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All Journal ILKOMNIKA: Journal of Computer Science and Applied Informatics Journal of Welding Technology
Abdullah, Hidayat
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

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Current progress on wire-arc and welding-based additive manufacturing with its potential application: a review Dioktyanto, Mudzakkir; Abdullah, Hidayat; Dewi, Yulia Puspa; Rokhim, Imam Nur; Kuswanto, Teguh Junian
Journal of Welding Technology Vol 7, No 2 (2025): December
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jowt.v7i2.8418

Abstract

Wire-Arc Additive Manufacturing (WAAM) is one of the leading metal additive manufacturing procedures for producing medium to large-scale components due to its high deposition rate, low material cost, and compatibility with common structural alloys. This paper review provides a focused and critical assessment of welding-based Additive Manufacturing (AM) technologies, particularly WAAM processes utilizing Gas Metal Arc Welding (GMAW), Gas Tungsten Arc Welding (GTAW), and Plasma Arc Welding (PAW). The scope covers the fundamental process principles, thermal–metallurgical behaviour, mechanical performance, and deposition control methods. The specific contribution of this review is: (i) explaining key process–structure–property relationships documented in recent studies, (ii) identifying core technological barriers—such as thermal distortion, porosity, residual stresses, and anisotropic microstructures—that limit industrial deployment, and (iii) outlining strategic future research directions that important for improving process stability and weld results. Key findings indicate that heat input management governs bead morphology, cooling rate, phase formation, and residual stress accumulation across multi-layer builds. Advances such as adaptive arc modes, interpass temperature control, closed-loop sensing, and hybrid subtractive–additive workflows have shown significant reductions in geometric deviation and defect formation. Nevertheless, reproducibility, dimensional accuracy, and mechanical property predictability remain persistent challenges. Overall, the review shows that integrating real-time monitoring, predictive simulation, alloy design tailored for WAAM, and intelligent control systems represents the most impactful pathway toward achieving certified and industrial-grade components.
Naive Bayes Predictive Model for Failure Detection in CODLAG Propulsion Systems Rokhim, Imam Nur; Kuswanto, Teguh Junian; Dioktyanto, Mudzakkir; Abdullah, Hidayat; Dewi, Yulia Puspa
ILKOMNIKA Vol 8 No 1 (2026): Volume 8, Number 1, April 2026
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v8i1.831

Abstract

This study implements a Naive Bayes Classifier algorithm to detect failures in gas turbines operating within a CODLAG (Combined Diesel-Electric and Gas) propulsion system. The complexity of hybrid propulsion systems necessitates reliable data-driven monitoring methods to support early anomaly detection and predictive maintenance. An open-access dataset from Kaggle was utilized as the source of gas turbine operational data, with five key parameters (GTn, T48, ṁf, P1, and P2) selected due to their strong correlation with turbine thermodynamic performance. Following data preprocessing and an 80:20 train–test split, the model was trained to classify operating conditions into Normal and Faulty states. The evaluation results demonstrate an accuracy of 86.89%, accompanied by high precision and recall values, indicating the model’s capability to identify anomalies with minimal misclassification. Furthermore, the Receiver Operating Characteristic (ROC) curve yields an Area Under the Curve (AUC) of 0.96, reflecting strong discriminative performance. These findings confirm that the Naive Bayes approach is computationally efficient and suitable for real-time implementation within shipboard Condition-Based Monitoring (CBM) systems, thereby enhancing the reliability and operational efficiency of CODLAG propulsion systems.
Co-Authors Dewi, Yulia Puspa Dioktyanto, Mudzakkir Kuswanto, Teguh Junian Rokhim, Imam Nur