Optimization of Mig Welding Process Parameters for Improving Welding Strength of Steel

2017 ◽  
Vol 50 (1) ◽  
pp. 26-33
Author(s):  
C. Labesh kumar ◽  
◽  
T. Van aja ◽  
KGK Murti
Keyword(s):  
Process Parameters ◽  
Welding Process ◽  
Mig Welding ◽  
Welding Strength

Optimization of MIG Welding Process Parameters: A Review

2017 ◽  
Vol 8 (3) ◽  
pp. 273 ◽  
Author(s):  
Sahil Angaria ◽  
P. S. Rao ◽  
S. S. Dhami
Keyword(s):  
Process Parameters ◽  
Welding Process ◽  
Mig Welding

scholarly journals Multi-Objective Optimization of MIG Welding and Preheat Parameters for 6061-T6 Al Alloy T-Joints Using Artificial Neural Networks Based on FEM

Coatings ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 998
Author(s):  
Qing Shao ◽  
Fuxing Tan ◽  
Kai Li ◽  
Tatsuo Yoshino ◽  
Guikai Guo
Keyword(s):  
Process Parameters ◽  
Welding Speed ◽  
Heat Input ◽  
Pareto Front ◽  
Welding Process ◽  
Residual Deformation ◽  
Welding Deformation ◽  
Mig Welding ◽  
Preheat Temperature ◽  
Multi Objective

To control the welding residual stress and deformation of metal inert gas (MIG) welding, the influence of welding process parameters and preheat parameters (welding speed, heat input, preheat temperature, and preheat area) is discussed, and a prediction model is established to select the optimal combination of process parameters. Thermomechanical numerical analysis was performed to obtain the residual welding deformation and stress according to a 100 × 150 × 50 × 4 mm aluminum alloy 6061-T6 T-joint. Owing to the complexity of the welding process, an optimal Latin hypercube sampling (OLHS) method was adopted for sampling with uniformity and stratification. Analysis of variance (ANOVA) was used to find the influence degree of welding speed (7.5–9 mm/s), heat input (1500–1700 W), preheat temperature (80–125 °C), and preheat area (12–36 mm). The range of research parameters are according to the material, welding method, thickness of the welding plate, and welding procedure specification. Artificial neural network (ANN) and multi-objective particle swarm optimization (MOPSO) was combined to find the effective parameters to minimize welding deformation and stress. The results showed that preheat temperature and welding speed had the greatest effect on the minimization of welding residual deformation and stress, followed by the preheat area, respectively. The Pareto front was obtained by using the MOPSO algorithm with ε-dominance. The welding residual deformation and stress are the minimum at the same time, when the welding parameters are selected as preheating temperature 85 °C and preheating area 12 mm, welding speed is 8.8 mm/s and heat input is 1535 W, respectively. The optimization results were validated by the finite element (FE) method. The error between the FE results and the Pareto optimal compromise solutions is less than 12.5%. The optimum solutions in the Pareto front can be chosen by designers according to actual demand.

Sustainable Analysis of Process Parameters during MIG Welding of 1018 Mild Steel

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Tensile Strength ◽  
Energy Consumption ◽  
Mild Steel ◽  
Process Parameters ◽  
Negative Impact ◽  
Sustainable Manufacturing ◽  
Welding Process ◽  
Travel Speed ◽  
Contour Plot ◽  
Mig Welding

The present work analyses MIG in terms of strength and consumption of energy during joining of similar AISI 1018 Mild Steel plates. Sustainable manufacturing is the creation of various manufactured products that generally use different processes that will minimize negative impact on environment, conserve natural resources and energy, are also safe for the employees, consumers and communities as well as economically sound. Sustainable manufacturing highlights on the necessity of an energy effective process that optimize consumption of energy. AISI 1018 mild steel is extensively used in automotive industries for pins, worms, dowels gears, non-critical tool components etc. Main important output responses are Tensile Strength and energy consumption during MIG Welding Process by taking Current, Travel Speed and Voltage as effective input variables. The main objective is to optimize energy consumption as well as tensile strength also determination of main influential process parameters on energy Consumption and tensile strength by using Taguchi Method. Contour plot has been also shown.

A hybrid methodology for optimizing MIG welding process parameters in joining of dissimilar metals

2020 ◽  
Vol 23 ◽  
pp. 507-512
Author(s):  
Prakash Babu Kanakavalli ◽  
B. Navaneeth Babu ◽  
Ch.P.N. Vishnu Sai
Keyword(s):  
Process Parameters ◽  
Welding Process ◽  
Dissimilar Metals ◽  
Mig Welding ◽  
Hybrid Methodology

Enhancement of welding strength on Eglin steel using MIG welding process

2020 ◽  
Vol 33 ◽  
pp. 4617-4620
Author(s):  
T. Louie Frango ◽  
M. Prabhakaran ◽  
C. Sivakandhan ◽  
K. Vinoth Babu ◽  
J. Vairamuthu
Keyword(s):  
Welding Process ◽  
Mig Welding ◽  
Welding Strength

scholarly journals Application of entropy measurement technique in grey based Taguchi method for optimization of MIG welding process parameters

1970 ◽  
Vol 8 (3) ◽  
pp. 13-26
Author(s):  
Rani Jatav ◽  
Vedansh Chaturvedi
Keyword(s):  
Taguchi Method ◽  
Process Parameters ◽  
Measurement Technique ◽  
Welding Process ◽  
Quality Characteristic ◽  
Mig Welding ◽  
Entropy Measurement ◽  
Grey Relational Grade ◽  
Grey Relational ◽  
Grey Taguchi

We seldom realized that without this form of metal work, many structures would cease to be in existence. A skilled journeymen welder joins metal in such a way that it is not able to be parted unless it is cut. Welding is an absolutely essential technique used in various industries like automotive industry, construction industry as well as in the aviation industry. Hence welding process parameters are required to be optimized for their responses or welding characteristics. This study incorporates entropy measurement technique based on grey Taguchi method to analyze multiple quality characteristic optimization of metal inert gas welding of low carbon steel plates. For this study, four control variables are selected current, voltage, gas flow rate and wire feed rate and analysing their effect on the four quality characteristics ultimate tensile strength, elongation %, bending strength and hardness of the weldments have been investigated in this paper. In order to optimize the multiple quality characteristic of the MIG welding, grey relational analysis coupled with entropy measurement method has been employed. Using entropy measurement method, value of weight corresponding to each quality characteristic has been assigned, so that the importance can be properly determined. Using the theory of grey relational analysis, these have been accumulated to calculate the overall grey relational grade. Signal to Noise ratio (S/N ratio) is applied to find the optimal parameter setting. To determine the contribution of MIG welding process parameters, Analysis of variance (ANOVA) on grey relational grade has been calculated. The confirmatory test also has been done for verifying the results. A foresaid methodology has been found fruitful in the cases where simultaneous optimization of huge number of responses is required.Keywords: Metal Inert Gas (MIG) Welding, Grey-Taguchi Method, Entropy Measurement Technique, Analysis of variance (ANOVA), S-N ratio.

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