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All Journal Frontier Advances in Applied Science and Engineering Journal Electrical and Computer Experiences
Jerry Heisye Purnama
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Physics

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Implemantation of Non-Sensor Based Fuzzy Logic Control for G-Code Parameter Optimization: Advanced Efficiency in Titanium Alloy CNC Processing I Made Aditya; Bryant Josua Runturambi; Jedithjah Naapia Tamedi Papia; Firmansyah Reskal Motulo; Jerry Heisye Purnama; Meike Negawati Kesek
Journal Electrical and Computer Experiences Vol. 2 No. 2 (2024): July-December
Publisher : Tinta Emas Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59535/jece.v2i2.363

Abstract

This research introduces an innovative algorithm for G-code modification using Fuzzy Logic Control (FLC) to optimize Computer Numerical Control (CNC) machining parameters without relying on additional hardware or sensors. The study develops a computational framework that processes G-code blocks with an average speed of 0.3ms while maintaining a minimal memory footprint of 1.2MB. Implementation results demonstrate an 18% reduction in total machining time, with the feed rate optimized from 1000 mm/min to 1180 mm/min for linear cutting and spindle speed enhanced from 3000 RPM to 3450 RPM, while maintaining conservative parameters for critical plunge cutting operations. The system achieved a 23% increase in tool life through intelligent parameter modulation. Testing on titanium alloy workpieces showed consistent performance with zero machining interruptions during parameter modification, marking a five-fold improvement in processing speed compared to existing sensor-based systems. This hardware-independent approach enables rapid deployment in existing CNC systems through simple software updates, offering a cost-effective solution for machining optimization. The research establishes a foundation for intelligent G-code generation that adapts to material properties and cutting conditions while maintaining operational safety and efficiency.
Multi-Stage Computer Vision Framework with Ensemble Learning for Real-Time Glass Packaging Defect Detection in Industrial Applications Jonah Alfred Mekel; Rick Resa Wahani; Motulo, Firmansyah Reskal; Alfred Noufie Mekel; Tineke Saroinsong; Tammy Tinny V. Pangow; Jerry Heisye Purnama; Jedithjah Naapia Tamedi Papia
Frontier Advances in Applied Science and Engineering Vol. 3 No. 2 (2025)
Publisher : Tinta Emas Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59535/faase.v3i2.572

Abstract

Transparent glass packaging inspection presents significant challenges for automated quality control systems due to optical complexities including reflections, refractions, and low-contrast defect patterns. This research develops a comprehensive multi-stage computer vision framework integrating specialized algorithmic modules with ensemble machine learning for real-time defect detection in industrial glass packaging lines. The framework implements four specialized detection stages: (1) meniscus-corrected liquid level measurement using dual-camera validation and polynomial surface fitting, (2) seal integrity assessment through Circular Hough Transform combined with geometric, texture, and color feature extraction, (3) lid positioning evaluation via calibrated geometric centroid analysis with tolerance-based classification, and (4) multi-method contamination detection integrating color aberration analysis, histogram-based particle detection, and morphological operations. The system employs an ensemble classification architecture combining modified MobileNetV2 convolutional neural network with Random Forest classifier, optimized for edge computing deployment. Industrial validation at PT AQUWAR Bintang Semesta demonstrated 91.6% overall detection accuracy with 347 milliseconds average processing time per container across 2,847 test samples spanning multiple defect categories. The modular framework architecture enables independent optimization of detection stages while maintaining real-time processing capabilities, providing a robust foundation for transparent packaging quality control in high-volume manufacturing environments.
Co-Authors Alfred Noufie Mekel Bryant Josua Runturambi Firmansyah Reskal Motulo I Made Aditya Jedithjah Naapia Tamedi Papia Jonah Alfred Mekel Meike Negawati Kesek Motulo, Firmansyah Reskal Rick Resa Wahani Tammy Tinny V. Pangow Tineke Saroinsong