Abstract
Ensuring precise diameter control in flux core wire drawing is crucial for maintaining welding wire
quality and performance. Conventional measurement methods, which often rely on manual inspection
or mechanical techniques, face limitations in accuracy, consistency, and responsiveness. This study
explores the application of AI vision-controlled systems to address these challenges by enabling realtime,
high-precision, non-contact diameter measurement. Through an extensive review of research
papers and patents, this paper examines the advancements in AI-based monitoring techniques and their
integration into industrial wire drawing processes. The AI-driven approach employs laser collimated
transmission and CCD sensor technology to capture and analyze wire dimensions with micro-level
precision. Unlike traditional methods, this system offers continuous measurement, adaptive corrections,
and seamless integration into automated production lines, leading to enhanced process stability and
reduced defect rates. The research highlights the advantages of AI-enhanced non-contact measurement,
including increased efficiency, reduced material wastage, and improved compliance with stringent
quality standards. Additionally, advanced predictive algorithms enable proactive parameter
adjustments, optimizing production workflows and minimizing downtime. Experimental trials validate
the effectiveness of this system, demonstrating significant improvements in accuracy and consistency.
By leveraging AI vision technology, manufacturers can achieve superior quality control, minimize
human error, and enhance the sustainability of flux core wire production. This study underscores the
transformative potential of AI-driven measurement solutions in modern manufacturing, paving the way
for intelligent automation and process optimization in the welding industry.

Keywords: Diameter measurement, optimization, automation, AI vision system, flux core wire
drawing, non-contact measurement, process automation, quality control, real-time monitoring,
digital image, welding wire industry.

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Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Starikova NS, Redko VV, Vavilova GV. Control of cable insulation quality by changing of electrical capacitance per unit during high voltage testing. J Phys Conf Ser. 2016;671:012056. doi:10.1088/1742-6596/671/1/012056.
2. Goldshtein AE, Vavilova GV, Belyankov VYu. An electro-capacitive measuring transducer for the process inspection of the cable capacitance per unit length in the process of production. Magn Electr Methods. 2015;51:86–93. doi:10.1134/S1061830915020047.
3. Goldshtein AE, Fedorov EM. A mutually inductive measuring transducer of transverse displacements of a rectilinear conductor. Magn Methods. 2010;46:424–30. doi:10.1134/S1061830910060069.
4. Fedorov EM, Bortnikov ID. Monitoring of the outer diameter of long items using the optical diffraction method. Optics. 2015;60:1689–92. doi:10.1134/S1063784215110110.
5. Fedorov E, Koba A. Diameter calculation in contactless three-axis measuring devices. MATEC Web Conf. 2016;79:01082. doi:10.1051/matecconf/20167901082.
6. Caetano E, Cunha Á. Dynamic testing of cable structures. MATEC Web Conf. 2015;24:01002. doi:10.1051/matecconf/20152401002.
7. Petrova AY, Chaikovskaya ON, Plotnikova IV. Investigation of bactericide systems using a microfiber polypropylene carrier. Exp Instrum Tech. 2015;60:592–4. doi:10.1134/S1063784215040222.
8. Vlasov VA, Lysenko EN, Surzhikov AP, Zhuravkov S. The oxidation kinetics study of ultrafine iron powders by thermogravimetric analysis. J Therm Anal Calorim. 2013;115(2). doi:10.1007/s10973-010-0912-.
9. Galtseva OV, Bordunov SV, Natalinova NM, Mazikov SV. Improvement of the quality of water purification from hydrocarbons using the fibers from recycled thermoplastics. IOP Conf Ser Mater Sci Eng. 2016;132:012003. doi:10.1088/1757-899X/132/1/012003.
10. Pritulov AM, Usmanov RU, Gal’tseva OV, Kondratyuk AA, Bezuglov VV, Serbin VI. Influence of the degree of compaction of a reagent powder mixture on solid-phase synthesis of lithium pentaferrite. Inorg Mater. 2007;50:187–92. doi:10.1007/s11182-007-0026-3.
11. Natalinova N, Avdeeva D, Kazakov V, Baranov V, Galtseva O, Ivashkov D. Computer spatially oriented reconstruction of a 3D heart shape based on its tomographic imaging. MATEC Web Conf. 2016;79:01005.
12. Payuk L, Voronina N, Korepanov S, Galtseva O, Natalinova N. Use of creeping speed mode in discrete control and measuring equipment with digital-program control. MATEC Web Conf. 2016;79:01060. doi:10.1051/confmatec/20164805004.
13. Bespal’ko AA, Surzhikov AP, Yavorovich LV, Fedotov PI. Controlling the structural distortions of mine-field rock masses using the parameters of mechanoelectric transformations. Magn Electromagn Methods. 2012;48:221–5. doi:10.1134/S1061830912040043.
14. Chursin YA, Fedorov EM. Methods of resolution enhancement of laser diameter measuring instruments. Opt Laser Technol. 2015;67:86–92. doi:10.1016/j.optlastec.2014.09.017.
15. Diwan YC, Rao K. Optical measurement systems for industrial inspection IV. Proc SPIE. 2005;5856:554–61.
16. Khodier SA. Measurement of wire diameter by optical diffraction. Opt Laser Technol. 2004;36(1):63–7.
17. Hartung GH, Blancq RJ, Lally DA, Krock LP. Estimation of aerobic capacity from submaximal cycle ergometry in women. Med Sci Sports Exerc. 1995;27(3):452–7.
18. Butler DJ, Forbes GW. Fiber-diameter measurement by occlusion of a Gaussian beam. Appl Opt. 1998;37(13):2598–607.
19. Zimmermann E, Dandliker R, Souli N, Krattiger B. Scattering of an off-axis Gaussian beam by a dielectric cylinder compared with a rigorous electromagnetic approach. J Opt Soc Am A. 1995;12:398–403.
20. Chaudhary KP, Sanjid MA, Moitra S. Modeling of laser scanning system to determine its associated uncertainty of measurement. MAPAN J Metrol Soc India. 2010;25:229–37.
21. Kee CW, Ratnam MM. A simple approach to fine wire diameter measurement using a high-resolution flatbed scanner. Opt Laser Technol. 2009;40:940–7.
22. Ericson M. Method and device for measuring a diameter. WO1997021073A1. 1997 Jun 12.
23. Keba Ges Mbh & Co. Cable diameter measuring device. GB2257512A. 1993 Jan 13.
24. Li W, Wang H, Feng Z. Ultrahigh-resolution and non-contact diameter measurement of metallic wire using eddy current sensor. Rev Sci Instrum. 2014;85(8):085001. doi:10.1063/1.4891699.
25. Sprecher Energie Osterreich Gmbh. Apparatus for measuring cable diameters. US5212539A. 1993 May 18.
26. Martinez-Anton JC, Serroukh I, Bernabeu E. Wire diameter determination by interferometry and diffraction. Proc SPIE. 1999;3745. doi:10.1117/12.357787.
27. Microtec S.r.l. Measuring device for wire or filament. EP0626560A2. 1994 Nov 30.
28. Beltronics Inc. Apparatus for measuring diameter of a wire. US4697088A. 1987 Sep 29.
29. Fedorov E, Koba A. Diameter calculation in contactless three-axis measuring devices. MATEC Web Conf. 2016;79:01082. doi:10.1051/matecconf/20167901082.
30. Tamayo Vegas S, Lafdi K. A literature review of non-contact tools and methods in structural health monitoring. Eng Technol Open Access J. 2021;4(1). doi:10.19080/ETOAJ.2021.04.555626.
31. Chang T-S, Huang H-H. Laser scanning measuring apparatus. US8233157B2. 2012 Jul 31.

Abstract
Ensuring precise diameter control in flux core wire drawing is crucial for maintaining welding wire
quality and performance. Conventional measurement methods, which often rely on manual inspection
or mechanical techniques, face limitations in accuracy, consistency, and responsiveness. This study
explores the application of AI vision-controlled systems to address these challenges by enabling realtime,
high-precision, non-contact diameter measurement. Through an extensive review of research
papers and patents, this paper examines the advancements in AI-based monitoring techniques and their
integration into industrial wire drawing processes. The AI-driven approach employs laser collimated
transmission and CCD sensor technology to capture and analyze wire dimensions with micro-level
precision. Unlike traditional methods, this system offers continuous measurement, adaptive corrections,
and seamless integration into automated production lines, leading to enhanced process stability and
reduced defect rates. The research highlights the advantages of AI-enhanced non-contact measurement,
including increased efficiency, reduced material wastage, and improved compliance with stringent
quality standards. Additionally, advanced predictive algorithms enable proactive parameter
adjustments, optimizing production workflows and minimizing downtime. Experimental trials validate
the effectiveness of this system, demonstrating significant improvements in accuracy and consistency.
By leveraging AI vision technology, manufacturers can achieve superior quality control, minimize
human error, and enhance the sustainability of flux core wire production. This study underscores the
transformative potential of AI-driven measurement solutions in modern manufacturing, paving the way
for intelligent automation and process optimization in the welding industry.

Keywords: Diameter measurement, optimization, automation, AI vision system, flux core wire
drawing, non-contact measurement, process automation, quality control, real-time monitoring,
digital image, welding wire industry.

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Starikova NS, Redko VV, Vavilova GV. Control of cable insulation quality by changing of electrical capacitance per unit during high voltage testing. J Phys Conf Ser. 2016;671:012056. doi:10.1088/1742-6596/671/1/012056.
2. Goldshtein AE, Vavilova GV, Belyankov VYu. An electro-capacitive measuring transducer for the process inspection of the cable capacitance per unit length in the process of production. Magn Electr Methods. 2015;51:86–93. doi:10.1134/S1061830915020047.
3. Goldshtein AE, Fedorov EM. A mutually inductive measuring transducer of transverse displacements of a rectilinear conductor. Magn Methods. 2010;46:424–30. doi:10.1134/S1061830910060069.
4. Fedorov EM, Bortnikov ID. Monitoring of the outer diameter of long items using the optical diffraction method. Optics. 2015;60:1689–92. doi:10.1134/S1063784215110110.
5. Fedorov E, Koba A. Diameter calculation in contactless three-axis measuring devices. MATEC Web Conf. 2016;79:01082. doi:10.1051/matecconf/20167901082.
6. Caetano E, Cunha Á. Dynamic testing of cable structures. MATEC Web Conf. 2015;24:01002. doi:10.1051/matecconf/20152401002.
7. Petrova AY, Chaikovskaya ON, Plotnikova IV. Investigation of bactericide systems using a microfiber polypropylene carrier. Exp Instrum Tech. 2015;60:592–4. doi:10.1134/S1063784215040222.
8. Vlasov VA, Lysenko EN, Surzhikov AP, Zhuravkov S. The oxidation kinetics study of ultrafine iron powders by thermogravimetric analysis. J Therm Anal Calorim. 2013;115(2). doi:10.1007/s10973-010-0912-.
9. Galtseva OV, Bordunov SV, Natalinova NM, Mazikov SV. Improvement of the quality of water purification from hydrocarbons using the fibers from recycled thermoplastics. IOP Conf Ser Mater Sci Eng. 2016;132:012003. doi:10.1088/1757-899X/132/1/012003.
10. Pritulov AM, Usmanov RU, Gal’tseva OV, Kondratyuk AA, Bezuglov VV, Serbin VI. Influence of the degree of compaction of a reagent powder mixture on solid-phase synthesis of lithium pentaferrite. Inorg Mater. 2007;50:187–92. doi:10.1007/s11182-007-0026-3.
11. Natalinova N, Avdeeva D, Kazakov V, Baranov V, Galtseva O, Ivashkov D. Computer spatially oriented reconstruction of a 3D heart shape based on its tomographic imaging. MATEC Web Conf. 2016;79:01005.
12. Payuk L, Voronina N, Korepanov S, Galtseva O, Natalinova N. Use of creeping speed mode in discrete control and measuring equipment with digital-program control. MATEC Web Conf. 2016;79:01060. doi:10.1051/confmatec/20164805004.
13. Bespal’ko AA, Surzhikov AP, Yavorovich LV, Fedotov PI. Controlling the structural distortions of mine-field rock masses using the parameters of mechanoelectric transformations. Magn Electromagn Methods. 2012;48:221–5. doi:10.1134/S1061830912040043.
14. Chursin YA, Fedorov EM. Methods of resolution enhancement of laser diameter measuring instruments. Opt Laser Technol. 2015;67:86–92. doi:10.1016/j.optlastec.2014.09.017.
15. Diwan YC, Rao K. Optical measurement systems for industrial inspection IV. Proc SPIE. 2005;5856:554–61.
16. Khodier SA. Measurement of wire diameter by optical diffraction. Opt Laser Technol. 2004;36(1):63–7.
17. Hartung GH, Blancq RJ, Lally DA, Krock LP. Estimation of aerobic capacity from submaximal cycle ergometry in women. Med Sci Sports Exerc. 1995;27(3):452–7.
18. Butler DJ, Forbes GW. Fiber-diameter measurement by occlusion of a Gaussian beam. Appl Opt. 1998;37(13):2598–607.
19. Zimmermann E, Dandliker R, Souli N, Krattiger B. Scattering of an off-axis Gaussian beam by a dielectric cylinder compared with a rigorous electromagnetic approach. J Opt Soc Am A. 1995;12:398–403.
20. Chaudhary KP, Sanjid MA, Moitra S. Modeling of laser scanning system to determine its associated uncertainty of measurement. MAPAN J Metrol Soc India. 2010;25:229–37.
21. Kee CW, Ratnam MM. A simple approach to fine wire diameter measurement using a high-resolution flatbed scanner. Opt Laser Technol. 2009;40:940–7.
22. Ericson M. Method and device for measuring a diameter. WO1997021073A1. 1997 Jun 12.
23. Keba Ges Mbh & Co. Cable diameter measuring device. GB2257512A. 1993 Jan 13.
24. Li W, Wang H, Feng Z. Ultrahigh-resolution and non-contact diameter measurement of metallic wire using eddy current sensor. Rev Sci Instrum. 2014;85(8):085001. doi:10.1063/1.4891699.
25. Sprecher Energie Osterreich Gmbh. Apparatus for measuring cable diameters. US5212539A. 1993 May 18.
26. Martinez-Anton JC, Serroukh I, Bernabeu E. Wire diameter determination by interferometry and diffraction. Proc SPIE. 1999;3745. doi:10.1117/12.357787.
27. Microtec S.r.l. Measuring device for wire or filament. EP0626560A2. 1994 Nov 30.
28. Beltronics Inc. Apparatus for measuring diameter of a wire. US4697088A. 1987 Sep 29.
29. Fedorov E, Koba A. Diameter calculation in contactless three-axis measuring devices. MATEC Web Conf. 2016;79:01082. doi:10.1051/matecconf/20167901082.
30. Tamayo Vegas S, Lafdi K. A literature review of non-contact tools and methods in structural health monitoring. Eng Technol Open Access J. 2021;4(1). doi:10.19080/ETOAJ.2021.04.555626.
31. Chang T-S, Huang H-H. Laser scanning measuring apparatus. US8233157B2. 2012 Jul 31.