Muhammad Lukman, Abdul Hamid, Muhammad Showi Nailul Ulum

OPTIMASI PAREMETER PENGELASAN SMAW TERHADAP HASIL UJI TARIK MENGGUNAKAN METODE TAGUCHI PADA POSISI 3G

Introduction

Optimasi paremeter pengelasan smaw terhadap hasil uji tarik menggunakan metode taguchi pada posisi 3g. Optimasi parameter las SMAW pada baja ASTM A36 untuk kekuatan tarik dengan metode Taguchi di posisi 3G. Menemukan kombinasi arus, elektroda, dan waktu rendam terbaik.

Abstract

This research aims to optimize the Shielded Metal Arc Welding (SMAW) process parameters on tensile strength using the Taguchi method in the 3G welding position. The study investigates the effects of welding current, electrode type, and electrode soaking time on the quality of weld joints. The base material used was ASTM A36 steel, welded with E7016 and E7018 electrodes at current settings of 85 A and 95 A. The experimental design employed a Taguchi L4 (2³) orthogonal array, resulting in four parameter combinations. Performance analysis used the Larger is Better Signal-to-Noise (S/N) ratio approach. The optimum parameter combination was found at 85 A welding current, E7016 electrode, and 30 minutes of electrode soaking, achieving a maximum average tensile strength of 404.35 MPa. Analysis of Variance (ANOVA) revealed that the most influential factor on tensile strength was electrode soaking time (57.65%), followed by electrode type (32.38%), and welding current (9.97%). The absence of residual error indicates a highly efficient and reliable experimental model. This study confirms that the Taguchi method is an effective approach to optimize SMAW parameters, enhance weld joint quality, and minimize trial-and-error efforts in the welding process. Keywords: SMAW Welding, Tensile Test, Taguchi Method, 3G Position, Parameter Optimization, ANOVA.

Review

This research, titled "OPTIMASI PAREMETER PENGELASAN SMAW TERHADAP HASIL UJI TARIK MENGGUNAKAN METODE TAGUCHI PADA POSISI 3G," presents a systematic investigation into optimizing Shielded Metal Arc Welding (SMAW) process parameters to enhance tensile strength, specifically for the 3G welding position. The study effectively targets a critical area in welding engineering, aiming to improve weld joint quality. Utilizing the robust Taguchi method, the authors explored the influence of key parameters: welding current, electrode type (E7016 and E7018), and electrode soaking time, on ASTM A36 steel. The experimental design, based on an L4 (2³) orthogonal array and analyzed using the Larger is Better Signal-to-Noise ratio, provides a sound methodological foundation for identifying optimal conditions. The findings reveal a clear optimal parameter combination for achieving maximum tensile strength: a welding current of 85 A, the use of an E7016 electrode, and an electrode soaking time of 30 minutes, which resulted in an impressive average tensile strength of 404.35 MPa. A crucial aspect of this study is the detailed Analysis of Variance (ANOVA), which quantitatively assessed the contribution of each factor. Electrode soaking time emerged as the most significant factor, accounting for 57.65% of the influence, followed by electrode type (32.38%), and welding current (9.97%). The reported absence of residual error in the ANOVA further underscores the efficiency and reliability of the experimental model, lending strong credibility to the identified optimal parameters. Overall, this research successfully demonstrates the efficacy of the Taguchi method as a powerful tool for optimizing SMAW parameters, substantially enhancing weld joint quality, and significantly minimizing the need for extensive trial-and-error. The practical implications are significant, providing valuable guidelines for welding practitioners aiming to improve process efficiency and material performance in real-world applications, particularly for the challenging 3G position. The clear methodology, quantitative results, and insightful ANOVA analysis make this study a strong contribution to the field of welding engineering, offering a reliable framework for optimizing similar welding processes.

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