Optimization of Tensile Strength in AA6063 Friction Stir Welds using ANOVA and ANFIS: A Statistical Analysis of Process Parameters

The tensile strength of 12 mm thick joints created by friction stir welding (FSW) on AA6063 was taken into account through statistical optimization of process parameters based on experimental research. The process variables including the spindle speed, the tool pin length, and the tool pin shoulder rise determine how well this friction stir welding type welds. Experiments were conducted as per full factorial and L9 orthogonal array. By using ANOVA (analysis of variance) the effect of parameters and its significance were examined on the tensile strength. The multiple variable regression equation and ANFIS (artificial neuro fuzzy inference system) based mathematical model were used to determine the optimized tensile strength value with the percent error of 23.27% and 6.04% respectively. As evident from the results the tool pin length has the higher impact with percentage contribution ratio (PCR) of 79.65.

Authors

Institutions

Publication Details

Journal
Mechanika
Published
2024-12-21
DOI
https://doi.org/10.5755/j02.mech.33779
Primary Topic
Advanced Welding Techniques Analysis
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Optimization of Tensile Strength in AA6063 Friction Stir Welds using ANOVA and ANFIS: A Statistical Analysis of Process Parameters

Deepika Mishra, Ravi Shankar Prasad, Sudhir Kumar
Mechanika
Advanced Welding Techniques Analysis
article

Optimization of Tensile Strength in AA6063 Friction Stir Welds using ANOVA and ANFIS: A Statistical Analysis of Process Parameters

Deepika Mishra, Ravi Shankar Prasad, Sudhir Kumar
article en

Abstract

The tensile strength of 12 mm thick joints created by friction stir welding (FSW) on AA6063 was taken into account through statistical optimization of process parameters based on experimental research. The process variables including the spindle speed, the tool pin length, and the tool pin shoulder rise determine how well this friction stir welding type welds. Experiments were conducted as per full factorial and L9 orthogonal array. By using ANOVA (analysis of variance) the effect of parameters and its significance were examined on the tensile strength. The multiple variable regression equation and ANFIS (artificial neuro fuzzy inference system) based mathematical model were used to determine the optimized tensile strength value with the percent error of 23.27% and 6.04% respectively. As evident from the results the tool pin length has the higher impact with percentage contribution ratio (PCR) of 79.65.

MechanikaVol. 30(6)
Jaypee University of Engineering and Technology, Guna (IN), CT Group Of Institutions (IN)
Affordable and clean energy
Openalex Percentile: Top 76%
Advanced Welding Techniques Analysis
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.