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Andi Firdaus Sudarma
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Universitas Mercu Buana Program Studi S2 Teknik Mesin Jl. Meruya Selatan No. 01, Kembangan, Jakarta Barat 11650, Indonesia
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INDONESIA
International Journal of Innovation in Mechanical Engineering and Advanced Materials
Published by Universitas Mercu Buana
ISSN : 2477541X     EISSN : 24775428     DOI : https://dx.doi.org/10.22441/ijimeam
Core Subject : Science, Engineering,
Automotive Engineering Energy Engineering Materials Science & Nanotechnology Mechanical Engineering
The journal publishes research manuscripts dealing with problems of modern technology (power and process engineering, structural and machine design, production engineering mechanism and materials, etc.). It considers activities such as design, construction, operation, environmental protection, etc. in the field of mechanical engineering and other related branches. In addition, the journal also publishes papers in advanced materials related with advanced electronic materials, advanced energy materials, advanced engineering materials, advanced functional materials, advanced materials interfaces, and advanced optical materials.
Arjuna Subject : Teknik (semua kategori) - Teknik Mesin
Articles 115 Documents
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Handling and Stability Analysis of an Autonomous Vehicle Using Model Predictive Control in a CarSim–Simulink Co-Simulation Environment Mohamad Yamin; Mega Maulida Mumtaz; Riyan Firmansyah
International Journal of Innovation in Mechanical Engineering and Advanced Materials Vol. 7 No. 2 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/ijimeam.v7i2.31812

Abstract

Cars 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.
Use of Hibiscus rosa-sinensis as a Green Corrosion Inhibitor for Valve Materials in RO Water Sri Pudjiwati; Yasa Sanusi; I Gusti Ayu Arwati
International Journal of Innovation in Mechanical Engineering and Advanced Materials Vol. 7 No. 2 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/ijimeam.v7i2.32806

Abstract

Valves 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.
IoT-Based Continuity Analysis of Oil Pipeline Leakages Nadia Sri Melati Malau; Nike Dwi Grevika Drantantiyas; Ferizandi Qauzar Gani
International Journal of Innovation in Mechanical Engineering and Advanced Materials Vol. 7 No. 2 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/ijimeam.v7i2.33360

Abstract

Oil 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.
Enhancing The Formability of SS304 in ISF via Pre-Heating Treatment Strategies Muhammad Aqib Raza Shah; Agung Shamsuddin Saragih
International Journal of Innovation in Mechanical Engineering and Advanced Materials Vol. 7 No. 3 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/ijimeam.v7i3.33596

Abstract

The 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.
Natural Inhibitors for Corrosion Protection of 6061 Aluminum Alloy: A Review Maulana Witanta; I Gusti Ayu Arwati; Edy Herianto Majlan
International Journal of Innovation in Mechanical Engineering and Advanced Materials Vol. 7 No. 3 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/ijimeam.v7i3.34713

Abstract

6061 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.
Enhancing Kiln Reliability in Cement Industry Using RCM II and FMEA Mustika Ratnawati Faizzah; Asri Amalia Muti; Sindy Nindia Maretha HarisTanti
International Journal of Innovation in Mechanical Engineering and Advanced Materials Vol. 7 No. 3 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/ijimeam.v7i3.35085

Abstract

This 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.
Utilization of Plastic Waste and Rice Husk Ash in Polyethylene-Based Composites for Ceiling Applications Gusti F. X. Wara Wangge; Yohanes Viva Servianus; Thadeus Rande
International Journal of Innovation in Mechanical Engineering and Advanced Materials Vol. 7 No. 3 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/ijimeam.v7i3.36658

Abstract

This 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 Agus Noviana; Muhamad Fitri; Dedik Romahadi
International Journal of Innovation in Mechanical Engineering and Advanced Materials Vol. 7 No. 3 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/ijimeam.v7i3.37302

Abstract

Brass 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.
Implementing OSA-CBM Architecture for Operational Readiness Assessment of Aging Motor Grader Engines Nova Pangastuti; Muhammad Ilham Muslimin; Sepriandi Parningotan
International Journal of Innovation in Mechanical Engineering and Advanced Materials Vol. 8 No. 1 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/ijimeam.v8i1.38575

Abstract

Traditional interval-based maintenance policies for diesel engines often rely on fixed overhaul schedules recommended by Original Equipment Manufacturers (OEM), which may not accurately reflect the actual health status of aging heavy equipment in harsh mining environments. Such static approaches risk either premature, costly overhauls or catastrophic, unexpected failures. This study proposes a robust methodology by integrating Interquartile Range (IQR)-based statistical control limits within the Open System Architecture for Condition-Based Maintenance (OSA-CBM) framework to evaluate the operational readiness of an aging motor grader engine. Historical oil analysis data from May 2024 to November 2025, covering an extended operational period from 13,515 to 19,755 hours, were examined to identify wear behavior and lubricant degradation. The implementation of the seven functional layers of OSA-CBM, from data acquisition to advisory generation, ensures a structured and traceable diagnostic process. The results indicate that primary wear metal parameters, specifically iron (Fe) and chromium (Cr), remained stable with Risk Index (RI) values below unity (RI < 1), signifying a steady-state wear condition despite the engine operating far beyond typical overhaul intervals. Although a significant isolated spike in aluminum (Al) was detected with a Risk Index of 3.20, the absence of correlated increases in Fe or Cr suggests episodic contamination or a transient event rather than progressive mechanical wear. Furthermore, lubricant condition indicators, including kinematic viscosity and Total Base Number (TBN), consistently complied with SAE 15W-40 specifications. These findings demonstrate that embedding unit-specific statistical boundaries within the OSA-CBM architecture provides reliable, data-driven decision support, enabling justified operational life extension and optimized maintenance strategies for aging heavy-duty engines.
Benchmarking Nine SMOTE-Balanced Classifiers Including Artificial Neural Network for CNC Predictive Maintenance Didiek Trisatya; Priyo Haryoko
International Journal of Innovation in Mechanical Engineering and Advanced Materials Vol. 8 No. 1 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/ijimeam.v8i1.38645

Abstract

Unplanned equipment failure in CNC manufacturing causes significant economic losses, driving demand for effective predictive maintenance (PdM). A critical research gap persists: existing studies on the AI4I 2020 Predictive Maintenance Dataset apply isolated classifiers under inconsistent preprocessing pipelines, preventing fair algorithmic comparison. No prior study has benchmarked nine diverse classifier families under a unified pipeline integrating SMOTE oversampling with domain-driven feature engineering. This study addresses that gap by systematically evaluating nine ML classifiers—Logistic Regression, K-Nearest Neighbors, Decision Tree, Random Forest, Gradient Boosting, AdaBoost, SVM (RBF kernel), Naive Bayes, and MLP Neural Network—on the AI4I 2020 dataset (10,000 records; 3.4% failure rate; 1:28 class imbalance). Two domain-engineered features were constructed: mechanical power (P = n × T × (π/30)) and thermal gradient (ΔT = T_process - T_air). Features were normalized; SMOTE was applied to training folds only; and 10-fold stratified cross-validation assessed six performance metrics. Three novel contributions are presented: (1) the first nine-classifier benchmark on AI4I 2020 under a unified SMOTE-and-feature-engineering pipeline enabling fair model comparison; (2) empirical demonstration that Average Precision is a more discriminating evaluation metric than AUC-ROC under severe 1:28 class imbalance; and (3) physical interpretation of feature importance linking dominant predictors to CNC failure mechanisms. Gradient Boosting achieved the best-balanced performance (F1-score: 0.6782, Accuracy: 97.20%, AUC-ROC: 0.9723); Random Forest attained the highest AUC-ROC (0.9772). Mechanical power (25.51%) and tool wear (23.91%) were dominant predictors, corresponding to tribological, fatigue loading, and thermal failure mechanisms. These findings support cost-effective condition-based maintenance strategies in industrial CNC environments.

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