Neural Networks applied to the Monitoring of Drilling Processes in Geological Material Samples

摘要

An Artificial Neural Network (ANN) is a mathematical computational model that, in a limited way, attempts to mimic the structure and functioning of biological neurons which, broadly speaking, take electrical input signals and produce an output signal called “response”. By combining many artificial neurons, they can have the ability to "learn" specific patterns in signal analysis and predict results. These networks can be used in a wide range of applications such as object or signal classification, function approximation, autonomous driving, speech recognition, image or text analysis and processing, etc. Self-Organized Map (SOM) or Kohonen ANN perform a weighted summation of multidimensional input data, which are individually affected by a numerical value called weight. Then, in a competition between neurons, a winner is selected, and a neighborhood weight is adjusted to generate an output more similar to the input. This generates a two-dimensional map where the information is shown organized in areas that have similar characteristics. The weights of each neuron are modified continuously and in different ways during several "epochs" (epoch is the name given to each step of weighting adjustment) until the response error is minimized. This process is called training, where specifically for this network, it is an unsupervised type of learning. In this work, SOM ANNs are used to study acoustic emission (AE) signals from a drilling process of geological materials arranged in layers bonded with cementitious material. With these elements, Laboratory size specimens (320x320x400 mm) were made. Drilling was carried out with 62 mm diameter drills, with single-wire tungsten carbide inserts, making four holes per multilayer specimen, and the AE was measured during each drilling. After a detailed training process and rigorous data selection, this ANN showed the ability to recognize the different layers of each specimen from the processing of the AE signals obtained during the drilling process.