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Development of empirical models for prediction of weld bead geometry in robotic - GMAW

Thamilarasi P., Ragunathan S., Mohankumar E.

Journal of Achievements in Materials and Manufacturing Engineering

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2014

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Vol. 67, nr 2

72--85

EN

Purpose: The objective of this paper is to determine the input-output relationship of robotic gas metal arc welding process using linear as well as second order linear regression analysis. Design/methodology/approach: Taguchi’s L27, 3 level 4 parameter orthogonal array design of experiments and multiple regression techniques has been utilized for the development of empirical model. Arc current, stick-out, arc voltage and travel speed is taken as input parameters and bead geometry has been taken as output responses. The effects and interaction terms on different responses of these selected welding parameters have been analyzed using ANOVA. Findings: Both techniques results were compared and concluding remarks have been made. The developed empirical model has been found good agreement with the experiment results and predicted error for second order polynomial regression equations lies between 0.58% to 14.86% for bead height, 0.93% to 9.44% for bead width and 0.34% to 2.56% for bead penetration using with actual experimental results. Research limitations/implications: It was noticed that interaction effects have considerable influence on the formation of weld bead geometry, so it cannot be ignored. Originality/value: In this present work, an effort has been made to carry out both first as well as second order linear regression analyses on robotic GMAW by L27, Taguchi’s design of experiments.

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Interaction model for predicting bead geometry for Lab Joint in GMA welding process

Thao D.T., Kim I.S.

Archives of Computational Materials Science and Surface Engineering

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2009

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Vol. 1, nr 4

237-244

EN

Purpose: The prediction of the optimal bead geometry is an important aspect in robotic welding process. Therefore, the mathematical models that predict and control the bead geometry require to be developed. This paper focuses on investigation of the development of the simple and accuracy interaction model for prediction of bead geometry for lab joint in robotic Gas Metal Arc (GMA) welding process. Design/methodology/approach: The sequent experiment based on full factorial design has been conducted with two levels of five process parameters to obtain bead geometry using a GMA welding process. The analysis of variance (ANOVA) has efficiently been used for identifying the significance of main and interaction effects of process parameters. General linear model and regression analysis has been employed as a guide to achieve the linear, curvilinear and interaction models. The fitting and the prediction of bead geometry given by these models were also carried out. Graphic results display the effects of process parameter and interaction effects on bead geometry. Findings: The fitting and the prediction capabilities of interaction models are reliable than the linear and curlinear models and it was found that welding voltage, arc current, welding speed and 2-way interaction CTWD welding angle have the large significant effects on bead geometry. Research limitations/implications: The these models developed are extended to shielding gas composition, weld joint position, polarity and many other parameters which are not included in this research in order to establish a closed loop feedback control system to minimize possible errors from uncontrolled variations. Practical implications: The developed models apply real-time control for bead geometry in GMA welding process and perform the Design of Experiments (DOE) analysis steps in order to solve optimisation problems in GMA welding process. Originality/value: The interaction factors, welding voltage arc current, CTWD welding angle, also imposes a significant effect on bead geometry. With the experimental data of this study, the interaction models have a more reliable fitting and better predicting than that of linear and curvilinear models.

3

Predicting Lap-Joint bead geometry in GMA welding process

Thao D., Jeong J., Kim I., Kim J.

Archives of Materials Science and Engineering

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2008

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Vol. 32, nr 2

121-124

EN

Purpose: The prediction of the optimal bead geometry is an important aspect in robotic welding process. Therefore, the mathematical models that predict and control the bead geometry require to be developed. This paper focuses on investigation of the development of the simple and accuracy interaction model for prediction of bead geometry for lap joint in robotic Gas Metal Arc (GMA) welding process. Design/methodology/approach: The sequent experiment based on full factorial design has been conducted with two levels of five process parameters to obtain bead geometry using a GMA welding process. The analysis of variance (ANOVA) has efficiently been used for identifying the significance of main and interaction effects of process parameters. General linear model and regression analysis in SPSS has been employed as a guide to achieve the linear, curvilinear and interaction models. The fitting and the prediction of bead geometry given by these models were also carried out. Graphic results display the effects of process parameter and interaction effects on bead geometry. Findings: The fitting and the prediction capabilities of interaction models are reliable than the linear and curvilinear models. It was found that welding voltage, arc current, welding speed and 2-way interaction CTWD×welding angle have the large significant effects on bead geometry. Practical implications: The model should also cover a wide range of material thicknesses and be applicable for all welding position. For the automatic welding system, the data must be available in the form of mathematical equations. Originality/value: It has been realized that with the use of the developed algorithm, the prediction of optimal bead dimensions becomes much simpler to even a novice user who has no prior knowledge of the robotic GMA welding process and optimization techniques.