B2 > B5 > B3 with all configurations showing proportional increases in strength with velocity. Configuration B5 composed of hybrid SRPP-GFRP demonstrated the best impact resistance outperforming CFRP systems by 20.5% in force resistance, while GFRP-based FMLs (B1) showed better energy absorption at 86.4 J which is critical for managing crash energy thus illustrating that the selection of materials relies on the designated zone of an automobile meant to endure a collision, prioritizing either force resistance or energy dissipation.

The rapid advancement of artificial intelligence has the potential to significantly enhance the aerospace industry, particularly through the development of intelligent engine control systems. This study seeks to tackle the challenges of controlling complex, nonlinear aero-engines by applying Deep Reinforcement Learning techniques. Specifically, the Deep Deterministic Policy Gradient (DDPG) algorithm within an actor-critic framework to design an adaptive controller for the nonlinear thermodynamic model of the J85 jet engine are employed. The proposed method is evaluated against traditional PI controllers under various operating conditions, including different altitudes, Mach numbers, and humidity levels. Simulation results reveal that the DDPG-based controller outperforms PI control by achieving faster response times, 1.75 seconds (7.18%) faster during acceleration and 0.55 seconds (1.24%) during deceleration in standard conditions, and 1.09 seconds (4.79%) and 3.44 seconds (7.13%), respectively, under altered conditions. Moreover, the DDPG controller reduces turbine inlet temperature by up to 44.97% in the first case and 38.21% in the second case, and decreases surge margin by 54.83% and 56.18%, respectively, ensuring safer operation within limits. These findings demonstrate the DDPG algorithm's potential for substantial engine control performance and safety improvements. The study underscores the transformative potential of AI-driven control systems in aerospace applications, paving the way for more intelligent and adaptable engine management solutions.

Wave energy converters can be a promising option for wave energy, which is among them, aims to be a crucial device for new studies and essential for energy transformation due to its geographical versatility and adaptation to different wave conditions, still being the subject of research and development in the field of renewable energy. The spatial study to evaluate the behavior of Oscillating Water Column (OWC) has been a major problem towards the simulated analysis of these devices due to their high computational cost. OWC is a device that harnesses the oscillatory motion of seawater inside a partially submerged chamber, this movement compresses and decompresses the air column above, driving a turbine connected to a generator and converting wave energy into usable electricity. This study aimed to evaluate the spatial and temporal mesh independence of an onshore OWC using Computational Fluid Dynamics (CFD) and Richardson’s Extrapolation (RE). The CFD analysis was performed using ANSYS-Fluent software, and an RE study was conducted to improve the accuracy of the results by extrapolated solutions for different efficiency values obtained from the domain discretization levels. Indeed, 10 treatments were carried out to study the spatial (M0, M1, M2, M3 and M4) and temporal (M2T1, M2T2, M2T1to2, M2T3 and M2T4). mesh independence. The results demonstrate, in addition to the inclusion of the analytical second-order Stokes equation, that RE was instrumental in testing the incoming wave front, observing the behavior of the OWC and reducing its computational cost. M2 and M2T1to2, were the treatments chosen for the spatial and temporal independence analysis, respectively. The extrapolated values correspond to about 28.0227% (M2) and 33.5412% (M2T1to2). These findings support the use of RE as a reliable tool for mesh validation in CFD simulations, optimizing computational efficiency while ensuring robust results.

Laser cleaning is an efficient, non-invasive method that utilizes high-energy laser beams to eliminate contaminants. However, variations in laser process parameters can lead to challenges such as inconsistent cleaning depth, thermal damage, and uneven surface treatment, ultimately compromising the quality of the cleaned surface. To address these issues, developing a predictive model for cleaning states is crucial to enhance online monitoring systems, enabling earlier detection of potential problems. This manuscript outlined the development of a classification model intended for predicting the states of laser cleaning by employing the EMD-1DCNN methodology. The primary objective of integrating Empirical Mode Decomposition (EMD) is to enhance the precision and reliability of the model generated from a one-dimensional Convolutional Neural Network (1D-CNN). The laser cleaning experiments were executed at velocities of 100 mm/s and 300 mm/s on corroded boron steel substrates. Acoustic signals within the frequency spectrum of 20 Hz to 10,000 Hz were systematically recorded during the entirety of the cleaning procedure.These signals were categorized into three phases, which were corrosion removal stage, low roughness formation, and engraving stage, which indicates the surface damage. The results show that the time-domain signal recorded a random non-linear pattern during the corrosion removal stage. The frequency was active at 6300 Hz for all laser cleaning conditions, but the peak amplitude decreased as the grooves started to form on the cleaned area. Instead, the peak at 1800 Hz was increased. However, the implementation of EMD revealed a significant trend that could separate corrosion removal and groove formation stage at another bandwidth, which was 20 Hz to 500 Hz. Moreover, the EMD-1D-CNN classification model achieved an average accuracy of 95.75% with a deviation of 1.99%, demonstrating enhanced performance compared to a model developed without EMD. This research highlights the importance of the classification model in predicting cleaning process stages, facilitating real-time monitoring and ensuring cleaning quality. The preprocessing methods employed not only enhanced model accuracy but also improved consistency, potentially reducing computational demands while fostering a stable model.

Modern concerns of pollution and the depletion of fossil fuels have drawn much research to alternative energy sources, and biodiesel is a prime example. Biodiesel special properties, such as cetane number higher than that of other diesel fuels, and volatility lower than that of other diesel fuels, can influence diesel combustion and inject fuel systems with the potential for power reduction and nitrogen oxides emissions. This study focuses on palm oil methyl ester (POME) biodiesel and used isobutanol as additives to investigate the potential for the improvement of engine performance. This experiment is precisely conducted from 2000 to 3000 RPM with the data step of 200 RPM. The fuel blends of 5%, 10%, 15%, and 20% isobutanol with B40 (POME) on a diesel engine. The reference fuel will be standard diesel. Among the tested fuels, the B40ISO10 blend achieved the highest torque at 2200 RPM and delivered peak brake power at 3000 RPM, highlighting its efficiency at mid to high engine speeds. While diesel performed well at lower RPMs due to its high calorific value, biodiesel blends, particularly those with isobutanol exhibited improved performance characteristics. Notably, B40ISO15 consistently showed lower brake specific fuel consumption, indicating better fuel economy at higher RPM. Additionally, B40ISO20 displayed significant efficiency gains at increased speeds. These findings suggest that carefully formulated isobutanol enriched biodiesel blends can provide an effective balance of engine performance and fuel efficiency.

The functionally graded materials (FGMs) have been realised to be potential candidates when it comes to high-pressure projects and applications where thermal and mechanical stability is to be ensured in extreme environments. In the research, the drawback of the widely used stainless steel SS316L facing high-stress conditions in the thermal environment will be overcome by the innovation of two new FGMs composed of SS316L and Inconel 625, Ti6Al4V, and Inconel 718. The aim was to conduct the fabrication and testing of a multi-phase FGM with the help of advanced techniques of manufacturing namely additive manufacturing and powder metallurgy, with the strict control of layer thickness of 0.2 mm and contents of its materials (60% SS316L, 20% Inconel 625 or Ti6Al4V, and 20% Inconel 718). Tensile testing, yield testing, fatigue and creep behaviour were performed at temperatures of −20°C and +60°C. The findings indicated that the FGM containing SS316L, Inconel 625, and Inconel 718 proved to be superior to SS316L at every point where its tensile strength is 992 MPa and its yield strength is 602 MPa, also at a temperature of +60 C versus 460 MPa and 186 MPa tensile and yield strengths in SS316L. The advanced fatigue performance and creep resistance were also indicated because of the better qualities of the alloys Inconel. Such results are indicative of gradient composition and layer formation in augmenting thermal and mechanical capabilities. The research ends up with a conclusion that these FGMs can be considered as excellent prospects in terms of the aerospace and power generation industries where strength and thermal endurance are of essence to the next generation of the industry.

Resonators represent a new generation of sensors with superior performance and high sensitivity, making them well-suited for mass sensing applications. While V-shaped cross-section beams have demonstrated enhanced particle absorption and improved performance over conventional rectangular beams in the first bending mode, their behavior in higher-order vibration modes including lateral bending, torsion, and in-plane bending remains unexplored. This study presents a dynamic model of the vibratory motion of V-shaped beams after particle adsorption, employing both modal analysis and finite element methods. A sensitivity analysis based on Sobol’s method is conducted to evaluate the influence of beam geometry on resonance frequency shifts post-adsorption and to quantify the extent of this effect. Simulation results reveal that V-shaped cross-section beams exhibit superior performance compared to rectangular beams not only in the fundamental bending mode but also in higher-order vibration modes, including lateral bending, torsion, and in-plane bending. These findings highlight the potential of V-shaped resonators for advanced mass sensing applications requiring multi-mode vibrational sensitivity.