EXPERIMENTAL APPLICATIONS OF ARTIFICIAL NEURAL NETWORKS IN ENGINEERING PROCESSING SYSTEM
Artificial Neural Networks along with Image Processing Systems have proven to be successful, particularly in the domains of mathematics, science and technology. They have gained quite notable advantages beyond classical learning, as their usable engagement are observable in many fields of scientific environment related to the relevant systems. This paper presents a model for identifying the small components parts. The model may be significant in various industries mainly in engineering processing system areas. The objective of the study is to apply Artificial Neural Networks (ANN) in Image Processing System (IPS) with feed forward structure to detect, and recognize different parts or any other environment products on a moving conveyor bel. In the proposed model, we have used appropriate method of edge detection. The edge detection realizes artificial neural network with noise. The paper emphasizes the implementation of the model considering functionality, parts images, accurate detection and identifying the different components. The result shows that the model can detect moving objects (products of many kinds) accurately on the conveyor belt with very high success rate and sort them accordingly for further processes.
Keywords: process engineering, model, ANN, detection, products