Global and Cluster-wise Regression Models for Total Bead Area as Welding Quality

Lee, Boram; Yun, Tae-Jong; Oh, Won-Bin; Zhang, Gang; Ki, Kyoung-Seo; Kim, Ill-Soo

doi:10.1016/j.promfg.2019.02.005

Procedia Manufacturing

2019

DOI: 10.1016/j.promfg.2019.02.005

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Global and Cluster-wise Regression Models for Total Bead Area as Welding Quality

Boram Lee

Tae-Jong Yun

Won-Bin Oh

et al.

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“…[18] A Taguchi method was used to optimize the different input parameters such as welding current, gas flow rate and nozzle to plate distance on the output responses such as ultimate tensile strength (UTS) and Yield Strength (YS) by using a MIG welding on the AISI409 ferritic stainless steel to AISI 316L Austenitic Stainless Steel materials. New modified model [19] and equation is developed for MIG welding and regression analysis is used to formulate the model for lap joint. Total bead area is taken as a output parameters and current, time, speed, voltage are taken as input parameters.…”

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Influence of GMAW Process Parameters and Selection Techniques on the Quality of a Welded Joint

Gandhe¹

2019

IJRASET

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Metal inert gas welding is one of the important joining processes in the industry. There is wide range of applications of MIG welding to join similar as well as dissimilar materials. This process is also employed for thickness of sheets from lower to higher thickness. It is essential to have an appropriate quality of welded joints for each application. The quality of joints is dependent on various factors such as tensile strength of the joint, microstructure, hardness and penetration of the joint. These factors are affected by different welding input process variables or their combinations such as welding voltage, welding current, wire feed rate, welding speed, etc. Higher quality of the joints is ensured by accurate combinations of these variables. This method to find out combination of variables such that the value of input is lower and output is higher is called an optimization. Various optimization techniques are available to finalize the parameters, which are Taguchi DOE, Taguchi Grey based, ANN network, surface response methodology, regression analysis, mathematical modeling, etc. In this paper weight age of input variables, output variables and optimization technique is discussed per literature. The study is limited for Carbon steel sheets having thickness up to 6mm. Based on the weight age the parameters and method of optimization is suggested. An overview of various techniques of optimization have been studied, effects of different parameters on the quality output parameters are discussed and appropriate range is suggested based on the literature review. I. INTRODUCTION MIG welding [8] can be used as a manual welding operation as well as automatic process. Main advantage of the MIG welding is automatic continuous feeding filler wire. It can also be used to join unalloyed steel, aluminum alloy and stainless steels. Robotic MIG welding is found to be a very effective tool to attain required quality, higher productivity, better working condition, and semiskilled operators can cooperate. MIG welding [35] can be operated manually as well as in automatic way. The use of robotic welding increases the productivity and the quality of the welded joints. Semiskilled operator can operate the machine. But operator should have a proper knowledge of programming of the machine. Specific design of work piece, equipment, shielding gas, electrode size and type of equipment are discussed in this study. Due to flexibility [6] and higher speed, the MIG welding is one of the widely used processes among all the welding processes. Input process parameters such as weld current, voltage and preheat temperature are optimized in order to attain output properties such as tensile strength and percentage elongation. Here grey based Taguchi analysis is used to optimize the parameters and mathematical equation is formed by using a Regression analysis. Results shows that preheat temperature are most affecting factors on the tensile strength. Quality of the welded joints [2] can be measured in terms of bead geometry. There are many input p...

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Influence of GMAW Process Parameters and Selection Techniques on the Quality of a Welded Joint

Gandhe¹

2019

IJRASET

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Global and Cluster-wise Regression Models for Total Bead Area as Welding Quality

Cited by 1 publication

References 14 publications

Influence of GMAW Process Parameters and Selection Techniques on the Quality of a Welded Joint

Influence of GMAW Process Parameters and Selection Techniques on the Quality of a Welded Joint

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