Browsing by Author "Tehranizadeh, Faraz"

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Citation Count: 0
Chatter Stability of Orthogonal Turn-Milling Process in Frequency and Discrete-Time Domains
(Asme, 2024) Berenji, Kaveh Rahimzadeh; Tehranizadeh, Faraz; Budak, Erhan
As the industry seeks better quality and efficiency, multitasking machine tools are becoming increasingly popular owing to their ability to create complex parts in one setup. Turn-milling, a type of multi-axis machining, combines milling and turning processes to remove material through simultaneous rotations of the cutter and workpiece with the translational feed of the tool. While turn-milling can be advantageous for large parts made of hard-to-cut materials, it also offers challenges in terms of surface form errors and process stability. Because tool eccentricity and workpiece rotation lead to more complexity in process mechanics and dynamics, traditional milling stability models cannot predict the stability of turn-milling processes. This study presents a mathematical model based on process mechanics and dynamics by incorporating the unique characteristics of the orthogonal turn-milling process to avoid self-excited chatter vibrations. A novel approach was employed to model time-varying delays considering the simultaneous rotation of the tool and workpiece. Stability analysis of the system was performed in both the discrete-time and frequency domains. The effects of eccentricity and workpiece speed on stability diagrams were demonstrated and validated through experiments. The results show that the tool eccentricity and workpiece speed alter the engagement geometry and delay in the regeneration mechanism, respectively, leading to significant stability diagram alterations. The proposed approach offers a comprehensive framework for the stability of orthogonal turn-milling and guidance for the selection of process conditions to achieve stable cuts with enhanced productivity.
Citation Count: 1
Improving milling force predictions: A hybrid approach integrating physics-based simulation and machine learning for remarkable accuracy across diverse unseen materials and tool types
(Elsevier Sci Ltd, 2024) Araghizad, Arash Ebrahimi; Pashmforoush, Farzad; Tehranizadeh, Faraz; Kilic, Kemal; Budak, Erhan
The prediction of milling forces has been addressed using a range of methods, including physics-based models and data-driven approaches. Analytical predictions that rely on mathematical models may not always provide the desired level of accuracy, whereas data-based approaches require extensive testing. A hybrid approach, which combines physics-based simulation results with machine-learning algorithms that integrate measurement data from a limited number of tests, can be employed as an effective alternative to improve the accuracy of milling force predictions. Through the implementation of this novel milling hybrid model, the accuracy of the milling force predictions is significantly improved to levels that cannot be achieved with process models alone. In this approach, a trained machine learning algorithm using simulation results and a small set of test data is a valuable tool for predicting milling forces under various conditions with high accuracy. One of the greatest advantages of this method is that the ML model trained on Al7075-T6, Steel 1050, and Ti6Al4V materials also improved the prediction accuracy for completely different materials, such as Inconel 625. This is mainly due to the way materials are defined in the machine learning system, that is, by their thermomechanical properties, which allow different materials to be input without additional testing. Furthermore, this method can be used to predict the cutting forces of special milling tools (i.e., serrated edges with cylindrical and tapered end mills with flat, ball, and round noses) with a high level of accuracy. It is demonstrated that the accuracy of the cutting force prediction in various cases can be increased up to 98 % (R2) through the implementation of this method. According to statistical error analysis, the majority of deviations between the improved model predictions and measured results fall within a narrow band of -5 % to 5 %, encompassing 90 % of the observations. It is important to note that this high prediction accuracy was achieved with very limited test data and simulation results.
Citation Count: 0
Milling Process Monitoring Based on Intelligent Real-Time Parameter Identification for Unmanned Manufacturing
(Elsevier, 2024) Araghizad, Arash Ebrahimi; Tehranizadeh, Faraz; Pashmforoush, Farzad; Budak, Erhan
This study addresses the critical need for intelligent process monitoring in unmanned manufacturing through real-time fault detection. The proposed hybrid approach, which is focused on overcoming the limitations of existing methods, utilizes machine learning (ML) for precise parameter identification in real-time to detect deviations. The ML system is developed using extensive data obtained from simulations based on enhanced force models also achieved through ML. Demonstrating over 96 % accuracy in real-time predictions, the method proves applicable for diverse unmanned manufacturing applications, including monitoring and process optimization, emphasizing its adaptability for industrial implementation using CNC controller signals. (c) 2024 CIRP. Published by Elsevier Ltd. All rights reserved.
Citation Count: 0
Special Milling Tools for Improving Productivity
(Nova Science Publishers, Inc., 2024) Tehranizadeh,F.; Barzegar,Z.; Budak,E.
During the milling operations a reduction in cutting forces combined with having stable cutting process conditions improves efficiency, productivity, and part quality. At this point, milling tools with special geometries such as variable pitch or/and variable helix tools, end mills with serrated or crest-cut edge forms can provide significant advantages to reach these goals. For this purpose, it is important to be able to design the cutting tool geometry considering process mechanics and dynamics. In this chapter, we aim to explore various aspects related to the design and performance of these special milling tools in a comparative manner. Our discussion will cover topics such as the tools' geometry, design, and performance, along with the underlying principles that govern their behavior in the milling process. © 2024 Nova Science Publishers, Inc. All rights reserved.