This study aims to develop asbestos-free and environmentally friendly brake pads using apus bamboo powder (Gigantochloa apus). In the experiments, bamboo powder, resin, and catalyst were used as the raw materials and varied to ensure the quality of the prepared brake pads. To analyze the performance of brake pads, the fabricated brake pads are subjected to physicochemical tests (such as microscopic tests and functional group analysis) and mechanical tests (such as puncture tests, compression tests, and friction tests). The research results showed that adjusting the composition of the raw materials allowed a change in the performance of the brake pad, including porosity, morphological structure, and mechanical properties. Indeed, the condition of the low porosity on the inside of the brake pad strategically optimizes the compression strength of the material, making this design ideal for applications that require high resistance to compression loads. This study shows the possibility of apus bamboo powder as an alternative to asbestos in the production of non-asbestos brake pads, offering a safer and environmentally friendly solution as well as giving ideas for supporting current issues in the sustainable development goals (SDGs).

Alternative fuels were developed by blending crude coconut oil and bio-carbon nanoparticles. Bio-carbon, derived from sago waste via pyrolysis and ground using a ball mill, underwent FTIR testing to assess its energy absorption capabilities. SEM analysis was conducted to examine the surface morphology of bio-catalyst with and without crude coconut oil. The findings indicate that incorporating bio-catalyst can enhance mechanical properties, facilitating rapid heat absorption, as evidenced by reduced flashpoint and viscosity. In addition, the results show an increase in fuel mass, broadening of molecular contacts, increased reactivity, and increased heat absorption for easier ignition. This phenomenon indicates that bio-carbon of sago waste have great potential for biofuel use as a homogeneous combustion catalyst.

An inspection is the most important step for the manufacturers producing their cars. This ensures the seamless compatibility of each car part, as even minor errors can lead to user discomfort during operation. To achieve that goal, the utilization of inspection tools, such as a checking fixture is essential. In this research, we will study the structure analysis of a checking fixture with Ansys software. This study aims to examine the structural strength by analyzing the impact of various design variations on the overall strength outcomes. The requirement for checking fixture is that it must meet the datum tolerance of the car with value of ± 2mm. Due to that factor, a rigid checking fixture is needed for inspecting the part without experiencing significant deformation. In static loading, the result of the first variation frame has a stress of 5.71 MPa and deformation of 0.051 mm, the second variation frame has a stress of 6.16 MPa and deformation of 0.049 mm and the third variation frame has a stress of 5.63 MPa and deformation 0.042 mm. In terms of weight, the first variation structure has 2470.48 kg, the second variation structure has 2179.93 kg and the third variation structure has 2210 kg. The second variation frame has the highest stress but it has the lightest weight, and the third variation frame has lower stress and deformation but it has a heavier weight than the second variation model. The study results that the second variation model is superior because it has the lightest weight while the three designs have small stress and deformation that still satisfy the requirement of the fixture.

Rubber Seed Biodiesel (RSB) and Plastic Pyrolysis Oil (PPO) deserve to be considered as alternative fuels for diesel engines, because of their advantages such as large raw material resources, derived from free or waste feedstock and the use of plastic waste as fuel can prevent environmental pollution. Due to their almost identical densities, RSB and PPO can be mixed homogeneously. In general, the use of a mixture of RSB and petroleum diesel in diesel engines shows positive performance, both engine performance and emissions, as well as the use of mixed PPO and diesel fuel. Although RSB has a good cetane number and flash point, on the other hand, RSB also has disadvantages in its physiochemical properties, such as low oxidation stability, high acid value, low heating value, and high viscosity. Likewise, PPO has good oxidation stability, acid value, and viscosity, but the flash point, CO, and HC emissions are also bad. This article tries to describe the opportunity to mix RSB and PPO, to find the best composition between RSB and PPO which shows the best fuel physiochemical properties and engine performance.

Identification of road profiles is needed to provide the input of automotive simulation and endurance testing. The analysis with estimation methods is mostly done to identify road profiles. The main goal of analysis methods is to obtain the data of vertical displacements due to road profile measurement. The acceleration data is obtained from measuring road profile by using 4 sensors of accelerometer placed on each car wheel. The measuring data is converted to be vertical displacement data by using a "double integrator", however, it is not easy to get accurate results since the signal obtained carries a lot of noise and it is necessary to design the right filter reduce the noise. In this study, the signal filtering methods reducing the noise were used Fast Fourier Transform (FFT) and Kalman Filter (KF) combination. Experiments were carried out by combining Fast Fourier Transform and Kalman Filters using an input signal with unit (volt) in the time domain. In addition, this research focused on preparing the survey data that has been obtained by eliminating the noise to convert becoming the displacement input data for providing the loads of automotive simulation testing.

Currently, research on natural fiber composites (NFC) for automotive applications has attracted the attention of researchers and academics. Natural fibers such as coconut fiber and wood powder are mixed with metal materials such as aluminum and copper to obtain the composite characteristics of automatic motorcycle clutch pad materials. Coconut fiber and wood powder are suitable natural materials for composites and are easily obtained from waste. Natural fiber materials with metal reinforcement into composites are expected to produce materials suitable for friction materials such as clutch pads and brake pads with a good characteristic. This study aims to determine the characteristics of the coefficient of friction, wear, and hardness of NFC materials reinforced aluminum and copper powder for the performance of automatic motorcycle clutch pads. Experiments were carried out on various compositions of aluminum and copper powder. Tribometer testing was carried out to determine the friction coefficient and wear. Hardness testing using the Vickers method and testing the performance of automatic motorbikes with a chassis dyno test. The results show that the performances of the clutch pad with NFC-reinforced aluminum and copper show power and torque results that resemble the performances of genuine part materials in each operating cycle. The value of friction coefficient, wear, and hardness of this material are a value close to that of a genuine part clutch pad material. The improved performance of this material is expected to be considered in the manufacture of future clutch pads.

Pedestrian crossing behavior can be studied by estimating critical gap, which is determined by analyzing accepted and rejected gaps by pedestrians. This can provide insight into safety levels at pedestrian facilities. The aim of this study is to determine critical gaps using various methods such as the Logit method, Raff's method, and Wu’s Method. These methods are then compared to identify the most appropriate one. Three locations in Malaysia were selected for data collection based on their land use, number of lanes, and carriageway width. Video cameras were used to capture mixed traffic flow and pedestrian crossing movements simultaneously at the selected sections. The results indicate that the critical gap values obtained from the three methods are highly comparable. Specifically, the Logit Method yielded a critical gap value of 8.4s, while Raff's Method and Wu's Method produced critical gap values of 7.7s and 7.12s, respectively. The study concludes that the Logit method is the most suitable for estimating critical gaps as it takes into account both pedestrian behavior and vehicular characteristics concurrently. The findings of this study have the potential to contribute to the review of design parameters for pedestrian crossing facilities, leading to the improvement of existing facilities and the enhancement of pedestrian safety.

This study investigates the barriers that the Lean Six Sigma implementation faces during the assembly of electric vehicles. In order to implement lean Six Sigma methodology in electric vehicle assembly processes effectively, it is crucial to identify and analyze the barriers that hinder process improvement. To identify the obstacles and create a conceptual model, a thorough literature review was conducted. Four factors, namely, integration of assembly, inspection, and testing, lack of trained and knowledgeable human resources, external and in-plant battery transportation, and manual assembly and rigid automation, were found to have the potential to affect the lean Six Sigma implementation. Three drivers, namely assembly cost, assembly time, and assembly effort were selected for the study. The model is then tested using the structural equation modeling and the gathered data. The results show a significant relationship between the three drivers and the four barriers of Lean Six Sigma implementation to the electric vehicle assembly.

In this paper, Metallic Catalytic Converter (MCC) is installed in motorcycle exhausts to produce the minimum CO as well as to produce the optimum engine power. The results from previous research were collected and then used to predict the best MCC design using the Artificial Neural Network Multi-Objective Genetic Algorithm (ANN-MOGA). In addition, the ANN parameter tuning process was also carried out using the Taguchi method to find the initial weighting and bias that is able to provide the best and the most stable performance to predict the best MCC design. The best two sets of design solutions out of 70 sets of Pareto solutions were obtained by ANN-MOGA. Those two MCC designs are the optimum emission design and the optimum multi-objective design. The verification results show that the optimum multi-objective design tends to be superior in terms of CO emissions and engine power. In terms of CO emissions, the optimum multi-objective design gets a larger S/N ratio of -10.98, while the optimum emission design only gets an S/N ratio of -11.21. Meanwhile, in terms of engine power, the optimum multi-objective design gets a larger S/N ratio of 16.13, while the optimum emission design only gets an S/N ratio of 15.86 S/N. It is in line with the ANOVA test results which show that the optimum multi-objective design is proven to be better than the optimum emission design.

Compressed Natural Gas (CNG) is an affordable fuel with a higher octane number. However, older CNG kits without electronic controls have the potential to supply more fuel when driving downhill due to the vacuum in the intake manifold. Therefore, this article presents a development of a CNG control system that accommodates road inclination angles to improve fuel efficiency. Machine learning is involved in this work to process engine speed, throttle valve position, and road slope angle. The control system is designed to ensure reduced fuel consumption when the vehicle is operating downhill. The results showed that the control system increases fuel consumption by 25.7% when driving downhill which an inclination of 5ᵒ. The AFR increased from 17.5 to 22 and the CNG flow rate decreased from 17.7 liters/min to 13.8 liters/min which is promising for applying to CNG vehicles.