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The state-of-the-art methodologies for quality analysis of arc welding process using weld data acquisition and analysis techniques

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Abstract

Arc welding, due to its simplicity, ease of use and low maintenance cost is one of the most widely used welding process in almost all types of modern industries. In this process, voltage, current and welding speeds are the major variable which influences the final weld product. Among these, monitoring welding speed is relatively easy, while monitoring voltage and current is not. This is because welding is a stochastic process in which wide variations in voltage and current occurs and durations of these variations are so short that the ordinary ammeters and voltmeters cannot measure these variations. However, using suitable sensors coupled with a high-speed data acquisition system, real time variations taking place in an actual welding process can be recorded and subsequently analyzed. A careful analysis of these variations using various signal processing, statistical and data mining techniques can provide a very useful information in estimating the quality of final weld product. In this research, a first of its kind, detailed review on various aspects of weld monitoring systems used for weld data acquisition and its subsequent analysis are presented. This will include an in-depth analysis of various electronic sensing and data sampling modules which can be used in the design and development of a Weld Monitoring System. Additionally, this review also includes a brief study on various soft computing, data mining and machine learning techniques on weld data in predicting the quality of different welding parameters. Finally, summary of the review is followed by the scope of future research to pave out some of the new dimensions in exploring the multi-disciplinary area of evaluating the arc welding quality using data acquisition and analysis techniques.

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References

  • Adolfsson S (1995) Quality monitoring in pulsed GMA welding using signal processing methods, licentiate thesis, Luleå University of Technology, Division of Signal Processing, ISSN 0280 – 8242

  • Adolfsson S, Ericson K, Grennberg A (1996a) Automatic detection of burn-through in GMA welding using a parametric model. Mech Syst Signal Process 10(5):633–651

    Article  Google Scholar 

  • Adolfsson S, Bahrami A, Claesson I (1996) Quality monitoring in robotized welding using sequential probability ratio test, Proceedings of TENCO ’96, Digital Signal Processing Applications 2, New York, N.Y., IEEE. pp. 635–640

  • Akkas N, Karayel D, Ozkan SS, Ogur A, Topal B (2013) Modeling and Analysis of the weld bead geometry in submerged arc welding by using adaptive neurofuzzy inference system mathematical problems in engineering 2013

  • American Society of Mechanical Engineers Section 2c (2015) Specifications for welding rods, electrodes, and filler metals, Boiler and pressure vessel code

  • Andrej L, Luka S, Peter B (2012) Online monitoring, analysis, and remote recording of welding parameters to the welding diary. Strojniškivestnik J Mech Eng 58(7–8):444–452

    Google Scholar 

  • Ang KH, Chong G, Li Y (2005) PID control system analysis, design, and technology, IEEE Trans Control Syst Technol 13(4)

  • Aviles-Viñas JF, Rios-Cabrera R, Lopez-Juarez I (2016) On-line learning of welding bead geometry in industrial robots. Int J Adv Manuf Technol 83(1–4):217–231

    Article  Google Scholar 

  • Bing X (2011) Welding arc signal acquisition and analysis system based on VC++ and MATLAB mixed programming. In: IEEE Proceedings 3rd third international conference on measuring technology and mechatronics automation (ICMTMA), 3, pp. 1150–1153

  • Bisgaard S, Pinho A (2004) Follow-up experiments to verify dispersion effects: Taguchi’s welding experiment. Qual Eng 16:335–343

    Article  Google Scholar 

  • Bo H, Can Y, Ji P (2005) Wavelet signal processing system in arc sensor. Trans China Weld Inst 26(1):61–68

    Google Scholar 

  • Bo C, Jifeng W, Shanben C (2010) Prediction of pulsed GTAW penetration status based on BP neural network and D-S evidence theory information fusion. Int J Adv ManufTechnol 48:83–94

    Article  Google Scholar 

  • Caglar R (2012) Wavelet transform and current signature analysis for welding machine measurement. J Vibroeng 14:805–812

    Google Scholar 

  • Chan B, Pacey J, Bibby M (1999) Modelling gas metal arc weld geometry using artificial neural network technology. Can Metall Q 38(1):43–51

    Google Scholar 

  • Chen B, Chen S (2010) Multi-sensor information fusion in pulsed GTAW based on fuzzy measure and fuzzy integral. Assem Autom 30(3):276–285

    Article  Google Scholar 

  • Chen B, Han F, Hhuang Y, Lu K, Llu Y, Li L (2009) Influence of nanoscale marble (calcium carbonate CaCO3) on properties of D600R surfacing electrode. Weld J 88:99–103

    Google Scholar 

  • Chen B, Feng J (2014) Modeling of underwater wet welding process based on visual and arc sensor. Ind Robot Int J 41(3):311–317

    Article  Google Scholar 

  • Cook GE, Maxwell JE, Barnett RJ, Strauss AM (1997) Statistical process control application to weld process. IEEE Trans Ind Appl 33(2)

  • Cudina M, Prezelj J, Polajnar I (2008) Use of audible sound for on-line monitoring of gas metal arc welding process. Metalurgia 47(2):81–85

    Google Scholar 

  • Dong H, Huff S, Cong M, Zhang Y, Chen H (2016) Backside weld bead shape modeling using support vector machine (Unpublished report). Texas State University, San Marcos, TX

  • Dong H, Cong M, Liu Y, Zhang Y, Chen H (2016) Predicting characteristic performance for arc welding process. In: 2016 IEEE international conference on cyber technology in automation, control and intelligent systems (CYBER). IEEE, pp. 7–12

  • Escribano-García R, Lostado-Lorza R, Fernández-Martínez R, Villanueva- Roldán P, Mac Donald BJ (2014) Improvement in manufacturing welded products through multiple response surface methodology and data mining techniques. Adv Intell Syst Comput 299:301–310

    Google Scholar 

  • Franco S (2002) Design with operation amplifiers and analog integrated circuits, 3rd edition, Tata Mc Graw Hill

  • Ganjigatti JP, Pratihar DK, Roy CA (2008) Modelling of the MIG welding process using statistical approaches. Int J Adv Manuf Technol 35:1166–1190

    Article  Google Scholar 

  • Gao J, Wu C, Hu J (2007) Real-time monitoring of abnormal conditions based on Fuzzy Kohonen clustering network in gas metal arc welding. Front Mater Sci China 1:134–139

    Article  Google Scholar 

  • Gao X, Ding D, Bai T, Katayama S (2011) Weld-pool image centroid algorithm for seam-tracking vision model in arc-welding process. IET Image Proc 5(5):410–419

    Article  Google Scholar 

  • Gu S, Ni J, Yuan J (2002) Non-stationary signal analysis and transient machining process condition monitoring. Int J Mach Tools Manuf 42:41–51

    Article  Google Scholar 

  • Hailin H, Jing L, Fang L, Wei Z, Heqiang P (2012) Neural-fuzzy variable gap control method for GMAW pipe-line welding with CCD camera. In: Zhao H (Ed.) Mechanical and electronics engineering III, Pts 1–5, Ser. Applied Mechanics and Materials. Proceedings Paper. Vol. 130–134. Hefei Univ Technol. Laublsrutistr 24, CH-8717 Stafa-Zurich, Switzerland: Trans Tech Publications Ltd, pp. 2358–2363

  • Heng Li, Yuhao C, Haitao X (2003) Selection on filter method of welding arc electric signal. Weld Join 11:29–32

    Google Scholar 

  • Horvat J, Prezelj J, Polajnar I, Cudina M (2011) Monitoring gas metal arc welding process by using audible sound signal. Strojniskivestnik- J Mech Eng 57(3):267–278

    Article  Google Scholar 

  • Iqbal A, Khan SM, Mukhtar HS (2011) ANN assisted prediction of weld bead geometry in gas tungsten arc welding of HSLA steels. In: Proceedings of the world congress on engineering, I, WCE, London, U.K

  • Ismail MIS, Okamoto Y, Okada A (2013) Neural network modeling for prediction of weld bead geometry in laser micro welding. Adv Opt Technol 7

  • Jiaxiang X, Zhiping Y, Ping F (2004) Study on virtual instrument for analyzing electrical signal of welding process[J]. Chin J Mech Eng 40(2):60–63

    Article  Google Scholar 

  • Kalaichelvi V, Karthikeyan R, Sivakumar D (2013) Analysis of gas metal arc welding process using GA tuned fuzzy rule-based system. J Intell Fuzzy Syst 25(2):429–440

    Article  Google Scholar 

  • Kang MJ, Rhee S (2000) A study on the development of the arc stability index using multiple regression analysis in short-circuit transfer region of gas metal arc welding. In: Proceedings of Institutions of Mechanical Engineers, 215 Part B, May

  • Kemppi pro evolution, digital control for the welding professional Kemppipvt. Limited. Web: - www.kemppi.com

  • Keshmiri S, Zheng X, Feng L, Pang C, Chew C (2015) Application of deep neural network in estimation of the weld bead parameters. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3518- 3523

  • Kim IS, Kwon WH, Siores E (1996) An investigation of a mathematical model for predicting weld bead geometry. Can Metall Q 35(4):385–392

    Article  Google Scholar 

  • Kim I-S, Son J-S, Lee S-H, Yarlagadda PKDV (2004) Optimal design of neural networks for control in robotic arc welding. Robot Comput Integr Manuf 20(1):57–63

    Article  Google Scholar 

  • Kolahan F, Heidari MA (2010) New Approach for Predicting and Optimizing Weld Bead Geometry in GMAW. Int J Mech Syst Sci Eng 2(2):138–142

    Google Scholar 

  • Krzysztof S, Wlodzimierz M, Antun S, Ivan S (2015) Defining the criteria to select the wavelet type for the assessment of surface quality. Tehnicki Vjesnik 22(3):781–784

    Article  Google Scholar 

  • Kuanfang H, Zhipeng Z, Chao W, Xuejun L (2016) Arc signal analysis of square wave alternating current submerged arc welding using local mean decomposition. J Adv Mech Des Syst Manuf 10(9):1–12

    Google Scholar 

  • Kuanfang H, Jigang W, Xuejun L (2011) Wavelet analysis for electronic signal of submerged arc welding process. In: Third international conference on measuring technology and mechatronics automation, pp. 1140–1141

  • Kumar V, Albert S, Chandrasekhar N, Venkatesan M (2016) Performance analysis of arc welding parameters using self organized maps and probability distributions. In: IEEE first international conference on control, measurement and instrumentation (CMI), Kolkata, India, 196–200

  • Li X, Simpson SW (2009) Parametric approach to positional fault detection in short arc welding. Sci Technol Weld Join 14:146–151

    Article  Google Scholar 

  • Li W, Gao K, Wu J, Hu T, Wang J (2014) SVM-based information fusion for weld deviation extraction and weld groove state identification in rotating arc narrow gap MAG welding. Int J Adv Manuf Technol 74(9–12):1355–1364

    Article  Google Scholar 

  • Li ZY, Gao XD (2014) Study on regression model of measuring weld position. applied mechanics and materials, 511–512, Trans Tech Publications, Ltd, pp. 514–517

  • Luksa K, Rymarski Z (2006) Collection of arc welding process data. J Achiev Mater Manuf Eng 17(1–2):377–379

    Google Scholar 

  • Lv N, Xu Y, Zhong J, Chen H (2013) Research on detection of welding penetration state during robotic GTAW process based on audible arc sound. Ind Robot Int J 40(5):474–493

    Article  Google Scholar 

  • Martinez R, Alfaro S (2020) Data analysis and modeling techniques of welding processes: the state-of-the-art. Intech Open Welding-Modern topics. pp. 1–25

  • Massimo L, Mirko S, Bruno R (2010) Seam welding monitoring system based on real-time electrical signal analysis. Weld J 89:218–223

    Google Scholar 

  • Matz V, Kreidl M, Šmíd R (2004) Signal-to-noise ratio improvement based on the discrete wavelet transform in ultrasonic defectoscopy. Acta Polytech 44:4

    Article  Google Scholar 

  • Misiti M, Misiti Y, Oppenheim G, Poggi JM (2007) Wavelet Toolbox 4 – User’s Guide. The MathWorks, Inc.

  • Modern Arc Welding Technology (1988) Ador welding limited. Oxford & IBH publishing Co. Pvt. Ltd, New Delhi (India)

  • Muniategui A, Eciolaza L, Ayuso M, Garmendia MJ, Alvarez P (2016) Electrode degradation analysis in aluminium-based resistance spot welding process. In: IEEE international conference on fuzzy systems (FUZZ-IEEE)

  • Muniategui A, Hériz B, Eciolaza L, Ayuso M, Iturrioz A, Quintana I, et al. (2017) Spot welding monitoring system based on fuzzy classification and deep learning. In: 2017 IEEE international conference on fuzzy systems (FUZZIEEE). IEEE, pp 1–6

  • Nandhitha NM (2016) Artificial neural network based prediction techniques for torch current deviation to produce defect-free welds in GTAW Using IR thermography, pp 137–142

  • Nishiguchi K (1975) Mechanism of bead formation in non-shielded arc welding, advanced welding technology, Japan Weld Soc, pp 339–344

  • Padovese LR (2004) Hybrid time-frequency methods for non-stationary mechanical signal analysis. Mech Syst Signal Process 18(5):1047–1064

    Article  Google Scholar 

  • Pallas-Areny R, Webster JG (1991) Common mode rejection ratio in differential amplifiers. IEEE Trans Instrum Meas 40(4):669–676

    Article  Google Scholar 

  • PaulS A (2004) The illustrated wavelet transform handbook, introductory theory and applications in science, engineering medicine and finance. Napier University, Edinburgh, UK, ISBN 07–50–30692–0

  • Rajesh G, Das Tapas K, Vivekanand V (2004) Wavelet-based multiscale statistical process monitoring: a literature review. IIE Trans 36:787–806

    Article  Google Scholar 

  • Ramirez JE, Johnson M (2010) Effect of welding parameters and electrode condition on alloying enrichment of weld metal deposited with coated cellulosic electrodes. Weld J 89:232–242

    Google Scholar 

  • Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. Cvpr, pp. 779–788

  • Rehfeldt D, Polite T (1998) Systems for processing monitoring and quality assurance in welding. In: Proceedings of 8th international conference on computer technology in welding, American Welding Society, Miami

  • Rehfeldt D, Rehfeldt M (2003) Computer-aided quality assurance (CAQ) of Al-GMAW-welding with analysator Hannover. In: Proceedings international forum on automobile welding, Mechanical Engineering Press, Beijing

  • Rong Y, Huang Y, Zhang G, Chang Y, Shao X (2016) Prediction of angular distortion in no gap butt joint using BPNN and inherent strain considering the actual bead geometry. Int J Adv Manuf Technol 86(1–4):59–69

    Article  Google Scholar 

  • Rosentha lD (1946) The theory of moving sources of heat and its applications io metal treatment, Transactions of the ASME, pp. 849–866

  • Raja SA, Rajasekaran N, Venkateswaran PR, Easwaran (2014) Evaluation of welding power sourced and fillers wires through signature analysis, welding research Institute, National Welding Seminar, Jamshedpur, 2014–15

  • Sarkar A, Dey P, Rai R, Saha S (2016) A comparative study of multiple regression analysis and back propagation neural network approaches on plain carbon steel in submerged-arc welding. Sadhana Acad Proc Eng Sci 41(5):549–559

    Google Scholar 

  • Savyasachi N, Chandrasekhar N, Albert SK, Surendranathan O (2015) Evaluation of arc welding process using digital storage oscilloscope and high-speed camera. Indian Weld J 48(4):35–43

    Article  Google Scholar 

  • Seyyedian Choobi M, Haghpanahi M, Sedighi M (2012) Prediction of welding-induced angular distortions in thin butt-welded plates using artificial neural networks. Comput Mater Sci 62:152–159

    Article  Google Scholar 

  • Shi DF, Tsung F, Unsworth PJ (2004) Adaptive time-frequency decomposition for transient vibration monitoring of rotating machinery. Mech Syst Signal Process 18(1):127–141

    Article  Google Scholar 

  • Shin S, Jin C, Yu J, Rhee S (2020) Real-time detection of weld defects for automated welding process base on deep neural network. Metals 10:389

    Article  Google Scholar 

  • Shinoda T, Doherty J (1978) The relationship between arc welding parameters and weld bead geometry - A literature survey, Weld lnst Research Report 7J, 1978PE, 77

  • Siewert T,SamardžićI K (2002) Application of an on-line weld monitoring system. In: Proceedings of 1st. DAAAM international conference on advanced technologies for developing countries, Sl. Brod, UDK 621(063)=111=163.42, ISBN 3–901509–32–1, pp. 227–232

  • Simpson SW (2007a) Signature image for arc welding fault detection. Sci Technol Weld Join 12:481–486

    Article  Google Scholar 

  • Simpson SW (2007b) Statistics of signature images for arc welding fault detection. Sci Technol Weld Join 12:556–563

    Article  Google Scholar 

  • Simpson SW (2008a) Fault identification in gas metal arc welding with signature images. Sci Technol Weld Join 13:87–96

    Article  Google Scholar 

  • Simpson SW (2008b) Signature image stability and metal transfer in gas metal arc welding. Sci Technol Weld Join 13:176–183

    Article  Google Scholar 

  • Simpson SW (2008c) through arc sensing in gas metal arc welding with signature images. Sci Technol Weld Join 13:80–86

    Article  Google Scholar 

  • Song Li D, Y and Ye and Fhanjie Xuebao, (2000) Identification of weld defects in GMAW based on arc sensing. Trans China Weld Inst 21:30–33

    Google Scholar 

  • Sreeraj P, Kannan T (2012) Modelling and prediction of stainless-steel clad bead geometry deposited by GMAW using regression and artificial neural network models. Adv Mech Eng 1–12

  • Sterling D, Sterling, T, Zhang Y, Chen H (2015) Welding parameter optimization based on process regression bayesian optimization algorithm. In: IEEE international conference on automation science and engineering (CASE), pp. 1490–1496

  • Szekely J (1986) The mathematical modeling of arc welding operations, advances m welding science and technology, ASM, pp. 3–14

  • Ujjwal K, Inderjeet Y, Shilpi K, Kanchan K, Ranjan Nitin K, KRam, Jain Rahul, Kumar Sachin, Pal Srikanta, Chakravarty Debasish, K. P Surjya, (2015) Defect identification in friction stir welding using discrete wavelet analysis. Adv Eng Softw 85:43–50

    Article  Google Scholar 

  • Vikas K, Albert S, Chandrasekhar N, Jayapandian J (2015) Analysis of shielded metal arc welding using digital storage oscilloscope. Measurement 81:1–12

    Google Scholar 

  • Vikas K, Albert S, Chandrasekhar N, Jayapandian J (2017) Evaluation of welding skill using probability density distributions and neural network analysis. Measurement 116:114–121

    Google Scholar 

  • Vikas K, Albert S, Chandrasekhar N, Jayapandian J (2018a) Performance analysis of arc welding process using weld data analysis. Int J Min Metals Mater Eng 71:1–13

    Google Scholar 

  • Vikas K, Albert S, Chandrasekhar N (2018b) Signal processing approach on weld data for evaluation of arc welding electrodes using probability density distributions. Measurement 133:23–32

    Google Scholar 

  • Vikas K, Manoj P, Verma OP (2019) Evaluation of power sources and the effect of varying current in SMAW process. Int J Syst Assur Eng Manag 1–11

  • Vikas K, Albert S, Chandrasekhar N (2020) Development of programmable system on chip-based weld monitoring system for quality analysis of arc welding process. Int J Comput Integr Manuf 9(33):925–935

    Google Scholar 

  • Wan X, Wang Y, Zhao D, Huang Y (2017) A comparison of two types of neural network for weld quality prediction in small scale resistance spot welding. Mech Syst Signal Process 93:634–644

    Article  Google Scholar 

  • Wang Y, Dalu G, Mingfu L (2005) A better method for detecting friction welding defect. J Northwestern Polytech Univ 23(4):496–499

    Google Scholar 

  • Wang D, Zhou YH (2003) Weld defect extraction based on adaptive morphology filering and edge detection by wavelet analysis. Chin J Electron 12(3):335–339

    Google Scholar 

  • Web (2016). http://www.cypress.com/

  • Web (2015). http://www.eetimes.com/document.asp?doc_id=1274125

  • Winder S (2002) Analog and digital filter design, 2nd edition, Newness-Elsevier

  • Wu CS, Polte T, Rehfeldt D (2000) Gas metal arc welding process monitoring and quality evaluation using neural networks. Sci Technol Weld Joining 5(5):324–328

    Article  Google Scholar 

  • Wu CS, Polte T, Rehfeldt D (2001) A fuzzy logic system for process monitoring and quality evaluation in GMAW. Weld J 80(2):16–22

    Google Scholar 

  • Wu D, Chen H, He Y, Song S, Lin T, Chen S (2016) A prediction model for keyhole geometry and acoustic signatures during variable polarity plasma arc welding based on extreme learning machine. Sens Rev 36(3):257–266

    Article  Google Scholar 

  • Wu C S, Hu Q X, Sun S, Polte T, Rehfeldt D (2004) Intelligent monitoring and recognition of the short-circuiting gas–metal arc welding process, Proceedings of the Institutions of Mechanical Engineers., Part B, J Eng Manuf, pp. 1145–1151

  • Wu CS, Gao JQ and Hu JK (2007) Real-time sensing and monitoring in robotic gas metal arc welding, Institute for Materials Joining, Shandong University, Jinan, People's Republic of China, January

  • Wu D, Huang Y, Chen H, He Y, Chen S (2017) VP-PAW penetration monitoring based on fusion of visual and acoustic signals using t-SNE and DBN model. Mater Des 123:1–14

    Article  Google Scholar 

  • Xiaoniu Z, Junyue L, Shisheng H (2002) Evaluation of technologic dynamic characteristic of CO2 arc welding power source basing on wavelet analysis. Chin J Mech Eng 38(1):112–116

    Article  Google Scholar 

  • Xue JX, Zhang LL, Peng YH, Jia L (2007) A wavelet transform-based approach for joint tracking in gas metal arc welding- 90s-96s, Weld J

  • XuejunLi, et al (2012) Arc stability analysis of square wave alternating current submerged arc welding based on wavelet energy entropy. J Converg Inform Technol 7:22

    Google Scholar 

  • You D, Gao X, Katayama S (2015) WPD-PCA-based laser welding process monitoring and defects diagnosis by using FNN and SVM. IEEE Trans Ind Electron 62(1):628–636

    Article  Google Scholar 

  • Yuezhou M, Pengxian Z, Weidong L (2001) Application of wavelet packet analysis and Welch method in power spectrum evaluation of arc sound. J Gansu Univ Technol 27(2):5–8

    Google Scholar 

  • Zamanzad Gavidel S, Lu S, Rickli JL (2019) Performance analysis and comparison of machine learning algorithms for predicting nugget width of resistance spot welding joints. Int J Adv Manuf Technol 105:3779–3796

    Article  Google Scholar 

  • Zhifen Z, Chen X, Chen H, Zhong J, Chen S (2014) Online welding quality monitoring based on feature extraction of arc voltage signal. Int J Adv Manuf Technol 70:1661–1671

    Article  Google Scholar 

  • Zhou Z, Guan C (2007) Wavelet image de-noising based on multi-scale edge detection and adaptive threshold. Chin J Sci Instr 28(2):288–292

    MathSciNet  Google Scholar 

  • Zhou W, QingLi ZZ (2001) Power quality detection using wavelet-multiresolution signal decomposition. Trans China Electrotechem Soc 16(6):81–84

    Google Scholar 

Download references

Acknowledgements

We thank Dr. A. K. Bhaduri, Director Indira Gandhi Centre for Atomic Research (IGCAR), Kalpakkam and Materials Testing Division of IGCAR for their support during this whole study. We also acknowledge the support and encouragement received from School of Electronics Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar.

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Kumar, V., Parida, M.K. & Albert, S.K. The state-of-the-art methodologies for quality analysis of arc welding process using weld data acquisition and analysis techniques. Int J Syst Assur Eng Manag 13, 34–56 (2022). https://doi.org/10.1007/s13198-021-01282-w

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