Neural models for predicting hole diameters in drilling processes

Abstract

The control of industrial manufacturing processes is of great economic importance due to the ongoing search to reduce raw materials and labor wastage. Indirect manufacturing operations such as dimensional quality control generate indirect costs that can be avoided or reduced through the use of control systems. The use of intelligent manufacturing systems, which is the next step in the monitoring of manufacturing processes, has been researched through the application of artificial neural networks in the last two decades. In this work, artificial intelligence systems were trained to estimate the diameter of holes in precision drilling processes. The methodology involved the use of an acoustic emission sensor, a three-dimensional dynamometer, an accelerometer, and a Hall effect sensor to monitor the drilling process. The method was applied to test specimens composed of packages of Ti6Al4V titanium alloy and 2024-T3 aluminum alloy sheets, which are widely employed in the aerospace industry. The collected signals were processed and the data were organized and fed into artificial intelligence systems, which consisted of an artificial multilayer perceptron (MLP) neural network and the adaptive neuro-fuzzy inference system (ANFIS). The results indicated that the MLP network was the most efficient of the two artificial intelligence techniques. The results also demonstrated a strong potential for the industrial application of the models.