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88. Kumari S, Jain R, Kumar U et al (2016) Defect identification in friction stir welding
using continuous wavelet transform. J Intell Manuf 1–12. https://doi.org/10.1007/s10845016-1259-1
89. Boldsaikhan E, Corwin EM, Logar AM, Arbegast WJ (2011) The use of neural network and
discrete Fourier transform for real-time evaluation of friction stir welding. Appl Soft Comput
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90. Cook GE, Crawford R, Clark DE, Strauss AM (2004) Robotic friction stir welding. Ind Robot
an Int J 31:55–63. https://doi.org/10.1108/01439910410512000
91. Longhurst WR, Strauss AM, Cook GE, Fleming PA (2010) Torque control of friction stir
welding for manufacturing and automation. Int J Adv Manuf Technol 51:905–913. https://
doi.org/10.1007/s00170-010-2678-3
92. Reisgen U, Mann S, Middeldorf K et al (2019) Connected, digitalized welding production—
Industrie 4.0 in gas metal arc welding. Weld World 63:1121–1131. https://doi.org/10.1007/
s40194-019-00723-2
93. Gräler I, Pöhler A (2018) Intelligent Devices in a Decentralized Production System Concept.
Procedia CIRP 67:116–121. https://doi.org/10.1016/j.procir.2017.12.186
94. Keller M, Rosenberg M, Brettel M, Friederichsen N (2014) How Virtualization, Decentrazliation and Network Building Change the Manufacturing Landscape: An Industry 4.0 Perspective. Int J Mech Aerospace, Ind Mechatron Manuf Eng 8:37–44. https://doi.org/10.1016/j.pro
cir.2015.02.213
95. Carvalho N, Chaim O, Cazarini E, Gerolamo M (2018) Manufacturing in the fourth industrial
revolution: A positive prospect in Sustainable Manufacturing. Procedia Manuf 21:671–678.
https://doi.org/10.1016/j.promfg.2018.02.170
96. Erboz G (2017) How to Define Industry 40: The Main Pillars of Industry 4.0. Manag Trends
Dev Enterp Glob Era 761767
97. Press I, Perez R (2018) Internet of Things A to Z. Internet Things a to Z. https://doi.org/10.
1002/9781119456735
98. Durao F, Carvalho JFS, Fonseka A, Garcia VC (2014) A systematic review on cloud
computing. J Supercomput 68:1321–1346. https://doi.org/10.1007/s11227-014-1089-x
99. Rossi B (2016) Why cloud technology is central to Industry 4.0. https://www.informationage.com/cloud-technology-central-industry-4-0-123462532/. Accessed 20 May 2020
100. Sakovich N Fog computing vs. cloud computing for IoT projects. https://www.sam-solutions.
com/blog/fog-computing-vs-cloud-computing-for-iot-projects/. Accessed 20 May 2020
101. Haffner O, Kuˇ cera E, Kozák Š, Stark E (2017) Application of Pattern Recognition for a
Welding Process. Communiation Pap 2017 Fed Conf Comput Sci Inf Syst 13:3–8. https://doi.
org/https://doi.org/10.15439/2017f115
102. Miskinis C What separates digital twin based simulations VS a reality that is augmented.
https://www.challenge.org/insights/digital-twin-vs-augmented-reality/. Accessed 20 May
2020
103. Berg LP, Vance JM (2017) Industry use of virtual reality in product design and manufacturing:
a survey. Virtual Real 21:1–17. https://doi.org/10.1007/s10055-016-0293-9
104. Zhang W Welding Simulation & Digital Twins. https://www.swantec.com/welding-simula
tion-digital-twins/. Accessed 20 May 2020
105. International F (2018) Big Data in Welding Technology. 1–10
106. Chen B, Wang J, Chen S (2009) Modeling of pulsed GTAW based on multi-sensor fusion.
Sens Rev 29:223–232. https://doi.org/10.1108/02602280910967639
107. Zhang Z, Wen G, Chen S (2016) Multisensory data fusion technique and its application to
welding process monitoring. Proc IEEE Work Adv Robot its Soc Impacts, ARSO 2016Novem:294–298. https://doi.org/https://doi.org/10.1109/ARSO.2016.7736298
108. Pal K, Pal SK (2011b) Soft computing methods used for the modelling and optimisation of
Gas Metal Arc Welding: A review. Int J Manuf Res 6:15–29. https://doi.org/10.1504/IJMR.
2011.037911
297
87. Kumar U, Yadav I, Kumari S et al (2015) Defect identification in friction stir welding using
discrete wavelet analysis. Adv Eng Softw 85:43–50. https://doi.org/10.1016/j.advengsoft.
2015.02.001
88. Kumari S, Jain R, Kumar U et al (2016) Defect identification in friction stir welding
using continuous wavelet transform. J Intell Manuf 1–12. https://doi.org/10.1007/s10845016-1259-1
89. Boldsaikhan E, Corwin EM, Logar AM, Arbegast WJ (2011) The use of neural network and
discrete Fourier transform for real-time evaluation of friction stir welding. Appl Soft Comput
J 11:4839–4846. https://doi.org/10.1016/j.asoc.2011.06.017
90. Cook GE, Crawford R, Clark DE, Strauss AM (2004) Robotic friction stir welding. Ind Robot
an Int J 31:55–63. https://doi.org/10.1108/01439910410512000
91. Longhurst WR, Strauss AM, Cook GE, Fleming PA (2010) Torque control of friction stir
welding for manufacturing and automation. Int J Adv Manuf Technol 51:905–913. https://
doi.org/10.1007/s00170-010-2678-3
92. Reisgen U, Mann S, Middeldorf K et al (2019) Connected, digitalized welding production—
Industrie 4.0 in gas metal arc welding. Weld World 63:1121–1131. https://doi.org/10.1007/
s40194-019-00723-2
93. Gräler I, Pöhler A (2018) Intelligent Devices in a Decentralized Production System Concept.
Procedia CIRP 67:116–121. https://doi.org/10.1016/j.procir.2017.12.186
94. Keller M, Rosenberg M, Brettel M, Friederichsen N (2014) How Virtualization, Decentrazliation and Network Building Change the Manufacturing Landscape: An Industry 4.0 Perspective. Int J Mech Aerospace, Ind Mechatron Manuf Eng 8:37–44. https://doi.org/10.1016/j.pro
cir.2015.02.213
95. Carvalho N, Chaim O, Cazarini E, Gerolamo M (2018) Manufacturing in the fourth industrial
revolution: A positive prospect in Sustainable Manufacturing. Procedia Manuf 21:671–678.
https://doi.org/10.1016/j.promfg.2018.02.170
96. Erboz G (2017) How to Define Industry 40: The Main Pillars of Industry 4.0. Manag Trends
Dev Enterp Glob Era 761767
97. Press I, Perez R (2018) Internet of Things A to Z. Internet Things a to Z. https://doi.org/10.
1002/9781119456735
98. Durao F, Carvalho JFS, Fonseka A, Garcia VC (2014) A systematic review on cloud
computing. J Supercomput 68:1321–1346. https://doi.org/10.1007/s11227-014-1089-x
99. Rossi B (2016) Why cloud technology is central to Industry 4.0. https://www.informationage.com/cloud-technology-central-industry-4-0-123462532/. Accessed 20 May 2020
100. Sakovich N Fog computing vs. cloud computing for IoT projects. https://www.sam-solutions.
com/blog/fog-computing-vs-cloud-computing-for-iot-projects/. Accessed 20 May 2020
101. Haffner O, Kuˇ cera E, Kozák Š, Stark E (2017) Application of Pattern Recognition for a
Welding Process. Communiation Pap 2017 Fed Conf Comput Sci Inf Syst 13:3–8. https://doi.
org/https://doi.org/10.15439/2017f115
102. Miskinis C What separates digital twin based simulations VS a reality that is augmented.
https://www.challenge.org/insights/digital-twin-vs-augmented-reality/. Accessed 20 May
2020
103. Berg LP, Vance JM (2017) Industry use of virtual reality in product design and manufacturing:
a survey. Virtual Real 21:1–17. https://doi.org/10.1007/s10055-016-0293-9
104. Zhang W Welding Simulation & Digital Twins. https://www.swantec.com/welding-simula
tion-digital-twins/. Accessed 20 May 2020
105. International F (2018) Big Data in Welding Technology. 1–10
106. Chen B, Wang J, Chen S (2009) Modeling of pulsed GTAW based on multi-sensor fusion.
Sens Rev 29:223–232. https://doi.org/10.1108/02602280910967639
107. Zhang Z, Wen G, Chen S (2016) Multisensory data fusion technique and its application to
welding process monitoring. Proc IEEE Work Adv Robot its Soc Impacts, ARSO 2016Novem:294–298. https://doi.org/https://doi.org/10.1109/ARSO.2016.7736298
108. Pal K, Pal SK (2011b) Soft computing methods used for the modelling and optimisation of
Gas Metal Arc Welding: A review. Int J Manuf Res 6:15–29. https://doi.org/10.1504/IJMR.
2011.037911