Comparison of Machine Learning Methods for Improving Assembly Operations

A comparison of four machine learning methods applied to computer prediction of assembly parameters is presented: neural networks, Support Vector Machines and Random Forest. Prediction is based on using coordinate measurement data of separate parts. Using the considered methods, the end run-out of an assembly unit consisting of a shaft, retainer and rotor disk of an aircraft engine turbine were calculated.