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 MATERIAL SCIENCE Vol. 26 No. 3 2024 Investigation of hardness behavior in aluminum matrix composites reinforced with coconut shell ash and red mud using Taguchi analysis Rishi Dewangan 1, a, Bhupendra Sharma 2, b, Shyam Sharma 3, с, * 1 Department of Mechanical Engineering, Amity University Rajasthan, Jaipur, 303002, India 2 Department of Mechanical Engineering, Amity University Uttar Pradesh, Noida, 201313, India 3 Department of Mechanical Engineering, Manipal University Jaipur, Jaipur, 303007, India a https://orcid.org/0000-0002-1973-6726, rdewangan@jpr.amity.edu; b https://orcid.org/ 0000-0002-3207-7286, bpsharma@amity.edu; c https://orcid.org/0000-0002-1510-5871, shyamsunder.sharma@jaipur.manipal.edu 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. 179–191 ISSN: 1994-6309 (print) / 2541-819X (online) DOI: 10.17212/1994-6309-2024-26.3-179-191 ART I CLE I NFO Article history: Received: 14 April 2024 Revised: 17 May 2024 Accepted: 22 June 2024 Available online: 15 September 2024 Keywords: Anova analysis Coconut Shell Ash Hardness Red Mud ABSTRACT Introduction: in present scenario, light and high strength aluminium metal matrix composite are extensively used due to its high mechanical and tribological properties. Aluminium metal matrix composite reinforced with ceramic and industrial waste can customize its mechanical-chemical behavior. The purpose of the work: to create an aluminum matrix composite material using ceramic (primary) and industrial (secondary) waste represented by red mud and coconut shell ash, respectively. The mass fraction of the strengthening phase varied from 5 to 12.5 wt. % respectively with the residual mass percentage of the aluminum alloy. Method of investigation: nine specimens of composite materials were prepared by stir casting. Stirring was carried out at a speed of 50 to 100 rpm for 20 minutes at a temperature of 800 °C. Result and Discussion: the hardness behavior of the aluminum metal matrix composite was studied at an indentation load of 10, 15 and 20 kN. Taguchi method with L27 orthogonal array was selected to conduct analysis of variance (ANOVA) and regression analysis by selecting the mass percentage of red mud and mass percentage of coconut shell ash. The indentation load was used as an input parameter, and the hardness behavior was taken as an output parameter. The signal-to-noise ratio, response rank table, contour plot, and normal probability plot are investigated and it is found that hardness values improve with the addition of both reinforcing components and indenter load. The results show that the hardness value varies from 33.34 HB to 53.44 HB, and the effect of red mud mass percentage is more significant than the indenter load and coconut shell ash mass percentage. For citation: 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. Obrabotka metallov (tekhnologiya, oborudovanie, instrumenty) = Metal Working and Material Science, 2024, vol. 26, no. 3, pp. 179–191. DOI: 10.17212/1994-6309-2024-26.3-179-191. (In Russian). ______ * Corresponding author Sharma Shyam S., D.Sc. (Engineering), Assistant Professor Manipal University Jaipur, 303007, Jaipur, India Tel.: +91-9887765320, e-mail: shyamsunder.sharma@jaipur.manipal.edu Introduction Currently, Aluminum Metal Matrix Composites (AL MMC) are widely used due to its high strengthto-weight ratio and good tribological properties. There are a large number of materials available in manufacturing industry, so we require cost-effective high-performance materials whose mechanical and chemical properties can be changed according to customer requirements. Due to the reinforcing materials in aluminum matrix composites, it is possible to adapt the mechanical, chemical and tribological properties of the latter in accordance with the requirements of the market and the consumer. In the last decade ceramic materials like silica, alumina, rutile etc. have been widely used to reinforce AL MMC but it may enhance the manufacturing and processing cost of Composite materials [1]. Industrial and bio wastes such as red mud, iron ore, rice husk ash, bagasse ash, coconut shell ash, etc. have the ability to replace these ceramic materials in the development of cost-effective composite materials by reducing manufacturing cost. In this
OBRABOTKAMETALLOV MATERIAL SCIENCE Vol. 26 No. 3 2024 research work, a hybrid MMC is prepared using industrial waste red mud and bio-waste coconut shell ash. Previously many researchers utilized coconut shell ash as an adsorbent to remove heavy metals and dyes from aqueous solutions [2], in the development of building materials such as brick tiles [3], cementitious and polymeric composites [4,5] and in the production of activated carbon [6]. Similarly, red mud is used as a coating material [7], as a mortar, aggregate tiles [8], a mineral cementitious material [9], a ceramic material [10] and for heavy metal leaching and wastewater treatment in general [11]. Some of the researchers have used the combination of bio-waste with ceramic material to create and evaluate a hybrid Al MMC. In [12] coconut shell ash and graphene are used to evaluate abrasive wear properties. In [13] and [14] a mixture of agricultural waste in the form of coconut shell ash and baggas ash with aluminum oxide was used to evaluate the mechanical properties of the developed hybrid Al MMC. In [15] and [16], a mixture of rice husk ash with red mud and alumina was used, respectively, to evaluate the tribomechanical behavior of hybrid Al MMC materials. Hardness Hardness is an important parameter to check mechanical strength of composite materials. The hardness of composite material depends on various parameters, such as the particle size, heat treatment, the weight ratio of the reinforcingmaterial and the interatomic bonds between the reinforcingmaterial and the parent matrix. Previously, many researchers optimized this hardness parameter and concluded that the hardness of composite increases with decreasing particle size and the heat treatment. Also, the increases to an optimal weight percentage, which varies from reinforcing material to the composite as there is good inter-atomic bonding between the reinforcing material and the matrix but at higher weight percentage the hardness value decreases due to agglomeration of the reinforcing material in the matrix layer, which leads to the formation of pits and cavities. Cavities lead to the propagation of cracks and a decrease in tensile strength and hardness [17]. The hardness values are dependent on many parameters such as reinforcing material weight fraction, indentation load, treatment behavior, inter-atomic bonding etc. For this reason, a large number of composite specimens are required for experiments and determining its characteristics becomes expensive and labor intensive. Therefore, experimental design and Taguchi analysis are suitable approaches for optimizing input and output parameters. In this paper, the aluminum composite material obtained through stir casting route and its Brinell hardness value are optimized using ANOVA and regression analysis. The selected orthogonal array L27 of the experimental plan and the effect of the signal-to-noise ratio, the graph of the normal probability of the remainder, response characteristics, contour diagrams are tabulated for various composite samples. L27 orthogonal array of design of experiment are chosen and the effect of the signal-to-noise ratio, normal probability plot of residual, response characteristics, contour plots are tabulated for various composite specimens. Method and Materials The 5051 series aluminumalloywas chosen as parent material due to high stiffness and strength-to-weight ratio, high corrosion resistance and optimum thermal properties and widespread use in the development of building materials, in the automotive and aerospace industries. Red mud and coconut shell ash were used as reinforcing material to develop a hybrid composite material. Here, red mud is bauxite residue and is used as the primary reinforcing material, the content of which varies from 5 to 12.5 % and is purchased from the Balco aluminum refinery. Around 100 coconuts were purchased from various temples in Jaipur to obtain ash by burning and sieving. The proportion of coconut shell ash used as a secondary supportive reinforcing material varied from 5 to 12.5 wt. %. Both red mud and coconut shell ash were properly sieved to obtain particles of about 50 μm in size as hardness increases with decreasing particle size. Composite preparation Nine aluminum specimens were prepared via stir casting process. Ceramic crucibles were used for casting of aluminum metal. Red mud and coconut shells were preheated to 200 °C to remove moisture
OBRABOTKAMETALLOV MATERIAL SCIENCE Vol. 26 No. 3 2024 before the casting process. Melting and stirring parameters were optimized using the control panel. The following parameter values were set: stirring speed = 50–100 rpm, stirring time = 20 minutes, melting temperature = 800 °C, preheating temperature = 200 °C. A cylindrical mould with a diameter of 20 mm and a length of 250 mm was made for pouring molten metal. Using an orthogonal array L9, nine specimens were prepared, while the weight percentage of red mud and coconut shell ash was selected separately in the amount of 5, 7.5 and 12.5 wt. %. The hardness of each composite was calculated via selecting three values of indentation load of 10, 20 and 30 kN, the control variables table of which is shown below. Design of Experiment Material characterization requires an optimal output response to reduce the number of variables and improve the performance and durability of composite specimens. This optimization is achieved by controlling the input parameter over the output response, and Taguchi analysis is the optimal platform for characterizing materials [14]. Here, three parameters as weight percentage of red mud, weight percentage of coconut shell ash and indentation load are selected to test the Brinell hardness response of the hybrid aluminum composite material. Three levels of input parameters were selected to evaluate the hardness response, therefore, an orthogonal array L27 was selected for ANOVA and regression analysis, which is an experimental and predictable result of regression analysis, summarized in Table 1. In this research work, ANOVA and regression analysis were performed using Minitab 17 software to check the hardness value of hybrid aluminum composite specimens. The weight percentage of coconut shell ash (CSA weight %), the weight percentage of red mud (RM weight %) and the indenter load were accepted as input parameters. The coconut shell ash and red mud levels are assumed to be equal to 5, 7.5 and 12.5 wt. % at loads of 10, 15 and 20 kN. The hardness of composite specimens with different parameters is shown in Table 2. Figure 1 shows the effect of hardness on the signal-to-noise (SN) ratio and here the response is optimized for a larger hardness value and mean of SN ratio varies from 31 to 32.6 hardness values, which shows the optimal variability of the output hardness response. The composite material with the highest percentage of red mud and coconut shell ash reinforcing with the highest indentation load has a maximum hardness of 52.44 HB, which is almost 95 % greater than the composite material with the lowest percentage of reinforcing material (5 wt. % coconut shell ash and 5 wt. % red mud) and indenter load (10 kN). Thus, the hardness value improves with increasing volume fraction of the reinforcing material under loading [19]. Hardness behavior The hardness behavior characterization of a hybrid aluminum composite material reinforced with red mud (RM) and coconut shell ash (CSA) is presented in Table 2. The hardness increases with increasing percentage of reinforcing martial because the hard and brittle phase of the reinforcing martial creates a lubricating dislocation density and while the application of a dislocation density load leads to the formation of new strain fields that resist the dislocation movement [19]. Also, the difference in melting temperatures between the reinforcing material and the aluminum matrix activates the mechanism of strain hardening due to the transfer of the strain field along the grain boundary, which creates a barrier field along the matrix and hinders the indentation load, therefore increasing the hardness of the composites [20]. Figure 2 shows that the hardness value increases with increasing load, since under high load conditions the lubricating layer (formed due to the thermal mismatch between the reinforcingmaterial and the aluminum Ta b l e 1 Level of Control variables for hardness Variable Unit Level I Level II Level III Red mud Weight % 5 7.5 12.5 CSA Weight % 5 7.5 12.5 Load kN 10 20 30
OBRABOTKAMETALLOV MATERIAL SCIENCE Vol. 26 No. 3 2024 Ta b l e 2 Brinell hardness characterization of aluminum composite specimens Red Mud (%) CSA (%) Load (kN) Observed hardness Predicted hardness Signal-tonoise ratio 5 5 10 33.52 32.78 30.50696 5 5 15 34.75 35.02 30.82105 5 5 20 36.61 37.30 31.27347 5 7.5 10 35.12 35.94 30.91267 5 7.5 15 36.11 38.22 31.15281 5 7.5 20 38.22 40.50 31.64756 5 12.5 10 35.47 35.71 30.99823 5 12.5 15 37.60 37.95 31.50574 5 12.5 20 38.38 40.27 31.68409 7.5 5 10 36.27 34.95 31.1922 7.5 5 15 38.60 37.22 31.73368 7.5 5 20 39.21 39.50 31.8698 7.5 7.5 10 37.64 35.94 31.51387 7.5 7.5 15 38.99 38.22 31.82011 7.5 7.5 20 39.93 40.50 32.02644 7.5 12.5 10 38.09 37.92 31.61843 7.5 12.5 15 39.85 40.20 32.00981 7.5 12.5 20 40.32 42.48 32.11192 12.5 5 10 38.49 39.36 31.70709 12.5 5 15 39.33 41.64 31.89633 12.5 5 20 42.39 43.94 32.54664 12.5 7.5 10 39.04 40.35 31.83158 12.5 7.5 15 40.60 42.63 32.17236 12.5 7.5 20 48.72 44.91 33.75489 12.5 12.5 10 41.53 42.34 32.3691 12.5 12.5 15 43.51 44.62 32.77234 12.5 12.5 20 52.44 46.9 34.39448 1 2.5 7.5 5.0 32.6 32.4 32.2 32.0 31 .8 31 .6 31 .4 31 .2 31 .0 1 2.5 7.5 5.0 20 1 5 1 0 Red Mud(%) Mean of SN ratios CSA (%) Load(kN) Main Effects Plot for SN ratios Data Means Signal-to-noise: Larger is better Fig. 1. Mean effect of signal-to-noise ratio on Brinell hardness
OBRABOTKAMETALLOV MATERIAL SCIENCE Vol. 26 No. 3 2024 matrix) creates a strong dislocation strain field along the aluminum grain boundaries, which supports the tendency of increasing hardness. Figure 2 also shows that hardness increases as the percentage of the reinforcing material increases, since the combination of both reinforcing components can refine grain the structure of the composite, and the presence of a hard and brittle phase of silicon oxide, aluminum oxide and iron oxide leads to the formation of a strong inter-atomic bond between the aluminum matrix and the reinforcing material. At the same time, a larger indentation load is required to facilitate scratching hence improves the hardness [21]. According to Table 2, nine specimens were used, while the weight percentage of red mud and coconut shell ash was selected separately in the amount of 5, 7.5 and 12.5 wt. %. In addition, three loads were selected to assess the hardness behavior: 10, 20 and 30 kN. The results of the hardness behavior assessment are given below (Figure 2). Result and Discussion ANOVA Table 3 presents the output hardness response data and shows that the weight percentage of red mud possesses higher rank than load and coconut shell ash. This is a very useful tool for testing the effect of an input parameter on the output response. Table 4 shows the results of ANOVA, which is a very valuable tool for testing the relevance of various input variables to the output results. The contribution of the weight percentage of red mud reaches 48.80 %, the weight percentage of coconut shell ash is 10.41 % and the indenter load is 23.01 %. The same type of results is presented in the response table. The effect of weight percentage of red mud on hardness is superior to the influence of weight percentage of coconut shell ash and indenter load because red mud contains industrial compounds such as Al2O3, Fe2O3, SiO2, TiO2, etc., which support the hardening mechanism of aluminum composite materials [14]. The value of the determination coefficient R2 and the adjusted value R2 drop by 97.02 % and 90.31 %, respectively, which shows the variability of the output response depending on different input parameters. Both R values are within a good range of variability and this analysis is also used to further verify the mechanical hardness of the hybrid aluminum composite material. Regression Analysis A linear regression equation was drawn for the hardness value using the parameters for red mud, coconut shell ash and indentation load taken as input parameters and Brinell hardness response was analyzed by Fig. 2. Hardness variation with respected to load Ta b l e 3 Response Table for Hardness Level Red Mud (%) CSA (%) Load (kN) 1 36.20 37.69 37.25 2 38.77 39.38 38.82 3 42.90 40.81 41.56 Delta 6.70 3.11 4.56 Rank 1 3 2
OBRABOTKAMETALLOV MATERIAL SCIENCE Vol. 26 No. 3 2024 Ta b l e 4 ANOVA for Brinell hardness Source Degree of variance DF Adjusted value of within-group variability (error variance) Adj SS Adjusted variance value Adj MS F-Value P-Value Contribution % Red mud (%) 2 205.446 102.723 65.54 0.000 48.80 CSA (%) 2 43.744 21.872 13.96 0.002 10.41 Load (kN) 2 96.652 48.326 30.83 0.000 23.01 Red Mud (%)* CSA (%) 4 16.548 4.137 2.64 0.113 3.94 Red Mud (%)* Load (kN) 4 40.826 10.206 6.51 0.012 9.73 CSA (%)* Load (kN) 4 4.641 1.160 0.74 0.590 1.15 Error 8 12.539 1.567 2.29 Total 26 420.396 *Standard deviation S = 1.2519; R2 = 97.02 %; adjusted R2 = 90.31 %. 5.0 2.5 0.0 -2.5 -5.0 99 95 90 80 70 60 50 40 30 20 1 0 5 1 Residual Percent Normal Probability Plot (response is HB) Fig. 3. Normal probability plot for residual on hardness of Hybrid Al composites 95 % probability level. Equation 1 shows the regression of hardness and in Table 1 shows the predicted value based on Equation 1 and it is found that the error of the predicted value compared with the experimental value is only 4 %, so this regression equation can be used for further analysis [22–23]. Brinell Hardness = 21.78 + 0.883 Red Mud + 0.397 CSA + 0.4562 Load (1) The normal probability plot is drawn for 95 % confidence level and the straight line shows the regression equation line (Figure 3). Using this residual value, it is shown that all hardness deviations are very close to the regression line, out of 27 data, about 4 falls outside the optimal residual value. Therefore, this hybrid compositional combination can be considered suitable for further hardness design analysis.
OBRABOTKAMETALLOV MATERIAL SCIENCE Vol. 26 No. 3 2024 Load(kN) Red Mud(%) 20 1 8 1 6 1 4 1 2 1 0 1 2 1 1 1 0 9 8 7 6 5 > – – – – – – < 35.0 35.0 37.5 37.5 40.0 40.0 42.5 42.5 45.0 45.0 47.5 47.5 50.0 50.0 HB Contour Plot of HB vs Red Mud(%), Load(kN) Red Mud(%) CSA (%) 1 2 1 1 1 0 9 8 7 6 5 1 2 1 1 1 0 9 8 7 6 5 > – – – – – – < 35.0 35.0 37.5 37.5 40.0 40.0 42.5 42.5 45.0 45.0 47.5 47.5 50.0 50.0 HB Contour Plot of HB vs CSA (%), Red Mud(%) CSA (%) Load(kN) 1 2 1 1 1 0 9 8 7 6 5 20 1 8 1 6 1 4 1 2 1 0 > – – – – – – < 35.0 35.0 37.5 37.5 40.0 40.0 42.5 42.5 45.0 45.0 47.5 47.5 50.0 50.0 HB Contour Plot of HB vs Load(kN), CSA (%) a b с Fig 4. Variation of contour plot of Hardness value on (a) red mud vs load (b) load vs coconut shell ash (c Coconut shell ash vs red mud Contour plot effect on Hardness Figure 4 shows contour plot variation of different input factor over Hardness output. The X and Y axes display the combination of weight percentages of red mud, coconut shell ash and indentation load. The results show that all combined maximum variations of hardness fall in the range of 37.5–40 HB , while the range of 47.5–50 HB have very low area range. The weight percentage of red mud has more influential effect than the weight percentage of coconut shell ash and the load variation. The hardness value increases with an increase in the weight percentage of red mud and a load variation; a slight increase in hardness occurs with increasing weight percentage of coconut shell ash because the volatile nature of the red mud in the aluminum matrix and the low specific gravity of the coconut shell ash improve the surface contact area along the matrix. Thus, more of the intermediate contact area is available to incorporate the reinforcing component, and due to the sintering effect, the red mud and coconut shell ash are placed in the intermediate area, which acts as a barrier to the deformation movement under indentation load, thereby increasing the hardness [24]. Conclusion A hybrid aluminum metal matrix composite has been successfully developed by reinforcing red mud and coconut shell ash through stir casting process while maintaining 800 °C furnace temperature. Nine composite specimens were prepared, and the Brinell hardness of the optimized specimens was calculated
OBRABOTKAMETALLOV MATERIAL SCIENCE Vol. 26 No. 3 2024 using a hardness tester. For ANOVA and regression analysis, the L27 orthogonal array was used according to the Taguchi method. Three parameters were taken as input: weight percentage of red mud, weight percentage of coconut shell ash and indentation load, and hardness was taken as output. As a result of the optimization analysis, it was concluded that the hardness of the composite increases due to an increase in the weight percentage of the reinforcing component and indenter load. According to the response analysis, the weight percentage of red mud has a maximum contribution in the range of 48.80 %, which is superior to the contribution of the weight percentage of coconut ash and the contribution of indentation load. The hardness value shows an error of only 4 % compared to the predicted regression value, and all hardness values falling within the regression equation range show less variability in the residual value. References 1. Verma N., Vettivel S.C. Characterization and experimental analysis of boron carbide and rice husk ash reinforced AA7075 aluminium alloy hybrid composite. Journal of Alloys and Compounds, 2018, vol. 741, PP. 981– 998. DOI: 10.1016/j.jallcom.2018.01.185. 2. Bharathi P., Kumar T.S. Mechanical characteristics and wear behaviour of Al/SiC and Al/SiC/B4C hybrid metal matrix composites fabricated through powder metallurgy route. Silicon, 2023, vol. 15 (10), pp. 4259–4275. DOI: 10.1007/s12633-023-02347-0. 3. Sundarababu J., Anandan S.S., Griskevicius P. Evaluation of mechanical properties of biodegradable coconut shell/rice husk powder polymer composites for light weight applications. Materials Today: Proceedings, 2021, vol. 39, pp. 1241–1247. 4. Da Silva E.J., Marques M.L., Velasco F.G., Junior C.F., Luzardo F.M., Tashima M.M. A new treatment for coconut fibers to improve the properties of cement-based composites – Combined effect of natural latex/pozzolanic materials. Sustainable Materials and Technologies, 2017, vol. 12, PP. 44–51. DOI: 10.1016/j.susmat.2017.04.003. 5. Lubis H., Sharman E., Chairina E., Siregar I., Rizky M., Maiya D., Machdhalie T. Fabrication and characterization of adding coconut shell actived nanocarbon to lightweight concrete. Journal of Physics: Conference Series, 2020, vol. 1428 (1), p. 012039. DOI: 10.1088/1742-6596/1428/1/012039. 6. Melnyk L., Myronyuk O., Ratushniy V., Baklan D. The feasibility of using red mud in coatings based on glyptal resins. French-Ukrainian Journal of Chemistry, 2020, vol. 8 (1), pp. 88–94. DOI: 10.17721/fujcV8I1P88-94. 7. Li Z., Zhang J., Li S., Lin C., Gao Y., Liu C. Feasibility of preparing red mud-based cementitious materials: Synergistic utilization of industrial solid waste, waste heat, and tail gas. Journal of Cleaner Production, 2021, vol. 285, p. 124896. DOI: 10.1016/j.jclepro.2020.124896. 8. Kang S.P., Kwon S.J. Effects of red mud and alkali-activated slag cement on efflorescence in cement mortar. Construction and Building Materials, 2017, vol. 133, pp. 459–467. DOI: 10.1016/j.conbuildmat.2016.12.123. 9. Scribot C., Maherzi W., Benzerzour M., Mamindy-Pajany Y., Abriak N.E. A laboratory-scale experimental investigation on the reuse of a modified red mud in ceramic materials production. Construction and Building Materials, 2018, vol. 163, pp. 21–31. DOI: 10.1016/j.conbuildmat.2017.12.092. 10. Peng Y., Luo L., Luo S., Peng K., Zhou Y., Mao Q., Yang Y. Efficient removal of antimony (III) in aqueous phase by nano-Fe3O4 modified high-iron red mud: study on its performance and mechanism. Water, 2021, vol. 13 (6), p. 809. DOI: 10.3390/w13060809. 11. Sharma R., Pradhan M.K., Jain P. Fabrication, characterization and optimal selection of aluminium alloy 8011 composites reinforced with B4C-aloe vera ash. Materials Research Express, 2023, vol. 10 (11), p. 116513. DOI: 10.1088/2053-1591/acec32. 12. Kumar L.J., Ganjigatti J.P., Irfan G., Thara R. Optimizing wear performance: comprehensive analysis of aluminium 6061 metal matrix composites reinforced with coconut shell ash and graphene. Journal of The Institution of Engineers (India): Series D, 2024, pp. 1–17. DOI: 10.1007/s40033-023-00630-3. 13. Kumar A., Singh R.C., Chaudhary R. Investigation of nano-Al2O3 and micro-coconut shell ash (CSA) reinforced AA7075 hybrid metal–matrix composite using two-stage stir casting. Arabian Journal for Science and Engineering, 2022, vol. 47 (12), pp. 15559–15573. DOI: 10.1007/s13369-022-06728-2. 14. Chandla N.K., Yashpal, Kant S., Goud M.M., Jawalkar C.S. Experimental analysis and mechanical characterization of Al 6061/alumina/bagasse ash hybrid reinforced metal matrix composite using vacuumassisted stir casting method. Journal of Composite Materials, 2020, vol. 54 (27), pp. 4283–4297. DOI: 10.1177/0021998320929417.
OBRABOTKAMETALLOV MATERIAL SCIENCE Vol. 26 No. 3 2024 15. Dewangan R., Pandey P.K., Dohare R. Analysis on mechanical behaviour of hybrid aluminium metal matrix composite material using rice husk ash and iron ore tailing. Turkish Online Journal of Qualitative Inquiry, 2021, vol. 12 (3). 16. Gupta V., Kumar R. Investigating the mechanical and tribological properties of aluminium metal matrix composite reinforced with rice husk ash and aluminium oxide. International Journal of Precision Technology, 2020, vol. 9 (4), pp. 324–334. DOI: 10.1504/IJPTECH.2020.112697. 17. Singh J., Chauhan A. Investigations on dry sliding frictional and wear characteristics of SiC and red mud reinforced Al2024 matrix hybrid composites using Taguchi’s approach. Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications, 2019, vol. 233 (9), pp. 1923–1938. DOI: 10.1177/1464420718803126. 18. Panda B., Niranjan C.A., Vishwanatha A.D., Harisha P., Chandan K.R., Kumar R. Development of novel stir cast aluminium composite with modified coconut shell ash filler. Materials Today: Proceedings, 2020, vol. 22, pp. 2715–2724. DOI: 10.1016/j.matpr.2020.03.402. 19. Kar C., Surekha B. Characterisation of aluminium metal matrix composites reinforced with titanium carbide and red mud. Materials Research Innovations, 2021, vol. 25 (2), pp. 67–75. DOI: 10.1080/14328917.202 0.1735683. 20. Harish T.M., Mathai S., Cherian J., Mathew K.M., Thomas T., Prasad K.V., Ravi V.C. Development of aluminium 5056/SiC/bagasse ash hybrid composites using stir casting method. Materials Today: Proceedings, 2020, vol. 27, pp. 2635–2639. DOI: 10.1016/j.matpr.2019.11.081. 21. Samal P., Raj R., Mandava R.K., Vundavilli P.R. Effect of red mud on mechanical and microstructural characteristics of aluminum matrix composites. Advances in Materials and Manufacturing Engineering: Proceedings of ICAMME 2019. Singapore, Springer, 2020, pp. 75–82. DOI: 10.1007/978-981-15-1307-7_8. 22. Biswas R., Sarkar A. A two-step approach for arsenic removal by exploiting an autochthonous Delftia sp. BAs29 and neutralized red mud. Environmental Science and Pollution Research, 2021, vol. 28, pp. 40665–40677. DOI: 10.1007/s11356-020-10665-8. 23. Kumar P.V., Paranthaman P. Friction stir welding process parametric optimization of hybrid aluminiumbagasse ash-graphite composite by Taguchi approach. Materials Today: Proceedings, 2021, vol. 37, pp. 764–768. DOI: 10.1016/j.matpr.2020.05.789. 24. Khalid M.Y., Umer R., Khan K.A. Review of recent trends and developments in aluminium 7075 alloys and metal matrix composites (MMCs) for aircraft applications. Results in Engineering, 2023, vol. 20, p. 101372. DOI: 10.1016/j.rineng.2023.101372. Conflicts of Interest The authors declare no conflict of interest. 2024 The Authors. Published by Novosibirsk State Technical University. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0).
RkJQdWJsaXNoZXIy MTk0ODM1