Soleymani Mehdi, Hadad Mohammadjafar
School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran P.O. Box 14155-6619, Iran.
Department of Mechanical Engineering, College of Engineering and Technology, University of Doha for Science and Technology, Doha P.O. Box 24449, Qatar.
Micromachines (Basel). 2025 Feb 27;16(3):274. doi: 10.3390/mi16030274.
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.
人工智能（AI）模型通过执行当前由人类处理的任务，在各个领域展示了它们的能力。然而，这些模型的训练面临一些限制，例如需要足够的数据。本研究建议使用声学信号作为训练数据，因为与传统方法相比，这种方法提供了一种更简单的方式来获取大型数据集。声学信号包含有关过程行为的有价值信息。我们研究了从声学数据中提取有用特征的能力，期望通过多标签分类器而不是多类分类器分别预测标签。本研究重点关注放电车削（EDT），它是放电加工（EDM）和车削的混合工艺，是一个具有多个影响参数的复杂工艺。记录了EDT过程中产生的声音并将其用作训练数据。在将数据输入人工神经网络（ANN）模型之前，对声音进行了预处理，以检查用于特征提取的参数的效果。研究的参数包括采样率、快速傅里叶变换（FFT）窗口长度、跳步长度和梅尔频率倒谱系数（MFCC）的数量。该研究旨在考虑最高精度、召回率和F1分数来确定最佳预处理参数。结果表明，与在Python包中使用默认设置值不同，有必要研究预处理参数以找到实现最大分类性能的最佳值。多标签分类模型的良好结果表明，有可能在接收单个数据的同时检测过程的各个方面，这在监测中非常有益。结果还表明，将采样率、FFT窗口长度、跳步长度和MFCC数量分别设置为4500 Hz、1024、256和80时，可以获得最高的预测分数。
