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Teknomekanik
Published by Universitas Negeri Padang
ISSN : 26219980     EISSN : 26218720     DOI : 10.24036/tm.
Core Subject : Engineering,
Mechanical Engineering
Teknomekanik is an international journal that publishes peer-reviewed research in engineering fields (miscellaneous) to the world community. Paper written collaboratively by researchers from various countries is encouraged. It aims to promote academic exchange and increase collaboration among scientists, engineers and researchers to support sustainable development goals.
Arjuna Subject : Teknik (semua kategori) - Teknik Mesin
Articles 2 Documents
 1 
Search results for , issue "Vol. 9 No. 1 (2026): Regular Issue" : 2 Documents clear
Multiclass gas pipeline leak detection using multi-domain signals and genetic algorithm-optimized classification models Suprihatiningsih, Wiwit; Romahadi, Dedik; Pranoto, Hadi; Youlia, Rikko Putra; Anggara, Fajar; Rahmatullah, Rizky
Teknomekanik Vol. 9 No. 1 (2026): Regular Issue
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/teknomekanik.v9i1.38372

Abstract

Pipeline networks are critical infrastructure for oil and gas transport because the occurrence of leaks can rapidly escalate into safety, economic, and environmental crises. Operators are practically required to identify the presence and type of leaks; however, applying multiclass recognition is challenging when labeled data and computing power are limited. Therefore, this study proposes a three-stage pipeline which consists of: (1) adopting the GPLA-12 dataset of acoustic or vibration signals spanning 12 leak types; (2) extracting multi-domain features by combining time-domain descriptors with Power Spectral Density (PSD)-based spectral features; and (3) applying a genetic algorithm (GA) as a wrapper for feature selection to enhance discriminability and reduce dimensionality, which was followed by benchmarking seven conventional classifiers and GA-based refinement of the top model with a focus on the feature subset and hyperparameters. A maximum accuracy of 96.35% was achieved on the GPLA-12 dataset with low computation time and a simple model architecture. The proposed pipeline also attained similar or better accuracy at substantially lower complexity and data requirements compared with prior deep CNN approaches. These results support timely multiclass decision-making in resource-constrained industrial settings. A key observation was that the focus was on supervised leak-type classification from acoustic or vibration signals, while localization, severity estimation, and multi-sensor fusion were beyond the scope of this study.
Comparative analysis of bio-inspired and topology-optimized lattices under compressive loading Arifin, Ahmad Anas; Batan, I Made Londen; Bici, Michele; Wahjudi, Arif; Pramono, Agus Sigit
Teknomekanik Vol. 9 No. 1 (2026): Regular Issue
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/teknomekanik.v9i1.45472

Abstract

Lattice structure design is still dominated by strut-based forms and surface-based shapes, such as triply periodic minimal surfaces (TPMS), which both exhibit overlapping limitations. Strut lattices often show strong anisotropy because their response depends heavily on cell orientation, while TPMS lattices are difficult to adjust when bounded by geometric constraints. These conditions eventually led to stagnation in the development of lattice morphology. Hybrid and topology-optimization methods have appeared as possible alternatives, but many of them still produce modified versions of classical patterns. This study examined two lattice geometries: the Pyramorph, inspired by the shape of a pyramid, and the Topomorph, generated through a topology optimization framework. Both structures were designed using a CAD unit cell patterning technique and manufactured using the FDM method, with relative densities ranging from 0.40 to 0.44. Their mechanical behaviour was examined through FEA simulation and uniaxial compression testing. The parameter variations included cell orientations of 0°, 15°, 30°, and 45°, and cell sizes of 8 mm and 12 mm within a 24 mm specimen. The Topomorph showed superior strength, reaching 15–20 MPa, while the Pyramorph reached only 7–8 MPa. The highest value, about 20.5 MPa, was obtained from the Topomorph at 0° and with an 8 mm cell size. Failure modes indicated buckling and delamination in the Pyramorph, while the Topomorph tended to collapse progressively. These findings indicate that topology optimization combined with CAD-based patterning could significantly improve lattice performance.

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