Vol. 27 No. 1 2025 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. 27 No. 1 2025 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. 27 No. 1 2025 5 CONTENTS OBRABOTKAMETALLOV TECHNOLOGY Umerov E.D., Skakun V.V., Dzhemalyadinov R.M., Egorov Y.A. Investigation of the eff ect of oil-based MWFs with enhanced tribological properties on cutting forces and roughness of the processed surfaces.............................................. 6 Manikanta J.E., Ambhore N., Thellaputta G.R. Investigation of vegetable oil-based cutting fl uids enhanced with nanoparticle additions in turning operations........................................................................................................................ 20 Shlykov E.S., Ablyaz T.R., Blokhin V.B., Muratov K.R. Improvement the manufacturing quality of new generation heat-resistant nickel alloy products using wire electrical discharge machining................................................................... 34 Ablyaz T.R., Osinnikov I.V., Shlykov E.S., Kamenskikh A.A., Gorohov A.Yu., Kropanev N.A., Muratov K.R. Prediction of changes in the surface layer during copy-piercing electrical discharge machining....................................... 48 Martyushev N.V., Kozlov V.N., Boltrushevich A.E., Kuznetsova Yu.S., Bovkun A.S. Milling of Inconel 625 blanks fabricated by wire arc additive manufacturing (WAAM)..................................................................................................... 61 Fatyukhin D.S., Nigmetzyanov R.I., Prikhodko V.M., Sundukov S.K., Sukhov A.V. Infl uence of the oscillating systems inclination angle on the surface properties of steel 45 during ultrasonic surface plastic deformation................... 77 EQUIPMENT. INSTRUMENTS Borisov M.A., Lobanov D.V., Skeeba V.Y., Nadezhdina O.A. Development of a device for studying and simulating the electrochemical grinding process................................................................................................................................... 93 Lapshin V.P., Gubanova A.A., Dudinov I.O. Predicting machined surface quality under conditions of increasing tool wear............................................................................................................................................................................... 106 Podgornyj Y.I., Skeeba V.Y., Martynova T.G., Sadykin A.V., Martyushev N.V., Lobanov D.V., Pelemeshko A.K., Popkov A.S. Designing the homogenization mechanism.................................................................................................... 129 MATERIAL SCIENCE Usanova O.Yu., Ryazantseva A.V., Vakhrusheva M.Yu., Modina M.A., Kuznetsova Yu.S. Improving the performance characteristics of grey cast iron parts via ion implantation.......................................................................... 143 Abdelaziz K., Saber D. Fabrication and characterization of Al-7Si alloy matrix nanocomposite by stir casting technique using multi-wall thickness steel mold................................................................................................................ 155 Dama Y.B., Jogi B.F., Pawade R., Pal S., Gaikwad Y.M. DLP 3D printing and characterization of PEEK-acrylate composite biomaterials for hip-joint implants....................................................................................................................... 172 Prudnikov A.N., Galachieva S.V., Absadykov B.N., Sharipzyanova G.Kh., Tsyganko E.N., Ivancivsky V.V. Eff ect of deformation thermocyclic treatment and normalizing on the mechanical properties of sheet Steel 10.......................... 192 Bhanavase V., Jogi B.F., Dama Y.B. Wear behavior study of glass fi ber and organic clay reinforced poly-phenylenesulfi de (PPS) composites material........................................................................................................................................ 203 EDITORIALMATERIALS 218 FOUNDERS MATERIALS 227 CONTENTS
OBRABOTKAMETALLOV Vol. 23 No. 3 2021 MATERIAL SCIENCE EQUIPMENT. INSTRUMENTS 7 1 5 Predicting machined surface quality under conditions of increasing tool wear Victor Lapshin a, *, Alexandra Gubanova b, Ilya Dudinov c Don State Technical University, 1 Gagarin square, Rostov-on-Don, 344000, Russian Federation a https://orcid.org/0000-0002-5114-0316, lapshin1917@yandex.ru; b https://orcid.org/0000-0002-9785-5384, anatoliya81@mail.ru; c https://orcid.org/0009-0009-0784-1287, ilya.sandman@yandex.ru 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. 2025 vol. 27 no. 1 pp. 106–128 ISSN: 1994-6309 (print) / 2541-819X (online) DOI: 10.17212/1994-6309-2025-27.1-106-128 ART I CLE I NFO Article history: Received: 29 November 2024 Revised: 19 December 2024 Accepted: 23 January 2025 Available online: 15 March 2025 Keywords: Wear Vibrations Cutting process Surface quality Funding The study was carried out with the financial support of the RSF grant No. 24-29-00287. ABSTRACT Introduction. The most important factor determining the efficiency of metal cutting is the quality of the surface of the part obtained during cutting. The surface quality of a machined part is directly dependent on the vibration activity of the cutting tool, the amplitude of which is influenced by the complex evolutionary dynamics of the cutting process. In light of this, modern digital twin technology, which allows predicting the surface quality values of the parts using virtual models, is becoming an extremely relevant way to improve the efficiency in metalworking control systems. The purpose of the work. This study aims to improve the prediction accuracy of a digital twin system for the surface quality of the machined parts under conditions of increasing cutting tool wear. The paper examines: the dynamics of the turning process of metal parts, as well as a mathematical model describing the dynamics of tool vibrations during metal machining on lathes, considering the influence of the thermodynamic subsystem of the cutting system. Research methods. An experimental approach was employed, utilizing a author-designed measuring stand along with a modern inverted metallographic microscope LaboMet-I version 4, equipped with wide-angle lenses 5/20, having a 20 mm linear field of view, and a digital camera for microscopes Ucam-1400 with a 1.4 μm×1.4 μm matrix, and a contour profile recorder T4HD. Furthermore, the study used mathematical modeling of the dynamic cutting system in the Matlab environment, for which the authors developed a specialized data processing program. Results and discussion. Curves depicting the tool wear rate, changes in the quality parameters of the machined surface as functions of cutting path, and as a function of cutting tool wear are constructed. Dynamic indicators suitable for parametric identification of virtual digital twin models are determined. The structure of these models is established, and parametric identification is performed. Numerical modeling is conducted in the Matlab environment, based on the results of which a curve depicting the change in average arithmetic surface roughness as a function of increasing tool wear is constructed. The convergence of the results of field and numerical experiments is evaluated, which shows a high reliability of the surface quality prediction achievable through the use of digital twin systems. For citation: Lapshin V.P., Gubanova A.A., Dudinov I.O. Predicting machined surface quality under conditions of increasing tool wear. Obrabotka metallov (tekhnologiya, oborudovanie, instrumenty) = Metal Working and Material Science, 2025, vol. 27, no. 1, pp. 106–128. DOI: 10.17212/1994-6309-2025-27.1-106-128. (In Russian). ______ * Corresponding author Lapshin Viktor P., Ph.D. (Engineering), Associate Professor Don State Technical University, 1 Gagarin square, 344000, Rostov-on-don, Russian Federation Tel.: +7 900 122-75-14, e-mail: lapshin1917@yandex.ru Introduction As is well known, improving the quality of metal part processing on metal-cutting machines can be achieved by increasing positioning accuracy or by reducing vibrations of both the cutting tool and the workpiece, which is fixed in the machine’s spindle [1]. A crucial factor determining the surface quality during cutting is the wear of the cutting tool. A particularly problematic situation arises when the tool reaches critical wear values, leading to a sharp increase in vibration activity and, consequently, a decrease in the obtained surface quality. Therefore, assessing the impact of cutting tool wear on the surface quality achieved during machining is a urgent scientific task.
OBRABOTKAMETALLOV MATERIAL SCIENCE Том 23 № 3 2021 EQUIPMEN . INSTRUM TS Vol. 7 No. 1 2025 In contemporary view, vibration monitoring systems enable the prediction of surface quality during cutting based on digital vibration measurements [2‑3]. However, such prediction relies on the development of complex mathematical models that capture the evolutionary dynamics of the cutting process [4]. The complexity of these models and the requirement for their parametric identification present a significant challenge. Addressing this challenge will substantially enhance the capabilities of modern metalworking systems. One promising solution involves leveraging a novel digital paradigm in metalworking control systems known as the “digital twin” [5‑7]. Specifically, the application of intelligent models that describe the complex dynamics of technological processes during metal cutting holds significant potential in this emerging field of scientific knowledge [8‑9]. For example, the work by Y. Altintas and his research groups, prominent experts in the field of digital twin-based metalworking control, proposes using digital twins to generate new CNC programs that allow part machining without preliminary setups and experiments [10]. This suggests that the selection of optimal processing modes, both in addressing current challenges and in reconfiguring the control system on a metalcutting machine (i.e., achieving flexibility), can be achieved using virtual models within a digital twin. From a modern perspective, the technology of constructing digital twins, particularly the synthesis of virtual models, relies on two primary paradigms: one based on deterministic mathematical models [10] and the other on the widespread use of neural networks [8‑9]. Acritical direction in the development of digital twin technology is the diagnosis of various malfunctions. For instance, in [11], the focus is on generating labeled training datasets for various bearing malfunctions to supplement limited measured data. The authors propose a novel digital twin approach to address the scarcity of measured data in bearing malfunction diagnosis. Experimental results demonstrate an increase in the accuracy of malfunction diagnosis [12]. A similar perspective, albeit with slight variations, is presented in [13]. The authors highlight the limitations of traditional malfunction diagnosis methods that rely on experimental data, noting that in mission-critical industrial scenarios, such data is not always available. Digital twin technology, by creating a virtual representation of a physical object that reflects its operating conditions, enables the diagnosis of technical system or technological process malfunctions even when insufficient data on those malfunctions exists. The authors propose a malfunction diagnosis system based on digital twins that leverages labeled simulated data and unlabeled measured data [13]. The construction of a digital twin system that integrates sensor data from malfunctioning bearings into the subspaces of virtual models in real time is presented [14]. The authors refine the parameters of the virtual models by comparing the results of digital modeling in the time domain with the measured and captured signals [14]. At the same time, accurately modeling the complex influence of cutting tool wear on the dynamics of the cutting process remains an extremely challenging task, which cannot be solved without considering the thermodynamic aspects of the processes occurring during metal cutting [15‑16]. Based on the analysis, digital twin technology has become widely adopted in the diagnosis of malfunctions, including bearing malfunctions. Therefore, an obvious development of digital twin technology is to leverage it for predicting the impact of cutting tool wear on the quality of the parts produced during machining. Based on this, we formulate the purpose of the study as improving the accuracy of the digital twin system’s prediction of surface quality during machining under conditions of progressive cutting tool wear through parametric identification of the digital twin’s virtual models using data obtained from a vibration monitoring system of the cutting process. Research methodology Vibration monitoring system for assessing cutting tool wear and machined surface quality The experimental component of this research utilizes a vibration diagnostic subsystem, positioned on the cutting tool itself, or rather on its holder, as depicted in Fig. 1. This system is based on an industrial IEPE (ICP) general-purpose accelerometer with an integrated A603C01T charge converter amplifier. The accelerometer has a frequency range of +/− 3 dB: 0.4‑15,000 Hz; a sensitivity of +/− 10 %: 10.2 mV/(m/s2). The system also includes a single-channel ICP (IEPE) converter with a frequency range of 0.1-50,000 Hz.
OBRABOTKAMETALLOV Vol. 23 No. 3 2021 MATERIAL SCIENCE EQUIPMENT. INSTRUMENTS 7 1 5 As can be seen from Fig. 1, the vibration monitoring system relies on piezoelectric vibration sensors of the cutting tool. However, new intelligent sensors are becoming increasingly prevalent, offering the capability to digitally display vibration accelerations, speeds, and tool displacements directly [17‑20]. To illustrate the system’s operation, consider a turning example where a steel shaft (steel 45) with a diameter of 50 mm was machined on a 1K625 lathe. The processing mode was: a cutting speed of 124 m/ min, a depth of cut of 1 mm, and a feed rate of 0.11 mm/rev. We performed sequential double integration of the measured vibration acceleration signals using a program written in the Matlab environment. The resulting vibration acceleration signals from the x, y, and z channels, along with the calculated vibration velocity and displacement values, are presented for a single measurement case in the aforementioned figures. The adequacy of the program for step-by-step integration of the captured vibration acceleration signal can be conveniently examined by analyzing the main carrier frequencies in the signal’s spectrum, as illustrated in Fig. 5. As observed in Fig. 5, the main carrier frequencies remain unchanged. However, the high-frequency component of the vibration signal is significantly attenuated. This attenuation is attributed to the fact that the integration process involves summation and averaging operations, which aligns with the inertial properties of the object (tool). The actual wear of the cutting tool can be influenced by random factors not accounted for in digital twin mathematical models. To assess the actual wear of the cutting tool, a separate experiment was conducted on the 1K625 lathe, machining the shaft (steel 45) using the previously specified cutting mode. To measure a b c d Fig. 1. Vibration monitoring system on the 1K625 lathe: a, b – industrial accelerometers; c, d – amplifier converter and ADC
OBRABOTKAMETALLOV MATERIAL SCIENCE Том 23 № 3 2021 EQUIPMEN . INSTRUM TS Vol. 7 No. 1 2025 Fig. 2. Vibration acceleration signals for cutting at a speed of 150 m/min: a – in the axial direction; b – in the radial direction; c – in the tangential direction a b c Fig. 3. Vibration velocity signals for cutting at a speed of 150 m/min: a – in the axial direction; b – in the radial direction; c – in the tangential direction a b c
OBRABOTKAMETALLOV Vol. 23 No. 3 2021 MATERIAL SCIENCE EQUIPMENT. INSTRUMENTS 7 1 5 Fig. 4. Vibration displacement signals for cutting at a speed of 150 m/min: a – in the axial direction; b – in the radial direction; c – in the tangential direction a b c the wear of the cutting wedge, at each of eight steps, the actual wear of the cutting wedge on the back face was evaluated using a inverted metallographic microscope LaboMet-I version 4, equipped with wide-angle lenses 5/20, having a 20 mm linear field of view, and a digital camera for microscopes Ucam-1400 with a 1.4 μm×1.4 μm matrix. The microscope appearance and measurement results are shown in Fig. 6 and 7. A portion of the cutting tool wear measurement results is shown in Fig. 7 As shown in Fig. 7, the back face wear measurements were 0.3 mm for the first trial and 0.33 mm for the second trial. The complete measurement results are presented in the table 1 below. The measurement results are shown in Fig. 8. The wear curve development of the cutting tool, illustrated in Fig. 9, identifies three characteristic measurement points. These points will be used later for parametric identification of virtual models within the digital twin. Now, consider the relationship between the cutting tool wear and the surface quality of the machined part. To perform this assessment, a Contour ELITE optical three-dimensional microscope and a T4HD contour profile recorder (shown in Fig. 9) were used. Due to the physical size of the measuring equipment (Contour ELITE optical three-dimensional microscope and T4HD contour profile recorder), surface quality measurements could not be performed directly on the machine. To facilitate quality assessment, it was necessary to isolate the regions of interest. Therefore, each step of the experiment, where tool wear was measured, also included the creation of a corresponding evaluation surface. Considering that the shaft was ground during the experiment, it was convenient to measure tool wear after each tool stroke along the shaft and to design each step such that the processed surface from the previous step was preserved. After a series of experiments in which force response and the power of irreversible transformations (temperature in the cutting zone) were also measured, the resulting machined shaft exhibited a conical shape with a discretely decreasing radius, as shown in Fig. 10.
OBRABOTKAMETALLOV MATERIAL SCIENCE Том 23 № 3 2021 EQUIPMEN . INSTRUM TS Vol. 7 No. 1 2025 a b c d e f g h i Fig. 5. Power spectra of cutting tool vibration activity signals for cutting at a speed of 150 m/min: a, b, c – in the axial direction; d, e, f – in the radial direction; g, h, i – in the tangential direction
OBRABOTKAMETALLOV Vol. 23 No. 3 2021 MATERIAL SCIENCE EQUIPMENT. INSTRUMENTS 7 1 5 Fig. 6. The appearance of the microscope Fig. 7. Photograph with measured wear value Ta b l e 1 Data on tool wear during processing Cutter travelled distance, m 0 202 552 840 1,375 2,010 3,061 Wear of the cutting plate, mm 0.01 0.11 0.20 0.23 0.24 0.26 0.36 Following machining on the lathe, this shaft was sectioned into smaller pieces, each representing the surface quality achieved at a specific level of tool wear (Fig. 11). These shaft sections were then used to evaluate the quality of the surface machined by tools with different degrees of wear using both the T4HD contour profile recorder and the Contour ELITE optical three-dimensional microscope.
OBRABOTKAMETALLOV MATERIAL SCIENCE Том 23 № 3 2021 EQUIPMEN . INSTRUM TS Vol. 7 No. 1 2025 h, mm L, m 0.4 0.35 0.3 0.25 0.2 0.15 0.1 0.05 0 0 500 1000 1500 2000 2500 3000 3500 critical wear zone run-in area wear rate stabilization zone Measuring point 1 2 3 4 Fig. 8. Measured wear curve of the cutting tool a b Fig. 9. Measuring device: a – contour profile recorder T4HD; b – microscope Contour ELITE Fig. 10. Part after the experiment is complete
OBRABOTKAMETALLOV Vol. 23 No. 3 2021 MATERIAL SCIENCE EQUIPMENT. INSTRUMENTS 7 1 5 Based on the data acquired from the Contour ELITE optical microscope, the changes in the Ra and Rz parameters (units of measurement: µm) across the experimental steps are shown in Table 2. Fig. 11. The part prepared for measurements Ta b l e 2 Results of measuring Contour ELITE roughness indicators Parameters Point 1 Point 2 Point 3 Point 4 Rz on the y axis (μm) 26.186 21.347 22.898 18.152 Ra on the y axis (μm) 2.41 1.493 1.794 2.975 As the data in Table 2 reveals, there is no clear, consistent trend in the change of Ra and Rz. A significant increase in the arithmetic mean deviation of the profile from the mean line along the y-axis can be observed, with some fluctuations at the fifth step. However, the changes in Ra and Rz along the x-axis are not substantial, and, in general, these two indicators even decrease along this axis. In the case shown in Fig. 12, obtained at the second measurement point, the changes in the part’s profile along the feed direction (y-axis) are clearly due to the size of the tool’s step movement along the feed line. Decreasing the feed rate will reduce the Ra and Rz parameters along the y-axis, while increasing the feed rate will increase them. To illustrate this more clearly, a 3D model of the machined surface obtained on the Contour ELITE microscope at the second experimental point is provided (Fig. 13). Fig. 12. Photograph of the machined surface, obtained with a microscope Contour ELITE, after the first step of the experiment
OBRABOTKAMETALLOV MATERIAL SCIENCE Том 23 № 3 2021 EQUIPMEN . INSTRUM TS Vol. 7 No. 1 2025 Fig. 13. 3D model of the surface after the first step of the experiment To illustrate the appearance of a surfacemachinedwith near-catastrophic tool wear, consider a photograph of the machined surface obtained using the Contour ELITE microscope at the fourth experimental point (Fig. 14). Fig. 14. Photograph of the machined surface, obtained with a microscope Contour ELITE, after the sixth step of the experiment As shown in Fig. 14, the quality of the machined surface significantly deteriorated at this step of the experiment. This is also evident from the Ra indicator along the y-axis, which reaches its maximum value in this case (see Table 2). While the arithmetic mean deviation of the profile from the midline along the y-axis is not the highest, this is explained by the specific location of the measurement axis, which did not align with the area exhibiting the greatest profile deviations. The latter case is further illustrated with a 3D model of the surface image (Fig. 15).
OBRABOTKAMETALLOV Vol. 23 No. 3 2021 MATERIAL SCIENCE EQUIPMENT. INSTRUMENTS 7 1 5 Fig. 15. 3D model of the surface after the sixth step of the experiment Comparing the 3D model in Fig. 15 to the case in Fig. 13 indicates a significant deterioration in the quality of the machined surface. A graph summarizing the experiments is shown below (Fig. 16). Fig. 16. Results of surface quality measurement at 4 points As can be seen in Fig. 16, the Ra indicator is the most informative parameter. In general, the data obtained from the Contour ELITE optical three-dimensional microscope, particularly the 3D models, are qualitative in nature. They do not provide a clear, quantitative depiction of changes in the roughness of the machined surface. For quantitative surface quality assessment, a T4HD contour profile recorder was used, and its measurement protocols for a representative experimental step are shown in Fig. 17. Note that the roughness parameters were evaluated in the direction orthogonal to the feed, that is, along the y-axis.
OBRABOTKAMETALLOV MATERIAL SCIENCE Том 23 № 3 2021 EQUIPMEN . INSTRUM TS Vol. 7 No. 1 2025 Ta b l e 3 Quality indicators for T4HD protocols Path (m) 0 202 552 840 1375 2010 3061 Rz (μm) 4.704 3.040 3.272 3.396 3.632 5.92 9.274 Ra (μm) 0.609 0.516 0.527 0.532 0.602 0.969 1.69 Fig. 17. Example of the protocol captured on the contour profile recorder T4HD All the roughness measurement results are conveniently summarized in the following table (Table 3). As can be seen in Table 3, both roughness parameters increase with increasing wear. A period of relative stabilization is observed after the second experimental step, followed by a sharp increase in roughness after the sixth step. The graph of the roughness change over time is shown in Fig. 18. Fig. 18. Change in roughness indicators along the cutting path
OBRABOTKAMETALLOV Vol. 23 No. 3 2021 MATERIAL SCIENCE EQUIPMENT. INSTRUMENTS 7 1 5 As shown in Fig. 18, the roughness indicators begin to increase with increasing wear. At the initial stage, the dependence between Ra, Rz on wear is decreasing, which is due to the running-in of the cutting tool. As the tool approaches the point of catastrophic wear, the increase in roughness becomes more pronounced, and the dependence becomes nonlinear. Virtual models for the digital twin The synthesis of virtual models for the digital twin begins with constructing a cutting tool wear curve using a Volterra integral operator of the second kind [20]. Here, it is necessary to consider the actual path traveled by the tool, including the path deviations resulting from vibrational movements of the tool tip. This path is calculated taking into account the processed data received from the vibration monitoring system. That is, the path traveled by the tool will be determined as the sum of the calculated path, denoted as L0, which is determined by the cutting speed and feed rate, and the virtual path traveled by the tool: 2 2 2 v L x y x = + + . (1) The simulation results of the digital twin’s equation systems allow for the calculation of the predicted cutting tool wear. Modeling must account for the fact that the cause of these evolutionary transformations is related to the power and action of cutting forces, specifically the energy of irreversible transformations in the machining zone. Volterra integral operators of the second kind are used to model these evolutionary changes, having the following structure [21‑22]: 0 ( ) ( ) 3 A h k w t N d = − ξ ξ ξ ∫ , (2) where ( ) w t − ξ is the kernel of integral operator; ( ) N ξ is the phase trajectory of the power of irreversible transformations based on perfect work А is the work of cutting forces. The wear depends on the intensity of irreversible transformations and its history, which is captured by the integral operator’s kernel. The kernel of the integral operator is expressed as: ( ) 1 2 ( ) ( ) 3 1 2 0 ( ) A A A h e e N d α ξ− α −ξ = β + β ξ ξ ∫ , (3) where ( ) 1 2 ( ) ( ) 1 2 A A e e α ξ− α −ξ β + β is the sum of the kernels of an integral operator, where 1( ) 1 A eα ξ− β is the kernel that determines the running-in processes of the tool; 2( ) 2 A eα −ξ β is the kernel that determines the processes of wear; 1 β , 2 β , 1 α , 2 α are the parameters to be identified; N is the power of irreversible transformations; A is the performance. The power of irreversible transformations is defined as 2 2 N R Vf V = + , where R is the cutting force. We define the working process in the form of the following integral: 0 ( ) t A N t dt = ∫ . In case N = N0 = const, (2) the following equation is used: ( ) 1 2 ( ) ( 3 0 0 ) A A A h N e e d α ξ− α −ξ = + ξ ∫ . (4) The solution for (4) will be as follows: ( ) ( ) 1 2 3 0 0 1 2 1 1 1 1 A A h N e N e −α α = − + − α α . (5)
OBRABOTKAMETALLOV MATERIAL SCIENCE Том 23 № 3 2021 EQUIPMEN . INSTRUM TS Vol. 7 No. 1 2025 If N(t) is not equal to N0 = const, and taking into consideration that N0, N1, …, Nn−1 due to the small size ΔA = Ak − Ak−1 are values close to constants, we obtain the following approximate sum describing the integral operator (5): ( ) 1 1 1 1 2 ( ) 1 3 0 1 0 2 1 1 ( ) ( ) ... A A A A A h N e N N e N N e α − −α α − β = − − − − − − α 1 1 ( ) 1 2 1 ( ) n A A n n n N N e N − α − − − − − − + + 2 2 1 2 2 ( ) ( ) 2 0 1 0 2 1 2 ( ) ( ) ... A A A A A N e N N e N N e α −α − −α − β + − + − + − + α 2 1 ( ) 1 2 1 ( ) , n A A n n n N N e N − −α − − − − + − − (6) where 1 β , 2 β , 1 α , 2 α are the parameters identified in the wear calculation model, based on the results of preliminary experiments. The graph of the cutting tool wear development along the back edge based on the calculated data is shown in Fig. 19. h, mm L, m 0.4 0.35 0.3 0.25 0.2 0.15 0.1 0.05 0 0 800 1600 2400 3200 4000 4800 сritical wear zone run-in area stabilization zone Fig. 19. Calculated cutting tool wear curve A comparative analysis of Fig. 8 and 19 shows that the critical wear zone begins earlier in Fig. 8 than in Fig. 19. This discrepancy is attributed to the chip formation process. During machining, we deliberately allowed cutting to occur with strong vibrations resulting from the accumulation of flow chips. Consequently, these vibrations caused changes in the contact between the tool back face and the workpiece, leading to the appearance of new elemens in the wear zone. As can be seen in Figs. 8 and 19, the wear development curve of the cutting tool along the workpiece is divided into three distinct areas: – the run-in area, where initial tool wear is established; – the area of wear stabilization, where the rate of wear increases slowly; – the area of formation of critical wear, where the rate of tool wear increases rapidly. As the fundamental virtual model for a digital twin, we will adopt a system of nonlinear equations. The of the cutting force on the temperature-speed coefficient of cutting will be represented as a decreasing exponential dependence:
OBRABOTKAMETALLOV Vol. 23 No. 3 2021 MATERIAL SCIENCE EQUIPMENT. INSTRUMENTS 7 1 5 0 1 , c dz V dt e µ −α − ρ = ρ + µ (7) where ρ0 is the lowest value ρ (the coefficient that depends on temperature and characterizes the chip pressure on the front edge of the cutting wedge); μ is a coefficient showing the curvature of the exponential decrease ρ as the contact temperature increases; αμ is a steepness coefficient of the characteristic ρ; c dz V dt − is a real cutting speed. Taking into account Equation (7), the cutting force can be expressed as the following: 1 0 1 ( ) c v dz t V dt p t T dx F e a y V dt f dt −α − − = ρ + µ − − ∫ , (8) where ( ) p a y − is a real cutting depth; v t f t T dx V dt dt − − ∫ is a real feed. The outward force originating from the cutting zone, which depends on wear of the tool along the back surface and the thermal expansion of the workpiece material, is expressed as: ( ) 0 3( ) h K x F h Q h p F k Q h a y e − = σ + − , (9) where σ0 represents the ultimate strength of the processed metal in compression, kg/mm2, depending on the temperature of the contact between the back surface of the tool Qh and the workpiece, °C; Kh, is a coefficient that characterizes a nonlinear increase in outward force when the back surface of the tool and the workpiece come closer. Through the main cutting edge angle φ, the resultant force can be resolved along the x and y axes of deformation as follows: ( ) ( ) cos ; sin . x h h y h h F F F F = ϕ = ϕ (10) The force component on the tool back face in the z-coordinate direction, representing the friction force, is: ( ) z h t h F k F = , (11) where ki is the coefficient of friction is interpreted as: 1 2 0 2 f h f K Q K Q h t t t k k k e e − = + ∆ + , (12) where k0t is minimum value of the coefficient of friction; Δkt is the incremental change in the friction coefficient with a change in temperature in the contact area; Kf1 and Kf2 are coefficients that determine the rate of decrease and increase, respectively, of the friction coefficient characteristic. The general expression describing the force reaction resolved along the axes of deformation takes the following form: ( ) 1 ( ) 2 ( ) 3 ; ; , x f h y p h z c h F F F F F F F F F = χ + = χ + = χ + (13)
OBRABOTKAMETALLOV MATERIAL SCIENCE Том 23 № 3 2021 EQUIPMEN . INSTRUM TS Vol. 7 No. 1 2025 where χi is the coefficient of the cutting force decomposition on the i-axis of tool deformation. The deformation movement model of the tool tip is as follows: 2 11 12 13 11 12 13 2 2 21 22 23 21 22 23 2 2 31 32 33 31 32 33 2 ; ; , f p c d x dx dy dz m h h h c x c y c z F dt dt dt dt d y dx dy dz m h h h c x c y c z F dt dt dt dt d z dx dy dz m h h h c x c y c z F dt dt dt dt + + + + + + = + + + + + + = + + + + + + = (14) where m [kg∙s2/mm]; h [kg∙s/mm]; c [kg/mm] are the inertia, dissipation, and stiffness matrices, respectively. The differential equation describing the temperature transfer through back surface of the tool from the previous spindle revolution to the current contact zone between the tool and the workpiece is given by: 2 1 2 1 2 2 ( ) h h h d Q dQ T T T T Q kN dt dt + + + = , (15) where 3 1 2 1 2 , c h T T V λ = = α α are time constants; 3 1 2 Q c k h k V λ = α α is the transmission ratio; α1, α2 are identifiable dimensionless scaling parameters of the integral operator; λ is the thermal conductivity coefficient; kQ is the coefficient characterizing the conversion of irreversible transformation power into temperature; ( ) ( ) 3 ( ) ( ) ( ) ( ) ( ) z v v ñ v ñ v v ñ h dz t T dz t T N F t T V F t T F t T V dt dt − − = − − = χ − + − − is the force of irreversible transformations. Thus, the system of equations (3) through (15) constitutes a virtual mathematical model for the digital twin of the metalworking process on a metal-cutting machine. Research Results and Discussion The most promising approach for parametric identification of virtual digital twin models is the use of data acquired from a vibration monitoring system [20]. Here, from the complete dataset, we propose focusing on so-called scattering ellipses, which can be obtained directly from the recorded vibration acceleration signal. Let us consider the scattering ellipses for the cases presented in Fig. 8, identifying the parameters of the digital twin model from these data. The results of comparing these two methods for calculating scattering ellipses, for equal values of cutting wedge wear, are shown in Fig. 20. As seen in Fig. 20, the span of the scattering ellipse axes coincides almost exactly. However, the vibration dispersion for the real machine is significantly higher. This is attributed to the presence of vibration activity not only from the cutting process but also from other system carriers within the real machine. Modeling all supporting systems in conjunction with the cutting system remains an unresolved task. To evaluate the quality of the surface obtained during cutting, only the vibration activity coordinate of the cutting tool along the y-axis was analyzed. This choice is based on the fact that vibrations at the cutting tool tip along the z- and x-axes will be attenuated during longitudinal turning, which forms the basis for modeling the cutting system. However, in other metalworking operations, the vibration signal in other directions can also be analyzed. To analyze the quality of the surface obtained by cutting, we needed to process the data to remove any constant component, considering only the vibrations within a defined time range. For this purpose, we developed a program that allows us to analyze the quality of the surface obtained during cutting, both within the digital twin system and based on data acquired by the vibration monitoring system. This resulted in a sufficiently powerful computational tool that can subsequently be used for parametric identification of virtual digital twin models on real metalworking machines.
OBRABOTKAMETALLOV Vol. 23 No. 3 2021 MATERIAL SCIENCE EQUIPMENT. INSTRUMENTS 7 1 5 Fig. 20. Ellipses of dispersion of the “rose” of vibrations: a, b – in the X/Y plane with a wear value of h = 0.24 mm; c, d – in the X/Y plane with a wear value of h = 0.27 mm; e, f – in the X/Y plane and a wear and tear h = 0.32 mm a b e f c d e f
OBRABOTKAMETALLOV MATERIAL SCIENCE Том 23 № 3 2021 EQUIPMEN . INSTRUM TS Vol. 7 No. 1 2025 The simulation results of the deformation motion along the y-axis, as well as the points at which the surface roughness was evaluated using the Ra indicator, are presented in the figures below (see Figs. 21-23). Fig. 21 illustrates the method for calculating machining quality based on the Ra indicator for a cutting scenario with 0.11 mm of cutting tool wear on the back face. As seen in Fig. 21, the calculation was performed using data obtained from the model, but not from the first second, i.e., the start of the simulation, but rather from the time when the cutting process reached a steady state. In the resulting sample, we only considered the minimum and maximum values, which were used to calculate the roughness parameter, which in this case is equal to 1.26 μm. Fig. 21. Example of calculating the quality index for the case of cutting with a wear of 0.11 mm Fig. 22. Example of calculating the quality index for the case of cutting with a wear of 0.29 mm
OBRABOTKAMETALLOV Vol. 23 No. 3 2021 MATERIAL SCIENCE EQUIPMENT. INSTRUMENTS 7 1 5 The simulation results for the coordinate representing the deformation motions of the cutting tool tip in the radial direction, with the wear of the cutting tool along the back face of 0.29 mm, are shown in the figure below. As can be seen from the figure, the amplitude of the deformation motions increases significantly, and for this cutting case, the calculated Ra value reaches 2.2 μm, which is substantially higher than the previous calculated values. This jump is due to the transition of the wear curve from the stabilization region to the region of critical wear. Let’s consider another cutting scenario, characterized by the wear of the cutting tool along the back face of 0.344 mm. The simulation results for the y-coordinate for this case are shown in Fig. 23. A comparison of Figs. 22 and 23 shows an even greater increase in the vibration amplitude of the cutting tool, including in the radial direction. The calculated surface quality parameter for this case reached a value of 4.17 μm, which is a very large value for the case of metal turning and can only be considered as a roughing or pre-finishing option. In total, we conducted nine experiments to construct the curve of surface quality variation during cutting. For clarity, let’s consider both the experimental evaluation and the model evaluation of surface quality variation. To do this, let’s examine the variation of Ra for the case of experimental data, based on the interpretation of this parameter using the data presented in Figs. 8 and 19. The interpretation of the data from Table 4, presented as a combined graph of two characteristics, is shown in the figure below. As seen in Fig. 25, in this case of comparing simulation results with experimentally obtained results, both characteristics exhibit a very high degree of similarity. There is a slight discrepancy in the region of average values of the value characterizing the wear of the cutting tool along the back face, but overall, the characteristics are almost identical. Fig. 23. Example of calculating the quality index at h = 0.314 mm Ta b l e 4 Values of the quality indicator Ra (experimental and modeled) Wear (mm) 0.11 0.16 0.22 0.23 0.26 0.29 0.314 0.344 0.402 Ra (μm) (modeled) 1.26 1.22 0.96 0.84 0.98 2.2 2.9 4.17 41 Ra (μm) (experimental) 0.516 0.52 0.53 0.532 0.602 0.97 1.45 1.6 39
OBRABOTKAMETALLOV MATERIAL SCIENCE Том 23 № 3 2021 EQUIPMEN . INSTRUM TS Vol. 7 No. 1 2025 Fig. 24. Dependence of the surface quality obtained during cutting on the cutting tool wear It should be noted that both the model and experimental characteristics show stabilization and even some decrease in the Ra value in the stabilization area of the cutting tool wear curve (see Fig. 24). The digital twin technology relies on the widespread use of virtual mathematical models, the numerical simulation of which can predict the development of various processes in technical systems. The importance of such a prediction largely depends on the accuracy of the models used in the simulation, which in turn require constant parametric adjustment of the parameters. Primarily, such adjustment is associated with the increasing wear of the cutting tool, which must also be modeled in the digital twin system. The particular cutting case that we have considered only allows us to conclude the possibility of predicting the surface quality obtained during cutting based on calculations performed on virtual models of the digital twin. Overall, the purpose of the study set at the beginning of the paper has been achieved, mainly due to the introduction of a module for calculating the increase in cutting tool wear into the model, and also due to the parametric identification of the remaining model parameters. Studies have shown that the accuracy of predicting the dependence of the surface quality obtained during cutting on the cutting tool wear is sufficiently high for this particular case. The largest discrepancies between the modeled and experimental characteristics are observed in the range from 0.25 mm to 0.36 mm of cutting tool wear. This range is characterized by the largest deviation of the experimental and simulated curves of cutting tool wear (see Figs. 8 and 19). In the example we provided, the cutting tool wear curve along the cutting path behaved classically, and three wear areas could be identified on it, as well as on the simulated characteristic. However, this is a specific case, and the cutting tool wear may increase abruptly, in a jump, due to various random factors. For this case, it is necessary to integrate an intelligent subsystem for recognizing such non-standard cases of cutting tool wear development into the parameter identification system of the virtual models of digital twins. Conclusion This work considered the issues of assessing the interrelationship between cutting tool wear and the quality of the surfaces obtained during cutting. The quality of the surfaces obtained during cutting was assessed using the Ra value, which proved to be the most informative for the case under consideration, both for processing experimental data and for simulating. Given the dependencies obtained in Fig. 24, it can be asserted that predicting the quality of the surface of parts obtained by cutting based on the results
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