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All Journal International Journal of Advances in Intelligent Informatics Mechanical Engineering for Society and Industry
Gembong Edhi Setyawan
Universitas Brawijaya
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Transportation

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Advanced computational techniques for predicting 3D printing distortion in selective laser melting processes of Aluminium AlSi10Mg Moch. Agus Choiron; Anindito Purnowidodo; Achfas Zacoeb; Gembong Edhi Setyawan; Willy Artha Wirawan; Yudhi Ariadi; Allan E.W. Rennie; Diva Kurnianingtyas
Mechanical Engineering for Society and Industry Vol 5 No 1 (2025)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/mesi.12581

Abstract

Distortion for 3D printing using Selective Laser Melting (SLM) on AlSi10Mg aluminium is an important issue that affects the final manufactured product. This research aims to develop a finite element method (FEM)-based computational simulation and experimental validation to predict distortion in 3D printed products using SLM. The study results found that the variation of 3D printing position affects the resulting product's distortion and mechanical properties. The 90° part print position results in smaller distortion of 0.303 and 0.335 mm than the 0° part print position of 0.329 and 0.378, respectively, making it more suitable for high-precision applications. This study confirms the importance of scan orientation in controlling distortion in the SLM process, which can be used as a guide for optimal printing parameters. With proper orientation selection, the risk of distortion or defects in SLM products can be minimised, and industrial production efficiency can be improved.
Augmented haar cascade classifier for real-time ball detection in humanoid robots under dynamic environments Gembong Edhi Setyawan; Edita Rosana Widasari; Barlian Henryranu Prasetio; Yasa Palaguna Umar; Ivan Rafli Adipratama
International Journal of Advances in Intelligent Informatics Vol 12, No 1 (2026): February 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i1.2146

Abstract

This study proposes an Augmented Haar Cascade Classifier (AHCC) to enhance real-time ball detection for humanoid robots operating in dynamic environments. The method integrates Convex Hull mapping, HSV-based segmentation, and Hough Circle validation to overcome challenges such as fluctuating illumination, complex backgrounds, and partial occlusions. Experiments were conducted entirely on a CPU-only Intel NUC platform running ROS without GPU acceleration, using a dataset containing variations in lighting, orientation, scale, and background clutter. Compared with baseline models (standard Haar Cascade Classifier (HCC) and YOLOv5) the proposed AHCC achieved 97% accuracy, 83% recall, 97% precision, and an 89% F1-score, while requiring only 0.00849 s per frame with 8.97% memory usage. Although YOLOv5 reached 99% accuracy, it demanded higher computational resources (0.0344 s per frame, 22.3% memory usage), limiting its practicality for embedded robotic systems. The AHCC therefore offers an optimal balance between detection reliability and computational efficiency, outperforming traditional HCC and providing a lightweight alternative to GPU-dependent detectors such as Tiny-YOLO and MobileNet-SSD.
Co-Authors Achfas Zacoeb Allan E.W. Rennie Anindito Purnowidodo Barlian Henryranu Prasetio Diva Kurnianingtyas Edita Rosana Widasari Ivan Rafli Adipratama Moch. Agus Choiron Wirawan, Willy Artha Yasa Palaguna Umar Yudhi Ariadi