- Email: [email protected]
Optimization of dry sliding wear behavior of flyash & zirconia reinforced Al-Mg-1Si-Cu HMMC using Taguchi technique
Materials Today: Proceedings xxx (xxxx) xxx
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Optimization of dry sliding wear behavior of flyash & zirconia reinforced Al-Mg-1Si-Cu HMMC using Taguchi technique M. Fakkir Mohamed ⇑, S. Yaknesh, G. Radhakrishnan, P. Mohankumar Department of Automobile Engineering, Tamilnadu College of Engineering, Coimbatore 641659, India
a r t i c l e
i n f o
Article history: Received 7 November 2019 Received in revised form 9 December 2019 Accepted 15 December 2019 Available online xxxx Keywords: HMMC Flyash Zirconia Taguchi technique Linear regression analysis SEM
a b s t r a c t This research represents an efficient conceptualization to improve the tribo characteristics of hybrid aluminium matrix composites (6061-Flyash-Zirconia) fictitious by stir casting method, with multiple performances supported by Taguchi Technique. During this study flyash was unbroken constant at 100% (6061, 6061–10% Flyash-2% Zirconia and 6061–10% Flyash-4% Zirconia) and Zirconia was varied from 0 to 4%. Dry sliding wear checks were conducted employing a well-planned experimental schedule supported by Taguchi’s orthogonal arrays. The input parameters like Load (L), sliding Speed (S), sliding Distance (D) and Reinforcement (R) are optimized with issues of multiple performance characteristics: Specific Wear Rate (SWR) and Co-efficient of Friction (COF). The optimum levels of input parameters were selected from response table &graph. SEM was done to look at the worn surface of composite specimen. The regression equation was developed and valid through confirmative experiment. Ó 2019 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International Conference on Materials Engineering and Characterization 2019.
1. Introduction Aluminium alloy is widely used in aerospace and automobile companies due to its exceptional mechanical properties and low thermal co-efficient of expansion compared to other metals and alloys. There is a huge demand for aluminium matrix based nano compositesas they offer high specific strength and wear resistance than pure aluminium. Al6061 is one of the aluminium alloys which are lighter in weight with good mechanical properties. Rathod et al. 2014., examined the behavior of aluminium cast alloy (6063) with alumina composite produced by the stir casting technique for different percentage of alumina powder was used as reinforcement phase in matrix metal. The experimental studies reveal the increase in percentage of alumina in aluminium matrix improves the strength of the matrix material. Since uniform distribution of alumina particles in aluminium matrix improves the hardness of the matrix material [1]. S.K. Rhee et al. 1970, analysed the aluminum (al6061) alloy reinforced with aluminium oxide (al2o3) particle using various size (25, 50 & 80 m) with constant volume fraction of about 10% is fabricated by stir casting method. The investigation shows that the results illustrate the hardness and
⇑ Corresponding author.
yield strength increases with decrease in particle size of alumina [2]. Y. Sahin et al 2003, deals with the fundamentals of composite and their classification. An elaborate review on aluminium matrix composites, types, properties, applications and opportunities are given, which provides a basic foundation for the research on AMC [3]. N. Natarajan et al. 2006, investigated the mechanical and wear behavior of aluminium 2014 reinforced with fly ash for brake pads. AMMC with fly ash provides the better strength & wear resistance compared to grey cast iron [4]. K. Radhakrishna et al. 2007, examined the wear property for polymers sliding against metal surfaces. In this property of the al6061 & fly ash is greater when compared to grey cast iron and steel [5]. Ashutosh et al. 2016, Investigated the wear behavior of aluminium alloy and its composites reinforced with fly ash particles were analyzed using statistical analysis [6]. A. Karthik et al. 2017, presented with epoxy resin matrix composite reinforced with fly ash particles was prepared by ultrasonic stirring method and examined the parameters like % of fly ash debris, typical load, sliding speed and track distance. S/N ratio analysis optimizes the parametric condition that produce minimum wear rate, frictional force and co-efficient of friction. TOPSIS is used to optimize the output and ANOVA shows that applied normal load plays a vital role in increasing dry sliding wear [7]. S. Dharmalingam et al. 2011, examined the friction, wear, hardness and
E-mail address: [email protected] (M. Fakkir Mohamed). https://doi.org/10.1016/j.matpr.2019.12.122 2214-7853/Ó 2019 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International Conference on Materials Engineering and Characterization 2019.
Please cite this article as: M. Fakkir Mohamed, S. Yaknesh, G. Radhakrishnan et al., Optimization of dry sliding wear behavior of flyash & zirconia reinforced Al-Mg-1Si-Cu HMMC using Taguchi technique, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.12.122
2
M. Fakkir Mohamed et al. / Materials Today: Proceedings xxx (xxxx) xxx
tensile tests have been carried out in this work. Taguchi method is used for optimization of parameters and ANOVA is carried out [8]. K. Panneer selvam et al. 2016, investigated the dry sliding performance on aluminium HMMC using GRA & Taguchi method with load, sliding speed, molybdenum disulfide as input and output as specific wear rate and coefficient of friction. ANOVA shows the significant parameters for controlling the friction and wear. Using SEM, wear surface morphology has been investigated [9]. Bheemireddy et al. 2017, investigated the two matrix materials were Polypropylene and Nylon 6 with Aluminium honeycomb core were fabricated using Compression moulding. The process parameters were normal load, sliding velocity, sliding distance and abrasive paper grit size. The output responses were Coefficient of Friction and Specific Wear Rate with L9 Taguchi orthogonal array. For polypropylene composite material the highest GFRG is obtained at 30 N normal load, 0.523 m/s sliding velocity, 450 m sliding distance, 320 grit size of abrasive paper and these are the optimum level of process parameters. For nylon composite material highest GFRG is obtained at 30 N normal load, 1.046 m/s sliding velocity, 150 m sliding distance, 400 grit size of abrasive paper [10]. Most of the researchers were studied aluminium matrix composite using Al2O3, WC, SiC, fly ash and ZnO, particulates reinforcement to improvise the tribological properties of aluminum matrix. From the literatures the researchers have not tried this, so a new attempt has been done to improve the tribological properties of al6061 by using zirconium (ZrO2) and flyash particles as reinforcement. 2. Experimental work 2.1. Materials and methods Aluminium 6061 is employed as a matrix material and is one in every of the foremost extensively used 6000 series metal. It’s a flexible heat treatable extruded metal with medium to high strength capabilities. Materials are selected by supporting the physical and mechanical properties. The composition of 6061 alloy given in Table 1. 2.2. Flyash Flyash is one in all the coal combustion yield, composed of the fine particles that are driven out of the boiler with the flue gases. Ash that drops within the bottom of the boiler is named bottom ash. In trendy coal-fired power plants, ash is usually captured by
electricity precipitators. The composition of ash is shown in Table 2. 2.3. Zirconia Zirconia is wide used as associate additive in varied materials and yield. This semiconductor has many favourable properties, as well as smart transparency, wide band gap and robust room-temperature luminescence. The amendment of volume caused by the structure transition from polygon to monoclinic to cube-shaped induces massive stresses, inflicting it to crack upon cooling from high temperatures. The composition of Zirconia is shown in Table 3. 2.4. Stir casting Stir casting method has been used for manufacturing discontinuous particles bolstered metal matrix composites for many years. Stir casting relies on the principle of reinforcing the metal with different metals. These ideally enforced processes combine the material while not dynamical part and make microstructure with fine grains. Discontinuous reinforcement is stirred into liquified metal that is allowed to solidify. The liquified metal was stirred to make a vortex and also the preheated (200 °C) ash particles and Zirconia (0, 2, 4 wt%) were introduced in to the soften and also the suspension was stirred at 380 rpm for 10 mins for manufacturing the 3 totally different hybrid composites (6061, 6061-10% Flyash-2% Zirconia and 606110% Flyash-4% Zirconia). The stirred spread liquified metal was poured into preheated (650 °C) forged iron moulds and cooled to temperature. The experimental setup was shown in Fig. 1. 2.5. Wear test Sliding wear tester is that the most well-liked tribometer for work wear similarly as resistance behavior of materials underneath condition of dry sliding. 2 discs are fastened to 2 parallel shafts and ironed against one another underneath a relentless contact load. Driven by a motor through a train of substances, the specimens are rotating alongside. The rotating speed will be controlled in order that once the linear speeds of 2 wheels are equal at the contact purpose a pure rolling contact is achieved. Once one in all the specimens is fastened and also the alternative is rotating then wear could be a pure slippery. During this fastened specimen will be a block in order that a reputation of block-on-
Table 1 Composition of Al6061. Element
Al 6061
Composition [%] Al
Si
Fe
Cu
Mn
Mg
Zn
Ti
Pb
97.15
0.35
0.65
0.21
0.65
0.68
0.15
0.1
0.03
Table 2 Composition of Flyash. Element
Composition [%]
Fly Ash
SiO2
Al2O3
Fe2O3
CaO
MgO
54.27
34.73
6.1
2.4
2.1
Table 3 Composition of Zirconia. Element
ZrO2
Composition [%] SiO2
Al2O3
Fe2O3
CaO
TiO2
Na2O
0.05
0.5
0.05
0.2
0.02
0.01
Please cite this article as: M. Fakkir Mohamed, S. Yaknesh, G. Radhakrishnan et al., Optimization of dry sliding wear behavior of flyash & zirconia reinforced Al-Mg-1Si-Cu HMMC using Taguchi technique, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.12.122
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M. Fakkir Mohamed et al. / Materials Today: Proceedings xxx (xxxx) xxx
3. Design of experiments The Design of the experiment (DOE) is employed in Taguchi methodology to attain the foremost important information whereas conducting borderline variety of experiments. Taguchi optimisation is experimental optimisation technique that uses the quality orthogonal arrays for forming the matrix of experiments, which allows not solely conducting borderline variety of experiments however conjointly reaching higher worth level of every parameter. Signal/Noise (S/N) ratios are employed in information analysis in Taguchi methodology. The 3 basic classes to hunt the simplest results of experiments were nominal-thebetter, larger-the-better and smaller-the-better. In this study, the experimental style was in line with L9 orthogonal array (OA) supported by Taguchi methodology, whereas the Taguchi OA would considerably scale back the quantity of experiments. Based on the literatures the process parameters had chosen was shown in Table 4. Fig. 1. Experimental Setup.
3.1. Taguchi technique Design of experiments is one in all the necessary and powerful applied mathematics techniques to check the result of multi variables at the same time. This technique drastically reduces the quantity of experiments that needed to model the response perform compared with the complete factorial style of experiments. The Taguchi technique is devised for method optimisation and identification of best combination of the factors for a given response. The objective of the tactic is to supply prime quality product at low price.
4. Results and discussion
Fig. 2. Sample specimen A, B & C.
Table 4 Levels of Process Parameters. Level
Load, L, (N)
Sliding Speed, S (m/s)
Sliding Distance, D (m)
Reinforcements, R (wt%)
1 2 3
10 20 30
1 2 3
400 800 1200
0 2 4
wheel is employed. Weight has been noted on before experimentation and once the experiment to calculate wear rate. Sample taken for wear check with following dimensions were ten cm height and ten cm breadth according to ASTM standard G99. The sample taken for wear and friction check was shown in Fig. 2.
The wear tests were performed to review the impact of control parameters over the output responses. The S/N magnitude relation of the response characteristics for every variable at totally different levels were determined from experimental information. The most effects of method variables of S/N magnitude relation were planned. The response graphs were used for examining the constant effects on the responses. The foremost best settings of control variables were established by analyzing the response graphs. The experimental results of wear test is shown in Table 5. The results are dissected using S/N Ratios, Response Table and Graphs with the assistance of Minitab software 18. Taguchi Analysis: SWR and COF versus Load, Sliding Speed, Sliding Distance, Reinforcements S/N ratios are shown in Figs. 3 and 4. Average of every levels in terms of S/N ratios were given in Tables 6 and 7. Once observing all response graphs, Table 8 shows the optimum Parameters Combination for SWR and COF.
Table 5 L9 Orthogonal Arrays for Experimentation. S. No
L
S
D
R
SWR
COF
S/N Ratio SWR
S/N Ratio COF
1 2 3 4 5 6 7 8 9
10 20 30 20 30 10 30 10 20
1 1 1 2 2 2 3 3 3
400 800 1200 400 800 1200 400 800 1200
0 2 4 4 0 2 2 4 0
7.198 9.338 7.841 6.273 6.406 1.946 23.05 4.800 4.116
0.499 0.699 0.799 0.599 0.333 0.199 0.366 0.233 0.180
17.1442 19.4051 17.8874 15.9495 16.1317 5.7829 27.2534 13.6248 12.2895
6.0380 3.1105 1.9491 4.4515 9.5511 14.0229 8.7304 12.6529 14.8945
Please cite this article as: M. Fakkir Mohamed, S. Yaknesh, G. Radhakrishnan et al., Optimization of dry sliding wear behavior of flyash & zirconia reinforced Al-Mg-1Si-Cu HMMC using Taguchi technique, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.12.122
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4.1. Microstructure study After wear test, scanning microscopy is finished to check the damage tracks of the samples. The microstructure study is performed to grasp the character of damage employing a scanning microscope once wear check is completed and shown in Fig. 5a–c. The grey portion indicates the Aluminium 6061, white portion indicates the Zirconia and the curvy path indicates the flyash particles. So, it represents the better bonding between the aluminium and the reinforcements. 4.2. Linear regression analysis The rectilinear regression equation was developed by victimizing the Minitab 18 applied mathematics software system. This
analysis was accustomed to establish the correlation between expected variable and therefore the response variable. The regression of y on x for Specific Wear Rate and Co-efficient of Friction is as follows:
SWR ¼ 4:91 þ 0:389 L þ 1:26S 0:00942 D þ 0:10R
ð1Þ
COF ¼ 0:643 þ 0:00945 L 0:2030 S 0:000119D þ 0:0516R ð2Þ where L is the applied load in (N), S is sliding speed in (m/s), D is sliding distance in (m) and Reinforcements (wt%). The positive/negative sign of this Eqs. (1) and (2) indicated the Specific Wear Rate and Co-efficient of Friction behavior of
Fig. 3. Effect of control factors on SWR.
Fig. 4. Effect of control factors on COF.
Table 6 Response Table for S/N Ratio (SWR). Level
L
S
D
R
1 2 3 Delta Rank
12.18 15.88 20.42 8.24 1
18.15 12.62 17.72 5.52 3
20.12 16.39 11.99 8.13 2
15.19 17.48 15.82 2.29 4
Please cite this article as: M. Fakkir Mohamed, S. Yaknesh, G. Radhakrishnan et al., Optimization of dry sliding wear behavior of flyash & zirconia reinforced Al-Mg-1Si-Cu HMMC using Taguchi technique, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.12.122
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M. Fakkir Mohamed et al. / Materials Today: Proceedings xxx (xxxx) xxx Table 7 Response Table for S/N Ratio (COF). Level
L
S
D
R
1 2 3 Delta Rank
10.905 7.485 6.744 4.161 2
3.699 9.342 12.093 8.393 1
6.407 8.438 10.289 3.882 3
10.161 8.621 6.351 3.810 4
Table 8 Optimum Conditions using Taguchi Method. S.No
Control Factors
SWR
COF
Best Level
Value
Best Level
Value
1 2 3 4
Load Sliding Speed Sliding Distance Reinforcements
3 1 1 2
30 1 400 2
3 1 1 3
30 1 400 4
Fig. 5. a. SEM for worn surface of Pure Al, b. SEM for worn surface of Al-10%Fly ash and 2%ZrO2, c. SEM for worn surface of Al-10%Fly ash and 4%ZrO2.
Distance 400 m, Reinforcement 4(wt%) that is validated through regression analysis.
Table 9 Result for Confirmation Experiment. Initial Parameter
Optimal Parameter
Output
Level
Experimental
Regression
SWR COF
L3S1D1R2 L3S1D1R3
10.4168 0.7238
14.272 0.8823
composite. The plus sign indicated increase in wear rate and minus sign indicated decrease in wear rate with parameter. The confirmation was performed with parameters apart from designated parameters as shown in Table 9 and therefore the wear check was conducted for the preferred levels. The regression wear rate results were closely kind of like experimental wear rate with minimum error and thence the developed model had higher potency to investigate the wear rate of composite.
5. Conclusion Based on the Results and Discussion the subsequent conclusions were defended, microstructure of the composite was discovered and results represented the uniform distribution of reinforcement particles within the matrix. An effort was created to figure out the various machining parameters on Pin on Disc equipment for Al-Flyash-Zirconia hybrid composite supported by Taguchi’s methodology. From that, the optimized input parameters to urge the minimum Specific Wear Rate are Load 30 N, sliding Speed 1 m/s, sliding Distance 400 m, Reinforcement 2 (wt%) and for Co-efficient of Friction Load 30 N, sliding Speed 1 m/s, sliding
CRediT authorship contribution statement S. Yaknesh: Resources, Software, Supervision. G. Radhakrishnan: Resources, Software, Supervision. P. Mohankumar: Resources, Software, Supervision. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References [1] Rathod Abhik, V. Umasankar, M. Anthony Xavior, Evaluation of properties for Al-SiC reinforced metal matrixcomposite for brake pads, Procedia Eng. 97 (2014) 941–950. [2] S.K. Rhee, Wear equation for polymers sliding against metal surfaces, Wear 16 (1970) 431–435. [3] Y. Sahin, Wear behavior of aluminium alloy and its composites reinforced by Fly ash particles using statistical analysis, Mater. Des. 24 (2003) 95–103. [4] N. Natarajan, S. Vijayarangan, Wear behavior of aluminium matrix composites sliding against automobile friction material, Wear 261 (2006) 812–822. [5] K. Radhakrishna, M. Ramachandra, Effect of reinforcement of fly ash on sliding wear, slurry erosive wear and corrosive behavior of Al matrix composite, Wear 262 (2007) 1450–1462. [6] Ashutosh Pattanaik, Mantra Prasad Satpathy, Subash Chandra Mishra, Dry sliding wear behavior of epoxy fly ash composite with Taguchi optimization, Eng. Sci. Technol., Int. J. 19 (2016) 710–716. [7] A. Karthik, M. Shivapratap Singh Yadav, H.N. Reddappa, M. Ravikumar, Tribological behavior of Al6061-beryl metal matrix composite and optimization of parameters using Taguchi method, Int. J. Res. Sci. Innov. IV (XI) (2017) 52–56.
Please cite this article as: M. Fakkir Mohamed, S. Yaknesh, G. Radhakrishnan et al., Optimization of dry sliding wear behavior of flyash & zirconia reinforced Al-Mg-1Si-Cu HMMC using Taguchi technique, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.12.122
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[8] S. Dharmalingam, R. Subramanian, K. Somasundara Vinoth, B. Anandavel, Optimization of tribological properties in aluminum hybrid metal matrix composites using gray-Taguchi method, JMEPEG 20 (2011) 1457–1466. [9] K. Panneerselvam, K. Lokesh, D. Chandresh, T.N.S. Ramakrishna, Optimization of tribological properties of aluminium honeycomb reinforced polymeric composites using grey based fuzzy algorithm, Mech., Mater. Sci. Eng. (2016).
[10] Bheemireddy Hemanth Reddy, S. Perumal, Optimization of tribology properties of A356 aluminium matrix composites using grey relational analysis, SSRG Int. J. Mech. Eng. (Special Issue) (2017) 16–20.
Please cite this article as: M. Fakkir Mohamed, S. Yaknesh, G. Radhakrishnan et al., Optimization of dry sliding wear behavior of flyash & zirconia reinforced Al-Mg-1Si-Cu HMMC using Taguchi technique, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.12.122
Contents lists available at ScienceDirect
Materials Today: Proceedings journal homepage: www.elsevier.com/locate/matpr
Optimization of dry sliding wear behavior of flyash & zirconia reinforced Al-Mg-1Si-Cu HMMC using Taguchi technique M. Fakkir Mohamed ⇑, S. Yaknesh, G. Radhakrishnan, P. Mohankumar Department of Automobile Engineering, Tamilnadu College of Engineering, Coimbatore 641659, India
a r t i c l e
i n f o
Article history: Received 7 November 2019 Received in revised form 9 December 2019 Accepted 15 December 2019 Available online xxxx Keywords: HMMC Flyash Zirconia Taguchi technique Linear regression analysis SEM
a b s t r a c t This research represents an efficient conceptualization to improve the tribo characteristics of hybrid aluminium matrix composites (6061-Flyash-Zirconia) fictitious by stir casting method, with multiple performances supported by Taguchi Technique. During this study flyash was unbroken constant at 100% (6061, 6061–10% Flyash-2% Zirconia and 6061–10% Flyash-4% Zirconia) and Zirconia was varied from 0 to 4%. Dry sliding wear checks were conducted employing a well-planned experimental schedule supported by Taguchi’s orthogonal arrays. The input parameters like Load (L), sliding Speed (S), sliding Distance (D) and Reinforcement (R) are optimized with issues of multiple performance characteristics: Specific Wear Rate (SWR) and Co-efficient of Friction (COF). The optimum levels of input parameters were selected from response table &graph. SEM was done to look at the worn surface of composite specimen. The regression equation was developed and valid through confirmative experiment. Ó 2019 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International Conference on Materials Engineering and Characterization 2019.
1. Introduction Aluminium alloy is widely used in aerospace and automobile companies due to its exceptional mechanical properties and low thermal co-efficient of expansion compared to other metals and alloys. There is a huge demand for aluminium matrix based nano compositesas they offer high specific strength and wear resistance than pure aluminium. Al6061 is one of the aluminium alloys which are lighter in weight with good mechanical properties. Rathod et al. 2014., examined the behavior of aluminium cast alloy (6063) with alumina composite produced by the stir casting technique for different percentage of alumina powder was used as reinforcement phase in matrix metal. The experimental studies reveal the increase in percentage of alumina in aluminium matrix improves the strength of the matrix material. Since uniform distribution of alumina particles in aluminium matrix improves the hardness of the matrix material [1]. S.K. Rhee et al. 1970, analysed the aluminum (al6061) alloy reinforced with aluminium oxide (al2o3) particle using various size (25, 50 & 80 m) with constant volume fraction of about 10% is fabricated by stir casting method. The investigation shows that the results illustrate the hardness and
⇑ Corresponding author.
yield strength increases with decrease in particle size of alumina [2]. Y. Sahin et al 2003, deals with the fundamentals of composite and their classification. An elaborate review on aluminium matrix composites, types, properties, applications and opportunities are given, which provides a basic foundation for the research on AMC [3]. N. Natarajan et al. 2006, investigated the mechanical and wear behavior of aluminium 2014 reinforced with fly ash for brake pads. AMMC with fly ash provides the better strength & wear resistance compared to grey cast iron [4]. K. Radhakrishna et al. 2007, examined the wear property for polymers sliding against metal surfaces. In this property of the al6061 & fly ash is greater when compared to grey cast iron and steel [5]. Ashutosh et al. 2016, Investigated the wear behavior of aluminium alloy and its composites reinforced with fly ash particles were analyzed using statistical analysis [6]. A. Karthik et al. 2017, presented with epoxy resin matrix composite reinforced with fly ash particles was prepared by ultrasonic stirring method and examined the parameters like % of fly ash debris, typical load, sliding speed and track distance. S/N ratio analysis optimizes the parametric condition that produce minimum wear rate, frictional force and co-efficient of friction. TOPSIS is used to optimize the output and ANOVA shows that applied normal load plays a vital role in increasing dry sliding wear [7]. S. Dharmalingam et al. 2011, examined the friction, wear, hardness and
E-mail address: [email protected] (M. Fakkir Mohamed). https://doi.org/10.1016/j.matpr.2019.12.122 2214-7853/Ó 2019 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International Conference on Materials Engineering and Characterization 2019.
Please cite this article as: M. Fakkir Mohamed, S. Yaknesh, G. Radhakrishnan et al., Optimization of dry sliding wear behavior of flyash & zirconia reinforced Al-Mg-1Si-Cu HMMC using Taguchi technique, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.12.122
2
M. Fakkir Mohamed et al. / Materials Today: Proceedings xxx (xxxx) xxx
tensile tests have been carried out in this work. Taguchi method is used for optimization of parameters and ANOVA is carried out [8]. K. Panneer selvam et al. 2016, investigated the dry sliding performance on aluminium HMMC using GRA & Taguchi method with load, sliding speed, molybdenum disulfide as input and output as specific wear rate and coefficient of friction. ANOVA shows the significant parameters for controlling the friction and wear. Using SEM, wear surface morphology has been investigated [9]. Bheemireddy et al. 2017, investigated the two matrix materials were Polypropylene and Nylon 6 with Aluminium honeycomb core were fabricated using Compression moulding. The process parameters were normal load, sliding velocity, sliding distance and abrasive paper grit size. The output responses were Coefficient of Friction and Specific Wear Rate with L9 Taguchi orthogonal array. For polypropylene composite material the highest GFRG is obtained at 30 N normal load, 0.523 m/s sliding velocity, 450 m sliding distance, 320 grit size of abrasive paper and these are the optimum level of process parameters. For nylon composite material highest GFRG is obtained at 30 N normal load, 1.046 m/s sliding velocity, 150 m sliding distance, 400 grit size of abrasive paper [10]. Most of the researchers were studied aluminium matrix composite using Al2O3, WC, SiC, fly ash and ZnO, particulates reinforcement to improvise the tribological properties of aluminum matrix. From the literatures the researchers have not tried this, so a new attempt has been done to improve the tribological properties of al6061 by using zirconium (ZrO2) and flyash particles as reinforcement. 2. Experimental work 2.1. Materials and methods Aluminium 6061 is employed as a matrix material and is one in every of the foremost extensively used 6000 series metal. It’s a flexible heat treatable extruded metal with medium to high strength capabilities. Materials are selected by supporting the physical and mechanical properties. The composition of 6061 alloy given in Table 1. 2.2. Flyash Flyash is one in all the coal combustion yield, composed of the fine particles that are driven out of the boiler with the flue gases. Ash that drops within the bottom of the boiler is named bottom ash. In trendy coal-fired power plants, ash is usually captured by
electricity precipitators. The composition of ash is shown in Table 2. 2.3. Zirconia Zirconia is wide used as associate additive in varied materials and yield. This semiconductor has many favourable properties, as well as smart transparency, wide band gap and robust room-temperature luminescence. The amendment of volume caused by the structure transition from polygon to monoclinic to cube-shaped induces massive stresses, inflicting it to crack upon cooling from high temperatures. The composition of Zirconia is shown in Table 3. 2.4. Stir casting Stir casting method has been used for manufacturing discontinuous particles bolstered metal matrix composites for many years. Stir casting relies on the principle of reinforcing the metal with different metals. These ideally enforced processes combine the material while not dynamical part and make microstructure with fine grains. Discontinuous reinforcement is stirred into liquified metal that is allowed to solidify. The liquified metal was stirred to make a vortex and also the preheated (200 °C) ash particles and Zirconia (0, 2, 4 wt%) were introduced in to the soften and also the suspension was stirred at 380 rpm for 10 mins for manufacturing the 3 totally different hybrid composites (6061, 6061-10% Flyash-2% Zirconia and 606110% Flyash-4% Zirconia). The stirred spread liquified metal was poured into preheated (650 °C) forged iron moulds and cooled to temperature. The experimental setup was shown in Fig. 1. 2.5. Wear test Sliding wear tester is that the most well-liked tribometer for work wear similarly as resistance behavior of materials underneath condition of dry sliding. 2 discs are fastened to 2 parallel shafts and ironed against one another underneath a relentless contact load. Driven by a motor through a train of substances, the specimens are rotating alongside. The rotating speed will be controlled in order that once the linear speeds of 2 wheels are equal at the contact purpose a pure rolling contact is achieved. Once one in all the specimens is fastened and also the alternative is rotating then wear could be a pure slippery. During this fastened specimen will be a block in order that a reputation of block-on-
Table 1 Composition of Al6061. Element
Al 6061
Composition [%] Al
Si
Fe
Cu
Mn
Mg
Zn
Ti
Pb
97.15
0.35
0.65
0.21
0.65
0.68
0.15
0.1
0.03
Table 2 Composition of Flyash. Element
Composition [%]
Fly Ash
SiO2
Al2O3
Fe2O3
CaO
MgO
54.27
34.73
6.1
2.4
2.1
Table 3 Composition of Zirconia. Element
ZrO2
Composition [%] SiO2
Al2O3
Fe2O3
CaO
TiO2
Na2O
0.05
0.5
0.05
0.2
0.02
0.01
Please cite this article as: M. Fakkir Mohamed, S. Yaknesh, G. Radhakrishnan et al., Optimization of dry sliding wear behavior of flyash & zirconia reinforced Al-Mg-1Si-Cu HMMC using Taguchi technique, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.12.122
3
M. Fakkir Mohamed et al. / Materials Today: Proceedings xxx (xxxx) xxx
3. Design of experiments The Design of the experiment (DOE) is employed in Taguchi methodology to attain the foremost important information whereas conducting borderline variety of experiments. Taguchi optimisation is experimental optimisation technique that uses the quality orthogonal arrays for forming the matrix of experiments, which allows not solely conducting borderline variety of experiments however conjointly reaching higher worth level of every parameter. Signal/Noise (S/N) ratios are employed in information analysis in Taguchi methodology. The 3 basic classes to hunt the simplest results of experiments were nominal-thebetter, larger-the-better and smaller-the-better. In this study, the experimental style was in line with L9 orthogonal array (OA) supported by Taguchi methodology, whereas the Taguchi OA would considerably scale back the quantity of experiments. Based on the literatures the process parameters had chosen was shown in Table 4. Fig. 1. Experimental Setup.
3.1. Taguchi technique Design of experiments is one in all the necessary and powerful applied mathematics techniques to check the result of multi variables at the same time. This technique drastically reduces the quantity of experiments that needed to model the response perform compared with the complete factorial style of experiments. The Taguchi technique is devised for method optimisation and identification of best combination of the factors for a given response. The objective of the tactic is to supply prime quality product at low price.
4. Results and discussion
Fig. 2. Sample specimen A, B & C.
Table 4 Levels of Process Parameters. Level
Load, L, (N)
Sliding Speed, S (m/s)
Sliding Distance, D (m)
Reinforcements, R (wt%)
1 2 3
10 20 30
1 2 3
400 800 1200
0 2 4
wheel is employed. Weight has been noted on before experimentation and once the experiment to calculate wear rate. Sample taken for wear check with following dimensions were ten cm height and ten cm breadth according to ASTM standard G99. The sample taken for wear and friction check was shown in Fig. 2.
The wear tests were performed to review the impact of control parameters over the output responses. The S/N magnitude relation of the response characteristics for every variable at totally different levels were determined from experimental information. The most effects of method variables of S/N magnitude relation were planned. The response graphs were used for examining the constant effects on the responses. The foremost best settings of control variables were established by analyzing the response graphs. The experimental results of wear test is shown in Table 5. The results are dissected using S/N Ratios, Response Table and Graphs with the assistance of Minitab software 18. Taguchi Analysis: SWR and COF versus Load, Sliding Speed, Sliding Distance, Reinforcements S/N ratios are shown in Figs. 3 and 4. Average of every levels in terms of S/N ratios were given in Tables 6 and 7. Once observing all response graphs, Table 8 shows the optimum Parameters Combination for SWR and COF.
Table 5 L9 Orthogonal Arrays for Experimentation. S. No
L
S
D
R
SWR
COF
S/N Ratio SWR
S/N Ratio COF
1 2 3 4 5 6 7 8 9
10 20 30 20 30 10 30 10 20
1 1 1 2 2 2 3 3 3
400 800 1200 400 800 1200 400 800 1200
0 2 4 4 0 2 2 4 0
7.198 9.338 7.841 6.273 6.406 1.946 23.05 4.800 4.116
0.499 0.699 0.799 0.599 0.333 0.199 0.366 0.233 0.180
17.1442 19.4051 17.8874 15.9495 16.1317 5.7829 27.2534 13.6248 12.2895
6.0380 3.1105 1.9491 4.4515 9.5511 14.0229 8.7304 12.6529 14.8945
Please cite this article as: M. Fakkir Mohamed, S. Yaknesh, G. Radhakrishnan et al., Optimization of dry sliding wear behavior of flyash & zirconia reinforced Al-Mg-1Si-Cu HMMC using Taguchi technique, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.12.122
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4.1. Microstructure study After wear test, scanning microscopy is finished to check the damage tracks of the samples. The microstructure study is performed to grasp the character of damage employing a scanning microscope once wear check is completed and shown in Fig. 5a–c. The grey portion indicates the Aluminium 6061, white portion indicates the Zirconia and the curvy path indicates the flyash particles. So, it represents the better bonding between the aluminium and the reinforcements. 4.2. Linear regression analysis The rectilinear regression equation was developed by victimizing the Minitab 18 applied mathematics software system. This
analysis was accustomed to establish the correlation between expected variable and therefore the response variable. The regression of y on x for Specific Wear Rate and Co-efficient of Friction is as follows:
SWR ¼ 4:91 þ 0:389 L þ 1:26S 0:00942 D þ 0:10R
ð1Þ
COF ¼ 0:643 þ 0:00945 L 0:2030 S 0:000119D þ 0:0516R ð2Þ where L is the applied load in (N), S is sliding speed in (m/s), D is sliding distance in (m) and Reinforcements (wt%). The positive/negative sign of this Eqs. (1) and (2) indicated the Specific Wear Rate and Co-efficient of Friction behavior of
Fig. 3. Effect of control factors on SWR.
Fig. 4. Effect of control factors on COF.
Table 6 Response Table for S/N Ratio (SWR). Level
L
S
D
R
1 2 3 Delta Rank
12.18 15.88 20.42 8.24 1
18.15 12.62 17.72 5.52 3
20.12 16.39 11.99 8.13 2
15.19 17.48 15.82 2.29 4
Please cite this article as: M. Fakkir Mohamed, S. Yaknesh, G. Radhakrishnan et al., Optimization of dry sliding wear behavior of flyash & zirconia reinforced Al-Mg-1Si-Cu HMMC using Taguchi technique, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.12.122
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M. Fakkir Mohamed et al. / Materials Today: Proceedings xxx (xxxx) xxx Table 7 Response Table for S/N Ratio (COF). Level
L
S
D
R
1 2 3 Delta Rank
10.905 7.485 6.744 4.161 2
3.699 9.342 12.093 8.393 1
6.407 8.438 10.289 3.882 3
10.161 8.621 6.351 3.810 4
Table 8 Optimum Conditions using Taguchi Method. S.No
Control Factors
SWR
COF
Best Level
Value
Best Level
Value
1 2 3 4
Load Sliding Speed Sliding Distance Reinforcements
3 1 1 2
30 1 400 2
3 1 1 3
30 1 400 4
Fig. 5. a. SEM for worn surface of Pure Al, b. SEM for worn surface of Al-10%Fly ash and 2%ZrO2, c. SEM for worn surface of Al-10%Fly ash and 4%ZrO2.
Distance 400 m, Reinforcement 4(wt%) that is validated through regression analysis.
Table 9 Result for Confirmation Experiment. Initial Parameter
Optimal Parameter
Output
Level
Experimental
Regression
SWR COF
L3S1D1R2 L3S1D1R3
10.4168 0.7238
14.272 0.8823
composite. The plus sign indicated increase in wear rate and minus sign indicated decrease in wear rate with parameter. The confirmation was performed with parameters apart from designated parameters as shown in Table 9 and therefore the wear check was conducted for the preferred levels. The regression wear rate results were closely kind of like experimental wear rate with minimum error and thence the developed model had higher potency to investigate the wear rate of composite.
5. Conclusion Based on the Results and Discussion the subsequent conclusions were defended, microstructure of the composite was discovered and results represented the uniform distribution of reinforcement particles within the matrix. An effort was created to figure out the various machining parameters on Pin on Disc equipment for Al-Flyash-Zirconia hybrid composite supported by Taguchi’s methodology. From that, the optimized input parameters to urge the minimum Specific Wear Rate are Load 30 N, sliding Speed 1 m/s, sliding Distance 400 m, Reinforcement 2 (wt%) and for Co-efficient of Friction Load 30 N, sliding Speed 1 m/s, sliding
CRediT authorship contribution statement S. Yaknesh: Resources, Software, Supervision. G. Radhakrishnan: Resources, Software, Supervision. P. Mohankumar: Resources, Software, Supervision. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References [1] Rathod Abhik, V. Umasankar, M. Anthony Xavior, Evaluation of properties for Al-SiC reinforced metal matrixcomposite for brake pads, Procedia Eng. 97 (2014) 941–950. [2] S.K. Rhee, Wear equation for polymers sliding against metal surfaces, Wear 16 (1970) 431–435. [3] Y. Sahin, Wear behavior of aluminium alloy and its composites reinforced by Fly ash particles using statistical analysis, Mater. Des. 24 (2003) 95–103. [4] N. Natarajan, S. Vijayarangan, Wear behavior of aluminium matrix composites sliding against automobile friction material, Wear 261 (2006) 812–822. [5] K. Radhakrishna, M. Ramachandra, Effect of reinforcement of fly ash on sliding wear, slurry erosive wear and corrosive behavior of Al matrix composite, Wear 262 (2007) 1450–1462. [6] Ashutosh Pattanaik, Mantra Prasad Satpathy, Subash Chandra Mishra, Dry sliding wear behavior of epoxy fly ash composite with Taguchi optimization, Eng. Sci. Technol., Int. J. 19 (2016) 710–716. [7] A. Karthik, M. Shivapratap Singh Yadav, H.N. Reddappa, M. Ravikumar, Tribological behavior of Al6061-beryl metal matrix composite and optimization of parameters using Taguchi method, Int. J. Res. Sci. Innov. IV (XI) (2017) 52–56.
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Please cite this article as: M. Fakkir Mohamed, S. Yaknesh, G. Radhakrishnan et al., Optimization of dry sliding wear behavior of flyash & zirconia reinforced Al-Mg-1Si-Cu HMMC using Taguchi technique, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.12.122