[1]
Energy Parameters of Weld Formation Process in MIG-MAG Welding [Electronic resource] / D.A. Chinakhov [et al.] // Materials Science Forum: Scientific Journal. – 2018. – Vol. 927: Materials and Processing Technology II. – [P. 99-105].
DOI: 10.4028/www.scientific.net/msf.927.99
Google Scholar
[2]
Mao, Cong, et al. Analysis of Influence Factors for the Contact Length between Wheel and Workpiece in Surface Grinding., Key Engineering Materials, vol. 359–360, Trans Tech Publications, Ltd., Nov. 2007, p.128–132. Crossref,.
DOI: 10.4028/www.scientific.net/kem.359-360.128
Google Scholar
[3]
C. Zhang, L. Zhou, S. Huang, Finite Element Simulation of Temperature Field during Grinding of SiCp/Al Composites,, Key Engineering Materials, Vol. 487, p.70, (2011).
DOI: 10.4028/www.scientific.net/kem.487.70
Google Scholar
[4]
Li W.S. Tang B.G. The development of China's steel, welding and welding materials as well as the problems required to be focused on. (2008).
Google Scholar
[5]
Yi J. et al. Investigations of Microstructure and Phase Composition of Different Self-shield Flux-cored Wire Slag System. Materials Science Forum. 2016. 850. P. 748-754.
DOI: 10.4028/www.scientific.net/msf.850.748
Google Scholar
[6]
Kudo T. HSS rolls: carbide morphology and properties // Rolls 2000 + Advances in Mill Roll Tcclmology, Conf. Papers – Birmingham, UK, April 12 – 14, 1999. P. 71–80.
Google Scholar
[7]
Forged semi-HSS and HSS rolls designed for cold rolling reduction mills / C. Gaspard, S. Bataille, et al // 41st Mechanical Working and Steel Processing Conf. Proceed. – Baltimore, M. D., USA, October 24–27, 1999. P. 559–565.
Google Scholar
[8]
G. Hoyle , Bsc, FIM , CEng , Metallurgist consultant , High Speed Steels , Butterworth & Co. (Publishers) Ltd. (1988).
Google Scholar
[9]
M. Šebek, L. Falat, M. Orečný, I. Petryshynets, F. Kováč, M. Černík, Abrasive wear resistance of modified X37CrMoV5-1 hot work tool steel after conventional and laser heat treatment,, International Journal of Materials Research, Vol. 109, p.460, (2018).
DOI: 10.3139/146.111624
Google Scholar
[10]
I.Kim, J. Lee, J. Malekani, P. Yarlagadda, Prediction of GMA Welding Characteristic Parameter by Artificial Neural Network System,, Advanced Materials Research, Vol. 1061-1062, p.481, (2014).
DOI: 10.4028/www.scientific.net/amr.1061-1062.481
Google Scholar