International Journal of Innovation in Mechanical Engineering and Advanced Materials (IJIMEAM)
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Utilization of Plastic Waste and Rice Husk Ash in Polyethylene-Based Composites for Ceiling Applications
Get PDFThis study aims to analyze the effect of polyethylene (PE) and rice husk ash (RHA) composition variations on the mechanical properties of recycled composites developed as environmentally friendly ceiling materials. Composite specimens were prepared through a systematic process involving shredding PE plastic waste into 3–5 mm particles, burning rice husks at 600–700 °C followed by sieving through a 200-mesh screen, melting the plastic at 160–170 °C, and mixing with RHA at three composition ratios: 80:20, 70:30, and 60:40 (PE:RHA). The mixtures were molded into 50 × 50 mm specimens and tested in accordance with ASTM D695 for compressive properties and ASTM D792 for density. The results show that composition variation significantly influences compressive strength, elastic modulus, and strain behavior. The 80:20 composition exhibited the highest elasticity, with a compressive strength of 15.59 MPa and an elastic modulus of 463.50 MPa; however, it fractured shortly after exceeding the elastic limit. The 60:40 composition achieved the highest compressive strength of 125 MPa with a strain of 56.6%, but showed brittle behavior due to its very low elastic modulus (7.5 MPa). The 70:30 composition demonstrated the most balanced mechanical performance, with a compressive strength of 61.65 MPa, a strain of 18.20%, and stable ductile behavior. Based on the overall mechanical performance, the 70:30 PE–RHA composition is recommended as the optimal formulation, as it provides the best balance between strength, stiffness, and deformation resistance. This composition is therefore considered the most suitable for non-structural ceiling applications requiring lightweight, mechanically stable, and environmentally sustainable materials
Optimization of CNC Turning Parameters for Surface Roughness of Brass 36000 Using the Taguchi Method
Get PDFBrass is widely used in industrial applications due to its excellent machinability and durability, making it well suited for CNC turning operations. Although numerous studies have investigated the optimization of turning parameters, variations in machine tools and cutting conditions often lead to differing conclusions. This study aims to optimize surface roughness in the CNC turning of Brass 36000 using the Taguchi method. An L9 orthogonal array was employed to evaluate the effects of spindle speed, feed rate, depth of cut, and coolant type. Experimental data were analyzed using signal-to-noise (S/N) ratio analysis and analysis of variance (ANOVA) to identify the most influential parameters and optimal cutting conditions. The results indicate that feed rate is the dominant factor affecting surface roughness, contributing 95.54% of the total variation, followed by spindle speed (1.88%), depth of cut (0.33%), and coolant type (0.18%). The optimal machining parameters were determined as a spindle speed of 1700 rpm, feed rate of 0.1 mm/rev, depth of cut of 1.0 mm, and the use of synthetic coolant (GT41), resulting in a minimum surface roughness of 0.67 µm. These findings demonstrate that precise control of feed rate is critical for achieving improved surface quality in CNC turning of brass
Mechanical Properties Analysis of Stainless Steel 304 Linear Guide Rail Using Autodesk Inventor and MATLAB
Get PDFThis study investigates the mechanical properties of a stainless steel 304 linear guide rail using a combination of Autodesk Inventor and MATLAB. The primary objective is to analyze the von Mises stress distribution, displacement, and safety factor of the linear guide rail under varying load conditions, as well as to develop a model representing the relationship between stress and strain. A detailed 3D model of the guide rail was created using Autodesk Inventor, followed by finite element analysis (FEA) to evaluate stress and strain distribution across different sections of the rail. The simulation was conducted to assess the structural response under multiple loading scenarios, ensuring its reliability for real-world applications. Furthermore, a linear regression analysis was performed using MATLAB to establish a predictive model correlating stress and strain, enabling more accurate forecasting of the material's mechanical behavior. The results revealed that the maximum von Mises stress obtained from the simulation was 23.595 MPa, with a corresponding maximum displacement of 0.397 mm. The safety factor analysis confirmed the rail's structural integrity, with a minimum safety factor of 10.595, well above the failure threshold. These findings indicate that the linear guide rail meets the necessary mechanical performance requirements for its intended application
Enhancing Homogeneity and Particle Size Reduction in Coffee–Creamer Mixtures Using Fluidized Bed Mixer
Get PDFThis study investigates the application of a fluidized bed mixer to improve the homogeneity, particle size distribution, and moisture reduction of coffee and creamer powder mixtures. The research focuses on three types of coffee particles—Type A (145 μm), Type B (100 μm), and Type C (50 μm)—which were mixed with creamer in a weight ratio of 1:0.7. The mixing process was conducted using a prototype fluidized bed mixer with a capacity of 1,000 grams and a blower speed range of 2,800–3,000 rpm. After 10 minutes of mixing, significant reductions in particle size were observed: Type A decreased by 20–30%, Type B by 10–15%, and Type C by 5–10%, with creamer particles also experiencing a 15% reduction. Moisture content dropped from 10.63% to 8.5%, demonstrating the system’s dual function of mixing and drying. Microscopic analysis revealed a uniform particle distribution with minimal agglomeration or segregation, confirming the effectiveness of the fluidized bed mixer in achieving a homogeneous blend. These findings underscore the potential of fluidized bed technology in improving the quality, stability, and handling properties of powder-based products. The results have important implications for instant beverage production, food formulation, and broader powder processing industries, where consistent product performance is essential
Handling and Stability Analysis of an Autonomous Vehicle Using Model Predictive Control in a CarSim–Simulink Co-Simulation Environment
Get PDFCars are a prevalent mode of transportation for both people and goods, with B-class hatchbacks being particularly popular in Indonesia. However, road traffic crashes remain a major concern, contributing millions of deaths annually, primarily due to human error. Autonomous vehicles offer a promising solution to mitigate these issues by reducing reliance on human control. In particular, Level 3 autonomous vehicles enhance road safety, enable independent mobility, reduce traffic congestion, and allow drivers to engage in non-driving tasks. This study proposes an autonomous vehicle model that employs a trajectory tracking approach using Model Predictive Control (MPC), a robust and widely adopted control strategy in autonomous systems. A three-degree-of-freedom (3-DOF) vehicle dynamic model was developed and analyzed through co-simulation using CarSim and Simulink to evaluate its performance during a double-lane change maneuver. The simulation results demonstrate that the vehicle accurately follows the reference trajectory and exhibits excellent dynamic performance. The roll angle remained consistently low, ranging between 0.024 and 0.026 radians—well below the rollover threshold of 0.14 radians—demonstrating strong roll stability. The slip angle varied between –0.013 and 0.0135 radians, nearly 12 times lower than the critical limit, indicating optimal traction and directional control. Lateral acceleration ranged from –3.59 m/s² to 3.41 m/s², and yaw rate remained within –7.78°/s to 7.25°/s, both well within safe operational bounds. These findings confirm that the proposed MPC-based control framework enables precise path tracking, robust stability, and reliable handling performance in dynamic driving scenarios
Natural Inhibitors for Corrosion Protection of 6061 Aluminum Alloy: A Review
Get PDF6061 aluminum alloys are widely used in automotive, marine, and aerospace industries, yet their high susceptibility to corrosion in acidic and chloride environments remains a challenge. Bio-based inhibitors from natural sources have emerged as sustainable alternatives to toxic synthetic chemicals. This review synthesizes findings from published studies on AA6061 alloys and composites, integrating evidence from Potentiodynamic Polarization (PDP), Electrochemical Impedance Spectroscopy (EIS), and Scanning Electron Microscopy (SEM). Cross-study evaluations show that inhibition efficiency depends on inhibitor type and mechanism. Reports indicate that Boswellia serrata provides only moderate protection (~70%) due to weak physiosorbed films that are unstable under flow, whereas Alocasia odora achieves higher efficiency (~94% in HCl) through chemisorption with cathodic inhibition. Aerva lanata demonstrates ~88% efficiency in chloride-based fiber-metal laminates via polyphenolic adsorption, while glutathione provides ~80% protection at 0.75 mM through multisite coordination. Pectin consistently achieves the highest efficiency (~95% in mild acidic media) by forming compact polymeric films that increase charge-transfer resistance and reduce double-layer capacitance. This synthesis indicates that chemisorption-based inhibitors (e.g., pectin, Alocasia) generally outperform physisorption-based systems (e.g., Boswellia) because they form stronger and more stable films. Reported studies highlight both advantages and limitations: natural inhibitors are effective and eco-friendly, but most evaluations remain short-term and laboratory-based. Key gaps include durability testing, advanced characterization (XPS, ToF-SIMS, Raman, AFM), galvanic effects in composites, and poor hydrodynamic stability of physisorption systems. Future work should explore hybrid strategies, synergistic multi-inhibitor approaches, and validation under real-sea conditions to enable scalable and industrially viable corrosion protection
Viability of R-290 Refrigerant as Residential AC Retrofit: Effect of Charge Mass Variations
Get PDFThe growing concerns over ozone depletion and global warming caused by refrigerants have led to the search for environmentally friendly alternatives. This study evaluates the impact of varying R-290 refrigerant charge masses on the performance of a wall-mounted residential air conditioner using the drop-in substitute method. A ¾ HP residential AC unit originally charged with 550 grams of R-22 refrigerant was retrofitted with R-290 and tested at charge masses of 140 grams, 165 grams, and 190 grams—approximately 25%, 30%, and 35% of the original R-22 charge, in accordance with the commonly applied “one-third rule.” The results showed that retrofitting with R-290 increased the Refrigeration Effect (RE) by up to 75%, Compression Work (Wc) by 68%, and Coefficient of Performance (COP) by up to 18%. The system with a 25% refrigerant charge was unable to reach the set temperature due to a 23% reduction in cooling capacity, while the 30% charge showed a 10% reduction. The 35% refrigerant mass retrofit proved the most suitable, achieving adequate cooling capacity, an 18% increase in COP, and a 14% reduction in power consumption. Additionally, the retrofit resulted in an indirect CO₂ emission reduction of 1.15 metric tons annually, highlighting the environmental and energy-saving advantages of using R-290. These findings provide empirical validation of the one-third rule for refrigerant mass variation in R-290 retrofits and offer valuable insights into optimizing performance and efficiency in residential AC units, with significant energy and environmental benefits
IoT-Based Continuity Analysis of Oil Pipeline Leakages
Get PDFOil pipeline leaks pose a serious challenge due to their potential to cause significant economic losses and severe environmental damage. These incidents can disrupt industrial operations and endanger nearby ecosystems and communities. Early detection and real-time monitoring are therefore essential for minimizing adverse impacts and enabling rapid response. This research develops an Internet of Things (IoT)-based oil pipeline leak monitoring system using integrated multi-sensor data collected from field-simulated scenarios, providing a realistic evaluation of system performance under near-operational conditions. The system incorporates an ultrasonic sensor (HC-SR04) to measure fluid levels, a temperature sensor (DS18B20) to detect thermal anomalies, and a pressure sensor to identify internal pressure fluctuations. Sensor data are wirelessly transmitted via a NodeMCU ESP32 microcontroller to a web-based dashboard for remote monitoring, while local readings are simultaneously displayed on an LCD screen for on-site observation. The system was evaluated through controlled experiments simulating variations in pressure, temperature, and induced leak conditions. Results showed that the system achieved over 95% accuracy in leak detection, with a response time of less than 60 seconds upon leak initiation. The flow rate deviations under leak conditions exceeded the ±3% detection threshold, triggering real-time alerts. In non-leak scenarios, flow rates remained steady between 1.5–2.1 L/min, with tank level variations within 1 cm, confirming strong mass balance and stability. Overall, the developed IoT-based monitoring platform demonstrated high reliability and effectiveness in real-time leak detection, enabling faster response and significantly reducing potential environmental and operational impacts
Correlation Analysis of Battery Capacity, Range, and Charging Time in Electric Vehicles Using Pearson Correlation and MATLAB Regression
Get PDFThe increasing adoption of electric vehicles (EVs) reflects growing global awareness of climate change and air pollution challenges. As a sustainable alternative to conventional internal combustion vehicles, EVs produce zero tailpipe emissions and can significantly reduce carbon emissions—particularly when powered by renewable energy sources. However, one of the primary barriers to widespread EV adoption remains the high cost of battery components, which are essential to vehicle performance and energy storage. In Indonesia, two dominant battery types used in EVs are Lithium Ferro Phosphate (LFP) and Nickel Manganese Cobalt (NMC), each offering distinct advantages. LFP batteries are recognized for their thermal stability and longer life cycles, making them suitable for everyday use, while NMC batteries offer higher energy density and are preferred for performance-focused and long-distance applications. This study aims to evaluate the correlation between battery capacity, driving range, and charging time for LFP and NMC batteries using Pearson correlation and regression analysis through MATLAB simulation. The results indicate a strong and statistically significant correlation among the key parameters, with a Pearson coefficient of 0.576 for battery capacity and range, and an R-square value of 0.99 for the regression model, demonstrating high predictive accuracy. Furthermore, the analysis reveals that LFP batteries have a higher average energy efficiency of 7.53 km/kWh compared to 6.84 km/kWh for NMC batteries, indicating more consistent performance in energy usage. These findings offer valuable insights for optimizing battery selection in EV applications and contribute to strategic planning for the development of more efficient electric vehicle systems. The combination of statistical and simulation-based analysis provides a robust foundation for future research and policy-making in the field of electric mobility
Fuel Efficiency Evaluation of Automatic Motorcycles in Indonesia Using MATLAB-Based Clustering
Get PDFThe continuous rise in fuel prices in Indonesia has made fuel efficiency a crucial factor for consumers when selecting vehicles, particularly motorcycles. Automatic scooters with engine capacities below 160 cc have become increasingly popular in urban areas due to their fuel-saving benefits. This study aims to analyze the influence of engine capacity, vehicle weight, and engine torque on the fuel consumption of automatic scooters with engine capacities ranging from 109 cc to 156.9 cc. The study also considers additional performance parameters, including average fuel consumption, power output, and Power-to-Weight Ratio (PWR). Using statistical analysis and MATLAB-based modeling, the data were classified into three distinct clusters. Cluster 1 comprises scooters with engine capacities between 109 and 125 cc; Cluster 2 includes those with capacities between 150 and 160 cc; and Cluster 3 represents scooters with unique component specifications. The results show that Cluster 2 records the highest average maximum power output at 11.47 kW and torque at 14.25 Nm, while Cluster 1 has the lowest at 6.1 kW and 9.64 Nm, respectively. In terms of weight, Cluster 3 is the heaviest, averaging 129.33 kg, while Cluster 1 is the lightest at 96.14 kg. Fuel efficiency is highest in Cluster 1 at 55.3 km/l and lowest in Cluster 3 at 38.67 km/l. Comparative analysis using MATLAB confirms that scooters with lower engine capacities and weights tend to be more fuel-efficient, whereas higher engine capacities lead to increased torque, power, weight, and fuel consumption. These findings can guide consumers in selecting motorcycles that align with their usage needs and assist manufacturers in developing more efficient and high-performing scooters tailored to diverse market segments
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