Deep learning-based supervised and unsupervised neural networks for analysing the characteristics of powder composite preforms. (2nd November 2021) - Pavanasam, Radha; G., Chandrasekaran; Selvakumar, N.
ABSTRACT: This highlights the importance of deep learning in analysing the characteristics of composite preforms in the manufacturing of mechanical components in the industries. The raw data of Powder Metallurgy Lab are having highly non-linear, noisy and interrelated data. To process this kind of data, both shallow and deep neural network models of supervised learning have been considered for predicting the properties of composites. This work proves that the deep forward neural networks have good generality in recognizing even 100% of independent unseen data. The composites under the manufacturing process may have pores, which will reduce the strength and life time of the materials. Hence the sintering and extrusion of material processes are being followed to reduce the strength of pores. The presence of pores are examined by generating Scanning Electron Microscope (SEM) images for Cu–(5–20%)W composite preforms with a density of 94% without destroying the materials before and after the sintering and extrusion process. The pores are analyzed using unsupervised neural network model with Deep learning paradigm. These deep-learning based supervised and unsupervised models will guide the Lab Engineers to avoid the expensive experimentation and risky environment while preparing sintered composite preforms.