Prediction of temperature and HAZ in thermal-based processes with Gaussian heat source by a hybrid GA-ANN model

Shahri, Hamid Reza Fazli; Mahdavinejad, Ramezanali

doi:10.1016/j.optlastec.2017.09.024

Cited by 19 publications

(1 citation statement)

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“…The ultimate tensile strength of the friction stir welded joints were predicted from the fed parameters such as spindle speed, plunge force, and welding speed [26]. The power, focal diameter, and radiation time of the thermal-based process that used Gaussian heat source were sufficient to determine the unknown heat affected zone and temperature using hybrid genetic algorithm-artificial neural network (GA-ANN) model [27]. In a gas metal arc welding process with CMT metal transfer mode, the bead characteristics such as bead width, bead height, penetration depth, and dilution area were predicted using the welding speed, peak welding current and heat input [28].…”

Section: Introductionmentioning

confidence: 99%

Investigation of feed-forward back propagation ANN using voltage signals for the early prediction of the welding defect

Thekkuden

Mourad

2019

SN Appl. Sci.

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The research paper investigates the prediction capability of the artificial neural network for weld quality assessment from the captured voltage signals in a gas metal arc welding process. The bead-on-plate welds and v-groove welds were made on SA 516 grade 70 material by altering different parameters such as stickout distance, gas flow rate and travel speed. The voltage signals of each weld were captured using a data acquisition system having 8000 Hz data acquisition rate. The descriptive statistics of the voltage data such as mean, standard error, median, mode, standard deviation, sample variance, kurtosis, skewness, minimum and maximum corresponding to bead-on-plate welds and v-groove welds were used for training and testing the neural network respectively. The quality of the weld was assessed by the visual inspection, and from control charts plotted using voltage data. Overall classification accuracy of 94.7% was achieved in the training process. The feed-forward back propagation neural network predicted the quality of test v-groove welds accurately with a 90.9% prediction rate. The results proved that the developed method is promising for the immediate and early prediction of the weld quality.

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Section: Introductionmentioning

confidence: 99%