Abstract
Artificial intelligence (AI) models have demonstrated their capabilities across various fields by performing tasks that are currently handled by humans. However, the training of these models faces several limitations, such as the need for sufficient data. This study proposes the use of acoustic signals as training data as this method offers a simpler way to obtain a large dataset compared to traditional approaches. Acoustic signals contain valuable information about the process behavior. We investigated the ability of extracting useful features from acoustic data expecting to predict labels separately by a multilabel classifier rather than as a multiclass classifier. This study focuses on electrical discharge turning (EDT) as a hybrid process of electrical discharge machining (EDM) and turning, an intricate process with multiple influencing parameters. The sounds generated during EDT were recorded and used as training data. The sounds underwent preprocessing to examine the effects of the parameters used for feature extraction prior to feeding the data into the ANN model. The parameters investigated included sample rate, length of the FFT window, hop length, and the number of mel-frequency cepstral coefficients (MFCC). The study aimed to determine the optimal preprocessing parameters considering the highest precision, recall, and F1 scores. The results revealed that instead of using the default set values in the python packages, it is necessary to investigate the preprocessing parameters to find the optimal values for the maximum classification performance. The promising results of the multi-label classification model depicted that it is possible to detect various aspects of a process simultaneously receiving single data, which is very beneficial in monitoring. The results also indicated that the highest prediction scores could be achieved by setting the sample rate, length of the FFT window, hop length, and number of MFCC to 4500 Hz, 1024, 256, and 80, respectively.