Surface Roughness Prediction Of BTA Drilling Based On Artificial Neural Network

With the development of the times, people are more and more attention on the deepprocessing, it occupies a pivotal position in the field of machining. It is much difficultmachined and each processing has heavy workload, processing time is often long, themanufacturing process is an important process. However, people have increasingly highdemand for the product, how to obtain a high surface quality has become a focus of attentionand key issues. In this paper, how to control the surface roughness were studied by usingneural network model, it provides a new way of thinking to predict the surface roughness andimprove the surface quality and optimized cutting parameters for the deep processing field, ithas important theoretical significance and practical value.In this paper, the factors of affecting the surface roughness make the orthogonal test andanalyze their significant influence, which draws on the influence of surface roughness onBTA deep hole drilling for the study, it provides a better prediction of surface roughnesstheory.In this paper, it builds predictive network models of surface roughness on BTA drillingby introducing the neural network theory into the deep processing field. It determines thenetwork structure by dynamically adjusting the hidden layer nodes, compare the predictionaccuracy and convergence capability model can meet the requirements.Research shows that the prediction error of the selected3-16-1three-layer BP networkmodel are less than3%, it can accurately predict the surface roughness. This makes itpossible to select the optimal cutting parameters，quantitative forecast of the surfaceroughness and solve the problems of BTA drilling to control surface quality on line，it hasimportant theoretical significance and practical value.