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Prediction of penetration depth by laser welding using Hablanian plot method and linear multiple regression analysis
Author(s) -
Heya Manabu,
Funayama Hiroto,
Tsuboi Akihiko,
Tagawa Masao
Publication year - 2020
Publication title -
electronics and communications in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.131
H-Index - 13
eISSN - 1942-9541
pISSN - 1942-9533
DOI - 10.1002/ecj.12274
Subject(s) - dimensionless quantity , penetration depth , curve fitting , welding , linear regression , polynomial regression , penetration (warfare) , mathematics , polynomial and rational function modeling , laser , spot welding , regression analysis , data point , polynomial , materials science , statistics , optics , mathematical analysis , mechanics , physics , metallurgy , operations research
In order to prove the validity of prediction of penetration depth using Hablanian plot, we have compared experimentally obtained penetration depths with those predicted by Hablanian plot and linear multiple regression analysis. The experimental results with ∼1200 data points were obtained from bead‐on‐plate welding tests by the laser job shop. Stainless steel plates and four types of industrial CW lasers were used in these tests. From linear multiple regression analysis, it was found that dominating factors were laser power, welding speed, and spot diameter. On the other hand, we can predict the penetration depth using the 3th‐degree polynomial fitting curve (standard curve) expressed by Hablanian plot with two dimensionless parameters. The fitting difference between the standard curve and a data point results in the error of prediction. From Hablanian plot, it showed (1) that this fitting difference was dependent on some factors (such as laser power, welding speed, power density, and penetration depth), (2) that the fitting difference was, respectively, negative and positive in the lower and middle welding speed ranges, and (3) that the penetration depth could be precisely predicted by correcting the fitting difference in welding speed from the standard curve compared to the results predicted by linear multiple regression analysis.