2021
DOI: 10.1007/s11340-021-00765-y
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Determination of Residual Stresses in Cylindrical Components by the Hole-drilling Method

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Cited by 4 publications
(7 citation statements)
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“…Practically speaking, these parts are flat enough to have insignificant errors using a flat‐plate assumption. Recently, Halabuk and Návrat [ 43 ] studied the influence of the radius of curvature on the measurement errors, and results in Table 1 show that radius of curvature of 100 mm introduced an average error on the order of 0.5%. In our studied case, the radii of curvature were larger than 2841 mm; therefore, the error of the residual stresses measurement should be less than 0.5% and can be ignored.…”
Section: Resultsmentioning
confidence: 99%
“…Practically speaking, these parts are flat enough to have insignificant errors using a flat‐plate assumption. Recently, Halabuk and Návrat [ 43 ] studied the influence of the radius of curvature on the measurement errors, and results in Table 1 show that radius of curvature of 100 mm introduced an average error on the order of 0.5%. In our studied case, the radii of curvature were larger than 2841 mm; therefore, the error of the residual stresses measurement should be less than 0.5% and can be ignored.…”
Section: Resultsmentioning
confidence: 99%
“…Before training, the data set was randomly divided into two subsets: the training subset, which contained 85% of the states and was used to compute the gradient and update the weights and biases of the network, and the validation subset, which contained 15% of the states and was used to monitor data overfitting. The training process, 3 which involves adjusting network weights and biases to optimize network performance, was performed in MATLAB software by the Levenberg-Marquardt backpropagation training algorithm with the mean squared error performance function [13]. Since each network training session starts with different initial weights and biases and different divisions of the data set into the training and validation subsets, different solutions can be found for the same problem.…”
Section: Neural Network In Correction Proceduresmentioning
confidence: 99%
“…The outputs from the transfer function y (i) k become the inputs of neurons in the next layer. The above-mentioned algorithm can be written in matrix form as: where ( 14) a (1) = w (1) ⋅ X + b (1) (15) a (2) = w (2) ⋅ y (1) + b (2) (16) a (3) = w (3) ⋅ y (2) + b (3)…”
Section: Neural Network In Correction Proceduresmentioning
confidence: 99%
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“…These methodologies (through-hole and incremental blind hole) are covered by the ASTM E837 standard [4,5], which provides a reference for the implementation of the method, including best practices and calculation instructions to obtain the calibration coefficients. The implementation of this measurement technique can, however, require attention to certain details that are disregarded by the standard, such as the plasticity effect [6][7][8], possible small thickness of the plate in which the residual stress needs to be measured [9], the possibility of a chamfer at the bottom of the hole [10], and imperfect local planarity of the specimen, such as when the residual stress measurement is performed at the external surface of a cylindrical shaft [11]. Another source of uncertainty is the hole's eccentricity; however, this can be corrected provided that the eccentricity itself is accurately known [12][13][14][15].…”
Section: Introductionmentioning
confidence: 99%