Vol. 27 No. 2 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. 2 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. 2 2025 5 CONTENTS OBRABOTKAMETALLOV TECHNOLOGY Sundukov S.K., Nigmetzyanov R.I., Prikhodko V.M., Fatyukhin D.S., Koldyushov V.K. Comparison of ultrasonic surface treatment methods applied to additively manufactured Ti-6Al-4V alloy................................................................ 6 Kate N., Kulkarni A.P., Dama Y.B. A comparative evaluation of friction and wear in alternative materials for brake friction composites............................................................................................................................................................... 29 Naumov S.V., Panov D.O., Sokolovsky V.S., Chernichenko R.S., Salishchev G.A., Belinin D.S., Lukianov V.V. Microstructure and mechanical properties of Ti2AlNb-based alloy weld joints as a function of gas tungsten arc welding parameters............................................................................................................................................................................. 43 Jatti V.S., Singarajan V., SaiyathibrahimA., Jatti V.S., KrishnanM.R., Jatti S.V. Enhancement of EDM performance for NiTi, NiCu, and BeCu alloys using a multi-criteria approach based on utility function................................................ 57 Stelmakov V.A., Gimadeev M.R., Nikitenko A.V. Ensuring hole shape accuracy in fi nish machining using boring...... 89 EQUIPMENT. INSTRUMENTS Patil N., Agarwal S., Kulkarni A.P., Saraf A., Rane M., Dama Y.B. Experimental investigation of graphene oxide-based nano cutting fl uid in drilling of aluminum matrix composite reinforced with SiC particles under nano-MQL conditions............................................................................................................................................................................. 103 Gimadeev M.R., Stelmakov V.A., Nikitenko A.V., Uliskov M.V. Prediction of surface roughness in milling with a ball end tool using an artifi cial neural network................................................................................................................. 126 Osipovich K.O., Sidorov E.A., Chumaevskii A.V., Nikonov S.N., Kolubaev E.A. Manufacturing conditions of bimetallic samples based on iron and copper alloys by wire-feed electron beam additive manufacturing......................... 142 Babaev A.S., Savchenko N.L., Kozlov V.N., Semenov A.R., Grigoriev M.V. Performance of Y-TZP-Al2O3 composite ceramics in dry high-speed turning of thermally hardened steel 0.4 C-Cr (AISI 5135)...................................................... 159 MATERIAL SCIENCE Sokolov R.A., Muratov K.R., Mamadaliev R.A. Morphological changes of deformed structural steel surface in corrosive environment......................................................................................................................................................... 174 Chernichenko R.S., Panov D.O., Naumov S.V., Kudryavtsev E.A., Salishchev G.A., Pertsev A.S. Eff ect of heterogeneous structure on mechanical behavior of austenitic stainless steel subjected to novel thermomechanical processing............................................................................................................................................................................. 189 Panov D.O., Chernichenko R.S., Naumov S.V., Kudryavtsev E.A., Salishchev G.A., Pertsev A.S. Eff ect of cold radial forging on structure, texture and mechanical properties of lightweight austenitic steel................................................ 206 Deshpande A., Kulkarni A.P., Anerao P., Deshpande L., Somatkar A. Integrated numerical and experimental investigation of tribological performance of PTFE based composite material.................................................................... 219 Vorontsov A.V., Panfi lov A.O., Nikolaeva A.V., Cheremnov A.V., Knyazhev E.O. Eff ect of impact processing on the structure and properties of nickel alloy ZhS6U produced by casting and electron beam additive manufacturing........ 238 Misochenko A.A. Martensitic transformations in TiNi-based alloys during rolling with pulsed current........................... 255 EDITORIALMATERIALS 270 FOUNDERS MATERIALS 279 CONTENTS
OBRABOTKAMETALLOV Vol. 27 No. 2 2025 technology Enhancement of EDM performance for NiTi, NiCu, and BeCu alloys using a multi-criteria approach based on utility function Vijaykumar Jatti 1, a, Vijayan Singarajan 2, b, A. Saiyathibrahim 3, c, Vinaykumar Jatti 4, d, Murali Krishnan 2, e, *, Savita Jatti 5, f 1 School of Engineering and Applied Sciences, Bennett University, Noida, 201310, India 2 Karpagam Institute of Technology, Coimbatore - 641105, Tamil Nadu, India 3 University Centre for Research and Development, Chandigarh University, 140413, Punjab, India 4 Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune, 412115, Maharashtra, India 5 D Y Patil College of Engineering, Savitribai Phule Pune University, Pune, India a https://orcid.org/0000-0001-7949-2551, vijaykjatti@gmail.com; b https://orcid.org/0000-0002-3636-7731, s.n.vijayan@gmail.com; c https://orcid.org/0000-0002-1968-0937, imsaiyath@gmail.com; d https://orcid.org/0000-0001-6016-0709, vinay.jatti89@gmail.com; e https://orcid.org/0009-0004-0107-0753, murali15091990@gmail.com; f https://orcid.org/0000-0001-5514-8078, savitabirajdardyp@gmail.com 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. 2 pp. 57–88 ISSN: 1994-6309 (print) / 2541-819X (online) DOI: 10.17212/1994-6309-2025-27.2-57-88 ART I CLE I NFO Article history: Received: 20 February 2025 Revised: 25 March 2025 Accepted: 27 March 2025 Available online: 15 June 2025 Keywords: Boron carbide EDM Shape memory alloy Multi-objective optimization Taguchi ANOVA and Utility concept ABSTRACT Introduction: Machining hard materials and shape memory alloys (SMAs), such as NiTi, NiCu, and BeCu, using conventional techniques is challenging due to excessive tool wear and poor surface finish. Non-conventional machining methods, particularly electrical discharge machining (EDM), offer improved precision and surface quality. However, the effectiveness of EDM is contingent upon the optimization of process parameters. The purpose of this study is to optimize EDM parameters to enhance the machining performance of SMAs by considering factors such as pulse-on time, pulse-off time, discharge current, gap voltage, and workpiece electrical conductivity. Methods. In this study, the Taguchi experimental design approach was employed to analyze the influence of key process parameters on the material removal rate (MRR), surface roughness (SR), and tool wear rate (TWR). Analysis of variance (ANOVA) was then applied to identify the most statistically significant factors affecting machining performance. A multi-objective optimization method, based on utility theory, was utilized to determine the optimal EDM settings that balance MRR, SR, and TWR. The results were validated through experimental trials. Results and Discussion. The experimental results indicated that Trial 15 achieved the highest MRR of 9.076 mm³/min, while Trial 1 produced the lowest SR of 2.238 µm. The minimum TWR of 0.041 mm³/min was observed in Trial 10, which contributes to increased tool lifespan. ANOVA revealed that gap voltage was the most influential factor, accounting for 85.98 % of the variation in machining performance, followed by discharge current (4.76 %) and pulse-off time (2.59 %). The multi-objective optimization process successfully identified parameter configurations that optimize MRR while minimizing SR and TWR. The prediction model developed in this study demonstrated high accuracy, with an R² value of 93.3% and an adjusted R² of 89.7%. Validation experiments confirmed the effectiveness of the optimized parameters, resulting in an average MRR of 8.852 mm³/min, SR of 2.818 µm, and TWR of 0.148 mm³/min. The findings presented herein confirm that careful optimization of EDM parameters significantly enhances the machining performance of SMAs, considerably improving machining efficiency and tool longevity. For citation: Jatti V.S., Singarajan V., Saiyathibrahim A., Jatti V.S., Krishnan M.R., Jatti S.V. Enhancement of EDM performance for NiTi, NiCu, and BeCu alloys using a multi-criteria approach based on utility function. Obrabotka metallov (tekhnologiya, oborudovanie, instrumenty) = Metal Working and Material Science, 2025, vol. 27, no. 2, pp. 57–88. DOI: 10.17212/1994-6309-2025-27.2-57-88. (In Russian). ______ * Corresponding author Krishnan Murali R., D.Sc. (Engineering), Assistant Professor Karpagam Institute of Technology, Coimbatore – 641105, Tamil Nadu, India Tel.: +91 9789339171, e-mail: murali15091990@gmail.com
OBRABOTKAMETALLOV technology Vol. 27 No. 2 2025 Introduction Advanced non-conventional electrical discharge machining (EDM) is an electro-thermal process where material is removed from a workpiece by means of electrical discharges (sparks). EDM is widely used in the manufacturing of shape memory alloy (SMA) components, ceramics, and composite materials due to its ability to provide high precision and geometric complexity [1]. EDM is considered one of the most effective methods for processing difficult-to-machine materials such as high-strength, brittle, and hard alloys, as it does not require the application of mechanical force [2]. During the EDM process, thermal energy required for material removal is generated by electrical sparks occurring in a dielectric fluid. Localized, intense heating caused by continuous electrical breakdowns leads to melting and vaporization of the workpiece material. The dielectric fluid performs several important functions: removing erosion products, cooling the workpiece, and preventing arc discharges [3]. Two types of EDM machines are distinguished: sinker EDM and wire EDM (WEDM). The selection of a specific type of EDM is determined by the application requirements, as well as the material properties and geometric parameters of the part being manufactured [4]. EDM enables the machining of electrically conductive materials with a wide range of mechanical properties. Due to its high precision and ability to meet specified surface quality requirements, EDM technology is in demand in the aerospace, automotive, biomedical industries, and in the manufacture of tools and dies [5]. EDM efficiency is determinedbynumerous process parameters, includingdischarge energycharacteristics (pulse-on time and pulse-off time, current, gap voltage, spark gap), the type of electrode and dielectric fluid, flushing pressure, and cycle duration. Optimizing these parameters is a key factor in achieving maximum productivity (material removal rate), minimum surface roughness, and increased tool life [6]. Research in the field of EDM machining of advanced materials often includes parametric studies aimed at studying the influence of process parameters on material removal rate (MRR, Q), surface roughness (SR, Ra), and tool wear rate (TWR, υh). These studies typically include an assessment of the underlying physical processes accompanied by parameter optimization methods. The results of such studies enable the development of EDM technologies suitable for high-performance applications requiring precise processing of difficult-to-machine materials [7]. Due to their improved mechanical and thermal properties, shape memory alloys (NiTi), Monel alloy (NiCu), and beryllium bronze (BeCu) are finding increasingly wide application, which increases the demand for EDM as an effective method for their processing. NiTi shape memory alloys exhibit both the shape memory effect and superelasticity, making them in demand in biomedical devices, the aerospace industry, and robotic systems [8]. Important properties of NiTi alloys include high corrosion resistance, biocompatibility, and the ability to elastically recover after deformation. EDM is the preferred method for processing such materials, as conventional machining methods are often ineffective due to the high strength and toughness of these alloys. The NiCu material known as Monel alloy is characterized by excellent corrosion resistance combined with high mechanical strength and thermal stability. These properties make Monel alloy suitable for applications in marine environments, the chemical industry, and the aerospace sector. The difficulty in machining Monel alloy is related to the effect of strain hardening and high toughness, which makes EDM an optimal solution. Beryllium bronze (BeCu) combines high strength, thermal conductivity, and corrosion resistance. The primary application areas of this alloy include electronic connectors, aerospace components, and tooling elements for injection casting. Hardening of beryllium bronze increases its strength, but the material becomes difficult to machine due to heat generation and tool wear [9]. To enhance the efficiency of the EDM process and reduce machining time, it is necessary to increase the material removal rate (MRR, Q). Surface roughness (SR, Ra) is an important quality indicator that determines the smoothness of the machined surface. SR is influenced by factors such as discharge energy, spark gap size, and dielectric fluid flushing conditions. When used in areas requiring precision machining, high surface quality requirements are imposed, which are achieved by minimizing SR values.
OBRABOTKAMETALLOV Vol. 27 No. 2 2025 technology Tool wear rate (TWR, υh) characterizes the rate of electrode material loss during the EDM process [10]. TWR depends on the gap current, electrode material, and dielectric fluid properties. Minimizing TWR is essential for reducing tool costs and increasing the economic efficiency of the process. As a result of rapid solidification of the molten material removed by electrical discharge, a hardened layer known as the “recast layer” of a certain thickness is formed. Controlling the thickness of the recast layer is achieved by optimizing EDM parameters [11]. The area around the machined surface is subjected to thermal effects, forming a heat-affected zone (HAZ). Significant HAZ dimensions can lead to residual stresses and microcracks that affect the mechanical properties of the component. Managing the pulse energy and effectively using the dielectric fluid allows for improved thermal management. The microhardness of the machined surface may change due to thermal effects, which must be considered when evaluating the material characteristics after EDM [12]. Dimensional accuracy and overcut characterize the deviation of the machined part’s dimensions from the specified values. The amount of overcut is influenced by the size of the spark gap, the pulse-on time, and tool wear. Achieving high dimensional accuracy is critical for the production of precision components. Adjusting the EDM process parameters allows for increased productivity, improved surface quality, and extended tool life in accordance with industry standards [13]. The Taguchi method is an effective statistical optimization technique widely used for various technological processes, including EDM. This method allows researchers to plan efficient experiments, optimizing process parameters with a minimal number of experimental runs. The main concept of the Taguchi method relies on orthogonal arrays (OAs) to simultaneously study the influence of several factors on the process output parameters [14]. The L18 OA is often used for EDM optimization, as it provides an effective assessment of the influence of various levels of process parameters. The L18 array allows the analysis of up to eight factors, using two or three different levels of parameters, which is suitable for studying the main EDM parameters, such as pulse-on time, pulse-off time, current, and voltage [15]. Process optimization using the Taguchi method is based on the analysis of the signal-to-noise (S/N) ratio to determine the optimal values of parameters that provide the desired machining results. Three standard S/N ratio criteria are used in EDM studies: “Smaller-the-better” for minimizing SR and TWR, “Largerthe-better” for maximizing MRR, and “Nominal-the-best” for ensuring precision dimensional control. The Taguchi method can improve the efficiency of EDM by identifying optimal machining conditions by minimizing number of experiments and reducing cost and execution time while improving surface integrity and output productivity [16]. In the EDM process, several performance metrics must be considered simultaneously, as it requires achieving extreme MRR along with minimum SR and TWR. For balanced optimization of these competing criteria, the Utility method is often used, which is a popular tool for multi-criteria optimization. The Utility method transforms different output variables into a single combined index, simplifying the decisionmaking process. The application of the Utility method for EDM optimization involves the following steps: normalization of response values (bringing different performance characteristics to a comparable scale), assigning weights to each response based on its relative importance, and calculating a single utility value by multiplying the normalized values by the corresponding weights and summing the results. The optimal combination of process parameters is determined based on the maximum utility value, after which experimental verification is performed. The application of the Utility method allows manufacturers to find optimal parameter settings, providing an effective framework for balanced optimization of EDM performance indicators [17]. As a result of applying the Utility method, optimization of three key performance parameters of the EDM process was achieved, namely, MRR, SR, and TWR. The use of this method made it possible to balance the requirements for production speed and the quality of the machined surface. The integration of weighted normalization methods into the decision-making system improved its accuracy and reliability. The high process efficiency was made possible through the application of the Taguchi method, which provides a systematic study of the influence of EDM parameters with a minimal experimental test runs. The analysis of the S/N ratio allowed identifying critical parameters needed for accurate process optimization.
OBRABOTKAMETALLOV technology Vol. 27 No. 2 2025 In addition, it was established that the electrical conductivity of the workpiece material, along with current and voltage measurements in the discharge, has a significant impact on machining performance and, in particular, on surface smoothness [18]. Adetailed study of the machining methods of shape memory alloys (SMAs) was carried out, in which the effectiveness of EDM and its variations, including conventional die-sinking EDM and die-sinking microEDM, were evaluated. SMAs, possessing unique properties such as the shape memory effect, superelasticity, high corrosion resistance, and biocompatibility, particularly NiTi-based alloys and copper-based alloys, are widely in demand in various applications. EDM is a promising alternative to conventional machining methods, as it can solve problems related to tool wear, ensure high machining accuracy, and enables lowaccuracy CNC machining. The present study focuses on analyzing the influence of EDM input parameters on response behavior when machining SMAs, with an emphasis on NiTi alloy systems. The review examines various optimization strategies for EDM parameters, focusing on non-conventional approaches in addition to widely used statistical methods and multi-criteria decision-making methods. Particular attention is paid to both hybrid EDM methods and advanced technological approaches used in the processing of shape memory alloys [19]. An extensive review is devoted to the machining of shape memory alloys by EDM, with an emphasis on methods for processing NiTi-based SMAs. The wide industrial implementation of SMAs as industrial materials is emphasized due to their remarkable properties, finding applications in orthopedic implants, actuators, aerospace components, and biomedical devices. It is noted that efficient machining of NiTi SMAs remains a complex challenge. This review analyzes experimental, theoretical as well as modeling and optimization-based approaches used to describe EDM, WEDM, and conventional machining processes for SMAs. It is emphasized that improving machining efficiency requires optimal selection of process parameters, suitable electrode tools, and dielectric fluids. Among EDM methods, WEDM is the most extensively studied in the context of SMA cutting, outpacing die-sinking EDM and powder-mixed EDM used to enhance SMA processing performance and accuracy [20]. Several studies have investigated the optimization of WEDM process parameters for Nitinol shape memory alloys (nitinol – nickel-titaniumalloy), which exhibit the ability to return to their original shape under the influence of thermal or mechanical factors. In [21], desirability function analysis (DFA) combined with the analytic hierarchy process (AHP) is used within a multi-criteria decision making (MCDM) framework to determine optimal machining conditions. The influence of four WEDM input parameters, namely, pulseon time, pulse-off time, wire tension, and wire feed, on kerf width, MRR, and SR was investigated. Based on DFA-AHP methods, the optimal machining parameters were determined to be: pulse-on time 120 μs, pulse-off time 55 μs, wire tension 8 kgf, and wire feed 3 m/min. The results were confirmed by S/N ratio analysis using the Taguchi method. The combination of results showed that the MCDM approach successfully identifies effective process parameters to enhance the performance during the WEDM processing of Nitinol [21]. In [22], the WEDM of superelastic nickel-titanium SMA (Ni54.1Ti), driven by the difficulties of traditional machining methods investigated is studied. NiTi-based alloys require precision machining methods, especially in critical applications such as the medical industry. The assessment focused on the impact of pulse-on time, pulse-off time, and gap current on two key output metrics: MRR and SR. Experiments that systematically assessed these parameters were designed using a Taguchi L27 mixed orthogonal array (L27 OA) and demonstrated that pulse-on time is a key parameter influencing the MRR and SR values [22]. The optimization of surface roughness of NiTi SMA in EDM using the Taguchi method was investigated in [23]. NiTi-based alloys are widely used as “smart” materials in various industries, including the security industry, the maritime sector, and the aerospace field, due to their unique properties. Due to the high hardness of this material, processing with conventional tools presents significant difficulties, making EDM a suitable solution. The machining quality of NiTi largely depends on surface roughness parameters. EDM process variables were optimized using a systematic Taguchi method to improve performance. The research results demonstrate the possibility of improving the surface quality of NiTi-based alloys and, therefore, confirm the effectiveness of EDM as a precision machining method for this challenging material [23].
OBRABOTKAMETALLOV Vol. 27 No. 2 2025 technology Despite the difficulty in machining nickel-titanium (NiTi) SMA using conventional methods, EDM provides optimal performance when working with this material. However, high tool wear in EDM of NiTi leads to a reduction in the material removal rate. The study presented in [24] was aimed at maximizing MRR and minimizing TWR using the Taguchi method and the utility principle. Experiments were conducted on a die-sinking EDM machine in a liquid dielectric using a Taguchi L36 mixed orthogonal array (22×33). NiTi alloy was used as the workpiece material, and copper was used as the electrode tool. Taguchi analysis revealed that workpiece and tool electrode electrical conductivity, gap current, and pulse-on time are the key factors influencing MRR and TWR. It was found that an MRR of 6.31 mm³/min and a TWR of 0.031 mm³/ min are achieved with the following parameters: workpiece conductivity of 4219 S/m, tool conductivity of 26316 S/m, gap current of 16 A, and pulse-on time of 38 µs [24]. Reference [25] investigates the feasibility of processing nickel-titanium (NiTi) SMAs using EDM with copper, graphite, and tungsten-copper electrodes, and dielectric 358 as the dielectric fluid. The EDM process parameters included three levels of current (6, 12, and 18 A) combined with three values of pulse-on time (200, 400, and 600 µs) at a constant voltage of 3 V and a fixed pulse-off time of 50 µs. The primary objective was to determine the optimal settings to maximize MRR and minimize SR for NiTi shape memory alloys. The surface analysis of the workpiece included the examination of the electrode size and length using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX) to assess electrode material adhesion to the workpiece. Analysis of variance (ANOVA) was employed as a statistical method to determine the significance of process parameters. The differences between electrode materials were found to be relatively minor, and overcut was identified as the dominant factor influencing MRR and SR. Surface examination revealed the presence of surface defects in the form of droplets, debris, lumps, microcracks, and holes. Elevated SR values were associated with Cu and W residues from the electrode adhering to the workpiece due to insufficient dielectric rinsing [25]. Study [26] optimized the experimental conditions for surface milling of NiTi SMA under dry cutting conditions. The research aimed to achieve the lowest Ra and the minimal Vb using an uncoated tungsten carbide tool with a nose radius of 0.4 mm or 0.8 mm. Milling experiments were conducted at three cutting speeds (20, 35, and 50 m/min) and three feed rates (0.03, 0.07, and 0.14 mm/tooth) with a fixed axial depth of cut of 0.7 mm. A Taguchi L18 orthogonal array was used as the design of experiment (DOE) method, utilizing Minitab 17 software for data analysis. The analysis of variance (ANOVA) revealed that the cutting tool nose radius is the primary factor determining surface roughness, while the feed rate (fz) has the greatest impact on flank wear (Vb). Confirmation tests verified that the optimal machining parameters accurately predict the results of the laboratory experiments, indicating the success of the optimization process [26]. The optimization of EDM parameters for Cu-based SMA components using machine learning (ML) algorithms is described in [27]. The optimization process focused on varying pulse-on time (Ton), pulse-off time (Toff), discharge current (Ip), and gap voltage (GV) to minimize tool wear rate (TWR). An empirical design of experiments (DoE) approach utilized a central composite design (CCD) in conjunction with response surface methodology (RSM) to analyze the machining behavior. The study employed both single- and multi-objective optimization using a desirability function approach, as well as genetic algorithms (GA) and teaching-learning-based optimization (TLBO) algorithms [27]. The optimization of process parameters significantly improved the efficiency of the corresponding machining methods. The innovative aspect of the presented research lies in the application of machine learning (ML)-based optimization techniques to the electrical discharge machining (EDM) of Cu-SMAs, opening new perspectives for the aerospace, biomedical, and automotive industries. Based on the results presented in [27], it can be concluded that precisionmachining benefits significantly from the implementation of “smart” materials and data-driven optimization methods. A comprehensive analysis of existing shape memory alloys (SMAs) processing methods was conducted, encompassing both conventional and non-conventional approaches. The review includes research on waterjet machining (WJM), cryogenic machining, wire electrical discharge machining (WEDM), electrical discharge machining (EDM), and electrochemical machining. Key factors determining the performance and limitations of the considered processes are material removal rate (MRR), tool wear rate (TWR), surface
OBRABOTKAMETALLOV technology Vol. 27 No. 2 2025 roughness (SR), and the integrity of the surface layer. Based on the analysis, the most effective SMAs machining methods were identified [28]. The optimization process of electrical discharge machining (EDM) for a high-temperature high-entropy shape memory alloy (HT-HE-SMA) with a composition of 35Ni-35Ti-15Zr-10Cu-5Sn using a copper electrode is considered. It is emphasized that EDM is an effective method for machining complex-geometry parts from difficult-to-machine materials, and optimizing EDM process parameters can significantly improve the productivity and quality of the machined surface. The relationship between the input EDM process parameters — discharge current (Ip), pulse-on time (Ton), and pulse-off time (Toff) — and the output parameters, such as material removal rate (MRR), tool wear rate (TWR), and surface roughness (SR), was investigated. Response surface methodology (RSM) using a central composite design (CCD) was applied to evaluate the influence of machining parameters, and experimental data collection was performed using Minitab 19 software. Based on analysis of variance (ANOVA) at a significance level of 5 %, the most significant factors were determined, and the adequacy of the second-order regression models was evaluated. It was found that discharge current, pulse-on time, and pulse-off time have a significant effect on MRR, TWR, and Ra. The high accuracy of the developed mathematical models was confirmed, as evidenced by the high coefficients of determination (R²), reaching 97.82% for MRR, 99.53% for SR, and 95.36% for TWR [29]. The optimization of EDM parameters to achieve maximum MRR for NiTi, NiCu, and BeCu alloys was performed. Due to the difficulty of processing these advanced materials using conventional methods, EDM is considered an effective alternative. It is emphasized that the stability of the EDM process is a complex challenge due to the influence of numerous factors. This study investigates the optimization of EDM parameters by analyzing the current and voltage in the inter-electrode gap, combined with the control of pulse-on time, pulse-off time, and workpiece conductivity. A Taguchi orthogonal array was used for design of experiments (DoE), and Taguchi’s S/N ratio and ANOVA were used to determine the most significant factors affecting MRR. The results of the study demonstrate that EDM performance is largely dependent on the control of current and voltage in the gap, as well as pulse-on and pulse-off time [30]. The surface roughness (SR) and surface crack length (SCL) transformation in the EDM of electrolytic oxygen-free copper were evaluated using different processing modes. The influence of cryogenic treatment of the workpiece on EDM process parameters was investigated, including workpiece electrical conductivity, pulse-on time, pulse-off time, gap voltage, and gap current. The experiments were designed using a Taguchi L18 orthogonal array and subjected to statistical analysis. The results showed that gap voltage, pulse-on time, and pulse-off time have the greatest influence on SR, while the interaction of workpiece conductivity with gap current, pulse-on time, and gap voltage affects the surface crack length. It was found that the surface cracks length initially decreases with increasing conductivity and then begins to increase. A decrease in gap current leads to an increase in crack length, while an increase in gap voltage promotes a decrease in crack length. Machine learning models applied for regression analysis demonstrated high accuracy in predicting SCL and SR parameters, achieving a coefficient of determination (R²) exceeding 0.90 [31]. Tool wear rate (TWR) was minimized by optimizing the EDM parameters that influence the accuracy and cost-effectiveness of the process. Electrolytic copper was used as the electrode when machining NiTi, NiCu, and BeCu alloy workpieces. A Taguchi L18 orthogonal array was used to analyze the influence of various factors on TWR. The factors considered were: workpiece conductivity, gap voltage and current, pulse-on time, and pulse-off time. ANOVA in combination with Taguchi S/N ratio analysis revealed that workpiece material conductivity, pulse-on time, and gap current have the greatest influence on TWR. Based on the results, a set of optimal parameters was determined, allowing for reduced tool wear and improved EDM productivity [32]. Another study investigated the effect of cryogenic treatment and an external magnetic field on the EDM of beryllium bronze (BeCu). Experiments were conducted using different values of gap current, magnetic field strength, and pulse-on time, as well as electrolytic copper electrodes. The highest MRR of 11.807 mm³/min was achieved when machining cryogenically treated BeCu workpieces with untreated copper electrodes. Among the parameters studied, only the gap current had a significant influence on MRR,
OBRABOTKAMETALLOV Vol. 27 No. 2 2025 technology while the influence of pulse-on time and magnetic field strength was insignificant. Analysis of the surface microstructure using scanning electron microscopy (SEM) showed that a white layer with a thickness of up to 20 μm formed on the BeCu alloy after EDM, with minimal number of surface cracks [33]. Powder-mixed electrical discharge machining (PMEDM), as a promising machining method for difficult-to-cut alloys, particularly beryllium bronze (BeCu), is also being considered. The addition of fine powder particles to the dielectric fluid in PMEDM promotes increased machining efficiency and stability, as well as an increased concentration of spark discharges. A copper electrode was used in the experiments with constant pulse-on time, pulse-off time, and gap voltage. The gap current (ranging from 8–14 A) and powder concentration (2–6 g/L) were varied. The results showed that increasing the gap current and powder concentration leads to an increase in MRR. However, the worsening of flushing conditions at greater depths led to an increase in TWR [34]. In addition, the methods of manufacturing and processing beryllium bronze (BeCu)-based composite materials were investigated. The composite materials were fabricated using a stir casting, and their properties were evaluated using SEM and EDX methods. It was found that increasing the silicon carbide (SiC) particle content leads to an increase in material hardness. Abrasive waterjet machining (AWJM) was used to evaluate the machining performance of the composites, assessing MRR and hole circularity. The obtained parameters were compared with those obtained during EDM. ANOVA allowed for the identification of the most significant factors influencing the machining process, and the Taguchi method was used to optimize the parameters for achieving high productivity and accuracy [35]. The presented research stands out due to its novel approach to studying the peculiarities of the EDM process for three different materials: a shape memory alloy (NiTi), a monel alloy (NiCu), and beryllium copper alloy (BeCu). Special attention is paid to the difficulties encountered in processing these materials, due to their resistance to strength loss, thermal effects, and mechanical impacts. The results of the research can be valuable in industries such as aerospace, biomedical, and tool manufacturing. The significance of the work is determined by the comprehensive approach, combining investigations of EDM characteristics for specific materials, multi-criteria optimization, and experimental verification, all of which are aimed at improving high-performance machining methods. Materials and Methods The primary purpose of this research was to identify optimal combinations of EDM parameters to achieve maximum productivity. The varied parameters included: workpiece material conductivity (S/m), gap current (A) and voltage (V), pulse-on time (µs), and pulse-off time (µs). The key output parameters characterizing process performance were material removal rate (MRR), surface roughness (SR), and tool wear rate (TWR). Therefore, the objective was to maximize the machining rate of difficult-to-machine materials through optimal selection of EDM parameters, followed by an evaluation of machinability. NiTi and NiCu alloys (20 mm diameter, 20 mm length) and BeCu (20×20×30 mm³) were used as workpiece materials. Electrolytic copper was selected as the tool electrode material due to its high electrical conductivity. A copper rod (6 mm in diameter, 2000 mm in length) was cut and processed on a milling machine to obtain rectangular-shaped blanks, from which test samples (4×4×25 mm) were made. A square cave measuring 3×3 mm and 5 mm deep was formed in the samples using a tool electrode. The use of oxygen-free electrolytic copper ensured high electrical conductivity and wear resistance of the tool during the machining process. The experiments were conducted on an Electronica Machine Tool Limited die-sinking EDM machine, model C400x250. Industrial EDM oil was used as the dielectric fluid. Side flushing at a pressure of 0.5 kg/cm² provided effective removal of erosion products and stability of the machining process. GR-300 digital scales (accuracy 0.0001 g) were used to measure MRR and TWR, and a Mitutoyo SJ 210 profilometer was used to measure surface roughness (SR). A more detailed description of the manufacturing process, experimental methods, and obtained results is presented in the previous work by Vijaykumar S Jatti et al., 2022 [36].
OBRABOTKAMETALLOV technology Vol. 27 No. 2 2025 A photograph of the die-sinking EDM machine used is shown in Fig. 1. The chemical, physical, and thermoelectric properties of the workpiece and tool materials are summarized in Tables 1 and 2, respectively. The research methodology is shown schematically in Fig. 2. Material removal rate (MRR) and tool wear rate (TWR) were calculated using equations (1) and (2): ∆ ρ w m W MRR t (1) where ΔW is the change in workpiece mass (g); ρw is the workpiece material density (g/cm³); tm is the machining time (min). ∆ ρ t m T TWR t (2) where ΔT is the change in tool electrode mass (g); ρt is the tool electrode material density (g/cm³); tm is the machining time (min). Fig. 1. EDM die-sinking machine Ta b l e 1 Chemical composition of materials used Name of the material Ni (%) Ti (%) Be (%) Cu (%) NiTi alloy 60 40 – – NiCu alloy 72 – – 28 BeCu alloy – – 2 98 Copper electrode – – – 99.9 The experimental design was developed and implemented using the Taguchi method. To enhance the statistical significance of the results, three repeated measurements were conducted for each parameter set, which is a requirement of the Taguchi method when using the signal-to-noise (S/N) ratio. The S/N ratio is a combined statistic that considers both the average value of the target characteristic and its variance distribution. Using this ratio allows for the optimization of process parameters to enhance overall performance.
OBRABOTKAMETALLOV Vol. 27 No. 2 2025 technology Ta b l e 2 Physical properties of materials used Name of the material Density (ρ), g/cc Specific heat ca - pacity (cp), J/gK Melting point (Hm), K Thermal conductivity (k), W/mk Electrical conductivity (σ), S/mm NiTi alloy 6.45 0.320 1583 10 3.268 NiCu alloy 8.8 0.427 1623 21.8 5.515 BeCu alloy 8.25 0.420 1253 130 5.645 Copper electrode 8.94 0.394 1356 391.1 10 Fig. 2. Methodology Three main types of quality characteristics were used in the calculation of the S/N ratio: “Larger-isBetter” (LB) for MRR (aiming for the maximum response value), and “Smaller-is-Better” (SB) for TWR and SR (aiming for minimization). The “Nominal-is-Best” (NB) category is applied in cases where it is necessary to ensure precise adherence to a target value, for example, when maintaining specified dimensions. The mathematical expressions for calculating the S/N ratio corresponding to different quality characteristics are presented below (3), (4) and (5):
OBRABOTKAMETALLOV technology Vol. 27 No. 2 2025 “Larger-is-Better” LB S N 2 1 1 1 ( / ) 10 log R j R y (3) “Smaller-is-Better” SB S N 2 1 1 ( / ) 10 log R j y R (4) “Nominal-is-Best” NB S N 2 0 1 1 ( / ) 10 log ( ) R j j y y R (5) where yi is the the value of the parameter obtained in the i-th trial; R is the the number of repetitions of the trial; μ is the mean value of the data; σ is the standard deviation of the data. In the Taguchi experimental design, an L18 orthogonal array was used, selected based on the number of process parameters and their defined levels. The design included five parameters: workpiece electrical conductivity, gap current, gap voltage, pulse-on time, and pulse-off time. One variable (workpiece electrical conductivity) was varied at six levels, and the remaining four were varied at three levels. These parameters are designated as A, B, C, D, and E. Table 3 presents the process parameters and corresponding levels used in the experiments. The Taguchi method requires the calculation of degrees of freedom (DoF) to select a suitable orthogonal array for design of experiments. The workpiece material electrical conductivity, having six measurement levels, determines five degrees of freedom. Each of the remaining four parameters (gap current, gap voltage, pulse-on time, and pulse-off time), varied at three levels, has two degrees of freedom per variable. Therefore, the total number of DoF is 13. Based on this, a mixed orthogonal array L18 (61 × 34) was chosen as satisfying the criterion of possessing seventeen degrees of freedom. The structure of the L18 array is presented in Table 4. The experiments were conducted in accordance with the Taguchi L18 orthogonal array methodology. Two key principles of design of experiments (DoE) were implemented in this study. First, to enhance the statistical reliability of the results, the principle of replication was used, involving conducting multiple repeated measurements for each parameter set. This allows for improved accuracy in the estimation of main effects and their interactions, as well as a proper assessment of experimental error. In this study, three repeated measurements were conducted for each parameter combination. Second, data were collected for each experimental condition. Based on the obtained data, the signal-to-noise (S/N) ratio was calculated for each experimental condition using equations (3)–(5), according to the selected quality characteristics (MRR, TWR, and SR). Analysis of variance (ANOVA) was used to determine the significance of the influence of various EDM process Ta b l e 3 Process parameters and its levels Parameters Code Levels Electrical conductivity of workpiece (S/m) A NiTi NiCu BeCu 3268 (untreated) 4219 (treated) 5515 (untreated) 5625 (treated) 5645 (untreated) 5902 (treated) Gap current (A) B 8 12 16 – – – Gap voltage (V) C 40 55 70 – – – Pulse on time (µs) D 13 26 38 – – – Pulse off time (µs) E 5 7 9 – – –
OBRABOTKAMETALLOV Vol. 27 No. 2 2025 technology Ta b l e 4 Mixed L18 (61 × 34) orthogonal array Trail No. Parameter A B C D E 1 1 1 1 1 1 2 1 2 2 2 2 3 1 3 3 3 3 4 2 1 1 2 2 5 2 2 2 3 3 6 2 3 3 1 1 7 3 1 2 1 3 8 3 2 3 2 1 9 3 3 1 3 2 10 4 1 3 3 2 11 4 2 1 1 3 12 4 3 2 2 1 13 5 1 2 3 1 14 5 2 3 1 2 15 5 3 1 2 3 16 6 1 3 2 3 17 6 2 1 3 1 18 6 3 2 1 2 parameters on the output characteristics. The significant and non-significant parameters of EDM process were identified by ANOVA. Statistical data processing was performed using MINITAB 15.0 software. The main effects plot visually displays the influence of each process parameter on the output characteristics, allowing for the assessment of trend changes. The response plot shows the change in the value of the output parameter as a function of the change in the level of the input parameter. The experimental program was executed three times for each parameter combination, after which data were collected. The analysis included both raw data analysis and S/N data analysis to determine the significance of the process parameters by comparing the main effects plots constructed based on S/N data and raw data. Utility theory Optimization based on utility theory allows for the quantitative assessment of product value, considering it as a combination of utility levels corresponding to different quality characteristics. The product optimization problem is reduced to maximizing overall utility by optimizing the individual utility of each characteristic. The first step is to determine the optimal levels of the process parameters using the Taguchi method, which helps improve performance indicators. Then, a preference scale is established for each response (MRR, SR, TWR), taking into account the optimal and minimum values obtained during the experiments. The preference scale is constructed based on the following equation (6): ' log i i i x P A x (6)
OBRABOTKAMETALLOV technology Vol. 27 No. 2 2025 where Pi is the preference value for the i-th response; xi is the raw data obtained from the experiment for the i-th response; x’i is the smallest acceptable value for the i-th response; A is a constant, defined as: 9 log i i A x x (under optimal conditions) After determining the preference values for each response, it is necessary to determine the weightage (Wi, i = 1, 2, ..., n) for each performance indicator, satisfying the condition (7): 1 1 n i i W . (7) Subsequently, for each test condition and repetition, the utility value (U(n,R)) is calculated based on equation (8): ( , ) 1 ( , ) n n R i i i U P n R xW (8) where n is the number of performance metrics (1, 2, 3, ..., 18); R is the number of repetitions of each trial (1, 2, 3). After calculating the utility values, to determine the ideal configurations of process parameters, the S/N ratio is calculated, considering utility as a “Larger-the-Better” type of characteristic. Then, the average response value and confidence interval are calculated using the values of significant parameters. Equations (9) and (10) are used to calculate 95 % confidence intervals for confirmation experiments (CICE) and populations (CIpop): α 1 1 (1, ) CE e e eff CI F f V n R (9) α (1, ) e e pop eff F f V CI n (10) Fα(1, fe) is the F ratio at the confidence level of (1-α) against DOF 1 and error degree of freedom fe; Ve is the error variance; R is the sample size for confirmation trials. 1 ( ) eff N N DOF associated inthe estimate of meanresponse neff is an effective sample size, calculated as N / (1 + DoF), where N is a total number of findings DoF is the total number of degrees of freedom associated with the estimation of the mean response. Specific values: Neff = 54/(1+6) = 7.714; N (total number of results) = 18×3 = 54; R (sample size for confirmatory trials) = 3; Ve (error variance) = 0.05087; fe (error degrees of freedom) = 11. Conduct the validation trials at the optimum process parameter settings and compare the results with the projected mean response values. The assumed weightage of quality characteristics was 0.33 for each MRR, SR and TWR (WMRR, WTWR and WSR), and the utility value was calculated using equation 14. The utility values are calculated for all 18 experimental conditions and three repetitions. Since utility is a quality criterion that favors higher values, the utility values were analyzed based on the average utility at each parameter level as well as the S/N ratio.
RkJQdWJsaXNoZXIy MTk0ODM1