Artificial neural network modelling of cold-crack resistance of high strength low alloy steel 950A

• Main Authors: Velumani Manivelmuralidaran, Krishnasamy Senthilkumar
• Format: Article
• Language: English
• Published: Wiley 2019-01-01
• Series: The Journal of Engineering
• Subjects: plates (structures); impact strength; cracks; welds; neural nets; sensitivity analysis; arc welding; welding; alloy steel; cold cracking resistance; high strength low; welded joints; artificial neural network model; ANN; plate welding; gas metal arc welding process; identified process parameters; preheating temperature; oxide particle content; heat input; weld metal; propagation model; experimental data; given input parameters; predicted observed values; oxide particles content; artificial neural network modelling; cold-crack resistance; current 950.0 A
• Online Access: https://digital-library.theiet.org/content/journals/10.1049/joe.2018.5277

The objective of the study is to predict the cold cracking resistance of high strength low alloy 950A welded joints using an artificial neural network (ANN) model. A bead on plate welding is carried out using the gas metal arc welding process. The identified process parameters for the ANN are preheating temperature, oxide particle content, and heat input. The impact strength of the weld metal is considered as the output parameter. A feed-forward back propagation model with ten neurons in the hidden layer is developed to predict the impact strength of the weld metal. The neural network model is created, trained, and tested with a set of experimental data. The proposed model correctly predicted the impact strength of the given input parameters. The predicted value of the impact strength is in agreement with the experimental data. The error percentage between the predicted and observed values is <5% and the root mean square error value is 2.2%. Sensitivity analysis is performed to identify the significance of input parameters. It is evident that the preheating temperature contributes 50.04%, oxide particles content contributes 37.15%, and heat input contributes 12.81% to impact strength.