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All Journal Automotive Experiences Jurnal Polimesin
Sarifudin, Alfan
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Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Materials Science & Nanotechnology Mechanical Engineering

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Optimization of plunger geometry and stroke settings on hydraulic performance of diaphragm metering pumps Sonjaya, Muhammad Luthfi; Setyawan, Hendra; Ritonga, Jhordan; Sarifudin, Alfan; Nury, Dennis Farina
Jurnal Polimesin Vol 23, No 4 (2025): August
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jpl.v23i4.7308

Abstract

This study examines the influence of plunger diameter and stroke length on the performance of a hydraulic diaphragm metering pump, focusing on two key indicators: discharge pressure and flow rate. Experiments were conducted with five plunger diameters (7.90–9.00 mm) and three-stroke settings (100%, 75%, and 50%), validated in accordance with API 675 standards. Results show that each 1 mm increase in plunger diameter produced a consistent rise in flow rate across all stroke settings. Linear regression analysis revealed strong correlations, with flow rate increments of 67.54 mL/min per mm at 100% stroke, 60.78 mL/min per mm at 75% stroke, and 25.34 mL/min per mm at 50% stroke. High coefficients of determination (R²) confirm the robustness and predictive accuracy of these models. In addition to regression analysis, a two-way ANOVA was performed to statistically evaluate the effects of plunger diameter and stroke length, as well as their interaction, on pump performance. The ANOVA results confirmed that both parameters significantly affected flow rate (p 0.05), while discharge pressure was largely unaffected by stroke variation. The optimal configuration was achieved at a plunger diameter of 8.00 mm and 100% stroke, delivering performance that meets API 675 requirements. Importantly, this study proposes a novel validation framework for hydraulic diaphragm metering pumps based on API 675—a gap not fully addressed in prior research. These findings provide practical guidance for improving the efficiency and reliability of pump systems through optimized geometric and operational parameters.
Visual Inspection Improvement of Engine Components Using Deep Learning with Pre-processed Dataset Augmentation: Case Study Salim, Steven; Wiratama, Sandy; Sarifudin, Alfan; Yuliatin, Eka Prita
Automotive Experiences Vol 8 No 3 (2025)
Publisher : Automotive Laboratory of Universitas Muhammadiyah Magelang in collaboration with Association of Indonesian Vocational Educators (AIVE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/ae.14207

Abstract

Lighting instability, sharp shadows, and visual disturbances caused by mechanical vibrations are significant challenges in the application of computer vision-based visual inspection systems in automotive industrial environments. This study aims to enhance the accuracy and robustness of the YOLOv8 object detection model for detecting machine component completeness by applying an adaptive pre-processing strategy. The techniques employed include grayscale conversion, brightness adjustment, and blurring to simulate common visual conditions encountered in real-world production processes. The model was trained using 1,281 instances from 52 component classes and evaluated based on the metrics of precision, recall, mAP@50, and mAP@50–95. The results show an average precision of 0.971, a recall of 0.990, and mAP@50 of 0.991, with spatial variation reflected in the standard deviation of mAP@50–95 of 0.149. The pre-processing technique improves the detection precision of shape-based components by up to 19% and colour-based components by up to 31%. Testing on ten appearance variations showed 100% detection accuracy with no misclassification, indicating the model’s generalizability to data in the training distribution. These findings confirm that visual modification of training data significantly improves the reliability and efficiency of the YOLOv8-based automated inspection system. Further implications include reduced human intervention, accelerated production flow, and optimization of operational energy consumption through faster and more accurate detection. Therefore, this system contributes to energy-efficient and sustainable innovative manufacturing practices.
Co-Authors Dennis Farina Nury Hendra Setyawan, Hendra Muhammad Luthfi Ritonga, Jhordan Salim, Steven Wiratama, Sandy Yuliatin, Eka Prita