Open AccessArtificial neural network (ANN) based prediction of process parameters in additive manufacturing
Author(s) -
Hardik D Sondagar,
Seema Bhadauria,
Vin Sharma
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1136/1/012026
Subject(s) - sigmoid function , artificial neural network , selective laser melting , mean squared error , perceptron , matlab , multilayer perceptron , process (computing) , computer science , activation function , algorithm , artificial intelligence , mathematics , laser , statistics , optics , physics , operating system
In recent years, selective laser melting (SLM), a part of additive manufacturing (AM) is one of the most encouraging ones that permit fabricating metallic parts from metal powder with complex geometry. Diversities in thesecycle boundaries become an imperative system to improve the nature of the outcome.Cycleboundaries, for example, laser power, scan speed, hatch spacing, layer height used as input parameters and have a significant impact on the mechanical property taken as an output parameter of the manufactured part. The Artificial Neural Network (ANN) model includes a multi-layer perceptron (MLP) learning algorithm named as Levenberg-Marquardt and tangent sigmoid function consider as preparing and testing functions respectively utilizing MATLAB toolkit. Ideal cycle boundaries are attained dependent on the mean square error function (MSE) and correlation coefficient(R 2 ).