International Journal of Innovation in Mechanical Engineering and Advanced Materials (IJIMEAM)
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Use of Hibiscus rosa-sinensis as a Green Corrosion Inhibitor for Valve Materials in RO Water
Get PDFValves are mechanical devices that regulate the flow of oil and gas fluids and are typically constructed from materials that are heat-resistant, corrosion-resistant, and capable of withstanding high pressure. However, observations from valve manufacturing companies in the Banten area have shown that valve components made from medium carbon steel ASTM A105N are susceptible to corrosion during hydrotesting, particularly when using reverse osmosis (RO) water as the testing medium. This corrosion can degrade product quality before delivery to customers. To address this issue, this study investigates the use of Hibiscus rosa-sinensis as a green corrosion inhibitor. The objective of this research is to evaluate the corrosion rate, inhibitor efficiency, and surface morphology of ASTM A105N valve materials using Hibiscus rosa-sinensis in RO water media, with varying inhibitor concentrations and immersion durations. The electrochemical methods used include Potentiodynamic Polarization, Electrochemical Impedance Spectroscopy (EIS), Chronoamperometry, and Scanning Electron Microscopy (SEM). Results from the corrosion rate tests indicated that the highest inhibitor efficiency—59.04%—was achieved at 24 hours of immersion with a 2 g inhibitor concentration. This condition also yielded the lowest corrosion rate of 1.2231 × 10⁻² mm/year and the lowest corrosion current (Icorr) of 3.2601 × 10⁻⁶ A/cm². Chronoamperometry testing confirmed these findings with the lowest electric charge value of 0.0125 C. SEM analysis further revealed a more uniform and homogeneous protective coating on the metal surface under these conditions. Based on these results, Hibiscus rosa-sinensis demonstrates promising performance as a green corrosion inhibitor and is recommended as an additive in RO water for valve hydrotesting. This study highlights the potential of environmentally friendly and cost-effective inhibitors in reducing corrosion risk in valve materials
Enhancing Kiln Reliability in Cement Industry Using RCM II and FMEA
Get PDFThis study applies to the Reliability-Centered Maintenance (RCM) II methodology to improve the reliability and cost efficiency of a kiln system in a cement manufacturing plant. Kiln failures are critical because they cause unplanned downtime, reduced productivity, and financial losses. Traditional corrective or time-based maintenance strategies often fail to address the stochastic nature of failures in such high-temperature rotary systems. To overcome this gap, the research integrates Failure Mode and Effect Analysis (FMEA) with RCM II decision logic to identify and prioritize maintenance actions. The analysis focused on five critical kiln components—crusher cooler, firebrick lining, thrust roller, grate cooler, and main drive—using 12 months of operational data supported by expert interviews and technical manuals. Reliability indicators, including Mean Time to Failure (MTTF), Mean Time to Repair (MTTR), and Mean Time Between Failures (MTBF), were calculated, while Risk Priority Numbers (RPN) were assigned to rank failure modes. Results showed that the crusher cooler had the highest risk, whereas the main drive required the longest repair duration. Implementation of RCM II recommendations increased MTBF by 29–38% across components and reduced maintenance costs by more than 50%. These findings confirm that RCM II provides a practical, data-driven framework for enhancing system availability. The study contributes to maintenance engineering by demonstrating a structured approach that supports risk-informed and condition-based maintenance strategies in continuous-process industries
Performance Analysis of Centrifugal Pumps Before and After Wear Ring Restoration
Get PDFA pump is a mechanical device used to move fluids from a lower elevation to a higher one. In general, pumps are classified into two types: positive displacement pumps and non-positive displacement pumps. Centrifugal pumps fall into the latter category and operate by converting mechanical energy into kinetic energy to transport fluids. A centrifugal pump consists of several key components, including the casing, shaft, bearing, coupling, and impeller. In the case of closed impeller-type centrifugal pumps, wear rings (wearing components) are installed to provide a clearance between the impeller and the casing, preventing physical contact during operation. The size of this clearance significantly affects pump performance. Wear ring damage can result from mechanical wear, corrosion, cavitation, and fatigue, leading to performance losses such as reduced flow rate, lower pressure, and decreased efficiency. This research aims to analyze the effect of wear ring damage on the performance of a centrifugal pump by comparing operational data before and after repair of the wearing components. The performance parameters evaluated include pump head, pressure, hydraulic power, motor power, and overall efficiency. Data were collected through a structured procedure consisting of preparation, testing, measurement, and analysis. Prior to repair, the pump operated with a wear ring clearance of 1.2 mm, resulting in an average efficiency of 8.5% and a flow rate of 0.000646 m³/s. After the clearance was restored to 0.43 mm, the average efficiency increased to 15.5%, with a corresponding flow rate of 0.000932 m³/s. These results demonstrate that maintaining wear ring clearance within recommended standards significantly improves pump performance, highlighting the importance of regular maintenance and timely component repair
Effect of Coconut Fiber and Coconut Shell Charcoal Composition on the Properties of PVC-Reinforced Composite Brake Pads
Get PDFThe increasing concern over the health hazards associated with asbestos-based brake pads has driven the development of eco-friendly alternatives using natural fiber-reinforced composites. This study aims to fabricate and evaluate a sustainable brake pad material using coconut fiber as reinforcement, coconut shell charcoal powder as filler, and polyvinyl chloride (PVC) as the matrix. The composite was manufactured using the hot press method at a temperature of 180°C and a pressure of 7 MPa, conditions selected to optimize resin curing and interfacial bonding. A key focus of this research was to investigate the effect of solvent volume (cyclohexanone) used in the PVC resin preparation on the mechanical properties of the resulting composites. Three composite formulations were prepared with a constant composition of 70% coconut fiber, 5% charcoal powder, and 25% PVC resin, but with varying amounts of cyclohexanone solvent (200 mL, 150 mL, and 100 mL). The results revealed that reducing solvent content led to higher resin viscosity, which improved matrix–fiber bonding and increased both tensile strength and surface hardness. The optimal formulation—PVC Resin 3 with 100 mL of solvent—achieved a maximum tensile strength of 7.7 MPa and Shore D hardness of 72.2 HD, both of which meet the SAE J661-1997 standards for brake pad materials. This study confirms that solvent content is a critical factor influencing the density, strength, and durability of the composite. The findings support the feasibility of utilizing coconut-based agricultural waste in producing environmentally friendly brake pads with adequate mechanical performance
Numerical Analysis of Heat Transfer Enhancement in Wavy Trapezoidal and Rectangular Microchannels
Get PDFThis study presents a comprehensive numerical investigation of heat transfer enhancement in microchannels with varying geometries, specifically focusing on wavy microchannels with trapezoidal and rectangular cross-sections. Water is used as the working fluid, and silicon is selected as the solid wall material. A three-dimensional conjugate heat transfer model is developed by solving the steady-state Navier–Stokes and energy equations using the finite volume method in ANSYS Fluent, with the SIMPLEC algorithm employed for pressure–velocity coupling. The analysis examines the influence of cross-sectional shape and wall waviness on thermal performance, while maintaining a constant hydraulic diameter across all configurations. Eight different geometries, including smooth and wavy versions of rectangular and trapezoidal cross-sections with varying top-to-bottom width ratios (0.075–0.055 mm), are evaluated over a Reynolds number range corresponding to inlet velocities of 0.5–4.0 m/s. Results show that wavy microchannels significantly enhance heat transfer compared to their smooth counterparts. For instance, at 4 m/s, the Nusselt number for the wavy rectangular microchannel reaches 9.48, compared to 7.19 for the smooth rectangular configuration, representing a 32% enhancement. Similarly, the wavy trapezoidal channel with a top width of 0.18 mm achieves a maximum Nusselt number of 9.25, compared to 7.19 for its smooth equivalent, indicating a 29% improvement. Additionally, the Nu/Nu₀ versus Re plots reveal a consistent trend of increased heat transfer due to wall waviness across all geometries, with negligible influence from cross-sectional shape when hydraulic diameter is kept constant. The study demonstrates that incorporating wavy structures into microchannel designs significantly improves thermal performance with minimal increases in pressure drop, and that the effect is driven more by wall geometry than by cross-sectional shape. These findings provide valuable insights for the development of compact and efficient microchannel heat sinks for electronic cooling applications
Enhancing The Formability of SS304 in ISF via Pre-Heating Treatment Strategies
Get PDFThe increasing demand for lightweight yet high-strength components in the automotive and aerospace industries has accelerated interest in Incremental Sheet Forming (ISF) as a flexible, dieless, and cost-effective manufacturing process, particularly for low-volume and customized production. Unlike conventional forming processes that rely on expensive dies, ISF offers greater geometric flexibility and rapid prototyping capabilities. However, its broader industrial adoption remains limited due to persistent challenges such as poor surface finish, springback, and restricted formability, especially when forming hard-to-deform materials like Stainless Steel Grade 304 (SS304). This study investigates the influence of customized heat treatment on the formability and deformation quality of SS304 sheets formed via ISF. Sheets were subjected to preheating at controlled temperatures ranging from room temperature to 700°C, followed by dieless forming using a CNC machining center equipped with a hemispherical tungsten carbide tool. Key process parameters, including a step size of 0.3 mm, a feed rate of 180 mm/min, and a tool speed of 500 mm/min, were maintained throughout forming. Comprehensive mechanical and microstructural analyses, including tensile testing, surface roughness evaluation, and optical metallography, were performed. Results revealed significant improvements in formability: ductility increased from 24.28% to 65%, and surface roughness (Ra) decreased from 9.7993 µm to 5.4809 µm after annealing at 700°C and tempering at 500°C. Microstructural analysis confirmed grain refinement and carbide dissolution, contributing to improved plastic flow and reduced surface defects. Integrating controlled heat treatment with ISF significantly enhances forming capabilities, surface quality, and geometric precision of SS304, making it a viable solution for manufacturing complex, high-performance components. These findings provide valuable insights for developing more efficient, defect-minimized, and adaptable forming strategies suitable for advanced manufacturing industries
Review: Optimizing Plastic Injection Processes for Enhanced Quality and Sustainable Manufacturing
Get PDFIn the automotive world, plastic products are components that cannot be separated. Almost all automotive products use plastic because it is easy to produce, and the price is relatively cheap compared to other materials. For applications such as covers, the demand on plastic surface quality are higher than for different uses. Therefore, a lot of costs are incurred to achieve this quality. However, ongoing efforts have decreased the time and expense of developing plastic molds. Many researchers have conducted studies to improve the quality of these products. This review consolidates several research articles on optimizing plastic injection processes to reduce defects and improve product quality. Techniques such as Taguchi Method, Response Surface Methodology (RSM), Artificial Neural Networks (ANN), and Finite Element Method (FEM) were evaluated in this research. This review highlights the importance of process parameters such as melt temperature, injection pressure, and cooling time, as well as the role of digital simulation in designing efficient and sustainable molds. The results of the study show that in several studies, defects often occur in the product without carrying out the optimization process. Still, the Taguchi and ANOVA methods can reduce the weld line and sink after optimizing the process parameters, such as melting temperature, injection pressure, cooling time, and injection speed. Mark up to 30%. These findings highlight the potential of these techniques to significantly improve product quality and support more sustainable manufacturing practices in the plastic injection molding industry
Study of Eigenvalues and Matrix Eigenvectors Using MATLAB: Vibration Systems of Multi-Purpose Vehicle (MPV)
Get PDFVehicle vibration is a critical factor influencing both passenger comfort and vehicle performance. In this study, we analyze the multi-degree-of-freedom (MDOF) vibrational behavior of a multi-purpose vehicle (MPV) using matrix eigenvalue and eigenvector methods. The vehicle’s dynamics are modeled by developing a set of equations of motion that account for the forces acting on the front and rear tires, car body, and pitch angle. MATLAB is utilized to numerically compute the system’s eigenvalues and eigenvectors, representing the natural frequencies and vibration modes of the vehicle, respectively. The analysis focuses on the vehicle’s response to a 50 mm displacement at the front tire, simulating the effect of road disturbances. The resulting vibrations in the front and rear tires, car body, and vehicle pitch are illustrated over a 1-second time frame. The findings show that the front tire experiences the largest oscillation amplitude of ±1 mm, while the rear tire exhibits a much smaller displacement of ±0.04 mm. The overall car body displacement reaches a maximum amplitude of ±1.3 mm, indicating partial damping of the front tire vibrations. However, the results reveal that the vehicle’s suspension system lacks effective damping, as the vibrations do not decrease over time. This behavior could negatively impact ride comfort and safety, particularly on uneven roads. The study concludes that improvements to the vehicle’s suspension system are necessary to enhance damping performance. The presented MATLAB-based approach offers a valuable tool for analyzing and optimizing vehicle vibration systems
Performance Evaluation of a Condenser at a Combined Cycle Power Plant Using the LMTD Method
Get PDFThis study evaluates the performance of the condenser at the Cilegon Combined Cycle Power Plant (CCPP) using the Logarithmic Mean Temperature Difference (LMTD) method to measure the heat transfer rate. Routine maintenance carried out on the condenser in the form of cleaning the condenser water box and condenser tube from garbage and crust on the condenser tube wall. Currently, condenser maintenance follows a routine schedule that is tied to steam turbine maintenance, without taking actual condenser performance into account. This can lead to inefficiencies and unnecessary downtime. The goal of this research is to assess the heat transfer rate of the condenser before and after maintenance to judge its effectiveness. Data on temperature changes were gathered in June 2023, before maintenance, and again in July 2023, after an overhaul. The analysis shows that the heat transfer rate increased from 51,362,294.48 kcal/h to 127,246,219.7 kcal/h, while the LMTD value rose from 0.76°C to 1.86°C. Based on these results, the study suggests a new approach to maintenance that focuses on performance. Specifically, maintenance should be done when the heat transfer rate drops below 110,000,000 kcal/h. This approach will help ensure the condenser works at its best, improve the plant's overall efficiency, and prevent the need for unnecessary maintenance. By aligning maintenance with performance data, the plant can boost output while lowering costs and downtime
COMPARING ROTATION-ROBUST MECHANISMS IN LOCAL FEATURE MATCHING: HAND-CRAFTED VS. DEEP LEARNING ALGORITHMS
Get PDFThe objective of this research is to conduct a performance comparison between hand-crafted feature matching algorithms and deep learning-based counterparts in the context of rotational variances. Hand-crafted algorithms underwent testing utilizing FLANN (Fast Library for Approximate Nearest Neighbors) as the matcher and RANSAC (Random sample consensus) for outlier detection and elimination, contributing to enhanced accuracy in the results. Surprisingly, experiments revealed that hand-crafted algorithms could yield comparable or superior results to deep learning-based algorithms when exposed to rotational variances. Notably, the application of horizontally flipped images showcased a distinct advantage for deep learning-based algorithms, demonstrating significantly improved results compared to their hand-crafted counterparts. While deep learning-based algorithms exhibit technological advancements, the study found that hand-crafted algorithms like AKAZE and AKAZE-SIFT could effectively compete with their deep learning counterparts, particularly in scenarios involving rotational variances. However, the same level of competitiveness was not observed in horizontally flipped cases, where hand-crafted algorithms exhibited suboptimal results. Conversely, deep learning algorithms such as DELF demonstrated superior results and accuracy in horizontally flipped scenarios. The research underscores that the choice between hand-crafted and deep learning-based algorithms depends on the specific use case. Hand-crafted algorithms exhibit competitiveness, especially in addressing rotational variances, while deep learning-based algorithms, exemplified by DELF, excel in scenarios involving horizontally flipped images, showcasing the unique advantages each approach holds in different contexts
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