International Journal of Automotive and Mechanical Engineering
IJAME aims to disseminate original research in Automotive and Mechanical Engineering and presents the latest development and achievements in scientific research to stimulate and promote academic exchange between scientists, engineers, and researchers. Original, innovative and novel contributions providing insight into analytical, computational modelling, and experimental research are encouraged. IJAME is indexed in Scopus, Web of Science (ESCI), Asean Citation Index (ACI), Index Copernicus, Ulrichsweb, MyCite, Google Scholar, ProQuest and Chemical Abstracts Service.
A comprehensive review of machine learning approaches for decision-making in autonomous vehicles collision avoidance systems
Autonomous vehicles (AVs) hold the potential to reduce road accidents statistics through intelligent collision avoidance systems. This paper presents a comprehensive review of machine learning (ML) approaches for decision-making in AV collision avoidance. The comprehensive review is systematically categorized into four categories which are supervised learning (SL), hybrid learning (HL), deep learning (DL), and reinforcement learning (RL). Each category is evaluated in terms of its applicability in decision-making tasks, which include perception for situational awareness task, behavioural-level decision-making task, motion planning task, and multi-agent coordination task. The review highlight that SL methods offer reliable performance in structured scenarios but have constraints under real-world uncertainties. For HL, the methods utilized enable for feature selection and risk prediction improvement but still face challenge of computational overhead. Meanwhile, DL models advance in perception and control enhancement through spatial-temporal reasoning but encounter large-scale data and high processing power demand issue. As for RL, the methods demonstrate advantages of strong adaptability and scalability, supporting multi-agent coordination and decision-making under uncertainty. However, they still face challenges in training efficiency and real-time responsiveness. Based on the findings, the results offer a thorough guide for creating ML-enabled collision avoidance frameworks that are secure, scalable, and reliable for autonomous driving applications. This review also emphasizes existing research challenges such as computational inefficiency, restricted generalization, sensor blockage, and ethical uncertainty in unavoidable collision situations. The review concludes by stating the future research directions toward hybrid architectures, lightweight and edge-deployable models, uncertainty-aware reasoning deployment, and ethically integrated decision-making frameworks. This review provides a structured classification and comparative analysis to guide researchers and practitioners in advancing safer, more robust and socially acceptable autonomous driving systems.
Copyright (c) 2026 The Author(s) https://creativecommons.org/licenses/by-nc/4.0/
MATERIAL REMOVAL RATE AND SURFACE ROUGHNESS ON GRINDING OF DUCTILE CAST IRON USING MINIMUM QUANTITY LUBRICATION
A promising alternative to conventional fluid coolant application is minimum quantity lubrication (MQL). Despite much research, there have been few investigations about the influence of MQL parameters on the process results, such as oil flow rate, workpiece speed and depth of cut. The objective of this project is to develop a mathematical model of the material removal rate and surface roughness on grinding of ductile cast iron using minimum quantity lubrication. The experiment was carried out according to the design of experiment principle, prepared based on central composite design. The experimental data was utilized to develop the mathematical model for first- and second-order models. The second order gives acceptable performance of the grinding. The result shows that the highest value of the grinding ratio is with single-pass MQL, and the lowest value is with multiple-pass conventional coolants. The model fit was adequate and acceptable for sustainable grinding using a 0.15% volume concentration of ethylene glycol. This paper quantifies the impact of water-based ethylene glycol on the surface quality achieved. It is concluded that the surface quality is most influenced by the depth of cut and table speed. It is recommended that future research is also conducted using another parameter such as the speed of the grinding wheel or the distance from the wheel– workpiece contact zone. Besides that, further research can be conducted using different nozzle angles and different types of grinding wheel to see how these affect the surface of the material.
Copyright (c) 2015 The Author(s) https://creativecommons.org/licenses/by-nc/4.0/
Pipe feature classification using support vector machine based on time-domain feature extraction
Water infrastructure deterioration threatens global water security, with utilities losing over $14 billion annually due to non-revenue water (NRW). Malaysia reported 37.1% NRW in 2023, significantly exceeding international benchmarks. This study develops an automated pipeline monitoring system integrating pressure transient analysis with machine learning for leak detection in water distribution networks. Three feature selection methods (minimum classification error, minimum redundancy maximum relevance, and sequential forward selection) were systematically evaluated to optimize classification performance. The proposed minimised classification error-surface vector machine model (MCE-SVM), which is based on two time-domain features slope sign change and integrated EMG to characterize three pipeline conditions, including elbow, junction, and leak, attained a test accuracy of 87.4%, substantially higher than those achieved by Minimum redundancy maximum relevance- surface vector machine (77.3%) and sequential forward selection-surface vector machine (65.8%), demonstrating the effectiveness of the minimised classification error based method in selecting highly discriminatory features with a minimal feature set. Moreover, the suggested system is capable of fast processing of the signal, which highlights the appropriateness of the proposed system to the report on near real-time monitoring of pipelines. The controlled laboratory experiments were conducted using 300 transient pressure signals recorded with one configuration under 4 bar in one pipe setup (63 mm medium-density polyethene).
Automated system for defect classification of images from 3D-printed additive-manufactured products
Manual inspection of additive manufacturing is time-consuming and error-prone, making it unsuitable for high-speed production. Real-time automated systems should ensure precision and consistency of defect detection. Hence, this paper presents the development and evaluation of an automated defect classification system for additive manufacturing (AM) products using You Only Look Once version 8 (YOLOv8) and LabVIEW. This study utilized a dataset of 1200 images of AM products from the Malaysia Automotive Robotics & IoT Institute (MARii). YOLOv8, a state-of-the-art object detection technique, was used to develop a defect classification model. Following the development of the classification model, it was implemented on the Karakuri machine by interfacing the hardware with National Instruments' myRIO and LabVIEW. An infra-red sensor triggers image capturing via a USB camera system, while the real-time classification system activates the servo-based sorting mechanisms. Data augmentation techniques were deliberately applied to improve robustness during model training. The system achieved an average accuracy of 93.39% and demonstrated satisfactory performance across all evaluation criteria: precision, recall, and F1-score; thereby confirming its effectiveness in classifying defect and non-defect products. In conclusion, the results validate the designed system as a practically feasible and efficient approach toward automating quality control in additive manufacturing, reducing dependence on manual inspection while improving consistency and operational efficiency.
Optimization of bus seat vibration isolation by experiment and metaheuristic approaches
One way of determining health risk to public transport passengers is by measuring whole-body vibration (WBV) according to ISO 2631-1 standard. This Standard specifies the measurement and evaluation of RMS acceleration and Vibration Dose Value (VDV) in the range 0.5–80 Hz along three axes, using specific frequency-weighting curves. On longer routes over rough road surfaces, this measurement will help assess short- and long-term health effects. Existing bus seats are commonly bolted to the floor without vibration isolation, which may amplify vibration transmitted to passengers. This study aims to characterize and optimize the use of a passive rubber damper by integrating an EMT-FEA-PSO framework into an unmodified twin-seat passenger bus structure. Experimental Modal Testing (EMT) identified six dominant vibration modes in the 20–100 Hz range, which are validated against a Finite Element Analysis (FEA) model with an error within 10–15%. On-road measurements revealed that SEAT values consistently above 100% at speeds of 30–80 km/h, confirming vibration amplification by the unmodified seat. In laboratory tests, three rubber isolators were evaluated for effectiveness, and one damper reduced RMS acceleration by approximately 33%. Next, Particle Swarm Optimization (PSO) was applied to a 2-DOF seat–floor model to determine optimal damper stiffness, where the PSO-simulated FRF shows clear resonance peak attenuation. The proposed methodology offers a practical, low-cost design guidance tool for improving WBV isolation in bus seating and can be extended to other vehicle or machinery support structures.
Cross-machine reliability and fault diagnosis using correlation alignment, feature alignment, and enhanced deep extreme learning machine
Reliability and efficiency are vital components in modern manufacturing systems, especially for rotating machinery that operates continuously under a variety of environments. Recently, there has been growing interest among researchers in developing and deploying a cross-machine fault-diagnosis system in real-world industrial settings. It allows engineers to perform fault diagnosis across multiple machines without having to build a new intelligent model each time. Unlike most studies that utilized complex transfer learning architectures, this paper proposes a simple and efficient cross-machine bearing fault diagnosis framework based on correlation alignment and deep extreme learning machine (DELM). It aligned time-domain statistical features from two datasets, Case Western Reserve University and the experimental dataset, to reduce the domain gap between them and improve generalization performance. The aligned features were classified using a DELM model. The experiment yielded reliable cross-machine generalization, with an average accuracy of 91.27%. These findings underscore the model’s capability to offer a simple, effective, and low-computational-demand solution for cross-machine applications, making it suitable and practical for real industrial use.
Effect of die orientation on the mechanical and physical properties of 356 aluminium alloy castings produced by gravity die casting
Al–Si–Mg alloys are widely used in automotive and aerospace applications due to their excellent castability and mechanical properties. Gravity die casting (GDC) is commonly employed to manufacture such components. While AISI H13 tool steel is typically preferred for GDC dies, mild steel dies may be used for low-volume production because of their lower cost and ease of fabrication. However, mild steel dies generally exhibit limited-service life due to lower hardness, reduced wear resistance, and poor thermal fatigue resistance. Therefore, optimisation of casting parameters is necessary to improve casting quality. This study evaluates the influence of die orientation and pouring temperature on the mechanical, physical, microhardness, and porosity characteristics of aluminium (Al) alloy 356 castings produced using an ASTM A36 mild steel gravity die. The casting process was conducted at different pouring temperatures using vertically oriented casting (VOC) and horizontally oriented casting (HOC) configurations. The resulting castings were evaluated through impact testing, microhardness measurements, and porosity analysis, including apparent porosity (AP) and bulk porosity (BP). Results show that VOC at a pouring temperature of 900 °C improved impact toughness by 59.3% (average 16 kJ/m2) compared with HOC, while reducing surface microhardness by 2.4% (82.7 HV). Additionally, VOC significantly reduced BP by 91.9% and AP by 69.5%. Compared with castings produced at 800 °C, VOC at 900 °C increased impact toughness by 34.3% and reduced BP by 75.15%. Overall, vertically oriented casting using mild steel dies significantly improves impact toughness and reduces porosity in 356 Al alloy castings.
Optimized drying of Thai Jinda chili peppers using a direct-heating air tray system: Enhancing efficiency and product quality
Since foods have naturally high amounts of water content, they are prone to microorganism growth and chemical decomposition. This research aims to explore the drying process of Thai Jinda chilies pepper (TJCPs) in the form of hot-air convection drying by means of a direct-heating perforated air tray (1.5×0.6×1.0 m3). The effects of different temperatures (55, 60, 65, 70, and 75 °C) and Reynolds numbers (20000, 30000, 40000, and 50000) were studied. As the results indicated, the increase in drying temperature and Reynolds number significantly accelerated the drying process of the samples. The optimal drying conditions were achieved by performing tests at 75 °C and Re = 50000; this led to the minimum drying time of about 6-7 hours without deteriorating the physical properties of TJCPs. The effective moisture diffusivity increased between 0.374×10-10 and 5.176×10-10 m²/s while the activation energy decreased in correlation with the airflow intensity. This can be explained by the enhancement of heat and mass transfer due to an intense airflow circulation, thinning of the thermal boundary layer, and efficient convective transport inside the drying chamber. It is concluded that the perforated air tray method is an effective way to enhance thermo-fluid drying of TJCPs.
Risk-aware buckling design of functionally graded porous beams via machine-learning surrogates and reliability analysis
Functionally graded porous beams offer high stiffness-to-weight ratios, but their buckling strength is sensitive to induced porosity variability. Designers, therefore, need tools that are both fast and explicitly risk-aware. This study develops and validates an interpretable methodology that combines higher-order shear deformation theory, machine learning surrogates, and structural reliability analysis to support buckling design of functionally graded porous beams. Deterministic buckling responses are first generated using a higher-order shear deformation theory for two boundary conditions (simply supported and clamped-clamped), two slenderness ratios (L/h=10 and 40), geometric controls (taper and width), porosity indices 0<α<0.3, and two porosity patterns (Regular and Uneven). A gradient-boosted tree surrogate is then trained on the log-transformed dimensionless critical buckling load using five-fold cross-validation. Manufacturing variability in porosity is propagated through the surrogate via Monte Carlo simulation, and a companion First-order Reliability Method (FORM) in the porosity dimension provides efficient estimates of failure probability. Risk is summarized through domain-wise mean buckling capacity and Conditional Value-at-Risk (CVaR) at the 5% level (CVaR5%), from which risk-return frontiers and “knee” designs distinguish risk-neutral from risk-averse choices. The surrogate generalizes strongly (out-of-fold log coefficient of determination, R2log=0.94; mean absolute error, MAE=2.7; mean absolute percentage error, MAPE=9.5%), while FORM tracks Monte Carlo with near-perfect concordance (average R2=0.9997; MAE(Pf)=0.0005; MAPE=0.33%), with small, interpretable deviations confined to slender, simply supported beams with uneven porosity. Across all regions, clamped-clamped, regular, and thick beams systematically dominate both mean and lower tail performance. Knee designs typically incur only 1-2% loss in mean capacity while retaining 95-99% of the best attainable CvaR, providing a reliable basis for compact, risk-aware buckling design guidance.
A hybrid entropy-VIKOR-Taguchi approach for sustainable optimization of diesel engine performance and emissions
The urgent demand for sustainable transport fuels highlights biodiesel-hydrogen dual-fueling as a promising pathway to improve engine efficiency while reducing carbon-based emissions. Nevertheless, optimizing combustion parameters to balance performance with stringent emission constraints remains a major challenge. This study introduces a hybrid Entropy-VIKOR-Taguchi framework to optimize the performance, combustion, and emission characteristics of a compression-ignition engine operating on a cottonseed biodiesel (B20)- hydrogen blend. The methodology integrates entropy-based weighting for objective parameter prioritization, VIKOR for compromise decision-making, and Taguchi design for efficient experimentation, thereby minimizing trial costs and enhancing reproducibility. Experiments evaluated three key parameters, namely injection pressure (200-240 bar), compression ratio (16.5-18.5), and hydrogen flow rate (10-20 L/min). Results showed that hydrogen enrichment improved combustion, with peak pressure reaching 85 bar and a maximum heat release rate of 86 J/deg. Engine efficiency increased to 33.52% BTE and a minimum BSFC of 0.213 kg/kWh, while emissions decreased (HC = 65 ppm, CO = 0.016%). However, NOx rose to 1380 ppm at high hydrogen flow, underscoring the trade-off between efficiency and emissions. The optimal condition, IP = 240 bar, CR = 17.5, and H2 = 10 L/min, yielded the lowest VIKOR index (Q = 0.0588), ensuring balanced sustainability. This framework offers a transferable optimization pathway for dual-fuel engines, supporting decarbonization targets under SDG7 and SDG13.
Process optimisation in laser metal deposition
Laser metal deposition (LMD) has become a compelling technique for fabricating high-entropy alloys (HEAs). However, its adoption on a commercial scale is limited, hindered by process-related defects and difficulty in controlling numerous interacting parameters. This review examines defect formation, process parameter-property relationships, and optimisation approaches for LMD-fabricated HEAs. The findings establish that the quality of LMD part is governed by about 39 process variables, including 13 key process parameters, out of which laser power, scan speed, powder feed rate, beam diameter, layer thickness, overlap fraction, and shield gas flow rate exercise dominant effects on the melt-pool stability, microstructure and mechanical properties. Cracking, lack of fusion, gas porosity, surface-connected porosity, and gas porosity constitute the main defects, with lack of fusion reported when scan speed deviates from the optimum range by more than 15%. Evidence from reviewed studies indicates that increasing laser power from 600 to 800 W enhances hardness from 200 to 600 HV in AlCoCrFeCu HEA, and from 500 to 850 HV in AlTiCrCoNi HEA. Optimised process windows for fabricating defect-free CoCrFeMnNi are 400-600 W and 10-30 mm/s. Nevertheless, excessive layer thickness of about 300 µm results in reduced elongation from 11.45% to 2.0%. Advanced optimisation techniques enhanced the reliability of prediction: Taguchi-grey relational analysis obtained 0.95% deviation, response surface methodology produced 5.27%-10%, and machine learning approaches achieved R2 values between 0.97-1.00, including phase-prediction precision of 98.5%. In general, hybrid machine learning, physics-based optimisation, and digital twin frameworks present the most compelling way for attaining defect-free, high-performance LMD-produced HEAs.
Tool Wear Identification and Monitoring of Hard Alloy End Mills Using an Improved WOA and ConvLSTM
The wear state of carbide end mills directly affects the processing efficiency and quality. Traditional methods (such as Support Vector Machine (SVM)) have problems such as insufficient feature extraction and poor robustness when dealing with high-dimensional nonlinear data. To this end, an SVM classification model optimized by the Whale Optimization Algorithm (WOA) is proposed. The study combines the Convolutional Long Short-Term Memory (ConvLSTM) network and the Attention Mechanism (AM) to construct the wear prediction model (ConvLSTM-AM). Experiments show that the average classification accuracy of SVM-WOA for the four types of wear exceeds 97%, and the classification time is only 1.15 seconds. The prediction accuracy of ConvLSTM-AM in the severe wear stage reaches 98.64%, and the prediction error is significantly lower than that of the comparison models. This method provides an efficient solution for real-time monitoring and intelligent maintenance of tool wear.
Copyright (c) 2025 The Author(s) https://creativecommons.org/licenses/by-nc/4.0/
Artificial intelligence of things -based Fuzzy C-Means Control for energy‑efficient greenhouse microclimate management
The aim of modern greenhouses is to keep the optimal environmental conditions for plant growth, but this goal usually involves a high energy consumption, especially for temperature and humidity control. In this paper, an intelligent control strategy is presented based upon the Internet of Things (IoT) and a Fuzzy C-Means (FCM) clustering-based control model. The model is designed for the regulation of the greenhouse microclimate with the observance of temperature and humidity requirements for melon plants, which will increase operational efficiency and reduce energy consumption. To evaluate the performance of the proposed model and to compare the results with traditional control strategies, a simulation environment was created. Both simulation and experimental outcomes indicate that the proposed FCM-based controller has decreased the fan operating time by 28.0% and the cooling system operating time by 33.1%, resulting in an estimated daily energy saving of 29.9% in comparison with the conventional ON/OFF control method while maintaining stable microclimate conditions in the greenhouse. The control system was deployed in a greenhouse with data acquisition, remote monitoring, and real-time control functions, improving energy management and equipment operation in small-scale and family-run greenhouses while supporting smart agriculture applications.
Generalized bearing fault diagnosis using comparative time-frequency scalogram features and machine learning with advanced feature ranking
Bearings are critical components of rotating machinery, ensuring smooth operation by supporting shafts and loads. Even minor defects can severely degrade performance, causing unplanned downtime and economic loss. Accurate and timely fault diagnosis is therefore essential for condition-based maintenance. Conventional time- and frequency-domain methods often fail to capture the non-stationary and transient nature of bearing vibration signals. This work proposes a time–frequency diagnostic framework using Continuous Wavelet Transform (CWT)–based scalograms and machine learning. Coiflet and Morlet wavelets are selected using the Maximum Relative Wavelet Energy (MRWE) criterion, yielding peak MRWE values of 0.1347 for the CWRU dataset and 0.0584 for the Machinery Fault Simulator (MFS) dataset, ensuring optimal fault localization. Scalograms are converted into RGB images, from which 21 texture features are extracted and ranked using the Fisher score. The method is validated on two independent datasets: the benchmark Case Western Reserve University (CWRU) data and an experimental MFS dataset using SKF 6004 bearings with induced inner-race, outer-race, and ball defects. Logistic Regression, SVM, KNN, and Bagged Tree classifiers are evaluated using tenfold cross-validation. On the CWRU dataset, KNN and Bagged Tree achieve accuracies of 98.3% with eight ranked features, while the Bagged Tree reaches 100% accuracy using all 21 features.
Effect of adding blood shells powder (Anadara Granosa) on wear rate, impact strength, and hardness test as a motorcycle disc brake pad composite
The automotive industry has always demanded innovation in renewable materials that are environmentally friendly. One of the natural materials that has the potential as a constituent material to make motor vehicle parts is blood clam shell waste (Anadara Granosa) which can be used as a raw material for making brake pads. This study has the purpose to analyze the effect of the addition of blood shell powder on the wear, toughness, and hardness value of epoxy resin matrix composite as a motorcycle brake pad. This study is a type of experimental research by comparing the experimental group with the control group (Honda Genuine Parts brand brake pads). The test results showed that the addition of 30% of blood shells powder got the most optimal results with a wear value of 1.27×10-6 mm2/kg, impact value 2.41×10-3 J/mm2, hardness of 19.98 kgf/mm2. The 30% variation has the closest results to the test value on the brake pads of the Honda Genuine Parts brand motorcycle. With this, it can be concluded that the more volume of blood shells powder, the more the strength of the composite increases. With these results, blood shell powder composite can be recommended as an alternative to brake pad friction material that is more environmentally friendly.
Thermal, mechanical, and sound absorption properties of sugarcane bagasse–rice husk hybrid polymer composites
Natural fibre reinforced composites are explored more as sustainable alternatives to synthetic materials, but the challenge of obtaining a balance between thermal insulation, mechanical strength and acoustic absorption is still an important problem. This study evaluates the performance of hybrid composites with sugarcane bagasse (SCB) and rice husk (RH) in weight ratios of 100%, 70/30, 50/50 and 30/70 using thermoset resin matrix. Thermal conductivity was measured by Lee's Disc Method, the 100% RH sample obtained the lowest thermal conductivity of 0.03502 W/mK, which showed a good insulation performance. Mechanical characterization according to the standards of tensile strength and flexural strength of the samples in accordance with ASTM D3039, D790, and D256 showed that the 50/50 SCB-RH composite had the highest tensile strength (80.1 MPa) and flexural strength (78.9 MPa). Acoustic performance, which was determined through ASTM E1050-12, and the 50/50 hybrid was determined as the most effective sound absorber with a peak coefficient of 0.75 at 1900 Hz. The Scanning Electron Microscopy analysis revealed that there was good dispersion of fibres and low voids in hybrid samples and this is one of the reasons why they exhibit better multifunctional behaviour. In general, the 50/50 SCB-RH composite has the best thermal, mechanical and acoustic performance and it has great potential in the application of insulation and noise control in the automotive and architectural industry.
Machine learning-based prediction of the coefficient of performance for low global warming potential refrigerants in a vapor compression system
This study addresses the energy significance of the coefficient of performance (COP) in vapor compression systems and the practical need to forecast COP quickly and reliably. Because COP directly reflects the amount of cooling delivered per unit of input power, accurate prediction supports energy savings, refrigerant selection, and early-stage design decisions, especially for low global warming potential (GWP) refrigerants. Authors develop data-driven models to estimate COP without full thermodynamic calculations. A synthetic dataset of 2,000 samples is generated in the Engineering Equation Solver (EES) for four refrigerants (R1234yf, R134a, R290, R600a) by using five inputs: refrigerant type, evaporation temperature, condensing temperature, subcooling, and superheat. Five supervised learning algorithms are trained and compared: linear regression, polynomial regression, random forests, decision trees, and support vector machines. The study evaluates model performance using the coefficient of determination (R²), root mean squared error (RMSE), and mean absolute error (MAE) based on an 80/20 train/test split. Results show Polynomial Regression (degree 3) delivers the highest accuracy (R² ≈ 0.9999; RMSE ≈ 0.0071; MAE ≈ 0.0053), with Random Forest as the next strongest baseline. The findings suggest that lightweight, well-tuned regressors can provide fast, precise COP predictions, reduce analysis time, and guide system design and parameter optimization. The approach offers an accessible tool for engineers seeking efficient, low-carbon refrigeration solutions.
Integrated controller design for rejecting load distribution disturbance in collision avoidance
A collision avoidance (CA) system has become a necessity in every vehicle due to its ability to prevent collisions. Numerous techniques have been developed to improve the tracking system, thereby reducing the possibility of collision by increasing its accuracy relative to the reference signal. The path-tracking system is an important element in the CA system, whose function is to prevent deviation from the reference path. However, a conventional path-tracking system without an estimator is unable to track the reference signal in the presence of an external load disturbance. Moreover, the vehicle velocity, as well as variations in load disturbance, retard the vehicle’s path tracking abilities while avoiding an obstacle. This deficiency can lead to fatal consequences during vehicle navigation due to understeering and oversteering. The primary purpose of this research is to design an integrated controller for a disturbance-rejection (IC-DR) path-tracking system to improve tracking performance by rejecting external disturbances while avoiding obstacle. The design process involved formulating a longitudinal force controller to track changes in vehicle acceleration during the CA scenario. Then, the lateral controller was formulated by following these orders: (1) Linearization of the model, (2) Optimal state estimator design by using a linear model, (3) Optimal state feedback regulator design. The CA scenario was simulated by using a nonlinear tire characteristic for vehicle model development in MATLAB Simulink. Next, both controllers were integrated with the vehicle system, and their performance was analyzed. The simulation results show that the path-tracking system prevents deviation from the reference trajectory in understeering and oversteering situations. The proposed path-tracking method can efficiently reduce external disturbances, and it is much simpler than other advanced controllers. The results show that by implementing IC-DR, the mean squared-error between the vehicle and reference trajectories is below 0.01 for all additional load disturbance percentages at different velocities.
A comparative study of power and vibration signals for different tool pin sizes of friction stir welding
Process signals play an important role in revealing weld formation, reflecting the interaction between mechanical mixing and process parameters. This study focuses on how different tool pin lengths and material thicknesses influence the signals generated during friction stir welding of AA6061. Three pin lengths that were 2 mm, 4 mm, and 6 mm were tested on plates with thicknesses of 4 mm, 6 mm, and 8 mm (made by stacking two 4 mm plates). Spindle speed of 1200 RPM and a traverse speed of 50 mm/min were constant. Shorter pins cannot fully penetrate thicker plates in a single pass; therefore, welding was performed in dual passes, top and bottom, to complete the joint. During welding, power consumption and vibration signals were recorded. Data collected from the bottom side were used for detailed analysis, as the top pass helped hold materials before full penetration. The signals were analyzed using the Fast Fourier Transform, and standard deviation and defect accuracy were calculated to evaluate the weld condition. The findings show that the 2 mm pin achieved the highest defect accuracy of 90.63% based on power signals, while the 6 mm pin achieved the highest accuracy of 85.10% based on vibration signals. Larger vibration standard deviations from the 6 mm pin indicate energetic material stirring. Hardness results suggest that the 4 mm pin provides the most favorable material consolidation. These outcomes highlight that pin length and plate thickness significantly affect process signals, making them crucial variables for real-time weld quality monitoring and optimization.
Unsteady Drag Analysis of Platoon Vehicles using Unsteady Reynolds-Averaged Navier-Stokes and Improved Delayed Detached Eddy Simulation
The aerodynamic interaction of vehicles in platoon formation generates complex unsteady flow phenomena that strongly influence drag performance. This study investigates the unsteady aerodynamic drag and associated flow structures of two simplified Ahmed body models (30° slant angle) traveling in platoon using transient Computational Fluid Dynamics (CFD). Unsteady Reynolds-Averaged Navier–Stokes (URANS) and Improved Delayed Detached Eddy Simulation (IDDES) are employed to analyze the effect of longitudinal spacing ratios (d/L) ranging from 0.1 to 2.0. Following a mesh independence study, a grid with 13.3 million cells is adopted to resolve the flow field and quantify drag unsteadiness for both the lead and trailing vehicles across spacing configurations. Validation is performed by comparing the predicted drag coefficients with experimental measurements. Results show that the lead vehicle experiences a substantial drag reduction, with the coefficient decreasing to 0.16 at close spacing, before rising to 0.40 (URANS) and 0.37 (IDDES) at d/L=2. In contrast, the trailing vehicle exhibits only minor drag variation over d/L = 0.5–2, ranging from 0.35 to 0.37. Additional analyses include pressure coefficient distributions, instantaneous velocity fields, and vortex structures identified through Q-criterion visualizations. Comparisons indicate that IDDES captures finer flow structures and better reflects the unsteadiness of drag than URANS. Overall, the results suggest that the lead vehicle benefits most from platooning, achieving up to 60% drag reduction at small spacings, whereas the trailing vehicle gains comparatively little.
Impact of Throat Diameter, Length, and Divergent Angle on Bubble Formation and Flow Characteristics in Venturi Microbubble Generators
The design of a Venturi microbubble generator (VMBG) is crucial in influencing the size and distribution of produced microbubbles, which are essential in several industrial, medicinal, and environmental applications. This study examines the impact of three critical geometric parameters: throat diameter, throat length, and divergent angle, on the formation of bubbles and the characteristics of flow. A combination of experimental visualization using Particle Image Velocimetry and computational fluid dynamics simulations using the Euler–Lagrange approach with the realizable k-epsilon turbulence model was employed. Nine VMBG configurations were tested, with throat diameters of 8.5 mm, 12 mm, and 20 mm, throat lengths of 10 mm, 15 mm, and 30 mm, and divergent angles of 30°, 45°, and 60°. The findings showed that decreasing the throat diameter from 12 mm to 8.5 mm produced smaller and denser bubbles, resulting in an increase in flow velocity by 68% and vorticity by 30%. Results showed that smaller throat diameters (8.5 mm) increased flow velocities, turbulence, and vorticity, while longer throat lengths (30 mm) provided smoother fluid transitions. Larger divergent angles caused a significant drop in velocity, increased turbulence, and produced smaller bubble sizes. However, they also promoted bubble expansion and denser formation in the divergent section. Both throat diameter and throat length also played a crucial role in undergoing complete bubble formation stages. Experimental results, which were very similar to the computer results, verified the four stages of bubble formation: nucleation, expansion, coalescence, and breakdown. The study provides valuable insights for enhancing VMBG designs, enabling the creation of microbubbles with features suitable for specific applications.
Multi-factorial analysis and optimization of delamination damage parameters in the drilling of woven ramie/epoxy resin composite
The growing demand for lightweight and eco-friendly materials has accelerated the use of natural fiber composites in structural applications. However, drilling-induced delamination remains a significant challenge in machining. This study investigates the influence of drilling parameters on the delamination behavior of woven ramie/epoxy resin composites using a TPR 1100 drilling machine. TPR is the model designation of the pillar-drilling machine used in the experiments. The composite was fabricated from S-type 12/3 woven ramie fiber reinforced with epoxy resin and a polyaminoamide hardener (60:40 ratio). Delamination was quantified using 2400 DPI macrophotography and analyzed with Image-Pro Plus v4.5 software. Four process parameters, namely number of layers (3, 4, and 5), drill bit diameter (6, 8, and 10 mm), spindle speed (88, 455, and 1500 rpm), and feed rate (0.05, 0.09, and 0.15 mm/rev) were optimized using the Taguchi method, with analysis of variance used to assess parameter significance. The results showed that drill bit diameter was the dominant factor influencing delamination (entry: 70.1%; exit: 44.8%), followed by the number of layers and spindle speed, while feed rate had the least effect. The optimal parameter combination (3 layers, 6 mm drill, 88 rpm spindle speed, and 0.09 mm/rev feed) reduced delamination by 7.87% at the entry side and 10.42% at the exit side. These findings provide practical guidelines for minimizing structural degradation during machining, thereby extending the mechanical reliability of ramie/epoxy composites. The outcomes are directly applicable to the automotive, construction, and sustainable product manufacturing sectors, advancing eco-friendly machining standards and providing significant benefits for future industrial applications.
Crystalline Nanocellulose as a Potential Alternative to Metallic Nanofuel Additives: An Experimental Investigation
Crystalline nanocellulose (CNC), derived from various biomass sources, is gaining attention as a sustainable nanomaterial for energy applications due to its high surface area-to-volume ratio, lightweight nature, biodegradability, and associated environmental benefits. This experimental research investigated the potential of CNC as an alternative to traditional metallic nanofuel additives, presenting preliminary results that have not been previously reported. Commercially available CNC was blended into standard diesel fuel at concentrations of 100 ppm, 250 ppm, and 500 ppm, and engine performance and emission tests were conducted. CNC demonstrated potential as a nanofuel additive in diesel, offering benefits like improved combustion, enhanced engine performance, and reduced NOx emissions and exhaust smoke opacity. However, higher concentrations were less effective in lowering nitrogen oxides (NOx) levels, and carbon dioxide (CO2) emissions increased due to more complete combustion. The highest engine performance was achieved with a CNC concentration of 500 ppm, resulting in an average reduction in brake-specific fuel consumption (BSFC) by 3.95% and an increase in brake thermal efficiency (BTE) by 2.73% compared to diesel fuel. The fuel cost analysis indicated that CNC is economically viable at a 100-ppm concentration; however, higher concentrations resulted in a substantial increase in fuel costs, despite reducing fuel consumption. The initial results are promising and suggest CNC could be a suitable alternative to traditional metallic nanofuel additives.
Material-adaptive kurtosis thresholding for real-time multi-parameter condition monitoring in CNC milling
Tool wear remains a major contributor to dimensional defects and unplanned downtime in Computer Numerical Control (CNC) machining. Most existing monitoring strategies employ fixed vibration thresholds that cannot accommodate the distinct dynamic responses of different workpiece materials. Thresholds calibrated for hard materials such as cast iron often fail to detect early wear, whereas the same settings applied to softer polymers lead to excessive false alarms. This limitation highlights the need for material-dependent condition assessment rather than universal thresholding. This study proposes a material-adaptive monitoring framework based on kurtosis thresholds that automatically adjust when the machined material changes. Experimental validation was conducted on three materials representing a wide hardness range: cast iron (220 to 260), aluminum (95 to 100), and polyvinyl chloride (PVC) (80 to 85). A full factorial design comprising 81 milling trials of 27 per material was performed using spindle speeds of 1,500 to 3,500 rev/min, feed rates of 125 to 250 mm/min, and axial depths of cut between 0.2 and 0.7 mm. Vibration signals were acquired using accelerometers mounted on both the spindle and workpiece, and material-specific kurtosis thresholds were derived by correlating statistical features with measured tool wear. The current method achieved classification accuracies of 95.2% for cast iron, 97.8% for aluminum, and 96.7% for PVC, representing improvements of 6-11% over conventional fixed-threshold approaches. The monitoring system was further implemented on an Internet of Things platform to enable real-time remote diagnostics and automated alerts. Pilot deployment indicated a 25-30% reduction in maintenance costs compared with the existing practice. These results demonstrate that material-adaptive thresholding substantially improves the reliability and practicality of vibration-based tool condition monitoring in CNC milling environments.
Effect of machining conditions for enhanced surface integrity and cutting temperature in trimming hybrid fibre-reinforced polymer composites
This study aims to evaluate how cutting parameters influence surface quality and cutting temperature during the trimming of Hybrid Fibre Reinforced Polymer (HFRP) used in aerospace components. Although HFRP is increasingly adopted in aircraft structures, it remains difficult to machine because trimming can trigger delamination, matrix degradation, and non-uniform heat generation across the laminate. To address this, a Taguchi L9 Orthogonal Array was applied to systematically examine the effects of spindle speed and feed per tooth on two key outcomes: surface roughness (Ra) and maximum cutting temperature (Tmax). Trimming experiments were performed on a Roland MDX540 CNC router, and surface integrity was assessed using optical microscopy, thermal imaging, and analysis of variance. Two parameter settings emerged as optimal, depending on the targeted response. The lowest surface roughness was achieved at 7518 RPM with 0.10 mm per tooth (Ra = 2.44 µm), whereas the lowest cutting temperature occurred at 5012 RPM with 0.15 mm per tooth (Tmax = 110.2 °C). Since surface integrity is the primary quality requirement for aerospace trimming, the condition of 7518 RPM / 0.10 mm per tooth was selected as the most practical optimum. This setting provides a noticeably improved surface finish while keeping the cutting temperature at a moderate level (approximately 115 °C), which remains safely below the threshold for polymer matrix degradation. Analysis of variance results further indicate that feed per tooth is the dominant factor governing surface roughness, while spindle speed has the strongest influence on cutting temperature. Overall, the findings support a dual-objective optimisation approach that balances mechanical surface integrity and thermal control, providing practical parameter guidance for consistent, high-quality trimming of HFRP aerospace parts.
A 14 V battery-supercapacitor hybrid power supply for vehicle low-voltage systems: Reducing battery stress and enhancing dynamic response
In response to the inherent limitations of single-battery low-voltage supply and the escalating demand for electrical equipment power in vehicles, the feasibility of replacing single-battery with battery/supercapacitor (SC) hybrid power supply (HPS) has been proposed and verified. This HPS directly connects a lithium-ion battery in parallel with the SC and combines a bidirectional DC-DC power converter to boost the voltage to the required level. It can drive the vehicle at low speeds and power electrical equipment. A MATLAB/Simulink model is constructed and simulated using the Federal Test Procedure-75 driving cycles. In addition, experiments are conducted to verify the HPS's boost characteristics. Furthermore, a case study is conducted to compare the proposed HPS with 14 V and 48 V single-battery systems under the China Light-duty Vehicle Test Cycle-Passenger Car operating condition. The results indicate that during frequent load fluctuations, the SC quickly responds to dynamic power and high-power demands, thereby significantly reducing battery stress. The proposed HPS demonstrates key advantages by enhancing dynamic response capability, reducing battery stress to extend lifespan, and improving energy efficiency. This study provides a new solution for the practical application of low-voltage power supply in vehicles.
Cooling Slope-Cast LM25 Aluminum Fabrication for Additive Friction Stir Deposition: A Microstructural and Mechanical Study
Additive Friction Stir Deposition (AFSD) is an innovative solid-state additive manufacturing (AM) technique that has offered a promising solution for joining and fabricating high-performance aluminum alloys over the past decade. However, the use of heat-treated feedstock produced by cooling slope casting (CSC) remains largely unexplored. This initial study investigates the feasibility of using CSC LM25 feedstock in AFSD to achieve better microstructure and hardness, while preventing early feedstock breakage. Microstructural examination indicated a shift from coarse dendritic to globular grains, with an average grain size of 1.828 µm (0.351-60.835 µm), a reduction of approximately 96.3% compared to the as-cast material. While some samples (notably C5 CSC_HT and D2 PMC_HT) achieved smooth, defect-free deposition, continuous deposits under higher rotational speed (800 rpm) and feed rate (3 mm/min), the hardness of deposited parts reduced to 63–72 HV compared to 89HV in the base material, reflecting a ~20–29% decline due to thermal softening, over-aging of precipitates, and Si coarsening during AFSD thermal cycles. Although CSC helped to refine the grain structure and enhance deposition flow, this alone was not enough to improve the mechanical behavior of the deposited parts. Additional processing steps, such as thixoforming or heat treatment after deposition, may be needed to achieve better results. This study offers early observations on how cast feedstock behaves in AFSD and identifies important challenges and future directions for improving the solid-state processing of aluminum alloys, especially for lightweight parts in aerospace and automotive applications.
Optimization of AA7075–SiC Composite Machining by WEDM Using Biosilica Additives
The study herein discusses the wire-cut electrical discharge machining (WEDM) with the addition of biosilica from maize cobs for the precision cutting of AA7075 aluminum and silicon carbide (SiC) metal matrix composites. The new, harmless addition of biosilica as a dielectric agent not only supports the sustainability of the process but also enhances surface quality and cutting speed. A Taguchi L9 design was used to conduct an experiment investigating the effects of peak current, gap voltage, and pulse-on time on material removal rate (MRR) and surface roughness (Ra). The optimal machining parameters were determined using Grey Relational Analysis and an artificial neural network (ANN). These model developments aimed to predict performance outcomes. The findings indicated that the dielectric fluid with biosilica increased MRR by 25% and simultaneously decreased Ra by 15% when compared with the typical dielectric. SEM and AFM analysis confirmed surface uniformity improvement and reduced microcrack formation. The artificial neural network model, trained using the backpropagation method on the experimental data, produced predictions for material removal rate and average roughness with an R² of 0.96, indicating that the model is highly reliable. In conclusion, the present research not only reveals a non-conventional machining process but also provides an eco-friendly approach to optimizing wire EDM of metal matrix composites with nano-reinforcement.
Numerical modelling of the thermomechanical behaviour of a polymer seal for an automotive cabin
Automotive door seals play a critical role in ensuring cabin tightness, acoustic comfort and thermal insulation, yet their performance is strongly influenced by temperature-dependent material behaviour. The objective of this study is to develop and validate a comprehensive nonlinear coupled thermo-mechanical finite element model capable of predicting the stiffness, deformation and heat-transfer characteristics of a T4-type elastomer seal under realistic operating conditions. A temperature-dependent Mooney–Rivlin hyperelastic formulation was implemented, with material coefficients calibrated using an inverse-fitting procedure based on manufacturer specification data. The model incorporates transient heat conduction, contact interactions and the effect of trapped air within the seal geometry. Quantitative results demonstrate that increasing temperature from −8°C to 34°C reduces peak contact stresses by approximately 38%, increases total displacement by more than 50%, and enhances heat flux from 10 W/m² to 57 W/m² due to improved thermal conformity. The findings highlight the significant impact of coupled thermo-mechanical effects on seal performance and provide a robust numerical framework for virtual prototyping and optimisation of sealing systems in modern vehicles, including EVs with stringent thermal management requirements.
CFD Analysis of Patient-Specific and Idealized Models for Predicting Stenosis Locations and Hemodynamic Disturbances in Peripheral Arterial Disease
Peripheral arterial disease (PAD), a condition caused by atherosclerosis, poses significant cardiovascular risks by disrupting blood flow. Computational fluid dynamics offers insights into vascular remodeling mechanisms, leveraging patient-specific anatomical data from computed tomography angiography (CTA) to enhance the accuracy of blood flow analysis. This study aims to evaluate the capability of an idealized arterial model in simulating hemodynamic parameters and blood flow patterns by comparing it with patient-specific geometries. Additionally, the study investigates the impact of stenosis at different locations within the femoral artery: upstream, downstream, and at the Profunda on flow disturbances and downstream regions. Blood flow was modeled as a Newtonian fluid, assuming a constant viscosity independent of shear rate, which is a reasonable approximation for femoral arteries where shear rates are typically high. Both an idealized PAD geometry and a patient-specific model derived from CTA data were employed for simulations. Results showed elevated blood velocities at bifurcations, notably at the superficial femoral artery (SFA) and profunda femoral artery, with peak velocities exceeding 1.90 m/s. Regions of low wall shear stress (WSS) were identified at key branching points and along arteries such as the popliteal and tibial arteries. The idealized model effectively replicated patient-specific flow patterns. Upstream stenosis caused severe flow disturbances, with velocities up to 3.9 m/s and Reynolds numbers (Re) of 1272 in the mainstream region, disrupting flow recovery. Downstream stenosis caused severe disturbances, with Re of 1835 beyond the bifurcations, whereas profunda stenosis had minimal effect, maintaining Re below 1000. This study emphasizes the importance of patient-specific anatomical factors in predicting stenosis and highlights the utility of idealized models for generating hemodynamic profiles. These findings enhance the pre-treatment planning and management methods for PAD patients.