Akademik Veri Yönetim Sistemi
Advances in Sustainable Machining and Manufacturing Processes, Kishor Kumar Gajrani,Arbind Prasad,Ashwani Kumar, Editör, CRC, New York , New York, ss.189-204, 2022
Artificial neural networks (ANNs) are computer systems developed with
the aim of automatically realizing abilities such as creating and
discovering new information by taking an example from the human brain
without any assistance. It emerged as a result of mathematical modeling
of the learning process. ANNs are used for prediction, classification,
data association, clustering, filtering, interpretation, optimization,
and control thanks to their nonlinear structures and continuities. In
addition, it is used in many fields such as image processing (face,
motion, and object recognition), automotive, aviation, and manufacturing
industries, as well as natural language processing (voice assistant,
emotion analysis) computational biology (DNA sequencing, tumor
detection, drug discovery). Because ANNs can process highly nonlinear
situations, many parameters and incomplete information. An ANN structure
consists of inputs, weights, addition function, activation function,
and outputs. Learning abilities improve thanks to the parameters entered
for each operation. Experimental outputs, such as surface roughness,
cutting force, and tool wear, can be successfully predicted according to
independent variables for each machining technique in manufacturing.
ANN models used in different machining techniques according to
independent variables are presented in this chapter. In addition, the
relationships between the current studies in the literature and the
mathematical models obtained in these studies and the experimental
outputs are discussed.