AI-Based Prediction of Ultrasonic Vibration-Assisted Milling Performance

Mohamed S. El-Asfoury; Mohamed Baraya; Eman El Shrief; Khaled Abdelgawad; Mahmoud Sultan; Ahmed Abass

doi:10.3390/s24175509

Sensors (Aug 2024)

AI-Based Prediction of Ultrasonic Vibration-Assisted Milling Performance

Mohamed S. El-Asfoury,
Mohamed Baraya,
Eman El Shrief,
Khaled Abdelgawad,
Mahmoud Sultan,
Ahmed Abass

Affiliations

Mohamed S. El-Asfoury: Department of Production Engineering and Mechanical Design, Faculty of Engineering, Port Said University, Port Fuad 42526, Egypt
Mohamed Baraya: Department of Production Engineering and Mechanical Design, Faculty of Engineering, Port Said University, Port Fuad 42526, Egypt
Eman El Shrief: Department of Production Engineering and Mechanical Design, Faculty of Engineering, Port Said University, Port Fuad 42526, Egypt
Khaled Abdelgawad: Department of Production Engineering and Mechanical Design, Faculty of Engineering, Port Said University, Port Fuad 42526, Egypt
Mahmoud Sultan: Department of Production Engineering and Mechanical Design, Faculty of Engineering, Port Said University, Port Fuad 42526, Egypt
Ahmed Abass: Department of Production Engineering and Mechanical Design, Faculty of Engineering, Port Said University, Port Fuad 42526, Egypt

DOI: https://doi.org/10.3390/s24175509
Journal volume & issue: Vol. 24, no. 17
p. 5509

Abstract

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The current study aims to evaluate the performance of the ultrasonic vibration-assisted milling (USVAM) process when machining two different materials with high deviations in mechanical properties, specifically 7075 aluminium alloy and Ti-6Al-4V titanium alloy. Additionally, this study seeks to develop an AI-based model to predict the process performance based on experimental data for the different workpiece characteristics. In this regard, an ultrasonic vibratory setup was designed to provide vibration oscillations at 28 kHz frequency and 8 µm amplitude in the cutting feed direction for the two characterised materials of 7075 aluminium alloy (150 BHN) and Ti-6Al-4V titanium alloy (350 BHN) workpieces. A series of slotting experiments were conducted using both conventional milling (CM) and USVAM techniques. The axial cutting force and machined slot surface roughness were evaluated for each method. Subsequently, Support Vector Regression (SVR) and artificial neural network (ANN) models were built, tested and compared. AI-based models were developed to analyse the experimental results and predict the process performance for both workpieces. The experiments demonstrated a significant reduction in cutting force by up to 30% and an improvement in surface roughness by approximately four times when using USVAM compared to CM for both materials. Validated by the experimental findings, the ANN model accurately and better predicted the performance metrics with RMSE = 0.11 µm and 0.12 N for Al surface roughness and cutting force. Regarding Ti, surface roughness and cutting force were predicted with RMSE of 0.12 µm and 0.14 N, respectively. The results indicate that USVAM significantly enhances milling performance in terms of a reduced cutting force and improved surface roughness for both 7075 aluminium alloy and Ti-6Al-4V titanium alloy. The ANN model proved to be an effective tool for predicting the outcomes of the USVAM process, offering valuable insights for optimising milling operations across different materials.

Published in Sensors

ISSN: 1424-8220 (Online)
Publisher: MDPI AG
Country of publisher: Switzerland
LCC subjects: Technology: Chemical technology
Website: http://www.mdpi.com/journal/sensors

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