Browsing by Author "Arezoo, Behrooz"

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    A Review in machining-induced residual stress
    (Oum-El-Bouaghi University, 2022) Soori, Mohsen; Arezoo, Behrooz
    Due to friction, chip forming, and the induced heat in the cutting area, produced parts by using machining operations have residual stress. Residual stresses caused by machining processes have a major effect on the fatigue life of machined components, which can shorten their service life. In order to increase the performance of machined parts in real-world applications, such as fatigue life, corrosion resistance, and component distortion, residual stress should be investigated and minimized. As a result, predicting and controlling residual stresses caused by machining operations is important in terms of quality enhancement of machined parts. This paper reviews the recent achievements in the machining-induced residual stress in order to be analyzed and decreased. Different methods of the residual stress measurement Destructive Methods, Semi-Destructive Methods and Non-Destructive Test (NDT) Methods are reviewed and compared in order to be developed. In order to minimize residual stress in machined parts, the study examines the effects of machining process parameters, high-speed machining conditions, coolant, cutting tool wear, edges, and radius on residual stress. Analytical and semi-analytical modeling, numerical and FEM simulation techniques of residual stress are reviewed to include advanced methods of residual stress modeling methodology to predict residual stress in machined components. Residual stress in various alloys such as AL alloys, biomedical implant materials, hard to cut materials such as nickel-based alloys, Titanium Based Alloys, Inconel Based Alloys, and stainless-steel alloys is investigated in order to provide efficient residual stress minimization methods in machined components. It has been realized that evaluating and analyzing recent advances in published papers will contribute to develop the research field.
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    Cutting tool wear prediction in machining operations, a review
    (Oum-El-Bouaghi University, 2022) Soori, Mohsen; Arezoo, Behrooz
    In the machining process, tool wear is an unavoidable reason for tool failure. Tool wear has an impact on not just tool life but also the quality of the finished product in terms of dimensional accuracy and surface integrity. Tool wear is a significant element in the annual cost of machining. It happens when the tool-work contact zone experiences abrupt geometrical damage, frictional force, and heat generation. It's essential to accurately evaluate tool wear during machining so that the cutting tool can be replaced before the workpiece surface sustains significant damage. The capacity to assess tool wear is crucial for ensuring high-quality workpieces. Artificial neural network, Deep learning and Machine learning systems, heat generation analysis, image data processing, finite element method and gaussian process are used in order to accurately predict the tool wear during machining operations. In this paper, cutting tool wear prediction in machining operations is reviewed in order to be analyzed and minimized. The main purpose of the study is to provide a useful resource for researchers in the field by presenting an overview of current research on cutting tool wear prediction in machining processes. As a consequence, the research area can be progressed by reading and assessing existing achievements in published articles in order to provide new ideas and methodologies in prediction and minimization of tool wear during machining operations.
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    Effect of cutting parameters on tool life and cutting temperature in milling of AISI 1038 carbon steel
    (Oum-El-Bouaghi University, 2023) Soori, Mohsen; Arezoo, Behrooz
    During chip formation process of machining operations, thermo-mechanical loads are generated which can decrease life of cutting tool and quality of machined components. As a result, analysing the cutting temperatures and cutting tool life during milling operations can enhance productivity in process of part manufacturing using CNC machine tools. To predict the cutting tool life and cutting temperature during machining operations of AISI 1038 Carbon Steel, an application of the virtual machining system is developed. The impact of machining parameters such as cutting speed, feed rate and depth of cut on the cutting tool life and temperature are investigated in order to enhance productivity of milling operations. The modified Johnson–Cook model is used to investigate the combined influence of strain rate and deformation temperature on yield stress during alloy milling operations. Finite element analysis of milling operations is implemented to obtain the cutting temperature of the milling tool during the chip formation process. Then, cutting tool life during milling operations is predicted in order to be analyzed and maximized. The results of virtual machining system in prediction of cutting temperature as well as life of cutting tool are compared with the experimental results in order to validate the developed methodology in the study. So, an advanced virtual machining system is developed in the study in order to decrease cutting temperatures and increase cutting tool life in terms of efficiency enhancement of part production using milling operation.