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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Inotera
Herianto
Universitas Gadjah Mada
Computer Science & IT Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

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Analysis and Comparison of 3D Printed Soft Pneumatic Actuator Configurations for Swaying Lateral Motion of Planar Objects Syahirul Alim Ritonga; Ifran N. Sah Putra Sidauruk; Herianto; Adriyan Christofer Sitanggang
Jurnal Inotera Vol. 9 No. 1 (2024): January-June 2024
Publisher : LPPM Politeknik Aceh Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31572/inotera.Vol9.Iss1.2024.ID297

Abstract

Soft pneumatic actuators (SPAs) offer unique possibilities in robotics and automation due to their compliance and versatility. Unlike conventional actuators that can move either revolute or prismatic, SPAs can produce complex motions, especially when configured in combinations. This paper systematically investigated the performance of four distinct SPA configurations (Types 1-4) concerning the task of moving a planar square object and swaying it laterally to the right and left. These configurations are analyzed for their ability to achieve desired movements and orientations. Type 4, involving two SPAs, emerges as the most effective configuration, successfully navigating the challenges posed by the desired task. In contrast, Type 2, consisting of four SPAs, encounters difficulties in executing the task attributed to increased inertia, which was similar as type 3. Types 1, while not precisely meeting the primary task, reveal distinctive movements - left-right motion without orientation change. This study contributes valuable insights into the complex dynamics of SPAs, offering considerations for optimal design and performance. The outcomes could be developed in the future in soft actuator applications, especially those involving configurations based on specific needs.
COMPARATIVE PERFORMANCE OF SEQUENTIAL CNN AND PRE-TRAINED LEARNING FOR 3D PRINTING DEFECT CLASSIFICATION Dwi Riyono; Cholid Mawardi; Herianto
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 3 (2026): JITK Issue February 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i3.7337

Abstract

3D Printing is currently needed in various industries, including education in terms of research development. In this study, researchers classify 3D printing defect images to recognize images that are difficult to see with the naked eye. With limited observation, an image classification method is needed to help users detect defects in the printing process with a Deep Learning model. The printing process uses PLA and ABS-based filament materials, which are mostly used in 3D Printing objects with fused deposition modeling (FDM)-based 3D Printer machines. In this study, there are several stages, including data augmentation, model development using sequential CNN, pre-trained CNN based with pre-trained models, namely VGG-16 and VGG-19, training, validation, and model evaluation. The dataset taken for training is 1557, with a ratio of 80 percent training and 20 percent validation between defective and non-defective objects. The results of this study have a good accuracy value on Sequential CNN with an accuracy of 99.68% compared to pre-trained CNN models, namely VGG-16 and VGG-19. The classification results are also compared with other additive manufacturing classification methods with different machines and materials such as metal and 3D Food Printing which are measured based on classification model optimization analysis
Co-Authors Adriyan Christofer Sitanggang Ifran N. Sah Putra Sidauruk M. Cholid Mawardi Riyono, Dwi Syahirul Alim Ritonga