Abstract: Today’s request for reducing the fuel consumption of Heavy vehicles is one of the most interesting issues within the automotive industry. Together with the increased fuel price, the development of more fuel efficient vehicles has intensified. Recent research about fuel-saving technologies for trucks displayed that aerodynamic improvement is one of the most essential technologies when it comes to reducing fuel. The Main objective of this study is to determine the aerodynamic impact (drag force) for various profiles in the truck - cabin shape and Aerodynamic devices added in truck cabin such as, [ Cap of truck (with different angle), Gap device (with different length)]. To measure the aerodynamic drag produced by the truck, numerical model studies are undertaken using a 1/50 scale model of standard heavy truck. In this research, a numerical validation procedure by ANSYS FLUENT ®, computational fluid dynamics software with various turbulence models is described for estimation aerodynamic characteristics. It is observed that at the present work a good agreement between the numerical study and the experimental work with the Realizable k-ε model with maximum error is about 8%. Then, computational fluid dynamic (CFD) investigation is utilized for each case to compare with respect to coefficient of drag, Turbulence Kinetic Energy contours, pressure contours, velocity contours, 3D streamlines and velocity vectors between a standard 3D truck model with and without aerodynamic profiles and devices. The results show that the front and mid fillet radius profile has a significant drag coefficient reduction is noticed by about 17.75% with optimum dimensions. The top fillet radius profile has an opposite effect on the drag coefficient due to the Coandă Effect without cap of truck. When top fillet radius is utilized with a cap of the truck, the drag coefficient improvement with an optimism cap truck angle by about 9.92%. By adding Gap device with different lengths, the drag coefficient decreasing by about 8.36%. Finally, by using all aerodynamic profiles and devices on the truck - cabin studied at the same time the improvement in drag coefficient is about 36.03% from a standard 3D truck model.

Abstract: The microstructure of a material is one of the main influences on its mechanical properties, and therefore it participates in determining its uses and possible applications in the field of manufacturing. The automatic recognition which used for microstructure classification is a major challenge. Nowadays, Deep learning is the most exciting method that used to classify the microstructure of matter automatically. In this work, six outstanding Fully Convolutional Neural Networks (FCNNs) architectures were used to study their capabilities in classifying Spheroidite microstructural images into classes (6classes and 3classes). Three tasks were accomplished to test the classification of different classes of Spheroidite micrographs that were divided into based on different annealing conditions. The constructed Datasets were comprised of the images that are taken over a range of magnifications. The six networks were compared to assess their performance during the supposed tests. The comparison includes the all possible combinations of training datasets size, the learning rate, the cropping method of images, number of classes and the magnification scale. Results showed that the transfer learning approach can represent the microstructure image data very well by using the pretrained models with validation accuracies about 90.83%, 98.33 %for 6classes and 3classes, respectively. When fine-tuning approach was used, the validation accuracy reached about 96.67%, 100% for 6classes and 3classes, respectively.

Abstract: Direct hot extrusion is an alternative process for recycling aluminium without melting the scrap. It utilizes low energy and is environmental friendly. This study shows the effects of preheating temperature (PHT), preheating time (PHti) and addition of volume fraction (VF) of micro alumina on the microhardness (MH), density and microstructure of the extruded profiles. Three values of PHT (450, 500, 550 °C), PHti (1, 2, 3 hours) and VF (5, 10, 15 %) were considered respectively. The full factorial design with center point analysis was used to demonstrate the effect of process variables on responses. A total of 19 experimental runs were performed through the hot extrusion process. The results show that the preheating temperature is the most important factor to be controlled in order to obtain the optimum MH and density, while preheating time and volume fraction trailed behind the former. It can be concluded that microhardness increases with the increase in PHT and decrease in PHti and VF. On the contrary, an increase in density was observed with a decrease in PHT, PHti and VF apiece. The impact of hot extrusion parameters on the average grain sizes and microstructural analysis of the recycled samples were equally investigated and discussed.

Abstract: Tooling design is the most remarkable factor in producing composite laminates. The integrally-heated tool is the most appropriate alternative of the autoclave that is utilized in processing large parts of advanced composites with lower cost and curing cycle time. The main issue of the integrally water-heated tool is the temperature distribution across the tool face. Therefore, the current study aims to improve temperature uniformity across the surface of a hat shape integrally water-heated tool for manufacturing integrally-stiffened low-temperature carbon fiber reinforced polymer (CFRP) composite panels. Accordingly, three design variables of pipe layout, rod section shape, and contact height between the rods and pipes, which are not documented well in literature, are considered to define the optimal tool design with the most acceptable heating performance. The design of experiments (DoE) has been applied by Taguchi’s Orthogonal Array (OA) method to set several combinations of the design parameters. The design combinations are analyzed numerically in ANSYS-CFX to evaluate the surface temperature variation across the hat-shaped tool surface. The obtained numerical results of temperature variance are converted to signal to noise (S/N) ratio and then analyzed in the statistical method of analysis of variance (ANOVA). Finally, the optimal design combination of parallel layout, triangle section rod and lower contact height that provides the best temperature uniformity is defined.

Abstract: It is vital to find alternative fuels to reduce fuel consumption and exhaust emissions. Biodiesel was produced from waste cooking oil through process of transesterification. Waste cooking oil biodiesel physical and chemical properties were measured in accordance with ASTM standards. Biodiesel blend was produced by mixing diesel and biodiesel by volume percentage of 20% as W20. TiO2 nano additive was mixed with biodiesel at 25 mg / l, 50 mg / l and 100 mg / l as 25, 50 and 100 ppm. The aim of this paper is to evaluate the performance, combustion characteristics and exhaust emissions of a diesel engine using biodiesel blend with nano additive. Tests were carried out on a diesel engine at different engine loads with rated speed of 1500 rpm. Biodiesel blending with nano B20T100 resulted in the highest improvement in BTE and decrease in BSFC by 8.5 and 7% relative to biodiesel blend, respectively. Nano additive with waste cooking oil biodiesel B20T100 achieved the highest reductions in CO, HC, NOx and smoke emissions by 22, 33, 15 and 37% relative to B20. Addition of nano TiO2 to biodiesel blend improved the combustion characteristics related to B20. Biodiesel blend with nano additive concentration of 100 ppm achieve the maximum enhancement in performance, combustion characteristics and emissions about biodiesel blend.

Abstract: Recognition of the vehicle existence within the blind spot area efficiently is an essential issue for active safety in the automotive industry. To reduce the cases of car crashing caused by the changing lane maneuver, the features of the blind spot system need to be upgraded periodically. To fulfill this requirement, this paper presents the performance of the developed radar-based blind spot system. The performance of the system will be evaluated through on-road testing using HONDA CRV on the two selected regions which were Selangor and Wilayah Persekutuan Kuala Lumpur. The developed system has been designed to detect any vehicles exist within the blind spot area by using a recorded video based on OpenMV camera and 24GHz radar sensor for real time sensor detection. By taking video frames and converting the images to the one-dimensional image, the information regarding the number of vehicles being detected can be located and analyzed.

Abstract: Collisions and road accidents are inevitable parts of road traffic. The vast majority is caused by drivers: mostly passenger car drivers, followed by truck drivers. The accidents caused by the latter are particularly tragic. Their victims are usually pedestrians, cyclists or motorcyclists, commonly described as “vulnerable road users". One of many causes of accidents is the existence of blind spots around vehicles. The paper discusses the legislation relating to the approval of types of devices for indirect vision that can be used in heavy vehicles, such as trucks, as well as current solutions for the extension of drivers’ field of view. It also presents the results of visibility tests. Their aim was to analyze the driver’s actual field of view, as well as to verify the existence of blind spots around a truck equipped with mirrors and additional devices for indirect vision, as required by the European Union. The visibility tests were conducted using three different models of heavy trucks, equipped with mirrors in accordance to the laws of the European Union. One of trucks was additionally equipped with a wide-angle camera.

Abstract: Digital Image Correlation results of in plane strain measurement is discussed and synthesized on deviation obtained from Finite Element Modelling strain contour. In plane deformation measurement technique of Digital Image Correlation (DIC) full field deformation on Hybrid Composite C/GFRP with comparison with Finite Element Modelling (FEM).The deformation displacement, in-plane strain xx, in-plane strain yy and in-plane shear strain xy are extracted from strain gauge and digital image correlation (DIC) technique via high-speed camera that captures during the experiment.Proposing the in plane deformation measurement technique of Digital Image Correlation (DIC) on Hybrid Composite C/GFRP with comparison to Finite Element Modelling (FEM) via Median Value

Abstract: The machining of AL6063 alloy is a challenging process because, when machined, it has adhesion problems, which increases heat generation between the cutting tool and the workpiece during machining. To achieve a minimum surface roughness of the AL6063 alloy, the need to use an eco-friendly lubricant with high pressure at the turning zone is essential. Therefore, this study aid in carrying out experimental analysis on the effect of mineral oil-based SiO2 nano-lubricant on AL6063 alloy surface roughness during the turning operation. The nano-lubricant was ultra-sonicated for five (5) hours to properly homogenize the mineral oil and the SiO2 nanoparticles. The mineral oil-based SiO2 nano-lubricant was engaged in the turning operation, to study the effects and also compared the performance with the dry, and mineral oil-lubricant (i.e., the control based fluid) machining. This research applied Box-Behnken experimental design for the turning operation. The result shows that the mineral oil-based SiO2 nano-lubricant reduces the surface roughness value with 17.14% and 9.57% when compared with the dry and mineral oil-lubricant and the mineral oil-lubricant reduces the surface roughness with 8.38% when compared with the dry turning operation. The study achieved the minimum surface roughness of 8.65 µm, 7.72 µm, and 6.78µm for dry, mineral oil and mineral oil-based SiO2 nano-lubricant machining, at the optimized machining parameter of spindle speed of 165 rev/min, depth of cut of 1.5 mm and feed rate of 0.5 mm/rev. Furthermore, the developed models predicted the experimental result with 94.9%, 95.55%, and 95.71%, respectively, which is workable and reasonable in lathes machining. The finding from this study will assist researchers and manufacturers in carrying out a turning process on aluminum alloy with mineral oil-based SiO2 nano-lubricant for greener machining.

Abstract: This paper experimentally probes the impacts of using nanoparticles with and without the use of a magnetic field throughout flow behaviour characteristics and the heat transfer rate on forced convection heat transfer of Fe3O4–water nanofluid with laminar flow regime in a horizontal tube under constant heat flux conditions. The base working fluid is distilled water, and the added nanoparticles are Iron Oxide (Fe3O4), with a volume fraction of (φ = 0. 3, 0.6 and 0.9%). The intensity of supplied magnetic field is (0.1Tesla). The experiments are conducted at different constant heat fluxes (12.7, 15.9, 19.8 kw/m2), two different inlet temperatures (23, 45 o C), four different flow rates (2, 3, 3.5, 4, l/min) and a wide range of Reynolds numbers (3930-7860). It was derived from the experiment that an increase in the concentration of nanofluids and in the heat flux, resulted in an increase in Nusselt number. Furthermore, it was found that the use of magnetic intensity enhances the heat transfer even further. The maximum enhancement in Nusselt number is about 18.3% without using a magnetic field, whereas it becomes approximately 20.1% when supplied with a magnetic field at a nanofluid concentration of (φ = 0.9%) and lower heat flux. It is proven that as the inlet temperature increases, the heat transfer decreases.