Vol. 26 No. 3 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. 3 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. 3 2024 5 CONTENTS OBRABOTKAMETALLOV TECHNOLOGY Sukhov A.V., Sundukov S.K., Fatyukhin D.S. Assembly of threaded and adhesive-threaded joints with the application of ultrasonic vibrations...................................................................................................................................... 6 Baraboshkin K.A., Adigamov R.R., Yusupov V.S., Kozhevnikova I.A., Karlina A.I. Thermomechanical rolling in well casing production (research review)......................................................................................................................... 24 Dwivedi R., Somatkar A., Chinchanikar S. Modeling and optimization of roller burnishing of Al6061-T6 process for minimum surface roughness, better microhardness and roundness................................................................................ 52 Ilinykh A.S., Pikalov A.S., Miloradovich V.K., Galay M.S. Experimental studies of rail grinding modes using a new high-speed electric drive...................................................................................................................................................... 66 Karlina Yu.I., Konyukhov V.Yu., Oparina T.A. Assessment of the possibility of resistance butt welding of pipes made of heat-resistant steel 0.15C-5Cr-Mo................................................................................................................................... 79 Gimadeev M.R., Stelmakov V.A., Shelenok E.A. Product life cycle: machining processes monitoring and vibroacoustic signals fi lterings.................................................................................................................................................................... 94 EQUIPMENT. INSTRUMENTS Zakovorotny V.L., Gvindjiliya V.E., Kislov K.V. Information properties of frequency characteristics of dynamic cutting systems in the diagnosis of tool wear....................................................................................................................... 114 Ablyaz T.R., Blokhin V.B., Shlykov E.S., Muratov K.R., Osinnikov I.V. Features of the use of tool electrodes manufactured by additive technologies in electrical discharge machining of products....................................................... 135 Sidorov E.A., GrinenkoA.V., ChumaevskyA.V., Panfi lovA.O., Knyazhev E.O., NikolaevaA.V., CheremnovA.M., Rubtsov V.E., Utyaganova V.R., Osipovich K.S., Kolubaev E.A. Patterns of reverse-polarity plasma torches wear during cutting of thick rolled sheets..................................................................................................................................... 149 MATERIAL SCIENCE Semin V.O., Panfi lov A.O., Utyaganova V.R., Vorontsov A.V., Zykova A.P. Corrosion properties of CuAl9Mn2/ER 321 composites formed by dual-wire-feed electron beam additive manufacturing................................ 163 Dewangan R., Sharma B.P., Sharma S.S. Investigation of hardness behavior in aluminum matrix composites reinforced with coconut shell ash and red mud using Taguchi analysis............................................................................ 179 Saprykina N.А., Saprykin A.А., Sharkeev Y.P., Ibragimov E.А. The eff ect of technological parameters on the microstructure and properties of the AlSiMg alloy obtained by selective laser melting......................................................... 192 Burdilov A.A., Dovzhenko G.D., Bataev I.A., Bataev A.A. Methods of synchrotron radiation monochromatization (research review).................................................................................................................................................................. 208 Burkov A.A., Dvornik M.A., Kulik M.A., Bytsura A.Yu. Wear resistance and corrosion behavior of Cu-Ti coatings in SBF solution..................................................................................................................................................................... 234 Pugacheva N.B., Bykova T.M., Sirosh V.A., MakarovA.V. Structural features and tribological properties of multilayer high-temperature plasma coatings........................................................................................................................................ 250 Sharma B.P., Dewangan R., Sharma S.S. Characterizing the mechanical behavior of eco-friendly hybrid polymer composites with jute and Sida cordifolia fi bers.................................................................................................................... 267 Kornienko E.E., Gulyaev I.P., Smirnov A.I., Plotnikova N.V., Kuzmin V.I., Golovakhin V., Tambovtsev A.S., Tyryshkin P.A., Sergachev D.V. Fine structure features of Ni-Al coatings obtained by high velocity atmospheric plasma spraying.................................................................................................................................................................... 286 EDITORIALMATERIALS 298 FOUNDERS MATERIALS 307 CONTENTS
OBRABOTKAMETALLOV Vol. 26 No. 3 2024 technology Product life cycle: machining processes monitoring and vibroacoustic signals filtering Mikhail Gimadeev a, *, Vadim Stelmakov b, Evgenii Shelenok c Pacific National University, 136 Tihookeanskaya st., Khabarovsk, 680035, Russian Federation a https://orcid.org/0000-0001-6685-519X, 009063@pnu.edu.ru; b https://orcid.org/0000-0003-2763-1956, 009062@pnu.edu.ru; c https://orcid.org/0000-0003-4495-9558, 007141@pnu.edu.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. 2024 vol. 26 no. 3 pp. 94–113 ISSN: 1994-6309 (print) / 2541-819X (online) DOI: 10.17212/1994-6309-2024-26.3-94-113 ART I CLE I NFO Article history: Received: 29 May 2024 Revised: 23 June 2024 Accepted: 12 July 2024 Available online: 15 September 2024 Keywords: Life cycle Equipment measurement sensors Acoustic diagnostics Vibration Tool status monitoring Online monitoring Machining process Funding This work has funded by the Ministry of science and higher education of Russian Federation (project № FEME– 2024–0010). ABSTRACT Introduction. In modern manufacturing, the product life cycle comprises various stages, from conception to disposal. Among these stages, machining plays a significant role, as it directly influences the durability and functionality of the finished product. With increasing competition and the need to reduce production costs, optimizing machining processes has become a crucial task. Traditionally, conservative technological approaches have been used to ensure processing quality. However, this often leads to decreased productivity and higher costs. Modern monitoring and diagnostic techniques can significantly improve process control, particularly through tool condition monitoring. The subject. This paper discusses the stages of the product life cycle and emphasizes the importance of monitoring machining processes. It explores the potential of using vibroacoustic signals to continuously monitor equipment and product conditions. Special attention is paid to the use of vibroacoustic signals for diagnostics and quality control. Modern approaches to filtering these signals, including the use of the fast Fourier transform and various window functions, are analyzed in order to improve the accuracy of the analysis and identify potential defects. The purpose of this work is to develop an algorithm for an online monitoring system that will monitor the condition of cutting tools based on the creation of a digital shadow using a vibroacoustic complex. The main tasks to be solved are to establish the ranges of applicability of frequency response of acoustic signals and optimal window functions, as well as to establish the relationship between the degree of wear on the cutting tool and the results of vibration diagnostics and surface roughness. The methods and technologies for filtering vibroacoustic signals and their application in real–world production settings are discussed. Special attention is given to the role of digital twins in integrating monitoring and filtering data, allowing for the creation of a virtual model of a product to predict its behavior and optimize processes throughout the life cycle. A comparison of various monitoring methods and technologies is conducted, as well as an analysis of practical examples of digital twin implementation in production processes and its impact on improved control. Results and discussion are presented, identifying current research and practical advancements, while also proposing existing challenges and promising areas for future research in the fields of monitoring, signal filtering, and the use of digital twins in mechanical manufacturing. For citation: Gimadeev M.R., Stelmakov V.A., Shelenok E.A. Product life cycle: machining processes monitoring and vibroacoustic signals filterings. Obrabotka metallov (tekhnologiya, oborudovanie, instrumenty) = Metal Working and Material Science, 2024, vol. 26, no. 3, pp. 94–113. DOI: 10.17212/1994-6309-2024-26.3-94-113. (In Russian). ______ * Corresponding author Stelmakov Vadim A., Ph.D. (Engineering), Associate Professor Pacific National University, 136 Tihookeanskaya st., 680035, Khabarovsk, Russian Federation Tel.: +7 962 221-74-60, e-mail: 009062@pnu.edu.ru Introduction The transformation of industry during the implementation of the «Industry 4.0» concept opens up prospects for the use of new high-performance approaches to management at all life cycle stages [1], incl. through the use of digital twins (DT) [2, 3]. DT technology is the main component of a cyber physical system (CPS) [4], which allows collecting and managing large amounts of data (Big Data) [5], determining the behavior and reflecting the state of the production system in real time [6], and analyzing, modeling, predicting and optimizing various production processes [7–9].
OBRABOTKAMETALLOV technology Vol. 26 No. 3 2024 Currently, the initiative shown by enterprises is aimed at the synthesis of science, technology, innovation using digitalization and automation of design and production processes [10, 11], etc. At the same time, the emphasis of this initiative is on improving the functioning Continuous Acquisition and Life cycle Support (CALS), in particular, on improving Electronic Technical Documentation (ETD), digital twin technology [12, 13] and Quality Management Systems (QMS) [14, 15]. Product Lifecycle Management (PLM) systems are used to manage ETD, provide Integrated Logistics Support (ILS) [16] data and provide access to it at any stage (Fig. 1). Fig. 1. Information support for the PLC stages At the design stage, Computer-Aided Design (CAD) systems and Component Supplier Management (SCM) systems are used. Product Data Management systems (PDM) are used to solve the problems of joint functioning of CAD/CAE components (CAE – Computer Aided Engineering) [17]. In digital production systems, technological preparation of production (TPP) is carried out in the CAD system of TPP, including automated TPP-CAPP (Computer Aided Process Planning) and the development of control programs for Technological Equipment (TE) with CNC-CAM (Computer Aided Manufacturing). Program control is performed through a CNC system based on specialized computers integrated into the maintenance facility. Information obtained during production can be sent to the Enterprise Resource Planning system (ERP). To perform the functions of collecting and processing data on the state of maintenance and Technological Processes (TP), a system of software and hardware complex for Supervisory Control and Data Acquisition (SCADA) is introduced into the Automated Control System (ACS) [18], interacting with the Manufacturing Execution System (MES). According to GOST 57700.37–202, a DT is a system consisting of a digital model of a product and twoway communication with the product and (or) its components. The work of the authors Lu et al. [7] describes a conceptual model of DT consisting of three elements: physical space, digital space and two-way dynamic communication between it; an information model that collects and integrates selected information from existing databases; a methodology describing decision support within the life cycle. To ensure the stability of TP and other production elements that directly affect the quality of the product [19–21], when introducing DT into the life cycle stages, it is necessary to pay attention to improving the complex processing and transmission of accumulated streaming data received from a real product and describing its behavior – the Digital Shadow (DS) [22, 23]. DS helps to concentrate data sets from different subsystems for the purpose of processing and filtering it for use in DT. For example, during machining by cutting, an excessively large array of different data on process parameters enters the digital space from the product, some of which are not directly related to DT.
OBRABOTKAMETALLOV Vol. 26 No. 3 2024 technology Redundant data arises mainly due to the lack of ranges of measured product parameters. At the same time, in repetitive or slightly different processes at the production stage, the actual task when using DS is to generate meaningful (effective) data. Riesener M., Schuh G., et al. proposed a DS framework that allows the collection and integration of information based on heterogeneous data sources [24, 25]. In the work of the authors Fedonin O. N. et al. a structure of automated systems is proposed that ensures the collection and analysis of data from CNC metal-cutting machines within the framework of the MES [26]. Integration of a DS simulation model with MES was proposed by Negri S. et al. [27] by creating a DT used for decision making including an intelligent system that contains rules and knowledge for choosing between alternatives. Thus, we can conclude that at the moment, various scientific groups are studying DT of various levels (hierarchies), starting from DT of the cutting tool [5] and ending with DT of the operating process. This fact tells us that PLM systems can be integrated with DT to provide more efficient management of the entire life cycle [7, 22, 28] and be used for modeling and analysis of various processes [29]. For example, through Online Monitoring (OM) [30, 31], it is possible to clarify the idea of the technical condition of a vehicle, carry out its diagnostics, predict the remaining service life, etc. [32–34]. Since one of the main elements of the technological system is a metal-cutting tool, then from the point of view of creating DT and DS, it deserves the greatest attention, both in terms of ensuring the stability of the machining process and in terms of the quality of the resulting surfaces. However, taking into account the multifactorial nature of the machining process, in order to generate DT and DS, it is necessary to constantly obtain data about the machining process in real time. The authors’ works [35–39] describe monitoring systems for Tool Condition Control (TCC) in real time at maintenance. An analysis of scientific works [40–42] made it possible to formulate the purpose of monitoring the ICS: assessing the state of the IR, detecting chips and breakage of the instrument. Considering the difficulty of predicting tool condition, multiple sensors are used [43–45]. However, the presence of a large number of sensors leads to repetitive (redundant) data, which ultimately reduces the efficiency of using TCC systems. Thus, the selection of suitable sensors and, accordingly, monitoring methods is important [46–49]. Studies by many authors describe the use of monitoring based on signals of force [40], acoustic emission (AE) [42, 50], power, current [51, 52], temperature, etc. [33, 45]. Dynamometers and AE devices are expensive equipment, and for measuring signals, incl. cutting force values require highly qualified specialists. The AE system is also quite complex and includes: a set of preamplifiers, cable lines, units for preprocessing and converting AE signals, a computer with the necessary software [53], information display tools, and system calibration units [54]. The results obtained using temperature sensors are often unreliable because infrared rays do not measure the actual temperature in the cutting zone [33]. Similarly, the use of thermocouple [53] has its disadvantages for milling operations due to the complexity of the process. At the same time, an analysis of scientific works in recent years shows that the topic of TCC using Vibroacoustic (VA) signals is studied in fragments. Research mainly focuses on two areas: online vibrationbased cutting tool wear analysis [55] and surface roughness analysis using sound signal during machining [51, 56]. Despite the fact that these areas have its own characteristics and methods, the integration of its results can lead to the creation of a more comprehensive and effective OM ТСС system. Such a system, with proper methodological description, configuration, recording and filtering of VA signals, will make it possible to obtain an easily reconfigurable, reliable monitoring complex with low cost, and will also ensure the required product quality, increase productivity and reduce costs due to more accurate measurement data and the processing process. To implement the concept of monitoring machining on Technological Equipment with CNC, it is proposed to form a DS using a VA complex, which transmits a signal to the software. At the same time, the applied software should have an intuitive, friendly user interface, the data should be structurally ordered, and the software implementation of application functions should be carried out using a client-server design [57–62].
OBRABOTKAMETALLOV technology Vol. 26 No. 3 2024 The business objective of using the proposed DS is to reduce the number of defects during program development and increase the economic efficiency of the machining process. Machining information is represented by various input data: name of the control program, tool identification number [63, 64], feed rate [65], spindle speed, etc. [66–68]. The monitoring method based on measurements of the VA signal, although it does not require accurate information about the absolute interaction of the cutting tool and the part, but in order to generate effective output data it is necessary to set restrictions. An FFT (Fast Fourier Transform) filter was used to isolate a narrow band of the sound wave [69–71]. This filter uses the FFT method, which allows to effectively analyze the frequency content of the signal. The FFT block size determines the frequency resolution of the analysis. The larger the block, the higher the frequency resolution [72]. For example, for a block size of 65,536 points and a sampling rate of 44.1 kHz, the frequency resolution is approximately 0.67 Hz. This allows to accurately determine the presence of certain frequencies in the signal. However, with a large block size, the temporal resolution deteriorates. To improve time resolution, a smaller FFT block can be used to better track rapid frequency changes, but frequency resolution will be significantly degraded. With an FFT size of 1,024, the frequency grid step will be approximately 43 Hz. This means that frequencies are 43, 86, 129 Hz, etc. will be determined with high accuracy, but intermediate frequencies, such as 50 Hz, may not be visible. Filtering is used to isolate useful frequency components of a signal and remove noise [73]. In machining applications, this can help isolate vibration frequencies of interest and eliminate unnecessary noise. The following types of filters are used: low-pass filters pass low-frequency components and suppress highfrequency ones; high-pass filters pass high-frequency components and suppress low-frequency ones; bandpass filters pass frequencies in a certain range and suppress frequencies outside this range. The use of window functions in FFT analysis is necessary to minimize side effects associated with window discontinuities in the time signal. When a signal is trimmed for analysis, abrupt changes may occur at the ends of the block, resulting in distortion in the spectrum (spectral leakage). The Hann window function has low sidelobes compared to the rectangular function, and low spectral leakage. Among the disadvantages, one can highlight the low frequency resolution. The Hamming window function has low side lobes compared to the Hann function, and low spectral leakage. Among the disadvantages, one can highlight the low frequency resolution in comparison with the Hann window function. The Blackman window function has a very low level of side lobes, which allows the level of spectral leakage to be minimized, but the frequency resolution is significantly reduced. Thus, the goal of this work is to develop an algorithm for the operation of an online monitoring system for monitoring the condition of a cutting tool, based on the creation of a digital shadow, using a vibroacoustic complex. To achieve this goal, it is planned to solve the following tasks: 1) to determine the frequency ranges of the frequency response of the acoustic signal obtained during machining used to analyze the level of wear of the cutting tool; 2) to determine the optimal window function when filtering the acoustic machining signal to isolate the useful signal; 3) to establish experimental relationships between the degree of tool wear, surface microrelief parameters and the frequency response of the vibroacoustic signal. Research methodology OM ТСС in machining plays a key role in improving production efficiency [74]. It allows for a quick response to wear and other changes in the active contact zone of the tool [35, 75] and the workpiece, thereby ensuring optimal use of the equipment and preventing the need for premature or delayed tool replacement, which in turn can lead to unnecessary downtime, such as planned, and unscheduled. In the case under consideration, optimization of the milling process was based on minimizing the target cost function: ( ) ( ) 1 min, n i i F x C x = → = ∑
OBRABOTKAMETALLOV Vol. 26 No. 3 2024 technology where ΣC consists of С1 is equipment operating costs; С2 is costs for changing tools; С3 is cost of standard hour; С4 is cost of the tool. Then the performance limitation system will have the following form: Q = {V, n, fz, ap, t} → max. whereV is a cutting speed, m/min; n is a rotation speed, min–1; fz is a feed per tooth, mm/tooth; a p is a cutting depth, mm; t is an allowance, mm. In this work, DS was considered from the side of information transfer from the Physical World (PW) to DT. The proposed Online Monitoring system, consisting of maintenance, SCADA and VA sensors (Sensors are piezoelectric accelerometers “BC 110”), due to the diagnostic function, allows to timely detect tool wear and make a decision on replacing the cutting tool, correction or changing the control program (Fig. 2). Fig. 2. The scheme of monitoring and the digital twin The Online Monitoring system analyzes various parameters such as vibration, acoustic signals and surface finish. This allows to not only determine when a tool has reached a critical level of wear and requires replacement [76], but also to monitor less obvious changes that may signal possible problems. Digital signal processing techniques including FFT, window functions, and filtering were used to analyze the resulting VA signals. The data received in the form of an acoustic signal is transferred to the software, where noise removal (De-Noising) and filtering [35] are carried out in the VST plugin using the Fourier transform. One of the key elements of the system for transmitting a signal in real time is a virtual cable, which allows displaying information on the operator’s graphical interface (Fig 3). In experimental studies, machining was carried out in the same direction using a cutting fluid, on workpieces having the properties of AISI 321, with cylindrical end mills with a diameter of D = 8 mm, z = 2.
OBRABOTKAMETALLOV technology Vol. 26 No. 3 2024 The tool material used is CVD coated carbide BC20HT, application range according ISO is K10–K20. The tool overhang ratio was assumed to be l/D = 4. The feed per tooth was fz = 0.2 mm/tooth, ap = 10 mm, t = 0.4 mm. The spindle speed (n) was 1,500 min–1. During milling cutting tools were measured along its length and radius using a Heidenhain TT140 contact sensor to monitor the degree of tool wear. Vibroacoustic diagnostics were carried out using the Spectrum Analyzer ZetLab 017–U2 device based on a DMG DMU (Germany) 50 Ecoline machining center. The amplitude of the acoustic signal A (dB), changing over time t (s), vibration acceleration a (m/s2), signal frequency ω (Hz) and the microrelief parameter – roughness – Rz (μm) were used as output indicators of the efficiency of machining. The range of perceived frequencies of the microphone was 20 Hz÷20 kHz, resolution was 16 bits and sampling frequency was 44.1/48 kHz. For the optimal balance between time and frequency resolution, the FFT size was chosen to be 16,384, which ensures sufficient detail and accuracy of the analysis. Roughness measurements after surface milling were carried out using a TIME TR 200 profilometer; for this device, the error according to the standard is 3 %. The primary profile filtering process was performed using a 50 % Gaussian filter. Results and discussion When machining by cutting, vibrations and noises that occur during operation play an important role. Digital signal processing techniques including FFT [77], window functions and filtering were used to analyze these signals. Fig. 4 clearly shows that the microphone records several sound sources, including sounds around the technological equipment, the drive system of the technological equipment, the spindle, the tool, and the cutting process. Therefore, for a more accurate analysis, an acoustic signal within the frequency range characteristic of the cutting process was considered. This allows to better identify specific characteristics and features that may not be obvious in the wider frequency spectrum. From Fig. 4 it can be seen that background noise can be identified in the range of 0÷2 kHz, this specified part of the signal can be easily filtered without losing the main signal. Spectrum analysis reveals dominant frequencies and amplitudes that may indicate resonances, defects and tool wear. After spectral subtraction of noise and filtering of the acoustic signal, the frequency response of oscillations is characterized by three main Fig. 3. The process of software processing of an acoustic signal Fig. 4. Frequency response of the acoustic signal: 1 – Initial; 2 – After noise removal; 3 – Filtered; 4 – Filtered and normalized
OBRABOTKAMETALLOV Vol. 26 No. 3 2024 technology frequencies (Fig. 4) in the range of 20÷200 Hz: ω0 = 25 Hz – harmonic, a multiple of the frequency of passage of the cutting edges; ω1 = 50 Hz, which corresponds to the cutting frequency modulated by the revolutions of a two–tooth cutter; resonant frequency ω2 = 100 Hz, which should be interpreted as the tool outrun. Thus, the results of acoustic signal processing made it possible to establish a pronounced low-frequency region of 20÷200 Hz, the use of which contributes to a more accurate identification and analysis of acoustic characteristics associated with the cutting process. This is especially important for VA diagnostics, where the accuracy and detail of the analysis can make a significant difference in identifying and diagnosing potential problems. Also in this study, different windows for filtering audio signals are considered. The use of various window functions during filtering allows optimizing the process of extracting a useful signal from the total mass of data. The results of the window functions that were used in this study are presented in Fig. 5. a b c d e f Fig. 5. The spectrum of signals of the passage of the milling cutter cutting edges through the processing zone after the application of window functions, recorded in the form of: a, c, e – vibration acceleration; b, d, f – acoustic signal; a, b – Hanning window; c, d – Hamming window; e, f – Blackman window
OBRABOTKAMETALLOV technology Vol. 26 No. 3 2024 a b Fig. 6. Dependence of the roughness parameter Rz on tool wear: a – distribution; b – correlation The rectangular window due to the high level of side lobes, which can lead to significant spectral leaks, and the Kaiser window due to the complexity of tuning the parameters to achieve the optimal result were not used in this work. As can be seen from Fig. 5, the Hamming window function effectively reduces the spectral leakage that occurs when applying the Fourier transform. At the same time, the Hamming window function provides a good compromise between the width of the main lobe and the level of side lobes in the spectral representation, is easy to implement and does not require significant computational resources. This makes it the preferred choice for many digital signals processing applications, providing the high quality and accuracy of analysis needed to implement an online monitoring system. To determine the influence of the degree of tool wear on the parameters of the microrelief and the frequency response of the acoustic signal, experiments were carried out, the results of which are shown in Fig. 6 and Fig. 7. As can be seen from Fig. 6, the surface roughness of the processed material directly depends on the degree of tool wear, and the following correlation dependence has been established: r = −0.9678 (strong, negative). The deviation between the tool diameter with increasing number of machined surfaces is different, at the beginning there is a small dimensional wear of 2–4 μm, which increases Rz by 20 %. Further, within 6 μm, the roughness increases by 50 % of the minimum values obtained. At the same time, the deflection of the tool increases (Fig. 7), as evidenced by the increase in the resonant frequency ω2 = 100 Hz. It is important to regularly monitor the condition of the tool and replace it in a timely manner. As can be seen from Fig. 7, the sound spectrograms of a worn tool contain higher values than the sound of a new tool, with identical cutting process parameters. This fact is also confirmed by the graph of vibration acceleration at various degrees of tool wear. It is worth noting the difference in density around 50 Hz, but as previously stated, further work is still needed to understand the discrepancies for each specific case. Thus, based on the conducted research, we can conclude that with the use of a VA complex based on the formation of DS, Online Monitoring is possible. However, special attention should be paid to the obtained frequency response data of the acoustic signal from the standpoint of its processing and filtering. A significant amount of data obtained should be optimally processed in order to analyze and correlate it with the condition of the cutting tool. To do this, the study determined the frequency ranges of the acoustic signal, within which it is possible to draw conclusions about the current state of the instrument. The next step was to select a specific window function. It depended on signal filtering requirements, such as the acceptable level of spectral leakage and the required frequency resolution. The study analyzed the results of applying these window functions to audio signals to determine the optimal acoustic signal from the point
OBRABOTKAMETALLOV Vol. 26 No. 3 2024 technology Fig. 7. The results of VA diagnostics: a – frequency response with wear less than 4 μm; б – frequency response with wear more than 4 μm; в – vibration acceleration а b c of view of computational resources and accuracy, as a result, the Hamming window function is determined as the most suitable for analyzing the machining process. The dependences of the frequency response of the acoustic signal and surface roughness on the degree of tool wear are established, on the basis of which conclusions can be drawn about the condition of the cutting tool at stages close to critical in terms of the quality of the resulting surfaces. Although the study was aimed at determining the relationships between tool wear level, tool vibroacoustic signal and surface finish quality, the results of this study can be used to develop new techniques for monitoring tool wear, increasing the efficiency of the material processing process and improving the quality of finished products. Also, the study made it possible to formulate a sequence of actions for VA analysis, which contributes to a more accurate identification and analysis of acoustic characteristics associated with the cutting process: 1) install sensors (accelerometers, microphones) near the working area of technological equipment to record VA signals; 2) collect time data of VA signals; 3) convert an analog signal to a digital one using an analog-to-digital converter; 4) apply a window function to the collected data to minimize spectral leakage before performing FFT; 5) perform FFT to convert time signal to frequency domain; 6) analyze the spectrum to identify dominant frequency components associated with instrument condition; 7) apply filters to isolate frequency components of interest (apply a low-pass filter to remove highfrequency noise; apply a band-pass filter to highlight frequencies associated with normal tool operation and anomalies; apply a high-pass filter to remove low-frequency noise and vibration.)
OBRABOTKAMETALLOV technology Vol. 26 No. 3 2024 8) perform additional analysis of the filtered signal (Compare filtered signals with reference signals to assess the condition of the tool. Identify changes in the sound and vibration spectrum, indicating wear or damage to the tool). Conclusions Based on the current state of research described in this paper, as well as an evaluation of the results of the experiments, the following conclusions can be drawn. 1. An algorithm is developed for the operation of an Online Monitoring system for monitoring the condition of a cutting tool during milling, filtering interference and noise in real time based on the formation of DS obtained during vibroacoustic analysis. This conclusion stimulates the formulation of new tasks for research in this area. 2. Analysis of the frequency response in the range of 20÷200 Hz made it possible to establish the difference in the spectral density of the acoustic signal over time, an increase in which makes it possible to record the degree of tool wear. 3. The Hamming window function is determined to be optimal in terms of computational resources and accuracy of the acoustic signal from the standpoint of its use in the analysis of the machining process. 4. The presence of an acoustic signal correlation is confirmed by measurements of wear on the cutter radius, roughness and the results of vibration diagnostics. At the same time, the Online Monitoring system makes it possible to determine earlier signs of changes in the condition of the tool’s cutting edge than measurements recorded by a cycle in the CNC control program or measurements of roughness parameters. References 1. GOST R 56136–2014. Upravlenie zhiznennym tsiklom produktsii. Terminy i opredeleniya [State Standard R 56136–2014. Life cycle management for military products. Terms and definitions]. Moscow, Standartinform Publ., 2016. 24 p. 2. Grieves M. Digital twin: manufacturing excellence through virtual factory replication: whitepaper. Melbourne, FL, LLC, 2014, pp. 1–7. 3. GOST R 57700.37–2021. Komp’yuternye modeli i modelirovanie. Tsifrovye dvoiniki izdelii. Obshchie polozheniya [State Standard R 57700.37–2021. Computer models and simulation. Digital twins of products. General provisions]. Moscow, Russian Institute of Standardization Publ., 2021. 15 p. 4. Ingemansson A.R. Sovremennaya nauchnaya problema povysheniya effektivnosti mekhanoobrabatyvayushchego proizvodstva putem vnedreniya kiberfizicheskikh sistem v ramkakh kontseptsii «Industriya 4.0» [Current scientific problem of efficiency increase in mechanical operation by cyber-physical systems introduction within “industry 4.0” concept]. Naukoemkie tekhnologii v mashinostroenii = Science Intensive Technologies in Mechanical Engineering, 2016, no. 12, pp. 40–44. DOI: 10.12737/23487. 5. Kabaldin Y.G., Shatagin D.A., Kuzmishina A.M. Razrabotka tsifrovogo dvoinika rezhushchego instrumenta dlya mekhanoobrabatyvayushchego proizvodstva [The development of a digital twin of a cutting tool for mechanical production]. Izvestiya vysshikh uchebnykh zavedenii. Mashinostroenie = Proceedings of Higher Educational Institutions. Маchine Building, 2019, no. 4 (709), pp. 11–17. DOI: 10.18698/0536-1044-2019-4-11-17. 6. Uhlemann T.H.J., Lehmann C., Steinhilper R. The digital twin: realizing the cyber-physical production system for Industry 4.0. Procedia CIRP, 2017, vol. 61, pp. 335–340. DOI: 10.1016/j.procir.2016.11.152. 7. Lu Y., Liu C., Wang K.I.-K., Huang H., Xu X. Digital Twin–driven smart manufacturing: Connotation, reference model, applications and research issues. Robotics and Computer-Integrated Manufacturing, 2020, vol. 61, p. 101837. DOI: 10.1016/j.rcim.2019.101837. 8. Schleich B., Anwer N., Mathieu L., Wartzack S. Shaping the digital twin for design and production engineering. CIRP Annals, 2017, vol. 66, pp. 141–144. DOI: 10.1016/j.cirp.2017.04.040. 9. Padovano A., Longo F., Nicoletti L., Mirabelli G. A Digital Twin based service oriented application for a 4.0 knowledge navigation in the smart factory. IFAC-PapersOnLine, 2018, vol. 51 (11), pp. 631–636. DOI: 10.1016/ j.ifacol.2018.08.389. 10. Tao F., Anwer N., Liu A., Wang L., Nee A.Y.C., Li L., Zhang M. Digital twin towards smart manufacturing and industry 4.0. Journal of Manufacturing Systems, 2021, vol. 58 (B), pp. 1–2. DOI: 10.1016/j.jmsy.2020.12.005.
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