Browsing by Author "Rafighi M."

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    Machinability of hardened AISI S1 cold work tool steel using cubic boron nitride
    (Sharif University of Technology, 2021) Şahinoglu A.; Rafighi M.
    Recently, hard turning has become an interesting method for manufacturers as an alternative to the grinding process due to its superior features such as good surface quality, good productivity, lower production costs, lower power consumption, and shorter processing time. Despite its considerable benefits, hard turning is a difficult process that needs advanced cutting inserts such as ceramics and cubic boron nitride. However, these cutting inserts are costly and should be used properly by choosing appropriate machining parameters. In the presented work, the hard turning process was employed to investigate the machinability of AISI S1 cold work tool steel using a cubic boron nitride insert. The relation between machining parameters, namely depth of cut, cutting speed, and feed rate, on the responses such as power consumption, surface roughness, and machining sound was found using a full factorial orthogonal array of response surface methodology. In addition, analysis of variance was used to identify the most important machining parameters that inuence output parameters. Based on the results, surface roughness was dominantly affected by feed rate, whereas sound and power consumption were inuenced by all machining parameters, especially cutting speed and feed rate. Good agreement between the experimental and predicted values was observed. © 2021 Sharif University of Technology.
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    Investigation of tool wear, surface roughness, sound intensity, and power consumption during hard turning of AISI 4140 steel using multilayer-coated carbide inserts
    (University of Kuwait, 2021) Şahinoǧlu A.; Rafighi M.
    The present study investigated the machinability aspects, namely, surface roughness, sound intensity, power consumption, and crater wear, during dry turning of hardened AISI 4140 steel (63 HRC) employing (TiCN/Al2O3/TiN) multilayer-coated carbide inserts under dry cutting condition. The relationship between machining parameters and output parameters was determined using the Taguchi design. The analysis of variance was employed to evaluate the contributions of input parameters on output parameters. The main effect plots illustrated the impacts of cutting speed, feed, and depth of cut on response variables. Results show that the feed was the most dominant factor that affects surface roughness. Increasing the feed value increases the surface roughness, power consumption, and sound intensity. In the other part of this study, the constant values for feed (0.3 mm/rev), depth of cut (0.7 mm), and cutting speed (150 m/min) have been selected to evaluate a tool life that has 0.3 mm crater wear criteria. The results indicated that multilayer-coated carbide inserts presented very good tool life and reached 0.3 mm in 90 min. The experimental study results showed that chipping and abrasion were found to be the significant wear mechanism during hard turning of AISI 4140 steel. The cutting speed was the most significant parameter on the tool wear, although high cutting speed results the good surface finish but adversely increases the tool crater wear. © 2021 University of Kuwait. All rights reserved.
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    EXPERIMENTAL ASSESSMENT and TOPSIS OPTIMIZATION of CUTTING FORCE, SURFACE ROUGHNESS, and SOUND INTENSITY in HARD TURNING of AISI 52100 STEEL
    (World Scientific, 2022) Rafighi M.; Özdemir M.; Şahinoǧlu A.; Kumar R.; Das S.R.
    In this work, initially, the raw AISI 52100 bearing steel was heat-treated to obtain 40 HRC and 45 HRC workpiece hardness. Further, dry hard turning tests were carried out to study the impact of workpiece hardness (H), cutting speed (v), feed (f), and depth of cut (a) on cutting force (Fy), surface roughness (Ra), and sound intensity (SI). An economically viable PVD-coated carbide turning tool was implemented for the experiments. The Taguchi L18 (2-3 mixed level) design of experiments was employed to establish the experimental plan in order to save the experimental time, energy, and cost of manufacturing. The results disclosed that the feed has the prevailing consequence on surface roughness with a 96.3% contribution, while it also significantly affects the cutting force with a contribution of 13.8%. The contribution of cutting speed and workpiece hardness on the cutting force was reported as 48.3% and 35.1%, respectively. Higher workpiece hardness required more energy for plastic deformation as a result the cutting force increases with leading hardness. The sound intensity was dominantly influenced by depth of cut (53.3%) and cutting speed (40%). Finally, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was performed to determine the optimum machining parameters. According to the TOPSIS, the optimum level of cutting parameters was predicted as 40 HRC hardness (H), 150m/min cutting speed (V), 0.15mm/rev feed (f), and 0.1mm depth of cut (a) while the optimal result of Fy, SI, and Ra were noted as 27.66N, 70.7dB, and 0.86μm individually. © 2022 World Scientific Publishing Company.
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    An investigation on cutting sound effect on power consumption and surface roughness in CBN tool-assisted hard turning
    (SAGE Publications Ltd, 2022) Şahinoğlu A.; Rafighi M.; Kumar R.
    In machining activities, sound emission is one of the key factors toward the operator's health and safety. Sound generation during cutting is the outcome of the interaction between tool and work. The intensity of sound greatly influences the cutting power consumption and surface finish obtained during machining. Therefore, the current work emphasized the analysis of sound emission, power consumption, and surface roughness in hard turning of AISI 4340 steel using a CBN tool which was rarely found in the literature. Response surface methodology (RSM) and artificial neural network (ANN) techniques were utilized to formulate the model for each response. The results indicated that the maximum value of input parameters exhibited the highest level of sound due to the creation of vibration in the machine and tool. Higher sound level indicates the generation of lower power consumption but at the same instant surface roughness was leading with increment in sound level. The feed rate exhibited the utmost noteworthy consequence on surface quality with 87.71% contribution. The cutting power can be decreased by choosing the high level of cutting parameters. The RSM and ANN have a good correlation with experimental data, but the accuracy of the ANN is better than the RSM. © IMechE 2021.
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    Analysis and optimisation of the cutting parameters based on machinability factors in turning AISI 4140 steel
    (Taylor and Francis Ltd., 2022) Özdemir M.; Şahinoğlu A.; Rafighi M.; Yilmaz V.
    AISI 4140 alloy steel has high abrasion resistance, toughness, torsional, and fatigue strength. Different types of this alloy with different hardness are used to manufacture gears, crankshafts, collars, jigs, and milling spindles. In this study, turning tests were carried out on AISI 4140 steel using coated carbide inserts considering Taguchi L9 orthogonal array at three different cutting speeds, feed rates, and cutting depths. The output parameters were selected as cutting forces, surface roughness, current and sound intensity. According to the ANOVA results, feed rate was the most effective parameter on the cutting force and surface roughness. As the feed rate increases, the cutting force and surface roughness value enhance. The feed rate was also the most important factor affecting the current with 81.61% contribution, followed by cutting depth with 12.67% contribution. The cutting depth with 66.95% contribution has the highest impact on the sound intensity. It was followed by a feed rate with 26.07% contribution. According to the optimisation results, the experimental and the estimated values ⁣⁣were significantly close to each other. © 2022 Canadian Institute of Mining, Metallurgy and Petroleum.
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    Modeling and optimization of hard turning: predictive analysis of surface roughness and cutting forces in AISI 52100 steel using machine learning
    (Springer-Verlag Italia s.r.l., 2024) Kumar R.; Rafighi M.; Özdemir M.; Şahinoğlu A.; Kulshreshta A.; Singh J.; Singh S.; Prakash C.; Bhowmik A.
    This study addresses the critical need for high-strength, corrosion-resistant materials in renewable energy, biomedical and maritime applications, necessitating effective heat treatment processes. Focusing on AISI 52100 steel, the research employs finish hard turning with coated carbide inserts under dry cutting conditions. Five machine learning methods are applied to model surface roughness (Ra) and cutting forces (Fx, Fy, Fz) using a Taguchi L36 orthogonal array. Results indicate SVM, XGB, DT, and XGB are superior algorithms for Ra, Fx, Fy, and Fz prediction. Key findings highlight feed rate predominant influence (96.55%) on surface roughness, while depth of cut significantly affects cutting forces. Optimal cutting parameters, 0.1 mm depth of cut, 0.15 mm/rev feed rate, 160 m/min cutting speed, 0.4 mm nose radius, and 40.9 HRC hardness are identified via response surface methodology (RSM) and desirability function. The study underscores the importance of optimizing cutting parameters to enhance surface quality and machining efficiency in challenging material processing scenarios. © The Author(s), under exclusive licence to Springer-Verlag France SAS, part of Springer Nature 2024.