Machine Learning based Bayesian Network Models for Reverse Engineering Data Optimization

For mechanical components, "reverse engineering" refers to the process of developing a geometric CAD model based on the dimensions of an existing object. This method can be used to create new items. It would be wonderful if the cloud data could be automatically divided up into the surface patches that make up the cloud, and then an exact solid model could be produced. The use of a neural network is what is responsible for searching and detecting features in the initial scan data. The proliferation of data sources and quantities, as well as the growing acceptance of Bayesian modelling methodologies, has caused artificial intelligence researchers to focus a lot of attention on the problem of causal discovery. Investigations into causes and effects, as well as probabilistic modelling, can both profit from the application of causal Bayesian networks (CBN). In the field of mechanical engineering, "reverse engineering" refers to the process of creating a geometric CAD model by employing measurements taken from an already-existing artefact. In an ideal scenario, the cloud data would be automatically divided up into surface patches, after which a solid model would be constructed. In order to accomplish this objective, a neural network is utilised to search for and identify features contained within the initial scan data set.