Vol. 26 No. 1 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. 1 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. 1 2024 5 CONTENTS OBRABOTKAMETALLOV TECHNOLOGY Kuts V.V., Oleshitsky A.V., Grechukhin A.N., Grigorov I.Y. Investigation of changes in geometrical parameters of GMAW surfaced specimens under the infl uence of longitudinal magnetic fi eld on electric arc....................................... 6 Saprykina N.А., Chebodaeva V.V., Saprykin A.А., Sharkeev Y.P., Ibragimov E.А., Guseva T.S. Optimization of selective laser melting modes of powder composition of the AlSiMg system................................................................. 22 Gubin D.S., Kisel’ A.G. Features of calculating the cutting temperature during high-speed milling of aluminum alloys without the use of cutting fl uid............................................................................................................................................. 38 EQUIPMENT. INSTRUMENTS Borisov M.A., Lobanov D.V., Zvorygin A.S., Skeeba V.Y. Adaptation of the CNC system of the machine to the conditions of combined processing...................................................................................................................................... 55 Nosenko V.A., Bagaiskov Y.S., Mirocedi A.E., GorbunovA.S. Elastic hones for polishing tooth profi les of heat-treated spur wheels for special applications..................................................................................................................................... 66 Podgornyj Y.I., Skeeba V.Y., Martynova T.G., Lobanov D.V., Martyushev N.V., Papko S.S., Rozhnov E.E., Yulusov I.S. Synthesis of the heddle drive mechanism....................................................................................................... 80 MATERIAL SCIENCE Ragazin A.A., Aryshenskii V.Y., Konovalov S.V., Aryshenskii E.V., Bakhtegareev I.D. Study of the eff ect of hafnium and erbium content on the formation of microstructure in aluminium alloy 1590 cast into a copper chill mold............................................................................................................................................................................ 99 Zorin I.A., Aryshenskii E.V., Drits A.M., Konovalov S.V. Study of evolution of microstructure and mechanical properties in aluminum alloy 1570 with the addition of 0.5 % hafnium........................................................................... 113 Karlina Y.I., Kononenko R.V., Ivantsivsky V.V., Popov M.A., Deryugin F.F., Byankin V.E. Relationship between microstructure and impact toughness of weld metals in pipe high-strength low-alloy steels (research review)..................... 129 Patil N.G., Saraf A.R., Kulkarni A.P Semi empirical modeling of cutting temperature and surface roughness in turning of engineering materials with TiAlN coated carbide tool................................................................................. 155 Sawant D., Bulakh R., Jatti V., Chinchanikar S., Mishra A., Sefene E.M. Investigation on the electrical discharge machining of cryogenic treated beryllium copper (BeCu) alloys........................................................................................ 175 Karlina A.I., Kondratiev V.V., Sysoev I.A., Kolosov A.D., Konstantinova M.V., Guseva E.A. Study of the eff ect of a combined modifi er from silicon production waste on the properties of gray cast iron................................................. 194 EDITORIALMATERIALS 212 FOUNDERS MATERIALS 223 CONTENTS
OBRABOTKAMETALLOV MATERIAL SCIENCE Vol. 26 No. 1 2024 Semi empirical modeling of cutting temperature and surface roughness in turning of engineering materials with TiAlN coated carbide tool Nilesh Patil 1, a,*, Atul Saraf 2, b, Atul Kulkarni 3, c 1 Marathwada Institute of Technology, Aurangabad-431010, Maharashtra State, India 2 National Institute of Technology, Surat, Gujarat 395007, India 3 Vishwakarma Institute of Information Technology, Survey No. 3/4, Kondhwa (Budruk), Pune – 411048, Maharashtra, India a https://orcid.org/0000-0002-4884-4267, nileshgpatil@rediff mail.com; b https://orcid.org/0000-0003-4776-6874, atul.saraf001@gmail.com; c https://orcid.org/0000-0002-6452-6349, atul.kulkarni@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. 1 pp. 155–174 ISSN: 1994-6309 (print) / 2541-819X (online) DOI: 10.17212/1994-6309-2024-26.1-155-174 ART I CLE I NFO Article history: Received: 20 September 2023 Revised: 31 October 2023 Accepted: 22 January 2024 Available online: 15 March 2024 Keywords: Semi-empirical model Regression model Temperature Surface roughness For citation: Patil N.G., Saraf A.R., Kulkarni A.P Semi empirical modeling of cutting temperature and surface roughness in turning of engineering materials with TiAlN coated carbide tool. Obrabotka metallov (tekhnologiya, oborudovanie, instrumenty) = Metal Working and Material Science, 2024, vol. 26, no. 1, pp. 155–174. DOI: 10.17212/1994-6309-2024-26.1-155-174. (In Russian). ______ * Corresponding author Kulkarni Atul P., Ph.D. (Engineering), Professor Vishwakarma Institute of Information Technology, Survey No. 3/4, Kondhwa (Budruk), Pune – 411048, Maharashtra, India Tel.: 91-2026950419, e-mail: atul.kulkarni@viit.ac.in Introduction. In manufacturing, obtaining a given surface roughness of the machined parts is of great importance to fulfi ll functional requirements. However, the surface roughness signifi cantly aff ected by the heat generated during the machining process, which can lead to a decrease in dimensional accuracy. The surface roughness signifi cantly aff ects the fatigue characteristics of the part, and the service life of the cutting tool is determined by the cutting temperature generation. The purpose of the work. The purpose of this study is to create semi-empirical models for predicting surface roughness and temperature of various work materials. Enhanced cutting performance is achieved by accurately determining the cutting temperature in the machined zone. However, calculating the cutting temperature for each specifi c case is fraught with diffi culties in terms of labor resources and fi nancial investments. This paper presents a comprehensive empirical formula designed to predict both theoretical temperature and surface roughness. Methodology, The performance of the surface roughness and temperature generation was evaluated for the EN 8, Al 380, SS 316 and SAE 8620 materials when processed with TiAlN-coated carbide tools. The TiAlN coating was obtained by Physical Vapor Deposition (PVD) technique. Response surface methodology was used to prepare predictive models. Cutting speed (from 140 to 340 m/min), feed (from 0.08 to 0.24 mm/rev) and depth of cut (from 0.6 to 1 mm) were used as input parameters to measure the characteristics of all materials in terms of surface roughness and cutting temperature. The tool-work thermocouple principle was used to measure the temperature at the chip-tool interface. Novel Calibration Setup was developed to establish the relationship between the Electromotive Force (EMF) generated during machining and the cutting temperature. Results and Discussion. It is observed that the energy required for mechanical processing was largely converted into heat. The highest cutting temperature is recorded with SS 316, followed by SAE 8620 and EN 8. However, low temperature was reported during machining of Al 380 and it was mainly governed by the thermal conductivity of the material. The lowest surface roughness is observed for SAE 8620, EN 8, followed by SS 316 and Al 380. The semi-empirical method and regression model equations are in good agreement with each other. Statistical analysis of the nonlinear evaluation reveals that cutting speed, feed rate, and material density have a greater infl uence on the surface roughness, whereas depth of cut has a greater infl uence on the temperature change. The study will be very useful for predicting industrial performance when machining EN 8, Al 380, SS 316 and SAE 8620 materials with TiAlN-coated carbide tools.
OBRABOTKAMETALLOV MATERIAL SCIENCE Vol. 26 No. 1 2024 List of symbols Symbol Description f Feed (mm) Vc Cutting speed (m/min) doc Depth of cut (mm) Ra Surface roughness (μm) MRR Material removal rate (mm3/rev) HSM High speed machining Fc Cutting forces (N) ρ Density (kg/m3) Cp Specifi c Heat (J/kg k) K Thermal Conductivity(W/mk) σ Yield strength (N/m2) α Coeffi cient of thermal expansion (m/mk) Ɵ Temperature (oC) SS 316 Stainless steel SS 316 SAE 8620 Low alloy case hardening steel SAE 8620 EN 8 Engineering steel EN 8 Al 380 Aluminium alloy Al 380 Ø Buckingham’s π theorem constant a1 a2 a3 a4 a5 Power indices b1 b2 b3 b4 b5 Power indices M L T Ɵ Dimensions CBN Cubic boron nitride RSM Response surface methodology CCD Central composite design ANN Artifi cial neural network LM Levenberg-Marquardt
OBRABOTKAMETALLOV MATERIAL SCIENCE Vol. 26 No. 1 2024 Introduction Surface fi nish is critical toqualitybecause it directly aff ects the appearance, functionality, andperformance of machined components. Precision machining is essential, especially in aerospace and medical applications where specifi ed surface fi nish is required to reduce friction, improve wear resistance, or improve corrosion resistance. The infl uence of surface fi nish on tribological parameters such as friction and lubrication is crucial to achieve maximum performance and durability. Increased temperatures during machining have a signifi cant impact on tool wear, material integrity and dimensional accuracy. Temperature control is critical for extending tool life and maintaining the structural integrity of machined parts. Predictive modelling optimizes processes by identifying optimal parameters for cost savings through increasing tool life, reducing scrap rates and increasing effi ciency. The use of cutting fl uid in hard turning is not recommended, since at elevated temperatures when processing materials with a hardness of 48 to 68 HRC, the coolant in the cutting zone begins to boil. This boiling phenomenon promotes thermal deformation, thereby reducing both Ra (surface roughness) and the service life of the cutting tool [1]. In case of machining diff erent materials, its machinability was evaluated using various process parameters such as tool life, material removal rate (MRR), cutting force (Fc), energy consumption, chip morphology and machined surface roughness (Ra). Using high speed machining (HSM) while maintaining surface integrity and maintaining tolerance limits requires optimal coordination of factors such as cutting force (Fc), process and machine parameters. The right combination of these elements is critical to increasing the effi ciency of HSM without compromising the quality of the machined surfaces or exceeding specifi ed tolerance limits. This balance ensures that machining can proceed without compromising accuracy and surface quality, contributing to the overall success of high-speed machining operations [2]. Zhao et al., [3] measured the cutting temperature of Inconel 718 using a two-color infrared thermometer with a ceramic whisker-reinforced tool, and concluded that the large amount of heat generated during machining deteriorates the surface quality of the machined material. Due to the increase in temperature in the cutting zone during machining, the surface quality deteriorated [4]. High tool wear and temperature increase during machining of hardened AISI 4340 steel can be eliminated using bio-cutting fl uid [5]. Postmachining operations are required to improve the surface quality of superalloys, [6]. Kumar et al. [7] compared a RSM model with an ANN model to analyze the turning performance of AISI D2 steel and concluded that that the RSM-based prediction model is more accurate than the ANN model for predicting surface quality and cutting temperature. Gosai and Bhavsar [8] used mathematical models and equations generated by CCD-based RSM to predict cutting temperature. The material removal rate during the turning process was higher compared to other traditional machining processes. Abhang et al. [9] experimentally measured the temperature of the EN 31 alloy during turning with tungsten carbide inserts using the natural thermocouple technique. F has a signifi cant eff ect on the surface roughness: as the f increases, the roughness increases, and as the Vc increases, the roughness decreases [10– 12]. Bhopal et al. [13] used RSM with CCD for turning austenized high-strength cast iron with a carbide tool and found that Vc has a more signifi cant eff ect on surface roughness. Aouici et al. [14] used a CBN tool for turning AISI H11 steel, as well as a mathematical model based on RSM for Ra and Fc, however, when processing materials reinforced with particles, the surface morphology was changed. Longbottom and Lanham [15] conducted a review of temperature measuring devices and found that the measured temperature varied in diff erent places. Korkut et al. [16] compared the ANN model and the RA model and found that the training ANN model with the LM algorithm demonstrated a higher prediction rate and was useful in measuring the cutting temperature when tested by a qualifi ed RA method during machining. Dhar and Kamruzzaman [17] found that an increase in temperature signifi cantly aff ects tool wear and surface roughness, and the use of cryogenic cooling gives good results. Patil and Brahmankar [18] developed a model for surface roughness that takes into account the input parameters, material properties, size of ceramic particles and its volume fraction, and found that the volume fraction and particle size signifi cantly aff ect the output parameters, as well as that the presence of ceramic particles aff ects the surface roughness. Patel and Kiran [19] used a linear regression model to analyze the assessment of the roughness of the surface
OBRABOTKAMETALLOV MATERIAL SCIENCE Vol. 26 No. 1 2024 when processing AISI 1040 steel. Patel and Gandhi [20] machined AISI D2 steel with an CBN tool and developed a mathematical model based on the simultaneous action of f, Vc and the nose radius, and is in good agreement with the experimental values. But none of them used more than one material for experiments, with the exception of Rodriguez et al., [21] who used SS 304, 316L and 420 materials for turning and developed a model of cutting temperature taking into account thermal conductivity and maximum strength. According to the literature reviewed, the cutting parameters, in particular the cutting speed and feed, have a signifi cant eff ect on the temperature of the chip-tool contact surface. Various predictive models have been developed, but each model predicted results in a specifi c parameter range. In addition, several studies have been reported on the eff ect of TiAlN cutting modes and coating parameters on cutting temperature and surface roughness when turning EN 8, Al 380, SS 316 and SAE 8620 materials. In this work, the simplest and most economical method for measuring temperature is developed, involving the use of a tool-work thermocouple. Further, response surface models were developed for the cutting temperature and roughness of these materials, the infl uence of technological parameters and thermal and physical properties of the materials of the processed parts on the response parameters are studied, and a semi-empirical model is developed to predict the cutting temperature and surface roughness. Materials and methods The experimental results were obtained on a CNC lathe machine. Vc, f and doc were the three adjustable factors in turning operation. In the present work, workpieces made of four materials were used, namely mild steel (EN 8) with a diameter of 75 mm, aluminum alloy (Al 380) with a diameter of 50 mm, stainless steel (SS 316) with a diameter of 75 mm and low alloy steel (SAE 8620) with a diameter of 75 mm. The length of each workpiece was 300 mm and each of it was machined. To determine the chemical composition of the above materials, spectroscopic analysis was carried out, the results of which are presented in Table 1. Since the literature indicates that TiAlN-coated carbide tools have minimal Ra and tool wear, Sandvik PVD (TiAlN) coated carbide inserts CNMG-120408 MS PR1310 (0.8 mm nose radius) with eight cutting edges were used in this work for 20 tests under dry conditions. The contact point between the tool and the workpiece was hot during machining, while the carbon brush touching the workpiece remained cold. The workpiece was mounted in a three-jaw chuck, and insulation was provided between the workpiece and the chuck. The experimental setup, temperature calibration setup, and workpiece material are shown in fi gure 1, a, b and c respectively. The cutting parameters used for machining are given in Table 2. Ta b l e 1 Chemical composition of work material Element, % SS 316 EN 8 SAE 8620 Al 380 C 0.07 0.39 0.22 – Mn 0.16 0.87 0.8 0.5 Si 0.9 0.22 0.28 8.5 P 0.05 0.04 0.031 – S 0.02 0.05 0.04 – Cr 18.50 – 0.49 – Mo 2.25 – 0.22 – Ni 12.23 – 0.52 0.5 Mg – – – 0.1 Cu – – – 3.6 Sn – – – 0.35 Zn – – – 3 Fe balance balance balance 1.3 Al – – – balance
OBRABOTKAMETALLOV MATERIAL SCIENCE Vol. 26 No. 1 2024 a b c Fig. 1. Machining Setup (a), Temperature calibration setup (b), Work materials (c) Ta b l e 2 Process parameters and experimental levels Parameters/Levels L 1 L 2 L 3 L 4 L 5 Vc (m/min) 140 190 240 290 340 f (mm/rev) 0.08 0.12 0.16 0.20 0.24 doc (mm) 0.6 0.7 0.8 0.9 1.0 Results and discussion The central composite design of the response surface method was used for the main experiments. Table 3 shows the experimental results. The objective of the experimental analysis was to identify the signifi cant factor that has a greater infl uence on the response variables and to develop a generalized empirical model to predict surface roughness and generated temperature using Buckingham’s π theorem. Statistical analysis of surface roughness and temperature rise was carried out using RSM. The main objective of this paper is to develop semi-empirical formulae using the Levenberg-Marquardt method to predict the surface roughness and temperature of various materials. Using the values from Table 2, individual regression equations were constructed and the full factorial values were extracted from the regression. These full factorial values are used to derive the semi-empirical formula. The regression equations for surface roughness of materials are given below. 2 0.60 0.00018 2.7 1.37 0.000003 19.03 a c c SSR V f d V f = + + - - + + 2 0.79 0.0050 0.00050 1.87 ; c c d V xf V xd fxd - + + (I) 2 2 0.31 0.00202 10.01 1.20 0.00005 31 61 a c c SAER V f d V c f = - + - - + - 2 0.11 0.2604 0.00908 5.1 ; c c d V xf V xd fxd - + - (II) 2 2 3.135 0.01331 9.76 1.09 0.000023 59.66 a c c ENR V f d V f = - - - + + + 2 0.670 0.00312 0.00125 0.31 ; c c d V xf V xf fxd - + + (III) 2 2 14.32 0.0478 12.4 12.97 0.000093 53.7 a c c AlR V f d V df = - - - + + + 2 7.97 0.0444 0.0027 16.6 . c c d V xf V xd fxd - - + (IV)
OBRABOTKAMETALLOV MATERIAL SCIENCE Vol. 26 No. 1 2024 Ta b l e 3 Experimental data of Ra and temperature for SS 316, EN 8, SAE 8620 and Al 380 materials Run No. Speed, Vc, (m/min) Feed, f, (mm/ rev) doc, d, (mm) SS 316 Ra EN 8 Ra SAE 8620 Ra AL 380 Ra SS 316 Temp EN 8 Temp SAE 8620 Temp Al 380 Temp 1 190 0.12 0.7 0.73 0.84 0.63 2.88 635 636 629 243 2 290 0.12 0.7 0.56 0.66 0.50 1.73 812 657 733 264 3 190 0.2 0.7 1.39 1.54 1.60 3.56 643 654 648 247 4 290 0.2 0.7 1.22 1.31 1.25 2.24 997 672 741 318 5 190 0.12 0.9 0.74 0.92 0.55 2.95 782 647 675 236 6 290 0.12 0.9 0.62 0.74 0.59 1.93 1082 665 782 271 7 190 0.2 0.9 1.47 1.6 1.42 4.08 815 664 735 274 8 290 0.2 0.9 1.27 1.42 1.27 2.52 1157 679 818 334 9 140 0.16 0.8 1.08 1.32 1.12 4.25 732 644 595 229 10 340 0.16 0.8 0.78 1.03 0.80 1.86 1243 689 837 323 11 240 0.08 0.8 0.3 0.59 0.47 2.01 619 629 625 216 12 240 0.24 0.8 1.86 2.06 1.96 2.92 883 666 718 306 13 240 0.16 0.6 0.91 0.92 0.98 2 646 644 693 289 14 240 0.16 1 1.07 1.02 1.04 2.88 1082 653 791 310 15 240 0.16 0.8 1.01 0.95 0.99 2.12 805 649 704 283 16 240 0.16 0.8 0.92 1 0.96 2.24 766 642 694 291 17 240 0.16 0.8 0.93 0.94 1.00 2.31 775 644 699 293 18 240 0.16 0.8 0.99 0.94 1.00 2.09 764 645 701 296 19 240 0.16 0.8 0.96 0.94 1.00 2.1 769 644 703 298 20 240 0.16 0.8 0.98 0.95 1.00 2.08 765 643 701 297 The regression equations for material temperature are given below. 2 2 3,517 2.74 696 8645 0.01054 3, 963 c c SSTemp V f d V f = - + - + + + 2 699 6.6 1.57 3, 281 ; c c d V xf V xd fxd + - - (V) 2 2 1, 073 0.57 457 1,899 0.00210 3, 672 c c SAETemp V f d V f = + + - + - + 2 1,175 2.14 0.175 2,156 ; c c d V xf V xd fxd - - + (VI) 2 2 2 748 0.787 87 175 0.002436 838 159 c c ENTemp V f d V d f = - + - + + + - 0.375 0.150 63 ; c c V xf V xd fxd - - (VII) 2 2 2 239 0.579 39 353 0.001918 5341 108 c c AlTemp V f d V d f = + + - - - + + 4.69 0.075 1,344 . c c V xf V xd fxd - + (VIII) Buckingham’s π theorem This study uses the dimensional homogeneity principle of Buckingham’s π theorem [22]. Table 4 shows the mechanical properties of the materials.
OBRABOTKAMETALLOV MATERIAL SCIENCE Vol. 26 No. 1 2024 Quantities of diff erent nature cannot be homogeneous.Applying dimensional analysis, surface roughness can be given by an equation of the following form, ( , , , , , , , , ) a p R f F V D K C θ σ ρ α = , (1) where the fundamental dimensions are ρ, L, T and Ɵ. Therefore, since the total number of variables is ten, there are four fundamental dimensions. The number of dependent and independent variables is n = 10, and the number of repeated variables is m = 4. Therefore, none of the π terms in the present study will be n−m = 6. Thus, 1 2 3 4 5 6 ( , , , , , ) 0. f π π π π π π = (2) Note that equation (2) can also be written as: 1 1 2 3 4 5 6 ( , , , , , ) f π π π π π π π = ; (3) 1 a R F π = ; (4) 1 2 2 a p C V θ π æç ö÷ ÷ = çç ÷÷ çè ø ; (5) 2 3 3 a K FV θ π ρ æ ö÷ ç ÷ = ç ÷ ç ÷ ç ÷ è ø ; (6) 3 4 a F αθ π æç ÷ö = çç ÷÷ è ø ; (7) Ta b l e 4 Units, dimensions and properties of the machined materials Variable Unit Symbol Dimensions Workpiece properties SS 316 EN 8 SAE 8620 Al 380 Feed mm f L – – – – Speed m/min Vc LT −1 – – – – Depth of cut mm d L – – – – Surface roughness μm Ra L – – – – Density kg/m3 Ρ M L−3 8,000 7,850 7,845 2,760 Specifi c Heat J/kg k Cp L 2 T−2 Ɵ −1 0.5 0.475 1.6 0.963 Thermal Conductivity W/mk K M L T −3 Ɵ−1 16.3 46.6 27 109 Yield Strength N/m2 σ M−1 T−2 240 560 450 159 Coeff . Of Thermal Exp. m/m×K α L Ɵ −1 16.18×10 − 6 12.2×10 −6 11.6×10−6 12.1×10−6 Temperature oC Ɵ Ɵ 1.371 2.600 1.400 650
OBRABOTKAMETALLOV MATERIAL SCIENCE Vol. 26 No. 1 2024 4 5 2 a V σ π ρ æç ö÷ = çç ÷÷÷ çè ÷ø ; (8) 5 6 a D F π æç ö÷ = çç ÷÷ è ø . (9) Therefore, the fi nal form of the equations can be written as Ø 2 4 1 3 5 2 3 2 a a a a a p a C K D F FV R F F V V θ θ αθ σ ρ ρ æ ö æ ö æ ö æ ö æ ö ÷ ÷ ÷ ç ç ç ÷ ÷ ç ç ÷ ÷ ÷ ç ç ⋅ ç ÷ ÷ ÷ ÷ ç ç ÷ ç ç ç ÷ ÷ ÷ ÷ ç ç ç ÷÷ è ø è ø ç ç ÷ ÷ è ø è ø è ø = . (10) Similarly, the temperature increase (T) can be given by an equation of the following form: ( ) , , , , , , p f F V K C θ σ ρ α = ; (11) 1 F π αθ = ; (12) 1 2 2 b p FC V π α æ ö÷ ç ÷ = ç ÷ ç ÷ çè ø ; (13) 2 3 2 b K V π α ρ æ ö÷ ç ÷ = ç ÷ ç ÷ ç ÷ è ø ; (14) 3 4 b a R F π æç ö÷ = çç ÷÷ è ø ; (15) 4 5 2 b V σ π ρ æç ö÷ = çç ÷÷÷ çè ÷ø ; (16) 5 6 b D F π æç ö÷ = çç ÷÷ è ø . (17) Thus, the fi nal form of the equation can be written as Ø 2 4 1 3 5 2 3 2 b b b b b p FC K Ra D F a F F V V V σ θ α α ρ ρ æ ö æ ö æ ö æ ö æ ö ÷ ÷ ÷ ç ç ç ÷ ÷ ç ç ÷ ÷ ÷ ç ç = ⋅ ç ÷ ÷ ÷ ÷ ç ç ÷ ç ç ç ÷ ÷ ÷ ÷ ç ç ÷ ç è ø è ø ç ç ÷ ÷ è ø è ø è ø . (18) Although α appears repeatedly, its’ infl uence on Ra appears to be quite signifi cant. In this work, energy indicators are determined using the Levenberg-Marquardt method (see Table 5). The adequacy of the model is further analyzed by comparing the regression of Ra and the predicted values of the semi-empirical model. Ta b l e 5 Coeffi cients and energy indicators of Ra and temperature model Energy indicators Surface roughness Energy indicators Temperature Ø 1.687688 Ø 0.098376 a1 0.118057 b1 −0.186434 a2 0.322659 b2 −0.384552 a3 −0.591654 b3 −0.177437 a4 −0.272547 b4 0.407445 a5 0.548434 b5 0.660121
OBRABOTKAMETALLOV MATERIAL SCIENCE Vol. 26 No. 1 2024 Comparison of surface roughness of SS 316, EN 8, SAE 8620 and Al 380 To obtain a complete understanding of the infl uence of input parameters on surface roughness, threedimensional (3D) surface diagrams were constructed for all cutting materials by varying process parameters. These visual representations use empirically derived equations to ensure accuracy. Figure 2 shows threedimensional surface diagrams illustrating the surface roughness changes during turning of SS 316, EN 8, SAE 8620 and Al 380 with PVD-coated (TiAlN) tools generated using Eqs. (I)–(IV). From fi gure 2 it becomes clear that the surface roughness is primarily aff ected by the feed. However, this eff ect can be considered to be more signifi cant for Al 380 and SS 316. During the processing of aluminum alloys, built-up edges are formed due to the adhesion of chips to the cutting tool, which leads to an increase in surface roughness. In the case of SS 316, there is a tendency for the formation of drain chips that spin around the work material, damaging the new surface, and this may be the cause of poor surface fi nish. EN 8 and SAE 8620 materials seem well suited for machining, mainly due to their low hot hardness and easy machinability. Therefore, the roughness of these materials is higher compared to others. It was also observed that as cutting speed increases, there is a tendency for surface roughness to improve for all materials. The literature reports that at high cutting speeds, the tool-chip contact length is reduced, thereby minimizing cutting tool vibration and improving surface roughness. In addition, at higher speeds, the cutting temperature increases; this contributes to the softening of the material. This in turn helps reduce cutting forces, thereby minimizing vibration and improving surface fi nish. Figure 3, a shows the eff ect of f on Ra at Vc = 140 m/min and doc = 0.6 mm for both regression and semiempirical values. Aluminum material has poor surface fi nish because aluminum produces more continuous chips than other materials. In addition, this continuous chip damages the fi nished parts [23]. Figure 3, b shows the eff ect of f on Ra at Vc = 190 m/min and doc = 0.7 mm. As f increases, Ra increases compared to other materials, the thermal conductivity of SS 316 is lower, due to the increase in temperature, a b c d Fig. 2. Surface roughness 3D-plots for SS 316 (a), EN 8 (b), SAE 8620 (c) and Al 380 (d)
OBRABOTKAMETALLOV MATERIAL SCIENCE Vol. 26 No. 1 2024 Fig. 3. Eff ect of feed rate on surface roughness at diff erent cutting speed and depth of cut for all materials using TiAlN-coated tool a b c d e the material becomes more ductile when cutting and a smoother cut is possible, which leads to better surface quality [11]. The minimum Ra is achieved by increasing Vc from 240 m/min to 340 m/min and doc from 0.8 mm to 1 mm, as shown in fi gure 3 c–e, since at higher Vc the strain rate in the shear zone is expected to be high, which will lead to an increase in temperature [2]. As Vc and f increase, the temperature increases because the heat dissipation time decreases and the larger chip-tool contact area increases friction. Vc and doc are signifi cant factors in increasing tool temperature for SS 316 and SAE 8620. Ra decreases due to increasing strain rate [24]. Figure 4, a–e clearly shows that higher Vc provides good surface roughness for almost all materials. However, as f and doc increase, the surface roughness increases fi rst for SS 316 and then for Al 380. EN 8 shows even better results due to low heat generation in the cutting zone, which maintains tool shape stability. Since the thermal conductivity of SS 316 is lower compared to other materials, it becomes more ductile during cutting due to increased temperature, and a smoother cut is possible due to better surface quality [2]. Ra was found to be the worst when machining Al 380 and was superior to SS 316 and SAE 8620. The sticking of Al 380 material results in a rough surface. Built-up edge occurs because the material easily adheres to the cutting edge, which ultimately changes the geometry of the tool and Ra increases [12]. Figure 5 a–e shows the eff ect of doc on various materials. It is observed that doc does not have a signifi cant eff ect on Ra. This may be due to the increase in strain volume with increasing doc. Thus, severe deformation of the workpiece results in more surface irregularities and hence poor surface quality. Zou et al. [25] also obtained similar results. Doc is less signifi cant for Ra than Vc and f [11]. At higher values of technological parameters, the thermal wear of the tool and surface roughness increase [3]. Comparison of cutting temperatures of SS 316, EN 8, SAE 8620 and Al 380 To obtain a comprehensive understanding of the infl uence of input parameters on cutting temperature, three-dimensional (3-D) surface plots are constructed by varying the process parameters for all cutting materials. These visual representations use empirically derived equations to ensure accuracy. Figure 6 shows three-dimensional diagrams illustrating the cutting temperature changes during turning of SS 316, EN 8, SAE 8620 and Al 380 stainless steels for PVD-coated (TiAlN) tools obtained using equations (V)–(VIII).
OBRABOTKAMETALLOV MATERIAL SCIENCE Vol. 26 No. 1 2024 Fig. 4. Eff ect of cutting speed on surface roughness at diff erent feed rate and depth of cut for all materials using TiAlN-coated tool a b c d e a b c d e Fig. 5. Eff ect of depth of cut on surface roughness at diff erent feed rate and cutting speed for all materials using TiAlN-coated tool In the case of cutting temperature, f does not have a signifi cant eff ect (see fi gure 6, a–d). Compared to other materials, Al 380 exhibits a less signifi cant temperature increase. In other materials such as SS 316, SAE 380 and EN 8, the temperature rise is linear, low thermal conductivity and specifi c heat capacity are responsible for the large variations in temperature rise in SS 316. Consequently, the temperature during
OBRABOTKAMETALLOV MATERIAL SCIENCE Vol. 26 No. 1 2024 a b c d Fig. 6. Cutting temperature 3D-plots for SS 316 (a), EN 8 (b), SAE 8620 (c) and Al 380 (d) processing of SS 316 increases as the process parameters increase. High-speed, high-temperature processing results were obtained with increasing Vc. Most of the heat is carried away by the chips, and little heat is lost into the workpiece. It can be seen that f aff ects the temperature slightly, but gradually the temperature continues to increase as f increases. The same result was obtained by Dessoly et al. [26] using a FEM model and an IR camera. Figure 7 a, b shows that with increasing f the temperature increases, since a larger surface area of the workpiece and the tool is in contact. Aluminum has the lowest yield strength, so heat generation in aluminum is less compared to other materials. Figure 7, c–e shows how the temperature increases with increasing f, doc, and Vc increases. Increasing f increases the temperature due to greater chip-tool contact and associated friction [27]. In aluminum, the temperature rises to a lesser extent because due to higher thermal conductivity, heat transfer occurs faster, so the material remains in the same state throughout, the material does not become more ductile, and the friction between the workpiece and the cutting tool is reduced [12]. As the process parameters increase, the temperature increases. Kitagawa et al. [28] used ceramic tools to turn Inconel 718 and found that the cutting temperature continued to increase with increasing process parameters as the workpiece material was deformed into chips by the cutting tools. Deformation of the workpiece, cohesion or friction of the chips on the rake surface of the tool leads to strong heating [3]. As Vc increases, the temperature continues to rise. As a result, the surface quality decreases and tool wear increases [1]. In fi gure 8 cutting temperature is directly proportional to cutting speed. However, it also depends on other factors such as f, doc, cutting width, machine operating conditions [27]. Figure 8, a–e shows the eff ect of doc on cutting temperature. The temperature continues to increase with increasing doc because at maximum feed and doc, large frictional heat is generated due to the friction between the work material and the cutting tool, which leads to thermal softening of the material [29]. According to semi-empirical and regression results, doc is a more signifi cant temperature parameter than f and Vc [1].
OBRABOTKAMETALLOV MATERIAL SCIENCE Vol. 26 No. 1 2024 Fig. 7. Eff ect of feed rate on cutting temperature at diff erent cutting speed and depth of cut for all materials using TiAlN-coated tool a b c d e a b c d e Fig. 8. Eff ect of cutting speed on cutting temperature at diff erent feed rate and depth of cut for all material using TiAlN-coated tool In fi gure 9, a–e the workpiece or tool is enlarged due to the heat generated. Cutting temperature greatly infl uences the mechanical properties of the workpiece and the forces acting on the workpiece and tool [30]. Most of the total heat is transferred to the chip, and this total heat in the chip fl ow is due to shear and friction at the chip-tool interface. Changing doc has a greater impact on the cutting temperature compared to f and Vc [8].
OBRABOTKAMETALLOV MATERIAL SCIENCE Vol. 26 No. 1 2024 Fig. 9. Eff ect of depth of cut on cutting temperature at diff erent feed rate and cutting speed for all material using TiAlN-coated tool a b c d e All fi gures show the results of regression values taken from the empirical model and experimental RSM values for temperature and surface roughness, which were found to be comparable. All values of the RSM output parameters and the empirical model values are in good agreement with each other. Therefore, Equations (10) and (18) can be used to determine the theoretical value of Ra and temperature at diff erent cutting parameters for diff erent work materials with TiAlN-coated carbide tool inserts. Conclusions A semi-empirical method, taking into account the dimensions of material properties, is proposed for estimating cutting temperature and surface roughness when turning SS 316, SAE 8620, EN 8 and Al 380 workpieces with PVD-coated carbide (TiAlN) inserts. In addition, a multilinear regression analysis was carried out and based on the analysis of the results of the regression and semi-empirical model, the following conclusions were drawn: ● At higher feed rates, low surface roughness is observed for all materials. However, as feed and depth of cut increase, surface roughness tends to increase more in SS 316, then Al 380. EN 8 shows better results due to low heat generation in the cutting zone, which maintains tool shape stability. ● The rapid work hardening of the chips in the case of SS 316, the toughness of the chip and built-up material, the stability of the tool shape in the case of EN 8 and SAE 8620 are the main reasons for the surface roughness quality. ● Higher cutting temperature is obtained when turning SS 316 and lower cutting temperature is obtained when turning Al 380. This is due to the signifi cant diff erence in thermal conductivity of these materials. ● When machining EN 8 and SAE 8620, the cutting temperature range is found to be moderate. ● Surface roughness is found to be worst for Al 380 and best for SS 316 and SAE 8620. ● In addition, using a dimensional analysis model, a generalized empirical formula is developed to predict the surface roughness and temperature encountered during metal cutting. These models are found to fi t well with regression equations derived from experimental values. ● The proposed method for measuring surface roughness and temperature can be conveniently used. This is a useful way to cost-eff ectively evaluate heat generation and surface roughness when turning various materials with TiAlN-coated carbide tools.
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