The objective of this paper was to investigate the influence of driver seating distance on injury outcome. The study employed controlled finite element analysis using a virtual dummy and a mid-sized sedan to examine the isolated effect of driver seating distance on injury severity. Three simulations were conducted by varying the seating distance from the closest position to the farthest position (corresponding horizontal positions from chest to steering distances 308 mm, 358 mm, and 408 mm, respectively), while maintaining an identical frontal crash into a rigid wall. Injury severity was analyzed using established biomechanical injury criteria, including Head Injury Criterion (HIC15) for head trauma, maximum rib deflection (Rmax) and Principal Component score for chest injury, anterior superior iliac spine (ASIS) and acetabulum forces for pelvic injury, and Tibia Index for lower extremity injury. The simulation results revealed that the driver's seating distance influenced the selected body regions in a distinct pattern. Contrary to the conventional assumption, the intermediate position produced the highest head injury risk (HIC15 = 824.5), compared to the closed position (HIC15 = 693.4) and the farthest position (HIC15 = 749.9). In the chest, the closest position (Rmax = 87.2mm) contributed 6.6% higher rib deflection than the farthest position (Rmax = 81.8mm). Conversely, pelvic loading (ASIS force) increased by 9.1% with increased seating distance (from the closest to the farthest position). Lower extremity injury risk was highest in the closest position due to early knee-dashboard contact. These results indicate that the distance between the driver and the steering wheel affects the injury risk to the occupant in a distinct manner rather than uniformly increasing or decreasing overall injury severity. The optimal seating position varies by body region due to differences in restraint system interaction timing, load distribution patterns, and contact mechanics, emphasizing the need for position-specific injury mitigation strategies.

This study presents a crashworthiness evaluation of a Parallel 5-Tube Circular Multi-Segment Crash Box with a cutting die trigger, designed to promote controlled progressive folding for high-speed railway applications. The crash box integrates four primary tubes and one secondary tube in a parallel configuration, featuring multi-segment wall thickness and a die-trigger mechanism designed to initiate controlled folding from the tube’s end. The die acts as a deformation initiator, guiding the folding sequence to occur progressively, thereby improving energy dissipation and reducing peak impact forces. Two impact models, namely the Cutting Die Model (CDM) and the Flat Model (FM), were employed to investigate the combined effects of trigger mechanism, multi-segment wall thickness, and impact direction using validated finite element simulations supported by quasi-static and drop-test experiments. Finite Element Analysis (FEA) simulations using ANSYS LS-DYNA were conducted to assess key crashworthiness indicators, including maximum crushing force (Fmax), energy absorption (EA), specific energy absorption (SEA), mean crushing force (Fmean), and crushing force efficiency (CFE). Experimental validation through quasi-static and drop tests confirmed the reliability of the simulation results. The findings reveal that the CDM configuration significantly outperforms the FM model, exhibiting lower Fmax and higher EA, SEA, and CFE values. Progressive folding initiated by the die mechanism enables more stable and efficient energy dissipation. Additionally, the impact direction influences deformation behavior, with the t₁–t₂ configuration yielding superior performance. These results demonstrate the effectiveness of the proposed crash box design in meeting the stringent safety and spatial requirements of modern railway systems.

Lighting instability, sharp shadows, and visual disturbances caused by mechanical vibrations are significant challenges in the application of computer vision-based visual inspection systems in automotive industrial environments. This study aims to enhance the accuracy and robustness of the YOLOv8 object detection model for detecting machine component completeness by applying an adaptive pre-processing strategy. The techniques employed include grayscale conversion, brightness adjustment, and blurring to simulate common visual conditions encountered in real-world production processes. The model was trained using 1,281 instances from 52 component classes and evaluated based on the metrics of precision, recall, mAP@50, and mAP@50–95. The results show an average precision of 0.971, a recall of 0.990, and mAP@50 of 0.991, with spatial variation reflected in the standard deviation of mAP@50–95 of 0.149. The pre-processing technique improves the detection precision of shape-based components by up to 19% and colour-based components by up to 31%. Testing on ten appearance variations showed 100% detection accuracy with no misclassification, indicating the model’s generalizability to data in the training distribution. These findings confirm that visual modification of training data significantly improves the reliability and efficiency of the YOLOv8-based automated inspection system. Further implications include reduced human intervention, accelerated production flow, and optimization of operational energy consumption through faster and more accurate detection. Therefore, this system contributes to energy-efficient and sustainable innovative manufacturing practices.

This paper develops the new leaf-spring suspension parameters synthesis methods based on the use of modern mathematical and computer modeling tools to provide vehicle high in-motion comfort. The research aims to develop the new loading characteristics and design parameters synthesis methodology for the vehicle suspension system components, exactly for the elastic element (leaf-spring), the damping element (hydraulic shock absorber) and the transverse stabilizer bar. The authors demonstrate approaches to the use of vehicle dynamics simulation modeling, topological and parametric optimization, and finite element method and explain their role in the new developed vehicle leaf-spring suspension system parameters synthesis methodology. As main results, the authors propose a formation method for a primary suspension system elastic element non-linear loading characteristic to provide for the in-motion vehicle comfort. They develop a synthesis technique for the variable profile longitudinal section geometry of a leaf-spring and its other design parameters to generate the loading characteristic close to the one required for high motion comfort. They also proposed a loading characteristics synthesis for the damping element of the primary suspension with a leaf-spring-type elastic element and selection the most suitable one for a specific vehicle method. The authors also propose a stiffness calculation procedure for a randomly shaped transverse stabilizer bar when selecting its design parameters. As a contribution, based on the comparative virtual tests results of the motion comfort indicators of a vehicle with the loading characteristics generated using the developed methodology for the primary leaf-spring suspension and the initial basic configuration of the classic leaf-spring suspension, it was recorded that the vehicle motion comfort improved by 10-25% when the developed synthesis methodology was used.

Waste collection and transportation are essential elements of effective waste management. However, despite their importance, previous studies have highlighted several challenges, such as routing inefficiencies and environmental concerns. This study seeks to develop an optimized approach for waste collection and transportation under conditions of demand uncertainty, capacity limitations, and traffic constraints, through the application of a simheuristics-based method. The methodology utilizes a simheuristics approach, integrating a Genetic Algorithm (GA) to determine optimal routing solutions, while employing Discrete Event Simulation (DES) to incorporate key economic, environmental, and social variables. Data were obtained from field experiments and Google Maps, and assumptions regarding capacity requirements, distances and collection points, transportation cost components, and road conditions were established to ensure the reliability of the simulation results. The application of the simheuristics approach effectively reduces total transportation costs by approximately 51%, while also significantly minimizing environmental impacts. This research contributes to the academic literature by presenting an innovative method that strengthens existing waste collection strategies with an emphasis on sustainability. Additionally, it offers valuable insights for waste management policy, enabling the optimization of waste collection without exceeding capacity limits.

Lithium iron phosphate (LiFePO4) batteries offer advantages such as low cost, safety, environmental compatibility, and stability over repeated cycles. However, when subjected to high currents, this battery generates thermal issues, particularly when arranged in packs. This study aims to maintain the LiFePO4 80Ah battery within an optimal temperature range (20 °C – 40 °C) while minimizing pumping power. The proposed research introduces a serpentine channel with additional branches. The design variations include a gradient in branch spacing and changes in channel width. Each design is evaluated using dimensionless parameters representing maximum temperature, temperature uniformity, pumping power, and cooling efficiency coefficient. The best design from each variation is then compared with the conventional serpentine (CS) channel design, which is well-known for its superior thermal performance. The gradient variation reduces 𝑇𝑚𝑎𝑥∗ and 𝑇𝜎 by 0.07 and by 0.42, respectively, compared to the non-gradient channel design, at a Re 400 and a C-rate 3 C. The design with the largest channel width reduces 𝑇𝑚𝑎𝑥∗ by 0.57 or 11.32 °C compared to the design with the smallest channel width. At a Re 1000 and C-rate 3 C, the reduction in 𝑇𝑚𝑎𝑥∗ for the proposed channel design compared to the CS design is 0.017. In terms of the friction factor (𝑓), the proposed design is 0.0149 lower than the CS design. The results indicate that the thermal performance of the proposed channel design is better than that of the CS design, with reduced pumping power.

Topology optimization has demonstrated its effectiveness in generating lightweight and structurally efficient designs. This study focuses on refining the geometry of an automatic coupler body for trains using solid isotropic material with penalization and a level set method. These optimization methods are applied to the numerical model of the automatic coupler, and their results are compared to select the optimal design. The tensile strength of the automatic coupler is examined through numerical simulations and validated by experimental tensile tests conducted on a 1:1 scale prototype. The optimization outcomes reveal a remarkable 46.41% reduction in the mass of the automatic coupler body compared to the initial model. An evaluation of the tensile strength of the prototype demonstrates the ability of the automatic coupler to withstand the primary load without undergoing plastic deformation. Furthermore, a strong correlation is observed between the numerical and experimental results. This research contributes to advancing the design of next-generation automatic couplers, emphasizing the crucial aspects of lightweight design and structural performance.

Waste lubricant oil is always found in motor vehicle repair shops. Utilizing waste lubricant oil by distilling it will provide benefits. For this reason, waste lubricant oil was distilled in this research. Several characterizations were conducted to determine the viscosity, density, low heat value (LHV), and flash point of waste lubricant oil and distillation products. The distillation product is less viscous, denser, LHV, and flash point than lubricant oil waste. The distillation product was mixed with Pertamina DEX (0, 5, 10, and 15 vol.%) and then filled into the fuel tank for the engine performance test. The present experiment utilized a compression-ignition (CI) engine to measure performance. CI engine speed variations were carried out at 1000, 1500, 2000, and 2500 to see the influence of the mixed fuel on torque, power, specific fuel consumption (SFC), thermal efficiency, and smoke opacity. The increase in CI engine speed leads to an increase in torque, power, thermal efficiency, and smoke opacity, but at the same time, SFC decreases to 2500 rpm. Increasing the distillation product content in the mixed fuel decreased torque, power, SFC, thermal efficiency, and increased smoke opacity.

Metallic catalytic converter (MCC) is one of the technologies widely applied to motorcycle exhausts which aims to improve exhaust emission to be more environmentally friendly. However, even though many studies have been conducted, optimal design has not been achieved compared to other designs. Through this research, the Taguchi method is proposed as an alternative method to find the optimum parameters of MCC. The Taguchi method was chosen because of its ability to find a robust combination of parameters. There are four MCC parameters used as inputs while each parameter consists of three levels, thus the design used is the L18 Orthogonal Array (OA) which each combination is tested on three types of motorcycles, namely Moped, Automatic, and Sports. The signal-to-noise ratio (S/N) was adopted as one of the quality indicators of each combination. The optimization results showed that the best MCC design to reduce CO emissions is STD PGM. However, the optimum CO design can be used as an alternative because the difference in the S/N ratio is only -0.372. Meanwhile, the optimum CO design has another advantage over the STD PGM, namely the S/N value of the power ratio which tends to be higher with a difference of 5.037 compared to the STD PGM. Then, the best MCC design capable of increasing power is the optimum power design. The optimum power design has a superior S/N ratio with a difference of 5.404. In terms of emission, the optimum power design tends to be lower by a difference of -1.875 compared to the STD PGM.

In this study, a packed-bed reactor was used to produce biodiesel from disposed soybean oil using a lipase-nanoparticle bio composite catalyst. During the transesterification process of the ‘disposed of/used soybean oil’, different nano catalysts were employed such as nanoparticles of Ni-doped ZnO, Fe3O4, Alkylcelite, Poly-acrylonitrile fibres and Poly-acrylonitrile nanofibrous membrane and they were abbreviated as CI, CII, CIII, CIV and CIV respectively. In each case of biodiesel production, there were two levels of process parameters like flow velocity (such as 0.25 mL/min-1.25 mL/min) and the reaction time (20 and 100 h) were considered for analyses. From the derived biofuel, the biodiesel blends were prepared as B50 (50% diesel and 50% biodiesel) and B75 (25% diesel and 75% biodiesel). The synthesis of biofuel results, in the biodiesel conversion of cepacia lipase with Poly-acrylonitrile nanofibrous membrane nanoparticles being recorded at about 85% at a 1.25 mL/min flow rate, which is the maximum biodiesel conversion among five grades. The shortlisted biodiesel performances were analyzed by varying the engine speed, grade and kind of biodiesel, The observed results were analyzed. The B50CIV and B75CIV blends recorded the maximum BSFC at 1800 rpm engine speed. CO2 emission by diesel is about 2.3 vol% was recorded. It is the highest value compared to biodiesel blends (B50 and B75). The emission of NOx with the B50CII blend was 220 ppm at the engine speed of 1800 rpm. Based on the experimental results, B50CIV serves affordable fuel and is recommended among those tested in this investigation for CI engines.