AIMS and SCOPE Jurnal Mekanikal is a refereed international journal established in 1996 by the Faculty of Mechanical Engineering, Universiti Teknologi Malaysia. Since then, it has become a flagship journal with a publication frequency of twice a year, in June and December. The journal aims to provide a platform for publishing articles in Mechanical Engineering and all its related areas. It is a publication devoted to the dissemination of research findings and applications in all aspects of Mechanical Engineering. The scope of the journal is closely aligned with the various academic degree programs offered at the Faculty. Hence, the scope of the journal is broad as it encourages multi-disciplinary research. The topics covered by the journal include, but are not limited to: • Applied Mechanics and Design • Thermofluids • Manufacturing Engineering • Industrial Engineering • Materials Science • Automotive Engineering • Aeronautics • Marine Technology TYPES OF CONTRIBUTION Original research papers, technical papers, literature review papers, conceptual papers and case study papers on the development, advancement and application of theories, algorithms, models, techniques, methods, concepts, frameworks, etc. are welcome. Articles should be between 5,000 and 10,000 words in length. This includes all text, abstract, references, tables, figures and appendices. NOTES FOR AUTHORS Jurnal Mekanikal commits in ensuring ethics in its publishing. Submission of paper to Jurnal Mekanikal implies that the Author(s) declare that the work is original, is not plagiarised, and is not previously published or is currently under consideration for publication elsewhere. All papers are refereed through a double blind review process. A guide for authors for submitting papers are available on the Guidelines web-page. Jurnal Mekanikal is published online with a frequency of two (2) issues per year (June & December). INDEXED BY: SCOPUS, MyCite, Asian Science Citation Index (ASCI), Google Scholar
Copyright of articles that appear in Jurnal Mekanikal belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions or any other reproductions of similar nature.
NUMERICAL AND EXPERIMENTAL EVALUATION OF CALM WATER RESISTANCE AND INTERFERENCE EFFECTS FOR HYDROFOIL-ASSISTED SWATH
The purpose of this study is to evaluate the interference factor of a conventional Small Waterplane Area Twin Hull (SWATH) vessel with and without the hydrofoils in calm water. While integrating hydrofoils into SWATH designs can reduce wetted surface area and improve hydrodynamic efficiency, it also introduces complex hydrodynamic phenomena and interference effects at the free surface. Conventional methods for calculating interference factors have proven inadequate for hydrofoil-assisted SWATH designs, necessitating a more robust analytical approach. To address this, a modified equation using the resistance coefficient was developed, specifically considering the running wetted surface area of each individual component of the vessel. The methodology involved the construction of a SWATH model featuring passive NACA 0015 hydrofoils in a tandem configuration at the forward and aft ends. Resistance tests were conducted in a towing tank across a range of Froude numbers (Fr) from 0.28 to 0.48, complemented by numerical simulations performed using OpenFOAM to analyze the flow field and wave patterns. A central aspect of this study is the implementation of a new formula for the interference factor that accounts for the dynamic change in the running wetted surface area of the demi-hulls and hydrofoils to improve calculation accuracy. Results indicated that the addition of hydrofoils increased the total resistance coefficient by average 21%. Despite this increase, the hydrofoils significantly improved pitch and heave motions by providing essential dynamic lift. Notably, numerical results identified a zone of beneficial (destructive) wave interference at Fr = 0.38, where overall wave making energy was effectively reduced. The study concludes that hydrofoil-assisted SWATH exhibits a higher interference factor than the bare hull. Furthermore, the findings demonstrate that conventional equations are unsuitable for hydrofoil-integrated multihulls, validating the necessity of the modified resistance coefficient equation introduced in this work.
HYBRID MULTIMODAL FUSION FRAMEWORK INTEGRATING EMG AND FORCE SIGNALS FOR ENHANCED HAND MOVEMENT PREDICTION
Accurate prediction of hand movements is important to improve human–machine interaction, especially in rehabilitation and assistive applications. However, the nonlinearity of the electromyography (EMG) signal often limits the reliability of motion classification. It causes the sensor fusion to be unstable and not intelligent enough to continuously predict the user hand movements. To address this, we propose a hybrid multimodal fusion framework that integrates EMG and force signals to improve prediction accuracy and robustness. The framework investigates the relationship between forearm EMG signals, various grasping tasks, and finger/wrist joint angles. It goes beyond discrete classification to allow continuous motion intention prediction of wearable hand control. The proposed hybrid multimodal fusion framework has two levels: feature-level fusion and decision-level fusion. Canonical Correlation Analysis (CCA) is used to extract highly correlated features across modalities at the feature level. Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), and Artificial Neural Networks (ANN) are used to classify these features to identify six different hand movements. To strengthening the decision consistency, majority voting is used at the classifier level. The performance of the system is evaluated based on the confusion matrices, accuracy, and F1-scores. Results show the proposed framework is significantly better than unimodal approaches, with the highest accuracy of 97.86% being achieved by the Waveform Length. Through experiments using data from 10 healthy subjects, it was established that multimodal fusion is effective in addressing the nonlinearity of the EMG signal, which results in more accurate hand gesture recognition. The findings assist in developing an efficient control scheme for wearable hand devices that provide smooth, user-intent-driven motions. By enhancing accuracy and responsiveness, the proposed approach improves support for activities of daily living (ADL), reducing the possibility of user dissatisfaction and the discontinuation of the device.
MODELING AND EXPERIMENTAL EVALUATION OF GEOMETRY AND WALL THICKNESS ON THE DYNAMIC PERFORMANCE OF SILICONE-BASED SOFT PNEUMATIC ACTUATOR
This paper presents the design, fabrication, and testing of a pneumatic actuator using silicone rubber. The study addressed the need for adaptable, flexible actuators in soft robotics applications. Single and multi-chamber designs are created using SolidWorks and fabricated through 3D printing and mold casting. Physical characterisation included stress-strain tests, repeatability assessments, and frequency response evaluations. Python is used to process video-tracked strain data and calculate mean and standard deviation across cycles. Different designs are explored in terms of key geometric parameters for bellow-shaped actuators, including multiple tapered edges of 7 and 13, tapered angles of 40° and 60°, and wall thicknesses of 2.5 mm, 3.0 mm, and 3.5 mm, allowing for comparison of actuation performance across configurations. The best-performing design achieved maximum displacement, high strain, and demonstrated strong durability based on repeatability performance, as indicated by a low standard deviation across multiple actuation cycles. The results revealed that design configuration 3 was the most efficient in terms of displacement and structural integrity. Among the thickness variations, Design 1.2 with a 3.5 mm wall thickness exhibited superior performance in structural stability and repeatability. The valuable insight highlighted is that the performance of silicone-based actuators is influenced by geometry and wall thickness, supporting the optimisation of soft actuators for precision control and efficiency.
OPTIMIZATION OF SURFACE ROUGHNESS IN MICRO-MILLING OF NITI SHAPE MEMORY ALLOYS USING MODIFIED DIFFERENTIAL EVOLUTION ALGORITHM
This study is conducted to observe the optimal effect of feed per tooth to cutting edge radius ratio, nanoparticle composition in percentage, and cutting environment on the surface roughness (Ra) of Nickel-Titanium (NiTi) Shape Memory Alloys (SMAs) in micro-milling. NiTi alloys are challenging to machine due to their high ductility and temperature sensitivity, often resulting in significant Ra. In this connection, surface quality was enhanced through Minimum Quantity Lubrication (MQL) in combination with solid lubrication using Boron Nitride (BN) nanoparticles. The Taguchi method developed a regression model from the experimental machining data. This Ra regression model was used as a fitness function for the Modified Differential Evolution (MDE) algorithm. Hence, the outcome of this study demonstrated that the MDE optimization technique can identify the process parameters that achieve reduced Ra. MDE application significantly reduces the Ra to 0.7115 µm, which is lower than experimental data (0.7210 µm) and standard Differential Evolution (DE) optimization (0.7122 µm). In conclusion, this research shows how the parameter optimization process can benefit from a combination of regression modeling with the MDE algorithm, yielding the results necessary for sustainable and efficient NiTi SMA machining.
COMPREHENSIVE REVIEW OF NANOPOROUS ANODIC ALUMINA: SYNTHESIS STRATEGIES AND FUTURE PERSPECTIVES
Nanoporous anodised aluminium (NAA) is a valuable substance characterised by a uniform pore structure, exceptional chemical stability, and robust mechanical qualities. It possesses extensive applicability in biology, energy, sensing, and optics. This research initially examines the historical development of NAA, elucidates the fundamental principles of its formation process, and elucidates the mechanism underlying its distinctive structure. This study systematically analyses the impact of key parameters—oxidation voltage, oxidation temperature, electrolyte type, and concentration—on the anodization process and their effects on the structural characteristics of NAA, including pore size, pore spacing, and oxide film thickness. The comparison of outcomes from various preparation circumstances elucidates the approach for optimising the procedure to get the desired structure. This research delineates the unique applications of NAA in the biomedical domain, encompassing drug carriers, tissue engineering, and antibacterial coatings, while also categorising its use in energy conversion and storage, including osmotic energy conversion and battery augmentation. The advanced developments of NAA in humidity, gas, and optical sensors are thoroughly examined, particularly their potential applications in the creation of photonic crystals, photocatalysis, and random laser platforms. This article anticipates the future trajectory of NAA, identifies its technical challenges and research priorities concerning large-scale preparation, precise structural control, and multifunctional applications, and offers a theoretical foundation and practical guidance for further investigation in this domain.
A DECISION MODEL FOR PRODUCTION LOGISTICS PLANNING CONSIDERING ELECTRIC VEHICLE USAGE
Logistics in production involves the movement of materials/parts to the workstation to ensure an efficient and effective production process. This study is motivated by the practices of production logistics within a manufacturing company and aims to minimize delays and total costs in transferring materials/parts to the workstation by utilizing electric vehicles (EV). The problem is formulated as an EV multi-period routing problem (EV-MPVRP), taking into account factors such as vehicle capacity, limited operation time, restricted distance, and charging time for EV. The results demonstrate that the proposed method effectively reduces delays and costs associated with the movement of materials/parts. Additionally, incorporating inventory into the production plan can lead to a decrease in both travel distance and charging time.
ENHANCING SYSTEM AVAILABILITY IN THE PAPER INDUSTRY: A MARKOV BIRTH-DEATH APPROACH
This study presents a methodological context for evaluating the transient-state statistical behavior of a washing system in the paper manufacturing sector. A Markov birth-death model is developed to characterize the system’s stochastic dynamics, with the dual objectives of optimizing availability and enhancing operational uptime through data-driven maintenance strategies. The system’s behavior is formalized using state transition diagrams and a set of differential equations derived for individual subsystems. These equations are computationally solved through matrix-based methods, implemented in Python programming for efficiency. In addition, a comprehensive regression analysis is conducted using SPSS software to examine the relationship between system availability and operational time. Multiple models—including linear, quadratic, exponential, and logarithmic regressions—are applied and compared to determine the best-fitting representation of availability trends. Numerical simulations reveal substantial improvements in system performance through targeted maintenance interventions, including reductions in failure rates. The results underscore the efficacy of both Markov-based modeling and regression analysis in boosting availability and operational efficiency, offering actionable insights for downtime mitigation and maintenance scheduling in industrial applications.
LAGRANGIAN APPROACH OF MODELLING THE PRODUCTION RATE OF CARBON NANOTUBES (CNTS) SYNTHESIS IN THE FCVD SYSTEM FOR SCALABILITY ANALYSIS.
Flame-assisted chemical vapour deposition (FCVD) is considered an effective method for synthesizing carbon nanotubes at relatively low cost. Large-scale production of carbon nanotubes via FCVD is essential to fully realize the technique's potential for creating nanomaterials for industrial applications. The lack of a comprehensive model for scaling up CNT synthesis in previous research highlights the need for further development. Therefore, this study develops a baseline model for scaling up the synthesis process. It investigates the factors contributing to the decline in CNT yield as bead mass increases—specifically, fluidization intensity, which is determined by particle velocity, average growth temperature, and methane concentration—using a computational approach. In this method, the flow domain temperature was initialised at 500°C, and the substrate was fluidized at varying nitrogen shield gas flow rates across bead masses from 10 g to 25 g for scalability analysis. A discrete phase model (DPM), integrated into ANSYS Fluent CFD software and coupled with the Lagrangian framework, was utilised to track particle trajectories, collect data, and perform analysis. The results were organized and incorporated into the growth rate model, a combined CFD and MATLAB computation, before conducting overall data analysis and presentation. The CNTs produced over a 10-minute growth period indicate the respective production rate for each bead mass. The study shows that increasing fluidization intensity from 11 slpm to 14 slpm and bead mass from 10 g to 25 g results in a decrease in temperature from 1200 K to 1000 K. The velocity magnitude, representing fluidization intensity, rises from 0.017 to 0.02 m/s. Conversely, methane concentration peaks at the highest temperature, which occurs at the lower bead mass of 10 g. Notably, this high methane concentration at elevated temperatures underscores its crucial role in methane decomposition.
EFFECT OF STIRRING SPEED, TEMPERATURE, AND REINFORCEMENT FRACTION ON THE MICROSTRUCTURAL CHARACTERISTICS OF AL–SI/FE₂O₃ COMPOSITES
This study investigates the influence of stirring speed, melting temperature, and reinforcement fraction on the microstructural characteristics of Al–Si/Fe₂O₃ composites produced using the stir casting method. A Taguchi-based experimental approach was employed to optimize the process variables through an orthogonal array design, enabling a systematic and efficient evaluation of parameter effects. The cast specimens were characterized using optical microscopy and SEM-EDX to examine morphology, particle distribution, and elemental composition. Grain size analysis was performed using Python-based image processing to obtain a more comprehensive quantification of the microstructure. The Taguchi analysis identified the optimal parameter combination at a stirring speed of 1000 rpm, a processing temperature of 600 °C, and a reinforcement fraction of 1 wt%. This setting produced a grain size of 235.92 μm, representing the most consistent performance based on the signal-to-noise ratio and demonstrating strong agreement between the predicted optimum and the observed microstructural results.
NUMERICAL PREDICTION AND HULL FORM REFINEMENT FOR SEAKEEPING IMPROVEMENT OF A CATAMARAN TYPE UNMANNED SURFACE VEHICLE
The unmanned surface vehicle (USV) is specifically designed for targeted applications such as offshore environmental monitoring, hydrographic surveys and others. However, excessive motions in rough sea conditions can affect the accuracy of sensor data and may lead to potential equipment damage. Hence, this study aims to enhance the USV’s seakeeping performance by improving the hull form design. The DELFT 372 model with a length between perpendiculars (Lpp) of 3.0 m and a hull separation of 0.46 m, was initially selected, modelled and validated before conducting seakeeping simulations using Ansys AQWA at a Froude number of 0.6. The simulated heave and pitch RAOs were then compared to published experimental results under Sea State 1 conditions for validation purposes. At Fr = 0.6 and a wave amplitude of 0.0179 m, the simulated heave and pitch RAOs were found to be 2.93 m/m and 94.25 °/m, with deviations of 4.61% and 8.96%, respectively compared to the published experimental data. The study then proceeds by slicing the original hull along its longitudinal centerline and repositioning the halves to form an outside-flat (OF) configuration, further modifying the bow to an X-bow design while maintaining the hull separation distance. The DELFT 372 OF-Xbow variant was also developed with an increased hull breadth from 0.12 m to 0.20 m. Seakeeping simulations were conducted for both modified configurations under Sea State 2 conditions at Fr = 0.6, where the wave amplitude is 0.25 m. The resulting heave and pitch RAOs were 0.97 and 0.96 respectively for the OF-Xbow at λ/Lpp of 9.69, and 0.90 and 2.09 for the OF-Xbow with 0.20 m demihull breadth, respectively. These values were compared to the symmetric hull, which exhibited heave and pitch RAOs of 0.80 and 2.67 respectively. The results indicate significant improvements in pitch motion of 64.17% for the OF-Xbow and 21.72% for the OF-Xbow with b = 0.20 m, compared to the symmetric hull. However, heave motion increased by 20.42% and 11.91% % for OF-Xbow and OF-Xbow with b = 0.20 m respectively. This study demonstrates that hull modifications can effectively reduce pitch motion, although this comes at the cost of increased heave motion.
MECHANIZATION IN MALAYSIAN AGRICULTURE: A SYSTEMATIC REVIEW OF PRODUCTIVITY, SUSTAINABILITY, AND SOCIO-ECONOMIC PERSPECTIVES
Mechanisation is in fact much needed by Malaysia’s agricultural sector that has been dealing with the issues of food security, labour shortage and environmental sustainability. Recent technologies advanced, especially in IoT (Internet of Things), AI (Artificial Intelligence) and Automation provide new revolution to improve productivity and operational efficiency. Nonetheless, the contribution of these technologies to sustainable agricultural development in Malaysia needs to be thoroughly assessed. This review followed a PRISMA based systematic literature review. Literature search was performed in Scopus and Web of Science for articles between 2022 and 2024 with defined search string and inclusion criteria. After screening, 27 articles published in peer-review journals were identified for further screening. Thia review was synthesized in the following three themes: (i) Smart Farming and IoT Applications, (ii) Artificial Intelligence and Computer Vision, (iii) Sustainable Agriculture and Related Technologies. The review concludes that IoT-based smart farming enhances irrigation management, live monitoring, and resource utilization. Crop and weed detection are enabled by AI, computer vision technology to support early disease diagnosis and yield estimation using next generation of imaging and machine learning. Energy use and environmental pollution can be reduced by sustainable mechanization technologies. Despite these advantages, the barriers of high implementation cost, low digital literacy, and inconsistent policy enforcement hinder the widespread adoption of Malaysian smallholders. Adoption of IoT and AI technology with sustainable mechanization as the solution can serve as a promising approach in modernizing Malaysian agriculture. Advance mechanisms, cost-effective innovations and focused training programmes are necessary to enhanced productivity, resilience and environmental sustainability of the farming system.
OPTIMIZATION OF MOLDING MATERIAL COMPOSITION FOR IMPROVED HARDNESS IN SAND-CAST ALUMINUM ALLOYS USING METAL CHIPS: TAGUCHI DESIGN APPROACH
This study presents an innovative and environmentally friendly method for enhancing the hardness and thermal stability of aluminum alloy castings by incorporating metallic machining chips into foundry sand. The research identifies the optimal combination of parameters using Taguchi optimization and regression modeling. Characterization techniques, including X-ray fluorescence (XRF), thermogravimetric analysis (TGA/DTA), scanning electron microscopy (SEM), and optical microscopy, were employed to validate the results. XRF analysis confirmed that the base aluminum alloy (engine block) contained aluminum, silicon, and magnesium, while brown sand consisted mainly of silicon, phosphorus, and potassium. Cast iron and brass chips contained iron, carbon, silicon, copper, zinc, and iron, respectively. Mechanical testing revealed a 21.2% improvement in the hardness of the modified aluminum alloy over the as-cast sample. Taguchi optimization identified the optimal conditions as brass chip type (level 4), 30 wt% content (level 3), and 100 μm particle size (level 1), which yielded a 26.08% increase in hardness. SEM and optical microscopy of the optimal sample showed significant grain refinement compared to the control. TGA/DTA results demonstrated a 12.99% increase in residual mass and a 28.37% improvement in thermal degradation resistance, indicating better thermal stability. The regression model developed for predicting hardness exhibited strong performance, with R², adjusted R², and predicted R² values of 97.81%, 96.35%, and 91.33%, respectively. Foundry tests further revealed 16.03%, 18.18%, and 6.82% improvements in green compressive strength, dry compressive strength, and compactibility, respectively, for brass chip-modified sand. These findings have significant implications for foundry operations. The integration of metallic machining chips into conventional sand molds can be practically adopted as a cost-effective and sustainable technique to enhance the mechanical properties of aluminum alloy castings without altering the alloy composition.
DEVELOPMENT OF A PERFORMANCE MEASUREMENT TOOL FOR EMPLOYEE INVOLVEMENT IN TOTAL PRODUCTIVE MAINTENANCE
Operational excellence in manufacturing depends not only on advanced equipment but also on active employee involvement. This study develops an Employee Involvement Index (EII) to systematically measure employee involvement in Total Productive Maintenance (TPM) and help managers identify actionable improvement measures. Based on pre-survey interviews and structured employee survey in the Assembly Department of a Malaysian stationery manufacturing company, key factors influencing employee involvement were identified and incorporated into the EII. The EII was pilot tested over 11 weeks with nine employees and one production machine alongside targeted initiatives. Results showed an increase in EII from 68% to 84%, with a concurrent improvement in Overall Equipment Effectiveness (OEE) from 50.33% to 82.23%. The findings strongly suggest an association between employee involvement and equipment performance, highlighting the value of a people-centred approach to TPM. Without claiming direct causation, this study contributes a practical tool to evaluate employee involvement and its relationship with equipment performance, supporting sustainable TPM implementation in real manufacturing environments.
FIBRE SELECTION VIA ANALYTIC HIERARCHY PROCESS AND FIBRE ORIENTATION IN KENAF/THERMOPLASTIC COMPOSITES FOR FRUIT PICKER POLE
This research is a preliminary investigation into identifying appropriate natural fibres for blending with glass fibres to create durable and high-performing hybrid composites. The Analytical Hierarchy Process (AHP) was employed to select most suitable fibre in the enhancement of hybrid natural/synthetic the composites designed for fruit picker pole. Following this analysis, flexural test was conducted on kenaf yarn/fibreglass mesh-reinforced Acrylonitrile Butadiene Styrene (ABS) composites, unveiling the bending performance under the influence of stacking sequence. The kenaf yarn/fibreglass mesh composites with various stacking sequences were prepared via the hot press moulding compression technique. It was found that stacking sequences, 0°/90°/0°/90°, 0°/+45°/-45°/0°, 0°/+45°/90°/-45°, and 0°/0°/0°/0°, significantly affect the flexural properties of the composites, offering freedom of material design for certain engineering applications. Notably, the composites with 0°/0°/0°/0° stacking sequence displayed superior bending strength and elastic properties of 65.76 MPa and 1657.57 MPa, respectively. Conversely, the lowest flexural strength and modulus of 52.05 MPa and 908.69 MPa were noticed in composites with fibre stacking sequence of 0°/+45°/90°/-45°. These findings underscore the impact of the stacking sequence on the bending performance of the FRP composites. Optimising fibre stacking sequence emerges as a practical approach to enhance performance and sustainability in fruit picker pole.
OPTIMIZING PAINTING OPERATION IN AN AUTOMOBILE PAINTSHOP USING ML APPROACH
Optimizing paint operations is crucial to ensure product quality, consistency, and customer satisfaction in the automobile paintshop. The detailed analyses and understanding of the paint process proffers a thorough optimization approach to increase productivity, lower the operational costs, while maintaining a competitive advantage. The study aims to improve the painting operation in an automobile paintshop through key painting parameters, implementing quantitative approach techniques, using both random forest regression and reinforcement ML model and evaluating the effectiveness of optimized model for enhancing the painting processes. The painting defects and the causes were analyzed using cause and effect diagram and the corresponding parameters associated with the defects were used for data modelling to optimize the process. Random forest regression model predicts the good products, number of scraps and number of reworks with an accuracy of 70.6% while the Q-learning model has a prediction accuracy of 94.6%. The model was validated by adjusting the system parameters to the predicted model data which indicates a significant improvement in the FTT, number of scraps and reworks. The significance of the study is its potential to drive operational and process improvements, to increase the organization’s productivity by reducing paint defects
DEVELOPMENT OF A PROTOTYPE THREE–FINGER TELEROBOTIC HAND WITH FORCE FEEDBACK FOR GRASPING MULTIFORM OBJECTS
the demand for adaptability in dynamically changing conditions, and the desire for enhanced precision in remote operations. These factors necessitate the deployment of highly dexterous robotic hands for mechanical tasks, with humans in the loop for high-level planning and decision-making. Despite their potential in healthcare, manufacturing, space, disaster response, and agriculture, widespread adoption of telerobotic hands is limited by high costs, restricted motion, and control challenges. To address this, a prototype low-cost, 3D-printed, three-finger telerobotic hand was developed. The development process began with the conceptual design of the hand’s mechanical structure, followed by design theories to determine finger length, grasping capability, load-carrying capacity, and power requirements. A 3D CAD model was created using SolidWorks and fabricated with a 3D printer, while circuit layout of the components was developed in Fritzing. The assembled components were integrated with electronic modules and programmed in the Arduino IDE to receive servomotors control signals from a joystick-based wireless controller and transmit force-sensitive resistor (FSR) force signals to the robotic hand via Bluetooth communication. The wireless controller itself was designed for ergonomic use. It features adjustable joysticks positions for different hand sizes, signal mapping strategies, and micro-vibration motors for haptic feedback. The platform for its hardware assembly was modeled in SolidWorks and fabricated via 3D printing. The circuit layout of the components was developed in Fritzing. The assembled components were integrated with electronic modules and programmed in the Arduino IDE to transmit servomotors control signals to the robot hand and receive micro-vibration motors control signals generated from FSRs via Bluetooth communication. The hand supports four distinct ranges of motion, namely flexion, extension, abduction, and adduction, and includes fingertip FSRs to measure the force exerted on objects. Experimental results show the telerobotic hand effectively replicated human thumb, index, and middle finger movements as controlled by the joysticks. The system achieved 96.2% motion accuracy resulting from flexion (99.1%), abduction (95%), and adduction (94.5%), with minimal deviation. It attained an 87% grasping success rate with an average grasp time of 2.17 seconds. A positive correlation between measured force and vibration intensity validated the haptic feedback's effectiveness. User feedback indicated an 84% average satisfaction across responsiveness, ease of use, comfort, haptic feedback, fatigue, and task completion. The prototype successfully demonstrated its ability to replicate human finger motions and provide intuitive control with meaningful force feedback.
APPLICATION OF THE SET BASED CONCURRENT ENGINEERING (SBCE) PROCESS: A SYSTEMATIC LITERATURE REVIEW
The purpose of this paper is to document a comprehensive review of the application of set based concurrent engineering (SBCE) in industries. Additionally, it aims to investigate the integrated technologies within the domain of SBCE applications and identify research gaps in the corresponding technological landscape. An in-depth examination of 48 research papers provides valuable insights into SBCE's application in a variety of industries, where SBCE serves as a tool for refining designs and improving product performance to meet changing market demands. Furthermore, the use of relevant technologies such as mathematical modelling, computer software, machine learning, data analytics, and lean principles strengthens SBCE's efficacy by allowing for data-driven decision-making and faster design processes. Despite its widespread use, SBCE has received limited study attention in the previous decade, therefore future research should focus on machine learning integration to improve product development efficiency. The paper analyses SBCE applications across several industries and acknowledges a wide range of industries. The research also emphasizes contemporary studies from 2013 to 2023 to provide current and forward-thinking views. Additionally, the study examines how researchers incorporate SBCE into development processes and its practical engineering applications
PID CONTROLLER TUNED BY METAHEURISTIC ALGORITHM USING ANT LION OPTIMIZER FOR VIBRATION CONTROL OF HORIZONTAL FLEXIBLE STRUCTURE
applications, including aerospace, robotics, and precision machinery. Passive vibration control approaches are effective for high-frequency applications, but often fail against low-frequency vibrations. Hence, active vibration control (AVC) is introduced to address this limitation. Among various AVC techniques, PID controller remains one of the most widely adopted methods. However, optimal tuning for PID parameters remains a challenge in complex dynamic systems. Thereore, this study explores the application of ant lion optimizer (ALO) to fine-tune PID parameters for vibration cancellation in a flexible structure. The optimization process is based on single objective function by minimizing the mean square error (MSE). Inspired by the predatory behaviour of antlions, ALO iteratively adjusts kp, ki, and kd values by updating the position of ants and antlions based on fitness evaluations. The performance of the PID-ALO controller is validated through MATLAB/ Simulink R2021a by assessing its effectiveness in attenuating vibrations induced by single and multiple sinusoidal disturbances. Results indicate that the PID-ALO controller outperformed the classical PID-ZN controller by achieving an attenuation level of 47.45 dB with 45.85% vibration reduction under single sinusoidal and 46.97 dB with 40.04% reduction under multiple sinusoidal disturbances. These findings highlight the potential of ALO in precise PID tuning, representing the first implementation of a PID-ALO controller on a horizontal flexible plate under dual excitation disturbances.
PROGRESSIVE DAMAGE MODEL FOR NON-CRIMP FABRIC COMPOSITE: MODELLING FRAMEWORK AND VERIFICATION
Non-crimp fabric (NCF) composites have become increasingly used for primary aircraft structures due to its similar mechanical performance as the conventional Carbon Fiber Reinforced Plastic (CFRP) prepreg but comes with a greater manufacturing advantage. Inherent to its manufacturing method such as the Resin Transfer Molding (RTM), the NCF textile comes in dry state and requires stitching to hold the fiber tows in bundle shape for handling purposes. Because of this unique architecture, the damage mechanisms of NCF composites are different compared to unidirectional prepreg, which often comes in tape format. In this study, a progressive damage model operating at mesoscale level is proposed to predict the stiffness and strength of NCF composite laminate. The strength-based model was derived from LaRC05 model with an addition of damage evolution model to account for fracture energy dissipation after damage is initiated. Verification steps were performed using available data on the literature, and comparisons with other damage models such as Hashin model and Tsai-Hill model are conducted to ensure model’s conformity and accuracy. The result from the proposed model demonstrates good agreement with experimental data published by other authors with maximum error up to 16%.
OPTIMISING VENTILATION FOR THERMAL COMFORT AND INFECTION CONTROL IN VEHICLES: A REVIEW ON CFD APPLICATIONS AND TECHNIQUES
Effective ventilation in vehicles is crucial for ensuring thermal comfort and controlling airborne infections. This review focuses on the application of Computational Fluid Dynamics (CFD) in optimising vehicle ventilation systems. It examines various CFD methodologies, including turbulence models, meshing techniques, and solver algorithms, as well as their impact on ventilation performance. The review highlights that RNG k-ε and SST k-ω are among the most commonly used turbulence models in vehicle cabin simulations, based on their suitability for modelling complex airflow patterns and their validation against measured data. It also examines meshing techniques that influence the reliability of CFD simulations, including structured, unstructured, and hybrid meshes. This review article also discusses solver algorithms and their role in efficiently solving the governing equations of fluid flow and heat transfer. Various CFD approaches have been employed to reduce the risk of particle transmission by improving airflow distribution and contaminant removal effectiveness. Moreover, CFD has been used to enhance thermal comfort by optimising temperature distribution and managing heat loads. By synthesising recent advancements and case studies, this review provides valuable insights for researchers and practitioners, aiming to enhance ventilation systems in vehicles through advanced CFD techniques. This work aligns with Sustainable Development Goal (SDG) 3 (Good Health and Well-Being), by improving air quality and reducing infection risks, and SDG 11 (Sustainable Cities and Communities), by contributing to safer and more comfortable transportation solutions. The integration of CFD into vehicle ventilation optimisation supports the creation of healthier and more efficient transportation systems, ultimately contributing to broader sustainability goals.
UNVEILING THE IMPACT OF AIR CHANGE RATES ON PARTICLE DISPERSION IN PATIENT WARDS
The purpose of this study is to examine the effect of air change rate (ACH) on reducing particle concentration in a patient ward using numerical simulation. A patient ward is a large room or space in a medical facility where individuals with similar health conditions or treatments are grouped. It is equipped with modern beds, medical care systems, lighting, and ventilation systems to maintain a high level of hygiene. Airborne transmission in patient wards can easily spread to other individuals. Therefore, the ACH in the patient ward needs to be increased. Three ACH cases were investigated along with a baseline case using a simplified CFD model. The patient ward model was constructed and validated based on past literature. The Renormalization Group (RNG) k-epsilon turbulence model was selected to simulate airflow velocity in the patient ward. The movement of airborne particles was simulated using the Discrete Phase Model (DPM). An effective ACH was determined from the three cases. Based on the results, the most effective ACH in reducing particle dispersion in the patient ward was identified. The findings indicate that in Case 3, where ACH = 25h-1 and the ventilation opening size was 1 m × 1 m, the high airflow velocity in the patient ward completely reduced airborne particle concentration (100%) above patient 1 and 100% around other patients and medical staff. By reducing particle concentration, the number of particles that could infect other patients was also significantly reduced.
Stress Amplification and Shielding of Double-Edge Cracks in Alveolar Bone Under Mode II Loading: A Comparative Analysis of Bone Material Heterogeneity
This paper aims to examine the response of double edge cracks in alveolar bone under Mode II loading with emphasis on differences in crack behaviour between homogeneous (cancellous) and heterogeneous (cortical-cancellous) bone types. Implant placement exerts load on the alveolar bone and consequently causes microcracks that would influence the stability of the implant and the bone. Such fractures and their relation to the composition of the human bones must be better understood to enhance the design of the implant. Crack behaviour was studied using a 2D continuum mechanics model of the dimension 1mm x 1mm. The crack length-to-width ratio of the upper crack varied in the range of 0.125 mm to 0.5 mm, and the lower crack was held constant at 0.125 mm. The goal of the study is to investigate Mode II loading of cancellous and cortical-cancellous bone materials where stress intensity factors (SIF) and normalized SIF values are a particular focus. In the homogeneous cancellous bone model, stress shielding occurred and normalized SIF values from 0.176 to 0.717 were consistently demonstrated. While the heterogeneous model initially showed similar shielding behavior (SIF ranging from 0.039 to 0.053), a transition was observed from shielding to stress amplification to SIF increasing from 0.053 to 1.205, with a unification point at a/W ≈ 0.3119. We observe that the cortical layer initially disperses the applied shear stress; however, as the crack length increases beyond a critical threshold, stress amplification occurs. Thus, the relevance of bone heterogeneity to the crack behavior is emphasized and the need to account for material composition in the implant design and placement for improving implant mechanical stability and durability is confirmed.
INFLUENCE OF MULTI-WALLED CARBON NANOTUBES (MWCNTs) CONTENT ON THE MORPHOLOGICAL AND MECHANICAL PROPERTIES OF TITANIUM-HYDROXYAPATITE (Ti-HA) COMPOSITE
Despite the promising properties of titanium-hydroxyapatite (Ti-HA) composites in biomedical applications, their mechanical performance needs further enhancement. The incorporation of multi-walled carbon nanotubes (MWCNTs) has been proposed to improve these properties, however, their tendency to agglomerate at higher concentrations may negatively impact the composite’s performance. Ti-HA was successfully reinforced with MWCNTs using a process involving mechanical alloying, powder metallurgy, compaction, and sintering. This study investigated the effect of MWCNTs content, specifically 5 and 10 wt.%, on the morphological and mechanical properties of Ti-HA-MWCNTs composite. Every composition was evaluated for its compressive strength and Young’s modulus. The values obtained for 5 wt.% were 176.39 MPa and 66.82 GPa, respectively. For 10 wt.%, the values were 61.50 MPa and 23.05 GPa. The porosity values were determined to be 15.19% for 5 wt.% and 26.02% for 10 wt.%. The decline observed with the addition of 10 wt.% MWCNTs was associated with the MWCNTs agglomeration within the Ti-HA matrix, and the weak interfacial bonding between them. Additionally, the density measurements yielded 3.40 g/cm³ for 5 wt.% and 2.76 g/cm³ for 10 wt.%. These findings emphasise the importance of optimising dispersion techniques and improving interfacial bonding strategies in order to fully utilise MWCNTs’ reinforcing potential in Ti-HA composites.
ANALYTICAL MODELING AND EXPERIMENTAL VALIDATION OF LOW-VELOCITY IMPACT RESPONSES IN UNIDIRECTIONAL CFRP COMPOSITE USING FSDT AND TDOF APPROACHES.
Metallic structures have been extensively replaced with carbon fiber-reinforced polymer (CFRP) composites in many industries due to their technical advantages and design versatility. Despite these advantages, CFRP composites are sensitive to dynamic loads such as low velocity impact, which can compromise structural integrity with internal damage. This study introduces a computationally efficient analytical model developed in MATLAB to predict impact damage in unidirectional (UD) CFRP composites, providing a faster and more cost-effective alternative to finite element simulations. The model used First Order Shear Deformation Theory (FSDT) and a Two Degrees of Freedom (TDOF) approach, incorporating a nonlinear contact force model to calculate displacements, absorbed energy and impact forces. A key novelty of this research lies in the multi-level experimental validation conducted with an Instron CEAST 9350 impact testing machine with hemispherical impactor tips at energy levels of 5.6 J, 10.3 J and 16.14 J. Results reveal that the model achieves good agreement with experimental data at lower energy levels, accurately captures the elastic behavior with a minimum error of 1%. However, the model's limitations in simulating nonlinear responses and material damage become more evident at higher energy levels, where the extent of damage has a pronounced effect on energy absorption and dissipation. This underscores the need for incorporating advanced material models that account for damage progression and strain-rate effects to enhance accuracy and reliability. Ultimately, this work supports the development of innovative composite solutions, contributing to more efficient and cost-effective engineering practices.
BOX BEHNKEN DESIGN METHOD TO OPTIMIZE PIEZOELECTRIC CANTILEVER-BASED ENERGY HARVESTING SYSTEM PARAMETERS
Vibration in rotating and reciprocating machines and structures will likely increase with machine age and prolong machine usage. Thus, converting such a threat to machines for an opportunity in innovative energy harvesting is a unique advantage to industrial decision-makers. This study optimises the energy harvesting parameters of a piezoelectric cantilever-based system using the Box Behnken Design (BBD) Methodology to provide a sustainable solution for energy conversion using piezoelectric materials. Literature data that distinguishes the important parameters of the systems as follows was considered: Load resistance, width of piezo-plate and length of the piezo plate. A comprehensive analysis of the parameters and responses of the optimum scheme is presented. The feasibility of applying the Box Behnken design as a response surface approach was ascertained. BBD approximates the process parameters as a quadratic function and uses blocks to explain results. The current research offers evidence of the optimization behaviour of the piezoelectric cantilever-based energy harvester, impacting the conversion of the negative aspect of vibrations into a positive form for industrial equipment sustainability. The results emphasise the urgent need for interventions and policies to improve industrial management.
CONVOLUTIONAL NEURAL NETWORKS FOR AUTISM SPECTRUM DISORDER DETECTION USING ELECTROENCEPHALOGRAPHY
Dealing with children with Autism Spectrum Disorder (ASD) is challenging due to their sensory reactions, leading to behavioral issues, self-injury, and safety concerns. Many individuals with ASD also exhibit atypical sensory processing, increasing anxiety and difficulty in daily life. Existing diagnostic tools like the Autism Diagnostic Observation Schedule (ADOS) are subjective, time-consuming, and heavily dependent on trained professionals. This work is presented as a pilot feasibility study, based on EEG spectrograms, to establish a baseline for future large-scale investigations. The EEG data obtained from King Abdulaziz University Hospital, from 16 participants (12 ASD, 4 neurotypical) were preprocessed to remove noise, segmented into 3.5-second windows, and transformed into time-frequency spectrogram images using the Short-Time Fourier Transform (STFT). These spectrograms were classified using both machine learning (ML) models, including Support Vector Machines (SVM), Decision Trees, and Ensemble Methods, and deep learning (DL) Convolutional Neural Networks (CNNs). While ML models achieved moderate accuracy, with Subspace KNN performing best at 90.27%, CNN architectures significantly outperformed them, Model 4 achieving accuracy of 99.89%, demonstrating stability. Smaller batch sizes (32–64) optimized performance, whereas larger batches (128) degraded accuracy by up to 22%. The results highlight the transformative potential of deep learning in automating ASD diagnosis, offering a rapid, and clinically alternative to traditional methods.
ANALYSIS OF ENGINE SPLIT COOLING SYSTEMS FOR INTERNAL COMBUSTION ENGINES: FOCUS ON TEMPERATURE DISTRIBUTION AND PERFORMANCE OPTIMIZATION
This research focuses on the optimisation and analysis of internal combustion engine (ICE) split cooling systems to enhance thermal management, improve engine efficiency, and reduce emissions. Split cooling systems separate the coolant circuits for the cylinder head and engine block, allowing for differential cooling that maximises warm-up time and operational efficiency. A one-dimensional flow model of the cooling system was developed using GT-Suite simulation software to analyze coolant flow, pressure drops, and temperature distribution across a range of engine speeds (from 1,000 rpm to 5,000 rpm). The simulation results indicate that the split cooling system accelerates the warm-up process, with the cylinder head temperature reaching 260°C compared to 250°C for traditional systems, representing a 10°C improvement in thermal efficiency. Additionally, the split system exhibited a 20°C higher piston temperature (210°C versus 190°C) during warm-up. This configuration enables the use of a higher compression ratio and improved thermal efficiency. The study identifies the limitations of conventional cooling systems in managing heat at elevated engine outputs. In contrast, the split cooling system demonstrates a more uniform temperature distribution and maintains superior performance under increased engine loads. Validation of the simulation results against design targets confirms that split cooling systems significantly enhance engine performance and thermal management, with implications for broader automotive thermal management applications. Future research should focus on on-road testing and further applications to advance engine efficiency and sustainability.
THE EFFECT OF CONICAL AND SPHERICAL SHAPE OF DIMPLE’S BOTTOM PROFILE FABRICATED ON THE SHAFT SURFACE IN JOURNAL BEARING
Growing concerns over engine emissions have accelerated the search for alternative energy sources, with hydrogen emerging as a promising solution to reduce dependence on non-renewable fuels. In modern automotive systems, bearings are essential for achieving performance targets by supporting rotating components, carrying loads, and facilitating torque transfer. The effectiveness of introducing dimples on the contact surfaces of journal bearing depends on various factors. Careful consideration needs to be clarified as the wrong selection would have a detrimental effect on the surface. In this study, the conical and spherical shapes of the dimple’s bottom profile were fabricated on the shaft surface. The experiment was conducted by using a journal-bearing test rig with a 0.5 length-to-diameter ratio, made of Stainless steel SS304, and lubricated by mineral-based oil VG68. The experiment was conducted under various operating conditions. It was observed that the spherical shape demonstrated significant improvement in terms of friction coefficient, load-carrying capacity, and minimum oil film thickness compared to that of the conical shape and smooth surface respectively.
Kinematics Characterisation of Deflection Technique in Seni Silat Cekak Using Motion Capture Analysis
This study investigates the effectiveness and kinematics of Kaedah A, a deflection technique in Seni Silat Cekak Malaysia (SSCM). As the scientific research on Malay Martial Arts (Silat) is limited, this study aims to analyze the execution time of Kaedah A and presents its kinematic profile. Twenty experienced SSCM practitioners performed Kaedah A, which was captured using a motion capture system (Microsoft Kinect via Cekak Visual 3D v1.0) at 30 frames per second. The captured hand positional data was analyzed to determine the hand's trajectory, velocity, and execution time. Results indicated that Kaedah A can be executed in less than 0.1 seconds, demonstrating its potential for rapid self-defense. The maximum average hand velocity was 5.02 m/s. Statistical analysis of kinematic variables, including velocity components, execution time and elbow joint angle, revealed normal distributions. The findings of this study contribute to a deeper understanding of SSCM techniques and its potential applications in self-defense.
Augmented Reality-Assisted PLC Learning: A Comparative Analysis of Student Motivation
Laboratory experience is crucial in engineering education, as it enables students to design and conduct real experiments, fostering the development of their specific skills and aptitudes. However, students often face challenges when working with complex laboratory equipment, leading to longer experiment completion times. Augmented Reality (AR) and Mixed Reality (MR) offer the potential to enhance the learning experience by alleviating these difficulties. These technologies also improve concept comprehension and overall understanding of experiments. In this paper, we present an augmented reality application for a PLC (Programmable Logic Controller) experiment, designed to assist students in operating laboratory equipment more easily. The study investigates the impact of AR on students' laboratory skills, learning methods, and motivation through effectiveness results. A total of 35 participants from the Department of Mechanical and Manufacturing Engineering were introduced to AR technology. The study validates the effectiveness of AR using a questionnaire-based survey completed at the end of the experiment. Additionally, the study provides a comparative analysis of improvement percentages (POI) by examining data such as Task Completion Time (TCT), error counts, and the number of hints used in both paper-based and AR-based instruction experiments. Furthermore, this study assesses the understanding of MR applications. The findings suggest that AR and MR technologies are highly suitable for engineering education, offering efficiency and substantial benefits to students.