Effect of Spatial Distribution of SiC Particles on the Tensile Deformation Behavior of Al-10 vol%sic Composites

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1 Materials Transactions, Vol. 48, No. 2 (27) pp. 171 to 177 #27 The Japan Institute of Metals Effect of Spatial Distribution of SiC Particles on the Tensile Deformation Behavior of Al-1 vol%sic Composites Di Zhang*, Kenjiro Sugio, Kazuyuki Sakai*, Hiroshi Fukushima and Osamu Yanagisawa Mechanical System Engineering, Graduate School of Engineering, Hiroshima University, Higashi-Hiroshima , Japan The effect of particle clustering distribution on the damage accumulation and the deformation behavior is investigated in the particle reinforced metal-matrix composites. The clustering tendency of the Al-1 vol%sic composite is evaluated by the normalized 2-dimensional local number density (LND2D) of the particle. It is found that the uniform material having a spatial distribution close to the random distribution has higher flow stress and larger elongation. The particle/matrix delamination is easy to occur preferentially at the particles of larger size in the more clustered regions in tensile deformation. Composites with lower clustering tendency have larger strain hardening capacity than those with more clustering one. [doi:1.232/matertrans ] (Received September 15, 26; Accepted November 2, 26; Published January 25, 27) Keywords: Al-1 vol%sic composite, particle clustering, tensile properties, particle/matrix delamination 1. Introduction Particle reinforced metal matrix composites (PMMCs) are now emerging as an important class of engineering materials. The aluminum and silicon carbide (Al-SiC) system, which is the most commonly studied composite, is particularly used in automotive, aerospace and other engineering systems during the last few decades due to their potentially superior mechanical properties, such as high specific modulus, strength and thermal stability. However the advantages of these kinds of materials often diminish by the particle fracture and interfacial delamination leading to void formation in the matrix. Most of the experimental and numerical studies 1 7) have been carried out to understand the effect of the morphological variables, such as the particle volume fraction, particle size, shape and orientation on the deformation and damage behavior of the alloy. Experimental evidence and numerical results 8 15) also demonstrated that the spatial distribution of the second phase particles played an important role on the mechanical properties and the damage mechanism of the composites. The spatial distribution of the second phase particles in composites is much more clustered when there is an obvious difference between the matrix particle sizes and the reinforcement particle sizes. The relative particle size (RPS) ratio, 16) which is defined as the ratio of the average powder particles sizes between the matrix and the reinforcement, is a beneficial method to control the clustering tendency. The optimization of this RPS ratio results in a homogeneous microstructure and better mechanical properties. Stone et al. 16) studied the effect of RPS ratio on the mechanical properties of the PMMCs, but they did not establish any quantitative relation between the RPS ratio and the mechanical properties. Prasad et al. 17,18) carried out a systematic study. They found that the decrease in the RPS ratio resulted in the increase in strength and elongation, as a result of homogeneous distribution of SiC particles. However this kind of research did not provide any systematic correlation *Graduate Student, Hiroshima University with the mechanism of the particle damage. In this paper, we introduce normalized 2-dimensional local number density ([LND2D]) from measurement as the clustering parameter instead of the RPS ratio and investigate the relation between LND2D and the mechanical properties for Al-1 vol%sic composite. We discuss how the clustering parameter LND2D exerts an influence on the deformation properties and its relation with the damage behavior of the particles. 2. Experimental Procedure 2.1 Material preparation The matrix material used in the present study was the pure Al powder having the composition of Cu, Fe, Si with different average particle sizes of about 1 mm, 3mm, 2 mm and 3 mm, respectively. The reinforced material was the SiC powder with particle size of 2 3 mm. The chemical composition of these powders is shown in Table 1. Five samples (Sample 1 Sample 5) were prepared. For the Sample 1, Al powders (9 vol%) and SiC powders (1 vol%) were mixed in ethyl alcohol for 6 minutes applying ultrasonic vibration and then blended for 24 hours using a blending machine. For the other four samples, the Al powders (9 vol%) and SiC powders (1 vol%) were blended in dry condition for 24 hours. The detail of the five samples is summarized in Table 2. The mixed powders were consolidated up to % of the theoretical density using a spark sintering technique Table 1 Chemical composition of Al and SiC powders (mass%). Particle size Al Cu Fe Si 1 mm mm mm mm Particle size SiC Fe Al Ca Cr Na Mn Ni 2 3 mm

2 172 D. Zhang, K. Sugio, K. Sakai, H. Fukushima and O. Yanagisawa Table 2 Volume fraction, relative particle size (RPS) ratio of Al and SiC powders and relative density. Volume fraction of the particle Sample RPS Relative Al Al Al Al SiC ratio density (%) 1 mm 3 mm 2 mm 3 mm 2 3 mm RPS ratio = Al (average particle size)/sic(average particle size) Pulse discharge time (minute) Table 3 Holding time (minute) described elsewhere. 19) The sintering process was carried out in a vacuum with an atmosphere less than 1: Pa to limit the oxidation of the sample. The sintering condition is given in Table 3. The uniaxial tensile tests were carried out at room temperature using tensile testing machine to obtain the basic mechanical properties such as flow stress, ultimate tensile strength and elongation. The cylindrical samples with a 2 mm gauge length and 8 mm diameter were loaded at a constant crosshead speed of 2: 1 3 s 1. The samples after failure were cut in the longitudinal direction and metallographic samples near fracture surface were prepared. A scanning electron microscopy (SEM) was used to observe the microstructure, such as the particle spatial distribution, particle damage and the interfacial delamination between the matrix and the reinforcement. 2.2 Definition of LND2D (2-dimensional Local Number Density) At first, particles are arranged in 2-dimensionally closest, hexagonal ordering as Fig. 1, keeping a given 2-dimensional particle number density, (particle number in unit area). Let us define a measuring circle with a gravity center (GC) of noticed particle and radius, R, so that the number density in the circle is equal to the given value, including the GCs of the noticed and nearest neighbor particles. Then ¼ 7=ðR 2 Þ and R is represented as eq. (1). In this case of ordering, local number (LN) of all GC is equal to 7, and probability (P) and cumulative probability (CP) of LN is shown in Fig. 1(a ). R ¼ ¼ 1: ð1þ In Poisson field 2) where points are randomly distributed in 2-dimensional space (schematically shown in Figure 1), probability of k, point number included in area A, at arbitrary position is represented by eq. (2). PðkÞ ¼ ðaþk k! The sintering conditions. Holding temperature (K) Max. load(mpa) expð AÞ k ¼ ; 1; 2 ð2þ Because measuring radius has been defined so that the included 7 particles correspond to a given number density, A is equal to 7. Then, in the case of spatially random distribution, adding GC of the noticed particle, probability of GC number k, included in the measuring circle around a noticed particle is represented by eq. (3) and shown in Fig. 1(b ). 7ðk 1Þ PðkÞ ¼ expð 7Þ k ¼ 1; 2; 3 ð3þ ðk 1Þ! When particles are arranged in clustering state (schematically shown in Fig. 1(c)), probability histogram of LN must extend to right, larger LN (schematically shown in Fig. 1(c )). Then we consider that the LN of GC in the measurement circle is a proper parameter to evaluate spatial arrangement of particulate second phase. In this paper, normalizing measured LN divided by 7 of the ordering arrangement (eq. (4)), ½LND2DŠ ¼ LN ð4þ 7 clustering tendency is evaluated by the relative frequency of [LND2D]. Hereafter, the normalized LND2D ([LND2D]) is referred to 2-dimensional local number density. 2.3 Quantitative characterization of microstructure In this study, image analysis was carried out using computer software developed by us to evaluate the spatial distribution of particles. After the images had been captured from a SEM, thresholding was carried out to identify SiC particles and the information of each SiC particle was recorded. [LND2D] was calculated from the GC of each particle using the computer software. It has been made clear that measurement for more than 2 particles is needed to obtain reliable values of [LND2D]. 21) 3. Results and Discussion 3.1 Undefomed microstructures The typical microstructure of the five samples, consists of reinforcement particles surrounded by Al matrix, is shown in Figures 2 to (e). As shown in the pictures, all sintered material exhibits very small voids at the particle/matrix inteface, mostly in the clustering parts. It is shown in Sample 4 and Sample 5 that SiC particles agglomerate in the interstices of Al matrix particles and this lead to a heterogeneous distribution of the reinforcement. Figure 3 presents the cumulative relative frequency (CRF) of [LND2D] for the five samples, along with a random distribution that is calculated by Poisson field theory. The average of [LND2D] ( ) and the standard deviation of [LND2D] ([LND2D] stdev ) are shown in Table 4. The CRF curves of [LND2D] shift to right from Sample 1 to 5. The difference of curves is significant especially at largest [LND2D]. The cumulative distribution of Sample 1 and Sample 2 is narrower than the other three samples. The clustering tendency of Sample 1 is smaller than Sample 2 though the RPS ratio of these samples is same. It means that wet blending with ultrasonic vibration is more effective than dry blending to prepare homogeneous samples. It is thought

3 Effect of Spatial Distribution of SiC Particles on the Tensile Deformation Behavior of Al-1 vol%sic Composites (a') CP, P (%) CP, P (%) (b') CP 2 P (c) 1 8 (c') CP, P (%) 6 4 CP LN Fig. 1 2-dimensional arrangement types, hexagonal closest ordering, random and (c) clustering arrangement of the gravity center of particles; and probability (P) and cumulative probability (CP) distribution of local number (LN) in the measuring circle, (a )-(c ). P that [LND2D] is an appropriate parameter for quantitative analysis of the clustering tendency. 3.2 Tensile properties Figures 4 and show flow stress (" ¼ :6), ultimate tensile strength and elongation of the five samples having different respectively. The uniaxial tensile tests were repeated for each with at least two samples, which gives an indication of scatter in values. An obvious tendency of decreasing flow stress, ultimate tensile strength and ductility with the increasing is observed from the figure. While the sample with lower than 1.15 has a higher flow stress, ultimate tensile strength and elongation than the other three samples, those with higher than 1.15 does not show an obvious variation of these values. This result seems to indicate that small variations in the degree of clustering, below the values of 1.15 for, lead to a substantial improvement of the tensile properties. The corresponding effects on the associated fracture process will be discussed in the next section. Thus the clustering parameter of [LND2D] gives a good indication of the tendency of flow stress, tensile strength and elongation. This experimental results is in agreement with the results of Prasad et al. 16) and Murphy et

174 D. Zhang, K. Sugio, K. Sakai, H. Fukushima and O. Yanagisawa (c) (d) (e) 4µm Fig. 2 Microstructure of the Al-1 vol%sic composites.

, 22) who reported almost the same tendency of decreased tensile strength and the elongation with the increasing clustering parameter.

6), d d" ¼ ð5þ The flow stress and the strain-hardening rate are plotted against plastic strain in Fig.

After the start of plastic deformation (.2% offset), the flow stress of both composites can be divided into two regions.

It is important to notice that strain hardening rate of the more clustered composites decreases faster with strain than it does in more homogeneous

4 174 D. Zhang, K. Sugio, K. Sakai, H. Fukushima and O. Yanagisawa (c) (d) (e) 4µm Fig. 2 Microstructure of the Al-1 vol%sic composites. to (e) correspond to sample 1 to 5. al., 22) who reported almost the same tendency of decreased tensile strength and the elongation with the increasing clustering parameter. The tensile instability can be described by the Considere criterion, in which the stress is related to the strainhardening rate d=d" as Ref. 6), d d" ¼ ð5þ The flow stress and the strain-hardening rate are plotted against plastic strain in Fig. 5 in order to describe the onset of instability in these materials. In Figure 5, the flow curves are truncated at the necking beginning strain. After the start of plastic deformation (.2% offset), the flow stress of both composites can be divided into two regions. First the work hardening rate decreases sharply with the increasing strain at a medium strain. It is important to notice that strain hardening rate of the more clustered composites decreases faster with strain than it does in more homogeneous material. Then, after a large strain, the work-hardening rate is almost constant with increasing strain. It is seen that the instability strain coincides closely with the intersection point of these curves, in accord with Considere criterion. 3.3 Particle damage and accumulation We now turn our attention to the development of the particle damage and accumulation during tensile deformation. Figure 6 shows that many particles fail by delamination

Effect of Spatial Distribution of SiC Particles on the Tensile Deformation Behavior of Al-1 vol%sic Composites 175 CRF(%) 1 8 6 4 2.

5 Effect of Spatial Distribution of SiC Particles on the Tensile Deformation Behavior of Al-1 vol%sic Composites 175 CRF(%) [LND2D] Random (theory) Sample1 Sample2 Sample3 Sample4 Sample5 Fig. 3 Cumulative relative frequency (CRF) of 2-dimensional local number density ([LND2D]) for the prepared samples. Table 4 Average of [LND2D] ( ) and standard deviation of [LND2D] ([LND2D] stdev ) for the five samples. Sample [LND2D] stdev Stress, σ/mpa Stress, σ/mpa stress = tangent modulus =1.13 stress =1.32 tangent modulus = Strain, ε Fig. 5 tensile flow curves tangent modulus for the samples with different average [LND2D] ( ). Stress, σ/mpa Elongation (%) Sample 1 Sample 2 Flow stress(ε=.6) Ultimate tensile strength Sample 3 Sample 4 Sample Fig. 4 Relation between average [LND2D] ( ) and the tensile properties, tensile flow stress, tensile strength, and elongation. Tensile direction Delamination 1µm Fig. 6 Microstructure of the damaged particles after tensile test. The arrows indicate the locations of the particle/matrix interface delamination. of a particle/matrix interface, while the particle crack does not play an important role on the particle failure. The small voids may grow along the particle/matrix interface between the particles very close to each other. Figures 7 to (d) show the CRF of [LND2D] and size of near the fractured surface after tensile test for sample 2 and sample 5. Figures 7 and (c) show that the for is larger than whole particles. Figures 7 and (d) also show that the particle/ matrix interface delaminates mostly at larger particles. All sintered material exhibits very small voids at the

6 176 D. Zhang, K. Sugio, K. Sakai, H. Fukushima and O. Yanagisawa 1 8 CRF (%) (c) random (theory) =1.13 [LND2D]av=1.57 (d) CRF (%) [LND2D] random (theory) =1.32 =1.67 Particle size, D/µm Fig. 7 Cumulative relative frequency (CRF) of 2-dimensitonal local number density ([LND2D]), and (c), and size of delaminated particle after tensile test, and (d).,: sample 2; (c),(d): sample 5. particle/matrix inteface, mostly in the clustering parts. On the other hand, probability of exsiting small voids at the interface may increase with the increasing particle size. Then, in conclusion, the SiC particles of larger particle size in more clustered regions are easy to be delaminated. Previous work 4) on the Al-SiC composites indicated that the particle fracture increases with increasing particle size. This is due to the consideration that the probability of defects in reinforcements increases with increasing particle size. This is coincidence with our reults, though there is a difference in damage mode. Figure 8 shows the relationship between the number fraction (NF) of and of initial undeformed samples, while Figure 8 illustrates the relationship between of the after tensile test and of initial undeformed samples. The NF and of increase with increasing initial up to 1.15 and then they are almost same when is higher than The experimental results show that the presence of a critical clustering degree, which provides a minimum strengthening corresponding to a maximum number fraction of. Figure 9 shows that the NF of is strain dependent. The NF of in the sample increases more dramatically in the sample with of 1.32 than that with of 1.13 at any given value of the strain, both of which increase with the increasing strain. The delamination is more frequent during tensile deformation for particles of the more inhomogeneous microstructures, because they have more regions of larger local number density of the particles. Stress in tensile deformation can be decided by balancing its increase by strain hardening of Al matrix and its decrease by the damage of reinforcement particle. The composite with more heterogeneous spatial distribution of particles has a substantial levels of delamination produced at small strain. This leads to a sharp reduction of the strain-hardening rate of the composite, and as a result, the composite fractures at lower plastic strains compared with more homogeneous samples. Previous studies 9) demonstrated that largest strain and stress concentrations occur in the matrix region between the closely packed particles. The nucleation of void by the interface delamination is more frequent and may grow more rapidly at the clustered regions due to the stress concentrations induced in the elastic particles and the plastic matrix. As a result, composites with stronger clustering tendency present lower strain hardening capacity than those of the homogeneous ones as shown in Figure 5 and the noticeable reduction in the tensile properties as shown in Figure 4.

7 Effect of Spatial Distribution of SiC Particles on the Tensile Deformation Behavior of Al-1 vol%sic Composites 177 NF of (%), del NF of (%) REFERENCES Strain, ε Fig. 9 Relation between plastic strain and number fraction (NF) of Conclusion Fig. 8 Relation between average [LND2D] ( ) of each sample and the number fraction (NF) of and the average [LND2D] of the (,del. ). In this investigation, the effect of reinforcement spatial distribution on the damage accumulation and the tensile behavior was studied in an Al matrix composite reinforced with 1 vol% SiC particles. From the experimental results, the following conclusions can be made: (1) The determination of the normalized 2-dimensional local number density [LND2D] is simpler and appears to be benefit for quantifying the clustering tendency of the composites. (2) In tensile deformation, SiC particles of larger particle size in more clustered regions are easy to be delaminated from matrix in the composites with reinforcement size range of 2 3 mm. (3) Composites with stronger clustering present lower strain hardening capacity than those of the homogeneous ones due to their stronger tendency of particle/ matrix delamination. (4) The more homogeneous spatial distribution of reinforcement particles brings improved tensile properties, higher flow stress, tensile strength and larger elongation. 1) C. W. Nan and D. B. Clarke: Acta Mater. 44 (1996) ) Y. L. Liu: Scripta Materialia 35 (1996) ) D. J. Lloyd: Acta Metall Mater. 39 (1991) ) M. Li, S. Ghosh and O. Richmond: Acta Mater. 47 (1999) ) E. Maire, D. S. Wilkson, J. D. Embury and R. Fougeres: Acta Mater. 45 (1997) ) A. F. Zimmerman, G. Palumbo, K. T. Aust and U. Erb: Mat. Sci. Engng. A 328 (22) ) LLorca and C. Gonzalez: J. Mech. Phys. Solids 46 (1998) ) D. S. Wilkson, W. Pompe and M. Oeschner: Prog. Mat. Sci. 46 (21) ) J. Segurado, C. Gonzalez and J. LLorca: Acta Mater. 51 (23) ) J. Segurado and J. LLorca: Mech. Mater. 38 (26) ) M. Geni and M. Kikuchi: Acta Mater. 46 (1998) ) K. T. Conlon and D. S. Wilkinson: Mat. Sci. Engng. A 317 (21) ) J. J. Lewandowski and C. Liu: Mat. Sci. Engng. A 17 (1989) ) I. G. Watson, M. F. Forster, P. D. Lee, R. J. Dashwood, R. W. Hamilton and A. Chirazi: Composites Parts A 36 (25) ) T. Oki, K. Matsugi, K. Shimizu and O. Yanagisawa: J. of Japan Institute of Light Metals 52 (22) (in Japanese). 16) I. C. Stone and P. Tsakirroponlos: Mat. Sci. Technol. 11 (1995) ) V. V. B. Prasad, B. V. R. Bhat, Y. R. Mahajan and P. Ramakrishnan: Mat. Sci. Engng. A 337 (22) ) V. V. B. Prasad, B. V. R. Bhat, Y. R. Mahajan and P. Ramakrishnan: Powder Metallurgy World Congress (2) ) K. Matsugi, H. Kuramoto, T. Hatayama and O. Yanagisawa: Journal of Materials Processing Technology 146 (24) ) J. Ohser and F. Mucklich: Statistical Analysis of Microstructures in Materials Science, (John Wiley & Sons, LTD, England 2) pp ) O. Yanagisawa, M. Morokuma, T. Hatayama and K. Matsugi: Journal of Japan Foundry Engineering Society. 73 (21) (in Japanese). 22) A. M. Murphy, S. J. Howard and T. W. Clyne: Mat. Sci. Technol. 14 (1998)

OPTIMAL PRODUCTION PROCESS OF PARTICULATE INTERMETALLIC COMPOUND REINFORCED ALUMINIUM METAL MARIX COMPOSITES

International Journal of Aerospace and Lightweight Structures Vol. 3, No. 4 (2014) 503 512 c Research Publishing Services DOI: 10.3850/S2010428614000051 OPTIMAL PRODUCTION PROCESS OF PARTICULATE INTERMETALLIC