Control of the weld quality using welding parameters in a robotic welding process

• Autorzy: Park M.-H. , Jin B.-J. , Yun T.-J. , Son J.-S. , Kim C.-G. , Kim I. S.

Identyfikatory

DOI

10.5604/01.3001.0012.0737

• Języki publikacji: EN

Abstrakty

EN

Purpose: Since the welding automations have widely been required for industries and engineering, the development of the predicted model has become more important for the increased demands for the automatic welding systems where a poor welding quality becomes apparent if the welding parameters are not controlled. The automated welding system must be modelling and controlling the changes in weld characteristics and produced the output that is in some way related to the change being detected as welding quality. To be acceptable a weld quality must be positioned accurately with respect to the joints, have good appearance with sufficient penetration and reduce low porosity and inclusion content. Design/methodology/approach: To achieve the objectives, two intelligent models involving the use of a neural network algorithm in arc welding process with the help of a numerical analysis program MATLAB have been developed. Findings: The results represented that welding quality was fully capable of quantifying and qualifying the welding faults. Research limitations/implications: Welding parameters in the arc welding process should be well established and categorized for development of the automatic welding system. Furthermore, typical characteristics of welding quality are the bead geometry, composition, microstructure and appearance. However, an intelligent algorithm that predicts the optimal bead geometry and accomplishes the desired mechanical properties of the weldment in the robotic GMA (Gas Metal Arc) welding should be required. The developed algorithm should expand a wide range of material thicknesses and be applicable in all welding position for arc welding process. Furthermore, the model must be available in the form of mathematical equations for the automatic welding system. Practical implications: The neural network models which called BP (Back Propagation) and LM (Levenberg-Marquardt) neural networks to predict optimal welding parameters on the required bead reinforcement area in lab joint in the robotic GMA welding process have been developed. Experimental results have been employed to find the optimal algorithm to predict bead reinforcement area by BP and LM neural networks in lab joint in the robotic GMA welding. The developed intelligent models can be estimated the optimal welding parameters on the desired bead reinforcement area and weld criteria, establish guidelines and criteria for the most effective joint design for the robotic arc welding process. Originality/value: In this study, intelligent models, which employed the neural network algorithms, one of AI (Artificial Intelligence) technologies have been developed to study the effects of welding parameters on bead reinforcement area and to predict the optimal bead reinforcement area for lab joint in the robotic GMA welding process. BP (Back Propagation) and LM (Levenberg-Marquardt) neural network algorithm have been used to develop the intelligent model.

Słowa kluczowe

EN

GMA (Gas Metal Arc) welding; BP (Back Propagation) neural network; LM (Levenberg-Marquardt) neural network; lab joint welding; bead reinforcement area

PL

spawanie GMA; sieć neuronowa BP; sieć neuronowa LM; propagacja wsteczna

• Wydawca: International OCSCO World Press
• Czasopismo: Journal of Achievements in Materials and Manufacturing Engineering
• Rocznik: 2018
• Tom: Vol. 87, nr 1
• Strony: 32--40
• Opis fizyczny: Bibliogr. 19 poz., rys., tab., wykr.

Twórcy

autor

Park M.-H.

• Department of Mechanical Engineering, Mokpo National University 1666, Yeongsan-ro, Cheonggye-myeon, Muan-gun, Jeonnam, 534-729, South Korea

autor

Jin B.-J.

• Department of Mechanical Engineering, Mokpo National University 1666, Yeongsan-ro, Cheonggye-myeon, Muan-gun, Jeonnam, 534-729, South Korea

autor

Yun T.-J.

• Department of Mechanical Engineering, Mokpo National University 1666, Yeongsan-ro, Cheonggye-myeon, Muan-gun, Jeonnam, 534-729, South Korea

autor

Son J.-S.

• Research Institute of Medium & Small Shipbuilding, 1703-8 Yongang-ri, Samho-eup, Yeongam, Jeonnam, 526-897, South Korea

autor

Kim C.-G.

• Department of Mechanical Engineering, Mokpo National University 1666, Yeongsan-ro, Cheonggye-myeon, Muan-gun, Jeonnam, 534-729, South Korea

autor

Kim I. S.

• Department of Mechanical Engineering, Mokpo National University 1666, Yeongsan-ro, Cheonggye-myeon, Muan-gun, Jeonnam, 534-729, South Korea

Bibliografia

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• [3] C.H. Tsai, K.H. Hou, H. Chuang, Fuzzy control of pulsed GTA welds by using real-time root bead image feedback, Journal of Materials Processing Technology 176/1 (2006) 158-167.
• [4] D.S. Nagesh. G.L. Datta, Prediction of weld bead geometry and prediction in shield metal-arc welding using artificial neural networks. Journal of Materials Process Technology 57 (2002) 1-10.
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• [17] E. Salari, M. Jahazi, A. Khodabandeh, H. Ghasemi Nanesa, Influence of tool geometry and rotational speed on mechanical properties and defect formation in friction stir lap welded 5456 aluminium alloy sheets, Journal of Materials and Design 58 (2014) 381-389.
• [18] K. Muzaka, M.H. Park, J.P. Lee, B.J. Jin, D.H. Kim, I.S. Kim, A study on prediction of welding quality for vertical-position welding using mahalanobis distance method, International Journal of Innonvative Research in Engineering & Management 3/3 (2016) 154-150.
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Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).