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25. Kleiner D, Bird CR (2004) Signal Processing for Quality Assurance in Friction Stir Welds.
46:2003–2005
26. Kah P, Shrestha M, Hiltunen E, Martikainen J (2015) Robotic arc welding sensors and
programming in industrial applications. Int J Mech Mater Eng 10. https://doi.org/10.1186/
s40712-015-0042-y
27. Xu Y, Yu H, Zhong J et al (2012) Real-time seam tracking control technology during welding
robot GTAW process based on passive vision sensor. J Mater Process Technol 212:1654–1662.
https://doi.org/10.1016/j.jmatprotec.2012.03.007
28. Yu J-Y, Na S-J (1998) A study on vision sensors for seam tracking of height-varying weldment.
Part 2: Applications. Mechatronics 8:21–36. https://doi.org/10.1016/s0957-4158(97)00024-x
29. Zou Y, Wang Y, Zhou W, Chen X (2018) Real-time seam tracking control system based on
line laser visions. Opt Laser Technol 103:182–192. https://doi.org/10.1016/j.optlastec.2018.
01.010
30. Zou Y, Chen X, Gong G, Li J (2018) A seam tracking system based on a laser vision sensor.
Meas J Int Meas Confed 127:489–500. https://doi.org/10.1016/j.measurement.2018.06.020
31. Kovacevic R, Zhang Y, Li L (1996) Monitoring of Weld Joint Penetrations Based on Weld
Pool Geometrical Appearance. Weld. Journal-Including Weld. Res. Suppl. 75:317–329
32. Kovacevic R, Zhang YM (1997) Real-Time Image Processing for Monitoring of Free Weld
Pool Surface. J Manuf Sci Eng Trans ASME 119:161–169. https://doi.org/10.1115/1.2831091
33. Scherler M Survey of Robotic Seam Tracking Systems for Arc Welding. https://www.rob
otics.org/content-detail.cfm/Industrial-Robotics-Tech-Papers/Survey-of-Robotic-Seam-Tra
cking-Systems-for-Arc-Welding/content_id/956. Accessed 15 May 2020
34. Sforza P, De Blasiis D (2002) On-line optical monitoring system for arc welding. NDT E Int
35:37–43. https://doi.org/10.1016/S0963-8695(01)00021-4
35. Murthy V, Ullegaddi K, Mahesh B, Rajaprakash BM (2017) Application of Image Processing
and Acoustic Emission Technique in Monitoring of Friction Stir Welding Process. Mater
Today Proc 4:9186–9195. https://doi.org/10.1016/j.matpr.2017.07.276
36. Bhat NN, Kumari K, Dutta S et al (2015) Friction stir weld classification by applying wavelet
analysis and support vector machine on weld surface images. J Manuf Process 20:274–281.
https://doi.org/10.1016/j.jmapro.2015.07.002
37. Sudhagar S, Sakthivel M, Ganeshkumar P (2019) Monitoring of friction stir welding based on
vision system coupled with Machine learning algorithm. Meas J Int Meas Confed 144:135–
143. https://doi.org/10.1016/j.measurement.2019.05.018
38. Rajashekar R, Rajaprakash BM (2013) Analysis of Banded Texture of Friction Stir Weld Bead
Surface by Image Processing Technique. 5:30–39
39. Ranjan R, Khan AR, Parikh C et al (2016) Classification and identification of surface defects
in friction stir welding: An image processing approach. J Manuf Process 22:237–253. https://
doi.org/10.1016/j.jmapro.2016.03.009
40. Parikh C, Ranjan R, Khan AR et al (2017) Volumetric defect analysis in friction stir welding
based on three dimensional reconstructed images. J Manuf Process 29:96–112. https://doi.
org/10.1016/j.jmapro.2017.07.006
41. Uhrlandt D (2016) Diagnostics of metal inert gas and metal active gas welding processes. J
Phys D Appl Phys 49. https://doi.org/10.1088/0022-3727/49/31/313001
42. Pal S, Pal SK, Samantaray AK (2008a) Artificial neural network modeling of weld joint
strength prediction of a pulsed metal inert gas welding process using arc signals. J Mater
Process Technol 202:464–474. https://doi.org/10.1016/j.jmatprotec.2007.09.039
43. Pal S, Pal SK, Samantaray AK (2008b) Neurowavelet packet analysis based on current signature for weld joint strength prediction in pulsed metal inert gas welding process. Sci Technol
Weld Join 13:638–645. https://doi.org/10.1179/174329308X299986
44. Pal S, Pal SK, Samantaray AK (2008c) Sensor based weld bead geometry prediction in pulsed
metal inert gas welding process through artificial neural networks. Int J Knowledge-Based
Intell Eng Syst 12:101–114. https://doi.org/10.3233/KES-2008-12202
45. Quinn TP, Smith C, McCowan CN et al (1999) Arc Sensing for Defects in Constant-Voltage
Gas Metal Arc Welding. Weld J (Miami, Fla) 78:322–328
D. Mishra et al.
25. Kleiner D, Bird CR (2004) Signal Processing for Quality Assurance in Friction Stir Welds.
46:2003–2005
26. Kah P, Shrestha M, Hiltunen E, Martikainen J (2015) Robotic arc welding sensors and
programming in industrial applications. Int J Mech Mater Eng 10. https://doi.org/10.1186/
s40712-015-0042-y
27. Xu Y, Yu H, Zhong J et al (2012) Real-time seam tracking control technology during welding
robot GTAW process based on passive vision sensor. J Mater Process Technol 212:1654–1662.
https://doi.org/10.1016/j.jmatprotec.2012.03.007
28. Yu J-Y, Na S-J (1998) A study on vision sensors for seam tracking of height-varying weldment.
Part 2: Applications. Mechatronics 8:21–36. https://doi.org/10.1016/s0957-4158(97)00024-x
29. Zou Y, Wang Y, Zhou W, Chen X (2018) Real-time seam tracking control system based on
line laser visions. Opt Laser Technol 103:182–192. https://doi.org/10.1016/j.optlastec.2018.
01.010
30. Zou Y, Chen X, Gong G, Li J (2018) A seam tracking system based on a laser vision sensor.
Meas J Int Meas Confed 127:489–500. https://doi.org/10.1016/j.measurement.2018.06.020
31. Kovacevic R, Zhang Y, Li L (1996) Monitoring of Weld Joint Penetrations Based on Weld
Pool Geometrical Appearance. Weld. Journal-Including Weld. Res. Suppl. 75:317–329
32. Kovacevic R, Zhang YM (1997) Real-Time Image Processing for Monitoring of Free Weld
Pool Surface. J Manuf Sci Eng Trans ASME 119:161–169. https://doi.org/10.1115/1.2831091
33. Scherler M Survey of Robotic Seam Tracking Systems for Arc Welding. https://www.rob
otics.org/content-detail.cfm/Industrial-Robotics-Tech-Papers/Survey-of-Robotic-Seam-Tra
cking-Systems-for-Arc-Welding/content_id/956. Accessed 15 May 2020
34. Sforza P, De Blasiis D (2002) On-line optical monitoring system for arc welding. NDT E Int
35:37–43. https://doi.org/10.1016/S0963-8695(01)00021-4
35. Murthy V, Ullegaddi K, Mahesh B, Rajaprakash BM (2017) Application of Image Processing
and Acoustic Emission Technique in Monitoring of Friction Stir Welding Process. Mater
Today Proc 4:9186–9195. https://doi.org/10.1016/j.matpr.2017.07.276
36. Bhat NN, Kumari K, Dutta S et al (2015) Friction stir weld classification by applying wavelet
analysis and support vector machine on weld surface images. J Manuf Process 20:274–281.
https://doi.org/10.1016/j.jmapro.2015.07.002
37. Sudhagar S, Sakthivel M, Ganeshkumar P (2019) Monitoring of friction stir welding based on
vision system coupled with Machine learning algorithm. Meas J Int Meas Confed 144:135–
143. https://doi.org/10.1016/j.measurement.2019.05.018
38. Rajashekar R, Rajaprakash BM (2013) Analysis of Banded Texture of Friction Stir Weld Bead
Surface by Image Processing Technique. 5:30–39
39. Ranjan R, Khan AR, Parikh C et al (2016) Classification and identification of surface defects
in friction stir welding: An image processing approach. J Manuf Process 22:237–253. https://
doi.org/10.1016/j.jmapro.2016.03.009
40. Parikh C, Ranjan R, Khan AR et al (2017) Volumetric defect analysis in friction stir welding
based on three dimensional reconstructed images. J Manuf Process 29:96–112. https://doi.
org/10.1016/j.jmapro.2017.07.006
41. Uhrlandt D (2016) Diagnostics of metal inert gas and metal active gas welding processes. J
Phys D Appl Phys 49. https://doi.org/10.1088/0022-3727/49/31/313001
42. Pal S, Pal SK, Samantaray AK (2008a) Artificial neural network modeling of weld joint
strength prediction of a pulsed metal inert gas welding process using arc signals. J Mater
Process Technol 202:464–474. https://doi.org/10.1016/j.jmatprotec.2007.09.039
43. Pal S, Pal SK, Samantaray AK (2008b) Neurowavelet packet analysis based on current signature for weld joint strength prediction in pulsed metal inert gas welding process. Sci Technol
Weld Join 13:638–645. https://doi.org/10.1179/174329308X299986
44. Pal S, Pal SK, Samantaray AK (2008c) Sensor based weld bead geometry prediction in pulsed
metal inert gas welding process through artificial neural networks. Int J Knowledge-Based
Intell Eng Syst 12:101–114. https://doi.org/10.3233/KES-2008-12202
45. Quinn TP, Smith C, McCowan CN et al (1999) Arc Sensing for Defects in Constant-Voltage
Gas Metal Arc Welding. Weld J (Miami, Fla) 78:322–328