Open AccessDevelopment of a bi- directional multi- input- multi-output predictive model for the fused deposition modelling process using co-active adaptive neuro-fuzzy inference system
Author(s) -
Ananda Rabi Dhar,
Dhrubajyoti Gupta,
Shibendu Shekhar Roy
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/1136/1/012007
Subject(s) - computer science , adaptive neuro fuzzy inference system , gaussian process , inference , process (computing) , parametric statistics , neuro fuzzy , set (abstract data type) , artificial intelligence , data mining , gaussian , machine learning , fuzzy logic , algorithm , fuzzy control system , mathematics , statistics , physics , quantum mechanics , programming language , operating system
In the automated manufacturing industries, modelling and prediction of the process parameters of additive manufacturing plays an important role. This paper proposes a computationally intelligent method using coactive-adaptive neuro-fuzzy inference system to establish relationships between the process parameters and the responses, in both forward and backward directions, for the fused deposition modelling process. Experimental data have been statistically analyzed and regression equations have been generated to produce large training samples. The model has been built using six inputs each with non-linear Gaussian membership function distributions, and three responses, each with linear membership function distributions for the forward-directed mapping. Similarly, three inputs and six outputs from the same training data set have been used to formulate the backward-directed inference model. The parametric study for the used back propagation algorithm has been conducted and validation has been accomplished with the optimal settings using actual experimental data.