Study of parametric influence and welding performance optimization during regulated metal deposition (RMD™) using grey integrated with fuzzy taguchi approach. (June 2020)

• Record Type: Journal Article
• Authors: Prajapati, Vishalkumar; Dinbandhu, ; Vora, Jay J.; Das, Subhash; Abhishek, Kumar
• Is Part Of: Journal of manufacturing processes. Volume 54(2020)
• Page Start: 286
• Page End: 300
• Publication Date: 2020-06
• Subjects: Regulated metal deposition (RMD) welding -- Grey relation analysis -- Fuzzy inference system; Production management -- Data processing -- Periodicals; Manufacturing processes -- Periodicals; Procestechnologie; Productietechniek; Production -- Gestion -- Informatique -- Périodiques; Fabrication -- Périodiques; Manufacturing processes; Production management -- Data processing; Periodicals; 670.5
• DOI: 10.1016/j.jmapro.2020.03.017
• Languages: English
• ISSNs: 1526-6125

Abstract

Abstract: Regulated Metal Deposition (RMD™) welding process is fundamentally a modified short-circuit gas metal arc welding (GMAW) process wherein a precisely-controlled metal transfer provides uniform droplet deposition, making it easier for the welder to control the puddle and hence achieve an enhanced quality of welded joints. RMD technique was experimented and implemented on a variety of steels such as stainless steel, carbon steels, etc. However, obtaining satisfactory welding performance is indeed a challenging task during RMD. Hence, this study aims to assess the favorable parameters settings in order to optimize welding performance characteristics such as Heat affected zone (HAZ), depth of penetration (DOP) and bead width (BW) of Regulated Metal Deposited arc weldment using grey relation analysis integrated with fuzzy inference system with Taguchi approach. From the adopted methodology the optimal parameter settings for current (135 A), voltage (14 V), and gas flow rate (13 L/min) have been achieved. The study also tells that the voltage is the most influencing parameter during RMD welding.