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2 Journal of Machine Engineering

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Ebrahimi Araghizad Arash, Tehranizadeh Faraz, Kilic Kemal, Budak Erhan

Journal of Machine Engineering

2023

Vol. 23, No. 4

18--32

In this paper a novel approach for monitoring tool-related faults in milling processes by utilizing process simulation-based machine learning algorithms, specifically Random Forest algorithms, for fault detection is presented. In order to train machine learning models in tool condition monitoring, laboratory tests have traditionally been required. This method eliminates the need for costly, time-consuming laboratory tests. The training process has been simplified by utilizing analytical simulation data and provides a more cost-effective solution by leveraging analytical simulation data. Based on the results of this study, the proposed approach has been demonstrated to be 94% accurate at predicting tool-related faults, demonstrating its potential to serve as an efficient and viable alternative to conventional methods. These findings have been supported by actual measurement data, with a notable accuracy rate of 93% in the predictions. Furthermore, the results indicate that process simulation-based machine learning algorithms will have a significant impact on the tools condition monitoring and the efficiency of manufacturing processes more generally. To further enhance the capabilities of the proposed fault monitoring system, process-related and machine-related faults will be investigated in future research. Several machine learning algorithms will be explored as well as additional data sources will be integrated in order to enhance the accuracy and reliability of fault detection.

Improved machining performance through turn-milling

Berenji Kaveh Rahimzadeh, Budak Erhan

Journal of Machine Engineering

2022

Vol. 22, No. 2

5--17

As a multi-axis metal cutting operation, turn-milling has the combined characteristics of conventional turning and milling operations involving rotating workpiece and milling tool with linear feed motion in the workpiece axis direction. Although turn-milling offers many advantages in machining complex and hard-to-cut materials due to its flexible kinematics, the process presents specific challenges. The main objective of this paper is to present an overview of turn-milling operations from different perspectives. In this regard, first, the advantages of turn-milling in terms of tool life are presented. An analytical approach is given based on process kinematics to achieve better surface quality and productivity simultaneously. Additionally, the uncut chip geometry and the cutting force models are presented with experimental verification.