محمد زنگنه نجفي

Surface roughness of machined parts is a crucial parameter in assessing surface quality, influenced by a wide range of factors. This report presents a framework for predicting and classifying surface roughness in milling operations using deep learning techniques applied to data. A model is proposed to predict and classify surface roughness values, utilizing data from 162 experiments conducted on three aluminum alloys: 7075, 6061, and 2024. The data include cutting speed, depth of cut, tool coating type, feed rate, and surface roughness output. In this study, acoustic emission signals recorded during milling tests were converted into 2D images and fed into convolutional neural networks (CNNs) such as ResNet18, ShuffleNet, MobileNet, and CNN-LSTM. Four methods were used to convert the time-series acoustic emission signals: Stacked Sample Permutation Channels (SSPC), Stacked Sampled Channels (SSSC/SSSC*) , which has two subsets, and Recurrence Plots (RP). Among these encoding techniques, SSPC achieved the highest accuracy, over 98%, in most models, attributed to the minimal preprocessing of the signals. MobileNet demonstrated a strong combination of accuracy (96- 98%) and low computational cost. The performance of these methods was also eva‎luated under two noise levels, 40% and 80%, with both zero-mean and non-zero-mean noise conditions. SSPC and SSSC were the most robust against noise, maintaining test accuracy above 90% even in high-noise conditions. Adding acoustic emission data alongside process parameters (cutting speed, depth of cut, feed rate, tool type) as additional inputs can enhance model accuracy and convergence speed, particularly for noisy data. Finally, ShuffleNet and MobileNet were identified as suitable architectures for real-time monitoring due to their high accuracy, noise resistance, and low computational cost. In summary, this study demonstrates the capability of deep convolutional networks combined with innovative signal encoding techniques to accurately predict surface roughness under varying conditions. Based on process parameters, this framework offers a data-driven approach for real-time monitoring and optimization of machining processes.

ماشينكاري , هوش مصنوعي , يادگيري عميق , كيفيت سطح , سيگنال نشر آوايي

Machining , artificial intelligence , deep learning , surface quality , , acoustic emission signals

Mohammad Zangene Najafi

Dr. Seyed Ali Niknam, Dr. Mohammad Shahbazi