Vol. 26 No. 4 2024 3 EDITORIAL COUNCIL EDITORIAL BOARD EDITOR-IN-CHIEF: Anatoliy A. Bataev, D.Sc. (Engineering), Professor, Rector, Novosibirsk State Technical University, Novosibirsk, Russian Federation DEPUTIES EDITOR-IN-CHIEF: Vladimir V. Ivancivsky, D.Sc. (Engineering), Associate Professor, Department of Industrial Machinery Design, Novosibirsk State Technical University, Novosibirsk, Russian Federation Vadim Y. Skeeba, Ph.D. (Engineering), Associate Professor, Department of Industrial Machinery Design, Novosibirsk State Technical University, Novosibirsk, Russian Federation Editor of the English translation: Elena A. Lozhkina, Ph.D. (Engineering), Department of Material Science in Mechanical Engineering, Novosibirsk State Technical University, Novosibirsk, Russian Federation The journal is issued since 1999 Publication frequency – 4 numbers a year Data on the journal are published in «Ulrich's Periodical Directory» Journal “Obrabotka Metallov” (“Metal Working and Material Science”) has been Indexed in Clarivate Analytics Services. Novosibirsk State Technical University, Prospekt K. Marksa, 20, Novosibirsk, 630073, Russia Tel.: +7 (383) 346-17-75 http://journals.nstu.ru/obrabotka_metallov E-mail: metal_working@mail.ru; metal_working@corp.nstu.ru Journal “Obrabotka Metallov – Metal Working and Material Science” is indexed in the world's largest abstracting bibliographic and scientometric databases Web of Science and Scopus. Journal “Obrabotka Metallov” (“Metal Working & Material Science”) has entered into an electronic licensing relationship with EBSCO Publishing, the world's leading aggregator of full text journals, magazines and eBooks. The full text of JOURNAL can be found in the EBSCOhost™ databases.
OBRABOTKAMETALLOV Vol. 26 No. 4 2024 4 EDITORIAL COUNCIL EDITORIAL COUNCIL CHAIRMAN: Nikolai V. Pustovoy, D.Sc. (Engineering), Professor, President, Novosibirsk State Technical University, Novosibirsk, Russian Federation MEMBERS: The Federative Republic of Brazil: Alberto Moreira Jorge Junior, Dr.-Ing., Full Professor; Federal University of São Carlos, São Carlos The Federal Republic of Germany: Moniko Greif, Dr.-Ing., Professor, Hochschule RheinMain University of Applied Sciences, Russelsheim Florian Nürnberger, Dr.-Ing., Chief Engineer and Head of the Department “Technology of Materials”, Leibniz Universität Hannover, Garbsen; Thomas Hassel, Dr.-Ing., Head of Underwater Technology Center Hanover, Leibniz Universität Hannover, Garbsen The Spain: Andrey L. Chuvilin, Ph.D. (Physics and Mathematics), Ikerbasque Research Professor, Head of Electron Microscopy Laboratory “CIC nanoGUNE”, San Sebastian The Republic of Belarus: Fyodor I. Panteleenko, D.Sc. (Engineering), Professor, First Vice-Rector, Corresponding Member of National Academy of Sciences of Belarus, Belarusian National Technical University, Minsk The Ukraine: Sergiy V. Kovalevskyy, D.Sc. (Engineering), Professor, Vice Rector for Research and Academic Aff airs, Donbass State Engineering Academy, Kramatorsk The Russian Federation: Vladimir G. Atapin, D.Sc. (Engineering), Professor, Novosibirsk State Technical University, Novosibirsk; Victor P. Balkov, Deputy general director, Research and Development Tooling Institute “VNIIINSTRUMENT”, Moscow; Vladimir A. Bataev, D.Sc. (Engineering), Professor, Novosibirsk State Technical University, Novosibirsk; Vladimir G. Burov, D.Sc. (Engineering), Professor, Novosibirsk State Technical University, Novosibirsk; Aleksandr N. Korotkov, D.Sc. (Engineering), Professor, Kuzbass State Technical University, Kemerovo; Dmitry V. Lobanov, D.Sc. (Engineering), Associate Professor, I.N. Ulianov Chuvash State University, Cheboksary; Aleksey V. Makarov, D.Sc. (Engineering), Corresponding Member of RAS, Head of division, Head of laboratory (Laboratory of Mechanical Properties) M.N. Miheev Institute of Metal Physics, Russian Academy of Sciences (Ural Branch), Yekaterinburg; Aleksandr G. Ovcharenko, D.Sc. (Engineering), Professor, Biysk Technological Institute, Biysk; Yuriy N. Saraev, D.Sc. (Engineering), Professor, V.P. Larionov Institute of the Physical-Technical Problems of the North of the Siberian Branch of the RAS, Yakutsk; Alexander S. Yanyushkin, D.Sc. (Engineering), Professor, I.N. Ulianov Chuvash State University, Cheboksary
Vol. 26 No. 4 2024 5 CONTENTS OBRABOTKAMETALLOV TECHNOLOGY Manikanta J.E., Ambhore N., Shamkuwar S., Gurajala N.K., Dakarapu S.R. Investigation of vegetable-based hybrid nanofl uids on machining performance in MQL turning........................................................................................... 6 Dama Y.B., Jogi B.F., Pawade R., Kulkarni A.P. Impact of print orientation on wear behavior in FDM printed PLA Biomaterial: Study for hip-joint implant...................................................................................................................... 19 GrinenkoA.V., ChumaevskyA.V., Sidorov E.A., Utyaganova V.R.,AmirovA.I., Kolubaev E.A. Geometry distortion, edge oxidation, structural changes and cut surface morphology of 100mm thick sheet product made of aluminum, copper and titanium alloys during reverse polarity plasma cutting...................................................................................... 41 Somatkar A., Dwivedi R., Chinchanikar S. Comparative evaluation of roller burnishing of Al6061-T6 alloy under dry and nanofl uid minimum quantity lubrication conditions............................................................................................... 57 Karlina Yu.I., Konyukhov V.Yu., Oparina T.A. Assessment of the quality and mechanical properties of metal layers from low-carbon steel obtained by the WAAM method with the use of additional using additional mechanical and ultrasonic processing..................................................................................................................................................... 75 EQUIPMENT. INSTRUMENTS Yusubov N.D., Abbasova H.M. Systematics of multi-tool setup on lathe group machines............................................... 92 Toshov J.B., Fozilov D.M., Yelemessov K.K., Ruziev U.N., Abdullayev D.N., Baskanbayeva D.D., Bekirova L.R. Increasing the durability of drill bit teeth by changing its manufacturing technology......................................................... 112 Pospelov I.D. Investigation of the distribution of normal contact stresses in deformation zone during hot rolling of strips made of structural low-alloy steels to increase the resistance of working rolls..................................................... 125 Ablyaz T.R., Blokhin V.B., Shlykov E.S., Muratov K.R., Osinnikov I.V. Manufacturing of tool electrodes with optimized confi guration for copy-piercing electrical discharge machining by rapid prototyping method.......................... 138 MATERIAL SCIENCE Shubert A.V., Konovalov S.V., Panchenko I.A. A review of research on high-entropy alloys, its properties, methods of creation and application.................................................................................................................................................. 153 Syusyuka E.N., Amineva E.H., Kabirov Yu.V., Prutsakova N.V. Analysis of changes in the microstructure of compression rings of an auxiliary marine engine.......................................................................................................... 180 Dudareva A.A., Bushueva E.G., Tyurin A.G., Domarov E.V., Nasennik I.E., Shikalov V.S., Skorokhod K.A., Legkodymov A.A. The eff ect of hot plastic deformation on the structure and properties of surface-modifi ed layers after non-vacuum electron beam surfacing of a powder mixture of composition 10Cr-30B on steel 0.12 C-18 Cr-9 Ni-Ti............................................................................................................................................................................. 192 Boltrushevich A.E., Martyushev N.V., Kozlov V.N., Kuznetsova Yu.S. Structure of Inconel 625 alloy blanks obtained by electric arc surfacing and electron beam surfacing........................................................................................... 206 Sablina T.Y., Panchenko M.Yu., Zyatikov I.A., Puchikin A.V., Konovalov I.N., Panchenko Yu.N. Study of surface hydrophilicity of metallic materials modifi ed by ultraviolet laser radiation........................................................................ 218 EDITORIALMATERIALS 234 FOUNDERS MATERIALS 243 CONTENTS
OBRABOTKAMETALLOV Vol. 26 No. 4 2024 TECHNOLOGY Comparative evaluation of roller burnishing of Al6061-T6 alloy under dry and nanofl uid minimum quantity lubrication conditions Avinash Somatkar1, 2, a, Rashmi Dwivedi 2, b, Satish Chinchanikar 3, c,* 1 Department of Mechanical Engineering, Vishwakarma Institute of Information Technology, Pune, 411048, India 2 Mechanical Engineering Department, Sri Satya Sai University of Technology & Medical Science, Sehore, Madhya Pradesh, 466001, India 3 Department of Mechanical Engineering, Vishwakarma Institute of Technology, Pune, 411037, India a https://orcid.org/0000-0002-2885-2104, avinash.somatkar@viit.ac.in; b https://orcid.org/0000-0002-9755-5330, rashmidwivedi29@gmail.com; c https://orcid.org/0000-0002-4175-3098, satish.chinchanikar@viit.ac.in Obrabotka metallov - Metal Working and Material Science Journal homepage: http://journals.nstu.ru/obrabotka_metallov Obrabotka metallov (tekhnologiya, oborudovanie, instrumenty) = Metal Working and Material Science. 2024 vol. 26 no. 4 pp. 57–74 ISSN: 1994-6309 (print) / 2541-819X (online) DOI: 10.17212/1994-6309-2024-26.4-57-74 ART I CLE I NFO Article history: Received: 30 September 2024 Revised: 10 October 2024 Accepted: 14 October 2024 Available online: 15 December 2024 Keywords: Roller burnishing Al6061-T6 Nanofl uid MQL Optimization ABSTRACT Introduction. Roller burnishing is one of the most popular methods for improving the surface quality of a workpiece, increasing its wear resistance, microhardness and corrosion resistance. During the processing, the workpiece is compressed and smoothed under the pressure of hardened roller. Purpose of the work. The results of the research show that the introduction of minimum quantity lubrication (MQL) during roller burnishing makes it possible to increase the effi ciency of the process by reducing friction and improving lubrication. Studies have shown that the use of nanofl uids under MQL conditions improves the machining performance. However, very little attention has been paid to the roll burnishing of Al6061-T6 alloy under nano minimum quantity lubrication (NFMQL) conditions. The methods of investigation. In light of this, this study compares the performance of roll burnishing of Al6061-T6 alloy under dry friction conditions and NFMQL conditions. The microhardness, roundness, and surface roughness are evaluated, modeled, and optimized in the study by considering the cutting speed, feed rate, and number of passes. Based on the experimental results, mathematical models are established to predict the surface roughness, microhardness, and roundness deviation. Results and Discussion. The developed models of surface roughness, microhardness and roundness deviation show the R-square value higher than 0.9, which allows these models to be confi dently used to predict the studied responses under dry friction conditions and under NFMQL conditions within the parameter domain selected in this work. According to this study, the machining performed in four passes at a cutting speed of 357 rpm and a tool feed of 0.17 mm/rev can obtain the lowest roundness deviation (3.514 μm), the best microhardness (130.19 HV) and the lowest surface roughness (0.64 μm). Further, the study shows that increasing the number of passes (more than four) does not lead to a signifi cant improvement in surface roughness or microhardness. However, it leads to a slight increase in roundness deviation. Therefore, it is recommended to use a maximum of four passes during roll burnishing of Al6061-T6 aluminum alloy specimens under dry friction conditions to achieve optimal results. The obtained results imply that roller burnishing can eff ectively improve the overall surface quality and hardness of the workpiece. In addition, roller burnishing is regarded as an aff ordable method to enhance the functionality and strength of the machined parts by reducing the occurrence of surface defects such as scratches and cracks. It is found that the surface roughness decreases with the increase of the cutting speed. However, it is observed to increase under both dry friction and NFMQL conditions when the cutting speed is increased to 360–380 rpm. Moreover, it is found to decrease with the increase of the feed and the number of passes. But after three or four passes at a feed rate of 0.2–0.25 mm/rev, a noticeable increase in the surface roughness is observed. It is noticed that with the increase of the feed, the microhardness and the roundness deviation increase. In addition, as the number of passes increases, the roundness deviation decreases and the microhardness increases. The number of passes under dry friction condition and feed rate under NFMQL rolling has signifi cant eff ects on the surface roughness. The cutting speed seems to have the greatest eff ect on the microhardness, followed by feed rate and the number of passes. On the other hand, the eff ect of increasing microhardness under NFMQL conditions seems to be stronger. Under dry friction condition, the cutting speed has a signifi cant eff ect on the roundness deviation, and under NFMQL conditions, the feed rate has an eff ect. For citation: Somatkar A., Dwivedi R., Chinchanikar S. Comparative evaluation of roller burnishing of Al6061-T6 alloy under dry and nanofl uid minimum quantity lubrication conditions. Obrabotka metallov (tekhnologiya, oborudovanie, instrumenty) = Metal Working and Material Science, 2024, vol. 26, no. 4, pp. 57–74. DOI: 10.17212/1994-6309-2024-26.4-57-74. (In Russian). ______ * Corresponding author Satish Chinchanikar, D.Sc. (Engineering), Professor Vishwakarma Institute of Technology, 411037, Pune, India Tel.: +91-2026950401, e-mail: satish.chinchanikar@viit.ac.in
OBRABOTKAMETALLOV TECHNOLOGY Vol. 26 No. 4 2024 Introduction The search for new processing methods that can achieve high surface quality and improve mechanical properties is currently of great interest. One such method is roller burnishing. It is aimed at improving the surface quality and dimensional accuracy of various metals. This process uses a hard roller to smooth out surface irregularities, resulting in a shiny fi nish. It can also make the material harder at the micro level [1]. Many industries use aluminum alloy 6061-T6 (Al 6061-T6) because it is strong yet lightweight, easy to work with, and does not rust. But getting the best surface quality and mechanical properties from Al 6061-T6 can be challenging using old-school fi nishing methods. Roller burnishing has shown promise in addressing these issues. It can smooth out rough surfaces and improve dimensional accuracy [2]. Minimum quantity lubrication (MQL) is a lubrication method in which a small amount of lubricant is applied directly to the cutting zone. This method reduces friction, extends tool life, and produces a smoother surface. All this is achieved without the environmental and fi nancial problems that come with using large quantities of lubricant. Recent studies have shown good results when combining MQL with various machining processes, including turning and milling [3–6]. Kurkute and Chavan [7] optimized surface roughness and microhardness during roller burnishing of Al63400 alloy. In their study, feed was considered as a signifi cant parameter aff ecting surface roughness. Patel and Brahmbhatt [8] found that spindle speed and burnishing depth were the most important parameters for improving microhardness by 28 % compared to pre-machined surfaces. A group of researchers performed roller burnishing by varying the process parameters such as feed, depth of cut, cutting speed, and number of passes. Most of the studies designed the experiments using the central composite design of response surface methodology. Some studies considered the cutting speed as the dominant parameter aff ecting the surface roughness, and some studies found that the feed signifi cantly aff ected the surface roughness. Some studies reported that the depth of cut signifi cantly aff ected the surface roughness, and the cutting speed and number of passes signifi cantly aff ected the microhardness. Some studies reported the interaction eff ect of burnishing force and number of passes on the surface roughness. The cutting speed, feed, and number of passes signifi cantly aff ected the surface roughness and microhardness. However, it can be noted that the signifi cance of process parameters aff ecting the process response can be assessed as varying depending on the process parameters, the workpiece material and the cooling conditions. Prasad and John [9] studied the roller burnishing process on Mg-SiC composite material. In their study, experiments were conducted by varying the cutting speed, feed, force, and number of passes. The authors observed a decrease in surface roughness at a speed of 171 rpm, a feed of 0.18 mm/rev, a force of 21 N, and three passes. The group of researchers observed changes in the surface and metallurgical textures due to the development of high contact stresses and an increase in plastic deformation of the surface layer of the component during roller burnishing [10]. The study showed an improvement in surface fi nish at lower burnishing speed and higher depth of penetration [11]. Okada et al. [12] analyzed the performance of roller burnishing under minimum quantity lubrication. In their study, an increase in workpiece hardness by 126–323 HV was observed. A group of researchers performed roller burnishing using diff erent coolingmethods such as cryogenic burnishing and using kerosene as a coolant. The group observed an increase in surface hardness and the surface fi nish when burnishing under MQL conditions and using kerosene as a cutting fl uid [13–15]. The group evaluated the surface integrity by varying parameters such as speed, feed, number of passes, and cooling conditions, namely fl ood cooling, MQL, cryogenic cooling, and hybrid cooling. The results showed that the use of cryogenic cooling increased the strength of the material, while the use of hybrid coolant decreased the surface roughness. It was noted that microhardness depends to a small extent on the type of cooling conditions. From the reviewed literature, it is evident that the roller burnishing process is eff ective in improving the overall surface quality and hardness of the workpiece. In addition, roller burnishing is considered as an aff ordable method to improve the functionality and reliability of the machined parts by reducing the occurrence of surface defects such as scratches and cracks. Studies have shown that the use of MQL in roller burnishing provides the opportunity to further improve the process by improving lubrication and reducing
OBRABOTKAMETALLOV Vol. 26 No. 4 2024 TECHNOLOGY friction. Over the past decade, studies have shown higher machining performance using nanofl uids under MQL conditions [16–19]. However, very few attempts have been made to process Al6061-T6 alloy by roller burnishing using nanofl uid under NFMQL process conditions. From this point of view, this study comparatively evaluates the roller burnishing of Al6061-T6 alloy in dry and nanofl uid conditions under MQL cutting condition. The study evaluated, simulated and optimized the microhardness, roundness and surface roughness by considering the factors such as cutting speed, feed and number of passes. Mathematical models for predicting the surface roughness, microhardness and roundness error were developed based on the experimental results. The chemical composition of the material, conditions of the forming process and details of the roller burnishing tool are presented in the next section. The third section discusses the development of experimental-based mathematical models for predicting the surface roughness, microhardness and roundness of burnished workpiece under both cooling conditions. In the fourth section, the parametric eff ects of roller burnishing on the responses namely surface roughness, microhardness and roundness of roller burnished workpiece under both cooling conditions are comparatively discussed. Then, the optimized process parameters for minimum surface roughness and better microhardness and surface roundness for both cooling conditions are presented. Finally, the important results of the present study and the scope for future research in this area are presented. Materials and Design This study uses aluminum alloy 6061 (Al6061-T6), which is often used for general purposes. Due to its strength-to-weight ratio, corrosion resistance, and weldability, this alloy is popular in manufacturing processes and is suitable for various structural components. It is a precipitation-hardening aluminum alloy. The two most important components are silicon and magnesium. Weldability is the main advantage of aluminum alloy 6061. The aerospace industry often uses aluminum alloy 6061 due to its exceptional strength and light weight. Due to its composition, it can also be used for automotive and marine parts. The selected specimen has a diameter of 30 mm and a length of 50 mm across all surfaces. Table 1 shows the characteristics and chemical composition of aluminum alloy 6061. Ta b l e 1 Chemical composition of Al6061-T6 alloy Element Al Cu Cr Mg Mn Si Zn Fe Ti Percentage 95.8 0.15 0.2 1.1 0.15 0.75 0.25 0.19 0.15 A single-roller burnishing tool with a carbide roller was used in this study. The carbide roller burnishing tool is versatile and can be used on a variety of machines for diff erent applications. Its ability to restore and extend the tool life makes it a cost-eff ective solution for achieving high-quality surface fi nishes. The carbide roller is springloaded in both axial directions to maintain proper pressure throughout the burnishing process. By regrinding or lapping the worn carbide roller, it can be restored and its service life can be extended. The carbide roller tool can be used on CNC lathes, turret lathes or conventional lathes and is suitable for all external surfaces of shafts, tapered shafts, radii, shoulders, etc. Burnishing of the machined surface is possible up to 0.1–0.2 μm. Fig. 1 shows the burnishing tool used in this study. Fig. 1. Roller burnishing tool used in the present study
OBRABOTKAMETALLOV TECHNOLOGY Vol. 26 No. 4 2024 A constant burnishing depth of 0.5 mm was maintained while varying the feed, cutting speed and the number of passes in the experiments without coolant and with nanofl uid as coolant under MQL conditions. Alumina nanoparticles (Al2O3) were combined with a vegetable-based sunfl ower oil base fl uid to create the nanofl uid. Surface roughness, microhardness and roundness error, three main characteristics that contribute to the impact of stability performance, were studied using the design of experiments (DOE) method. All responses were analyzed and empirical models were developed using the central composite design (CCD). The experiments were designed using the central composite rotatable design (CCRD) test matrix, which has an alpha value of 1.6817. Five levels were used to vary each numerical parameter: center point, plus and minus 1 (factorial points) and plus and minus alpha (axial points). In this work, twenty roller burnishing tests were conducted under NFMQL and dry conditions with diff erent process parameters to construct models of surface roughness, microhardness and roundness error. Table 2 lists the coded levels along with the corresponding actual cutting parameter values. Ta b l e 2 Coded levels and corresponding actual cutting parameters Parameters Levels for alpha value −1.6817 −1 0 +1 +1.6817 Cutting speed (V) (rpm) 100 200 300 400 500 Feed (f) (mm/rev) 0.1 0.15 0.2 0.25 0.3 Number of passes (N) 0.5 1 1.5 2 2.5 Taylor Hobson Talysurf, Surtronic Duo and an off -line surface roughness measuring device were used to determine the average values of surface roughness. The surface roughness was measured at three equally spaced points around the perimeter of the workpiece to obtain a statistically signifi cant value. The surface quality assessment was performed accurately and consistently using this approach. A bridge type CMM (Manufacturer: Zeiss, Model: Contura, Range: 1,200×800×800 mm) was used to test the roundness. Geometrical errors were determined by measuring the roundness in twelve parts of a calibrated area using a millesimal dial indicator having a measuring range of 12.5 mm, a scale division value of 0.001 mm and a maximum permissible error (MPE) of 4 μm. Additionally, a Vickers microhardness tester was used to evaluate the microhardness using a 136° diamond indenter at 100 grams and a 20-second dwell time. Using surface roughness, microhardness tests and roundness measures together allowed a thorough examination of the workpiece properties. Results and Discussion In this section, the impact of roller burnishing process parameters on the process responses under dry and NFMQL cutting conditions is discussed based on the established regression equations. The curves showing the diff erent responses are plotted by varying one of the input parameters while keeping the other parameters constant in order to understand the physics of the process and the interaction eff ects of the cutting parameters on the diff erent responses. It also gives the contribution of the cutting parameters to the diff erent responses. Finally, the optimization of the process responses in roller burnishing of Al6061-T6 alloy is considered using the desirability function method. The cutting speed, feed and number of passes (input parameters) were varied during the experiments. Table 3 shows the experimental matrix and the results of the largest roundness error (roundness error), microhardness and surface roughness in roller burnishing of Al6061-T6 alloy under dry and NFMQL cutting
OBRABOTKAMETALLOV Vol. 26 No. 4 2024 TECHNOLOGY conditions. The experimental results of roller burnishing of Al6061-T6 alloy under dry cutting conditions are given in [20]. Response Surface Methodology (RSM) was the main method used to analyze the experimental results. Finding the region of interest where the response(s) reach its optimal or near-optimal value was another goal of using RSM in addition to investigating the response(s) in the entire factor space. In order to understand the physics of the process, an analysis of the experimental data was performed to create regression equations for surface roughness (Ra), microhardness (HV), and roundness error (Re). Using Stat-Ease Design Expert® (version 7.0), the regression approach was used to determine the values of the coeffi cients in the equation. The equations developed for the responses in terms of actual values of surface roughness, microhardness and roundness error under dry conditions can be found in [20], and the equations for determining the responses when using nanofl uid under MQL conditions are given below: 1.4209 0.00064 4.0943 0.2224 0.00275 0.00019 0.025 Ra V f N Vf VN fN = − − − − − + + 6 2 2 2 3.11 10 13.4545 20.042 V f N − + × + + (1) 85.3181 0.213 40.1136 6.3238 0.325 0.00625 47.5 HV V f N Vf VN fN = + + − − + + − 2 2 2 0.0002 90.91 0.3522 V f N − − − (2) Ta b l e 3 Roller burnishing experimental matrix with responses Cutting speed (V) (rpm) Feed (f) (mm/rev) No. of passes Surface roughness (Ra) (μm) Microhardness (HV) Roundness error ( Re) (μm) Dry NFMQL Dry NFMQL Dry NFMQL 300 0.2 3 0.81 0.62 117 128 7.7 4.8 200 0.15 2 0.82 0.68 114 120 9.6 5.6 200 0.15 4 0.89 0.69 116 118 8.6 4.8 200 0.25 2 0.92 0.77 116 122 5.4 8.8 200 0.25 4 0.9 0.78 125 131 8.7 7.7 400 0.15 2 0.94 0.78 118 129 10.1 3.4 400 0.15 4 0.84 0.71 111 131 1.6 3.2 400 0.25 2 0.97 0.81 110 126 8.4 9.2 400 0.25 4 0.79 0.75 113 136 2.9 7.4 300 0.2 3 0.81 0.61 117 128 8.4 5.5 300 0.2 3 0.81 0.61 117 128 8.6 5.7 100 0.2 3 0.92 0.72 112 108 13.2 9 500 0.2 3 0.93 0.75 104 131 4.2 6.1 300 0.1 3 0.94 0.70 123 121 1.5 2.8 300 0.3 3 0.96 0.79 124 132 2 9.2 300 0.2 1 0.95 0.83 123 119 8.7 7.7 300 0.2 5 0.86 0.73 125 133 4 3.6 300 0.2 3 0.83 0.65 117 125 6.9 5.2 300 0.2 3 0.82 0.61 113 128 8.3 5.4 300 0.2 3 0.81 0.62 118 131 8.7 5.9
OBRABOTKAMETALLOV TECHNOLOGY Vol. 26 No. 4 2024 4 Re 13.142 0.058 3.78 0.18 0.0975 1.3 10 V f N Vf VN − = − − − + − × − 5 2 2 2 4.75 5.43 10 62.27 0.0682 fN V f N − − + × + + (3) Using the analysis of variance (ANOVA) method, the adequacy of the resulting equations was verifi ed. The data point variation proportion is measured by the coeffi cient of multiple determinations, or R-squared. A correlation coeffi cient (R-squared) that is between −1 and +1 is always ideal. If R is really close to +1, the equation is important. Ameasure of how much of the variance around the mean is explained by a model is called adjusted R-squared. A measure of the predictive accuracy of the model for the response value is the predicted R-squared. It is considered reasonable agreement when the adjusted and predicted R-squared values are within around 0.20 of each other. Otherwise, there may be a problem with the model or the data. The signal-to-noise ratio, or the range in the expected response relative to the corresponding error, is what is called adequate precision. Four or more is ideal value. The ANOVA for surface roughness, microhardness and roundness error during roller burnishing under dry condition can be referred to [20], and that under NFMQL cutting condition is given in Table 4. The ANOVA for the investigated responses under dry condition is also mentioned in Table 4 for comparative evaluation. The ANOVA results for surface roughness under dry condition and NFMQL condition show model F-values of 46.91 and 19.51, respectively, which means that the models are signifi cant. The “Prob > F” values less than 0.05 indicate that the model terms are signifi cant. The signifi cant model terms observed for surface roughness under dry cutting conditions are V×f, V×N, f×N, V 2, f 2, N 2, and for NFMQL the signifi cant model terms are V, f, N, V×N, V 2, f 2, N2. Ta b l e 4 ANOVA for investigated responses under dry [20] and NFMQL cutting conditions Factors Surface roughness (Ra) (μm) Microhardness (HV) Roundness error (Re) (μm) Dry NFMQL Dry NFMQL Dry NFMQL R-squared 0.9769 0.9461 0.9152 0.9377 0.9407 0.9609 Adj. R-Squared 0.956 0.89765 0.8389 0.8816 0.8873 0.9258 Pred. R-Squared 0.8472 0.848529 0.855 0.8389 0.8933 0.7421 Adeq. Precision 19.328 12.74978 15.464 16.5655 16.002 18.2847 Model F-value 46.91 19.51 11.99 16.71 17.62 27.35 The ANOVA results for microhardness show that the model F-values are 11.99 and 16.71 for dry and NFMQL conditions, which means that the models are signifi cant. There is only a 0.03 % chance that such a large “model F-value” may be due to noise. In this case, V, V×f, V×N, f×N, V 2, f 2, N 2 for dry conditions and V, f, N, V×f, f×N, V 2 for NFMQL conditions are signifi cant model terms. And the ANOVA results for roundness show that the model F-values are 17.62 and 27.35 for dry and NFMQL conditions, which means that the models are signifi cant. In this case, V, N, V×N, f×N, f 2 for dry conditions and V, f, N, V×f, V 2 for NFMQL conditions are signifi cant model terms. Each model created for dry and NFMQL cutting conditions has an R-squared value above 0.9, indicating the proportion of variation in the data points. Therefore, during the roller burnishing of Al6061-T6 alloy, the microhardness, surface roughness and roundness error can be accurately predicted by the established empirical equations. To improve understanding, two-dimensional graphs are created for NFMQL cutting settings by adjusting the feed, speed, and number of passes using the derived equations 1–3. In order to facilitate comparison and better understanding, curves are also plotted for the studied responses under dry conditions using the
OBRABOTKAMETALLOV Vol. 26 No. 4 2024 TECHNOLOGY models derived in [20]. Plotting the curves for surface roughness, microhardness, and roundness error involves changing one input parameter while maintaining the other two constant. Using a feed value of 0.2 mm/rev and three passes, the variation in surface roughness with cutting speed is shown in Fig. 2, a. Fig. 2, b shows the dependence of surface roughness on feed at a cutting speed of 300 rpm and three passes. And Fig. 2, c shows the dependence of surface roughness on the number of passes at a cutting speed of 300 rpm and a feed of 0.2 mm/rev. Comparing the NFMQL cutting condition with the dry cutting condition, lower levels of surface roughness are observed. It can also be observed that as the cutting speed increases to 360–380 rpm, the surface roughness decreases before increasing. In addition, it decreases with the increase of feed and the number of passes. However, an increase in surface roughness can be seen beyond feeds of 0.2–0.25 mm/rev and 3–4 passes. From Fig. 2, b, it can be seen that the optimum responses with varying feed can be obtained. The minimum surface roughness and roundness error can be obtained by using the feed values in the range of 0.18–0.22 mm/rev and the cutting speed and number of passes of 250–350 rpm and three, respectively. Fig. 3, a and Fig. 4, a depict the variation of microhardness and roundness error, respectively, depending on the cutting speed, obtained at a constant feed of 0.2 mm/rev and three passes. It can be seen that the microhardness increases with the cutting speed. However, this eff ect was more prominent for the NFMQL cutting condition. Higher microhardness values can be seen for the NFMQL cutting condition. It can be seen that the microhardness decreases beyond the cutting speed of 280–300 rpm. On the other hand, it can be seen that the roundness error decreases with the increase of the cutting speed (Fig. 4, a). However, it can be seen that it increases beyond the cutting speed of 300–350 m/min. The lower roundness error values can be seen when roller burnishing under NFMQL cutting conditions. Fig. 3, b and Fig. 4, b show the variation of microhardness and roundness error, respectively, depending on the feed, plotted using the cutting speed value of 300 rpm and three passes. Fig. 3, c and Fig. 4, c show the variation of microhardness and roundness error, respectively, depending on the number of passes, plotted using the cutting speed value of 300 rpm and the feed of 0.2 mm/rev. a b c Fig. 2. Surface roughness varying with a) cutting speed, b) feed, and c) number of passes a b c Fig. 3. Microhardness varying with a) cutting speed, b) feed, and c) number of passes
OBRABOTKAMETALLOV TECHNOLOGY Vol. 26 No. 4 2024 a b c Fig. 4. Roundness error varying with a) cutting speed, b) feed, and c) number of passes It can be seen that the maximum microhardness, lower surface roughness and roundness error can be obtained by roller burnishing under NFMQL cutting condition compared with dry roller burnishing. The microhardness and roundness error can increase with the increase of feed. And the increase of microhardness and decrease of roundness error can be seen with the increase of the number of passes. It can be seen that the increase of feed leads to contradictory responses for surface roughness and microhardness. A compromise between roundness error and microhardness and lower surface roughness can be obtained by using a feed value in the range of 0.18–0.22 mm/rev. The surface roughness can decrease with the increase of the number of passes. However, no signifi cant benefi t is observed in reducing the surface roughness beyond the use of four passes. The roundness error can be minimized by using more passes. Similarly, the maximum microhardness can be obtained by using more passes. Tables 5–7 show the ANOVA fi ndings for the F-values of surface roughness, microhardness, and roundness error for roller burnishing under dry and NFMQL cutting conditions, respectively. Referred to in [20], the ANOVA examined the responses for roller burnishing under dry cutting conditions. The F-value is highlighted to indicate the factors that signifi cantly infl uenced the responses. Tables 5–7 also include the percentage contributions of the various elements, which are calculated by dividing the F-value of each element by the F-value of the entire element. Table 5 shows that under dry conditions, the surface roughness is primarily aff ected by the higher order of feed (contribution of about 30.76 %), the higher order of cutting speed, and the interaction eff ects Ta b l e 5 ANOVA for surface roughness (Ra): F-values and % contribution of diff erent parameters Elements Dry NFMQL F-Values % contribution F-Values % contribution Cutting speed (V) 0.3382 0.07 4.2267 1.85 Feed (f) 6.3512 1.23 21.6487 9.46 Number of passes (N) 63.1738 12.25 11.2517 4.92 Interaction V x f 12.7024 2.46 2.8334 1.24 Interaction V x N 81.8517 15.88 5.2688 2.30 Interaction f x N 21.7218 4.21 0.0234 0.01 V 2 103.2749 20.03 45.6632 19.96 f 2 158.5728 30.76 53.2904 23.29 N 2 67.5406 13.10 84.6219 36.98 Total F-value 515.5274 100.00 228.8283 100.00 * Signifi cant elements are shown as underlined and contributions are in bold-case.
OBRABOTKAMETALLOV Vol. 26 No. 4 2024 TECHNOLOGY Ta b l e 6 ANOVA for microhardness: F-values and % contribution of diff erent parameters Elements Dry NFMQL F-Values % contribution F-Values % contribution Cutting speed (V) 15.8251 16.91 72.1848 47.77 Feed (f) 0.6335 0.68 18.5180 12.25 Number of passes (N) 1.5631 1.67 26.8943 17.80 Interaction V x f 7.4668 7.98 4.1151 2.72 Interaction V x N 5.8132 6.21 0.6087 0.40 Interaction f x N 7.4668 7.98 8.7903 5.82 V 2 29.0338 31.02 19.1484 12.67 f 2 11.8708 12.68 0.2530 0.17 N 2 13.9156 14.87 0.6078 0.40 Total F-value 93.5887 100 151.1204 100.00 of cutting speed and number of passes (contributions of about 20 % and 15.88 %, respectively). Cutting speed and feed, on the other hand, have minimal infl uence. However, it can be considered that the number of passes is crucial in reducing the surface roughness. Conversely, under NFMQL conditions, the surface roughness is most aff ected by the higher order of passes (contribution of about 36.98 %), followed by the higher order of feed and cutting speed (contributions of about 23.29 % and 19.96 %, respectively). Table 6 shows the ANOVA results for the F-values of roller burnishing microhardness under dry and NFMQL conditions. It is obvious that under dry burnishing condition, the higher order of cutting speed V 2 (contribution of about 31.02 %), cutting speed V (contribution of about 16.91 %), and the higher order of feed f 2 and passes N 2 (contributions of about 12.68 % and 14.87 %, respectively) have the greatest infl uence on the microhardness, while the feed f and the number of passes N have the least infl uence (contributions of about 0.68 % and 1.67 %, respectively). On the contrary, under NFMQL condition, the experimental results show that the number of passes N (contribution of about 17.8 %), feed f (contribution of about 12.25 %), Ta b l e 7 ANOVA for roundness error: F-values and % contribution of diff erent parameters Elements Dry NFMQL F-Values % contribution F-Values % contribution Cutting speed (V) 40.2758 25.89 18.0635 7.28 Feed (f) 0.6619 0.43 167.1666 67.36 Number of passes (N) 24.0589 15.47 29.3038 11.81 Interaction V x f 1.4796 0.95 6.0885 2.45 Interaction V x N 28.7154 18.46 0.0040 0.00 Interaction f x N 5.7595 3.70 1.4451 0.58 V 2 0.9816 0.63 23.7563 9.57 f 2 50.5574 32.50 1.9515 0.79 N 2 3.0571 1.97 0.3743 0.15 Total F-value 155.5472 100 248.1536 100.00
OBRABOTKAMETALLOV TECHNOLOGY Vol. 26 No. 4 2024 fi rst order cutting speed V (contribution of about 47.77 %) and second order cutting speed V2 (contribution of about 12.67 %) have the greatest infl uence on the microhardness. Table 7 shows the ANOVA results for the F-values of the roundness error for roller burnishing under dry and NFMQL cutting conditions. Under dry conditions, the roundness error is signifi cantly aff ected by the higher order of feed (contribution of about 32.5 %), cutting speed (contribution of about 25.89 %), number of passes (about 15.47 %), as well as the eff ect of interaction between the cutting speed and the number of passes (contribution of about 18.46 %). On the other hand, the roundness error for NFMQL condition is observed to be highly aff ected by the feed (contribution of about 67.36 %), number of passes (contribution of about 11.81 %), and higher order of cutting speed (contribution of about 9.57 %). It is obvious that the feed under NFMQL cutting conditions and the number of passes under dry conditions had a signifi cant eff ect on the surface roughness. Cutting speed seems to have the greatest eff ect on microhardness, and feed rate and number of passes come in second and third place. On the other hand, this eff ect seems to be more pronounced under NFMQL cutting conditions. Under dry conditions, cutting speed has a signifi cant eff ect on roundness error; under NFMQL cutting conditions, feed has a signifi cant eff ect. It is evident from Figs. 2-4 and Tables 5-7 that the process parameters are in contradiction with the benefi cial responses. In addition, multi-objective optimization of these competing parameters is necessary to obtain the desired results. In the current work, the desirability function method is used to optimize the parameters of the roller burnishing process under NFMQL conditions to achieve the minimum roundness error, maximum microhardness and minimum surface roughness. Using this method, the optimization of several response variables becomes the optimization of a single desire function, and each response variable is converted into a desirability function [20–23]. Table 8 lists the range of the response function and the process variables. Table 8 illustrates the minimum and maximum limits of surface roughness, microhardness and roundness error based on the experimental results. A one-way transformation is used to transform each response into its corresponding desirability function [20–23]. Using the Design-Expert® software optimization module, a multi-objective optimization of roller burnishing was carried out in this study. The desirability of surface roughness, microhardness and roundness error was evaluated for each level of independent factors. The desirability of minimum surface roughness, maximum microhardness and minimum roundness error was then computed into one desirability function. The optimal process parameters for the smallest surface roughness, maximum microhardness and minimum roundness error under NFMQL conditions are shown in Table 9. The current study reveals that the ideal parameters for roller burnishing of Al6061-T6 alloy are a cutting speed of 357 rpm, a feed of 0.17 mm/rev and four passes. These results give a minimum surface roughness of 0.64 μm, a maximum microhardness of 130.19 HV and a minimum roundness error of 3.514 μm. However, it was found that the ideal parameters for roller burnishing of Al6061-T6 alloy under dry conditions are a cutting speed of 344 rpm, a feed of 0.25 mm/rev and four passes. This gives a minimum surface roughness of 0.807 μm, a maximum microhardness of 119.2 HV and a minimum roundness error of 4.282 μm. Ta b l e 8 Constraints for optimization of process parameters for NFMQL cutting conditions Parameters Goal Min.limit Max.limit Cutting speed, V (rpm) Is in range 100 500 Feed, f (mm/rev) Is in range 0.1 0.2 Number of passes, N (mm) Is in range 1 5 Surface roughness (Ra) (μm) Minimize 0.61 0.83 Microhardness (HV) Minimize 108 136 Roundness error (Re) (μm) Minimize 2.8 9.2
OBRABOTKAMETALLOV Vol. 26 No. 4 2024 TECHNOLOGY Ta b l e 9 A family of optimized process parameters for NFMQL cutting conditions Sr. No. Cutting speed (V) (rpm) Feed (f) (mm/rev) No. of passes Surface roughness (Ra) (μm) Microhardness (HV) Roundness error (Re) (μm) Desirability 1 357.6 0.17 3.68 0.6435 130.1976 3.519 0.8417 2 357.64 0.16 3.68 0.6436 130.1916 3.515 0.8417 3 357.81 0.16 3.68 0.6436 130.1988 3.514 0.8417 4 357.68 0.17 3.68 0.6436 130.2047 3.518 0.8417 5 357.85 0.16 3.68 0.6436 130.1997 3.515 0.8417 6 357.67 0.16 3.68 0.6436 130.1962 3.515 0.8417 As can be seen, roller burnishing under NFMQL cutting conditions gives reduced values for surface roughness, roundness error, and maximum microhardness compared to dry conditions. The lowest surface roughness found was 0.64 μm. However, this study highlights the need for additional investigation on roller burnishing of Al6061-T6 alloy to obtain improved fi nished work geometries that approach surface roughness of up to 0.3–0.4 μm with increased microhardness. Conclusions In the present work, an attempt is made to investigate the roller burnishing of Al6061-T6 alloy. In this study, the roller burnishing of Al6061-T6 alloy in dry condition and using nanofl uid under minimum quantity lubrication (NFMQL) conditions is comparatively evaluated. The study evaluates, simulates and optimizes the microhardness, roundness and surface roughness by considering the factors such as cutting speed, feed and number of passes. Based on the experimental results, mathematical models are developed to predict the surface roughness, microhardness and roundness error. The following conclusions can be drawn: ● R-square value above 0.9 was observed for the surface roughness, microhardness and roundness error models that represent the developed models and can be reliably used to predict the studied responses under dry and NFMQL cutting conditions and within the domain of the parameters selected in the present study. ● Roller burnishing under NFMQL cutting conditions gives reduced values of surface roughness (0.64 μm), roundness error (3.514 μm) and maximum microhardness (130.19 HV) compared with dry conditions. However, roller burnishing under dry cutting conditions gives comparatively higher surface roughness (0.807 μm), roundness error (4.282 μm) and lower microhardness (119.2 H reduced). ● Surface roughness is observed to decrease with increasing cutting speed. However, it increases with increasing cutting speed to 360–380 rpm under both dry and NFMQL cutting conditions. Furthermore, it is observed to decrease with increasing feed and number of passes. However, after three to four passes with a feed of 0.2–0.25 mm/rev, an increase in surface roughness is noticeable. ● Microhardness and roundness error increase with increasing feed. And an increase in microhardness and a decrease in roundness error are observed with an increase in the number of passes. ● Increasing feed is seen to result in inconsistent responses for surface roughness and microhardness. A compromise between roundness error and microhardness and lower surface roughness is obtained using a feed value in the range of 0.18-0.22 mm/rev. It is observed that roundness error decreases with higher pass counts and maximum microhardness was observed with higher number of passes. ● Surface roughness is signifi cantly aff ected by the feed under NFMQL cutting conditions and the number of passes under dry conditions. Microhardness appears to be the most aff ected by cutting speed, with feed and number of passes coming in second and third. However, this eff ect appears to be more
OBRABOTKAMETALLOV TECHNOLOGY Vol. 26 No. 4 2024 noticeable when using NFMQL cutting conditions. Roundness error is signifi cantly aff ected by dry cutting speed and feed under NFMQL cutting conditions. ● The cutting speed of 357 rpm, feed of 0.17 mm/rev and four passes are found as the optimum parameters for roller burnishing of Al6061-T6 alloy to obtain the minimum surface roughness of 0.64 μm, maximum microhardness of 130.19 HV and minimum roundness error of 3.514 μm. References 1. Rodríguez A., López de Lacalle L.N., Celaya A., Fernández A., Lamikiz A. Ball burnishing application for fi nishing sculptured surfaces in multi-axis machines. International Journal of Mechatronics and Manufacturing Systems, 2011, vol. 4, pp. 220–237. DOI: 10.1504/IJMMS.2011.041470. 2. Saff ar S., Eslami H. Increasing the fatigue life and surface improvement ofAL7075 alloy T6 by using ultrasonic ball burnishing process. International Journal of Surface Science and Engineering, 2022, vol. 16 (3), pp. 181–206. DOI: 10.1504/IJSURFSE.2022.125438. 3. Somatkar A.A., Dwivedi R., Chinchanikar S. Enhancing surface integrity and quality through roller burnishing: a comprehensive review of parameters optimization, and applications. Communications on Applied Nonlinear Analysis, 2024, vol. 31 (1s), pp. 51–69. DOI: 10.52783/cana.v31.563. 4. Nguyen T.-T., Nguyen T.-A., Trinh Q.-H., Le X.-B., Pham L.-H., Le X.-H. Artifi cial neural network-based optimization of operating parameters for minimum quantity lubrication-assisted burnishing process in terms of surface characteristics. Neural Computing and Applications, 2022, vol. 34 (9), pp. 7005–7031. DOI: 10.1007/s00521-02106834-6. 5. Nguyen T.-T. Multi-response performance optimization of burnishing operation for improving hole quality. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2021, vol. 43 (12), p. 560. DOI: 10.1007/ s40430-021-03274-0. 6. Shirsat U., Ahuja B., Dhuttargaon M. Eff ect of burnishing parameters on surface fi nish. Journal of The Institution of Engineers (India): Series C, 2017, vol. 98, pp. 431–436. DOI: 10.1007/s40032-016-0320-3. 7. Kurkute V., Chavan S.T. Modeling and optimization of surface roughness and microhardness for roller burnishing process using response surface methodology for Aluminum 63400 alloy. Procedia Manufacturing, 2018, vol. 20, pp. 542–547. DOI: 10.1016/j.promfg.2018.02.081. 8. Patel K.A., Brahmbhatt P.K. Response surface methodology-based desirability approach for optimization of roller burnishing process parameter. Journal of the Institution of Engineers (India): Series C, 2018, vol. 99, pp. 729– 736. DOI: 10.1007/s40032-017-0368-8. 9. Prasad K.A., John M.R.S. Optimization of external roller burnishing process on magnesium silicon carbide metal matrix composite using response surface methodology. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2021, vol. 43 (7), p. 342. DOI: 10.1007/s40430-021-03069-3. 10. Tadic B., Todorovic P.M., Luzanin O., Miljanic D., Jeremic B.M., Bogdanovic B., Vukelic D. Using specially designed high-stiff ness burnishing tool to achieve high-quality surface fi nish. The International Journal of Advanced Manufacturing Technology, 2013, vol. 67, pp. 601–611. DOI: 10.1007/s00170-012-4508-2. 11. El-KhabeeryM.M., El-Axir M.H. Experimental techniques for studying the eff ects of milling roller-burnishing parameters on surface integrity. International Journal of Machine Tools and Manufacture, 2001, vol. 41 (12), pp. 1705–1719. DOI: 10.1016/S0890-6955(01)00036-0. 12. Okada M., Suenobu S., Watanabe K., Yamashita Y., Asakawa N. Development and burnishing characteristics of roller burnishing method with rolling and sliding eff ects. Mechatronics, 2015, vol. 29, pp. 110–118. DOI: 10.1016/j. mechatronics.2014.11.002. 13. Huang B., Kaynak Y., Sun Y., Jawahir I.S. Surface layer modifi cation by cryogenic burnishing of Al 7050T7451 alloy and validation with FEM-based burnishing model. Procedia CIRP, 2015, vol. 31, pp. 1–6. DOI: 10.1016/j. procir.2015.03.097. 14. Caudill J., Schoop J., Jawahir I.S. Correlation of surface integrity with processing parameters and advanced interface cooling/lubrication in burnishing of Ti-6Al-4V alloy. Advances in Materials and Processing Technologies, 2019, vol. 5 (1), pp. 53–66. DOI: 10.1080/2374068X.2018.1511215. 15. Rotella G., Rinaldi S., Filice L. Roller burnishing of Ti6Al4V under diff erent cooling/lubrication conditions and tool design: eff ects on surface integrity. The International Journal of Advanced Manufacturing Technology, 2020, vol. 106 (1), pp. 431–440. DOI: 10.1007/s00170-019-04631-z.
RkJQdWJsaXNoZXIy MTk0ODM1