Authors :
Penta. Shreenivasarao; G. Phanindra; P. Anoop Kumar; U. ChandraRao; V. Chiranjeevi
Volume/Issue :
Volume 10 - 2025, Issue 4 - April
Google Scholar :
https://tinyurl.com/5n6vac77
Scribd :
https://tinyurl.com/4hcfhxc3
DOI :
https://doi.org/10.38124/ijisrt/25apr1183
Google Scholar
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Abstract :
Weld joint quality depends on welding process settings. High-quality welds need controlled input settings. This
study looks at four input settings: arc speed, wire feed to travel speed ratio, wire feed rate, and eccentricity. Tests used a
full factorial design on a mild steel joint. Bead penetration, height, and width were measured. Fuzzy logic created models
for these output measures. This fuzzy model predicts the output settings. Also, weld shape accuracy was checked. The
inaccuracy is below 20% for bead penetration. Bead height and width inaccuracy is usually less than 10%.
References :
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- Chan, Pacey, and Bibby (1999) modeled gas metal arc welds with neural networks.
- Carrino et al. (2007) used a neuro-fuzzy method to boost welding output. Ganjigatti, Pratihar, and Choudhury (2008) modeled MIG welding using stats.
- Kannan and Yoganandh (2010) studied how process affected clad shape in GMAW.
- Kim et al. (2002) used a neural net to predict bead height in arc welding. Kim et al.
- (2003) checked how GMA welding steps affect the process. Lee,
- Pi-Cheng, and Wen-Hou (2006) used fuzzy control for arc welding.
- Lee (2000) predicted welding steps by looking at back-bead shape.
- Montgomery (2006) wrote about experiment design and analysis.
- Manonmani, Muruga, and Buvanasekaran (2007) saw how steps change laser-welded steel. Pandu et al. (2012) built a fuzzy system to predict abrasive water jet cuts.
- Palani and Murugan (2006) made math models to guess weld bead shape in arc welding.
- Rao et al. (2009) studied how steps and math models predict bead shape in GMA welding. Sreeraj,
- Kannan, and Maji (2000) used math to predict weld bead shape.
Weld joint quality depends on welding process settings. High-quality welds need controlled input settings. This
study looks at four input settings: arc speed, wire feed to travel speed ratio, wire feed rate, and eccentricity. Tests used a
full factorial design on a mild steel joint. Bead penetration, height, and width were measured. Fuzzy logic created models
for these output measures. This fuzzy model predicts the output settings. Also, weld shape accuracy was checked. The
inaccuracy is below 20% for bead penetration. Bead height and width inaccuracy is usually less than 10%.
