High-energy ball milling/mechanical alloying is used for producing a wide range of engineering materials due to several advantages, such as synthesis of homogeneous nanostructured materials [1], composite materials [2], metallic solid solutions [3], and intermetallics [4]. Ball milling displays a significant advantage in terms of alloying immiscible elements [5] and forming stable and metastable phases [6]. Several techniques are available for material processing, among which solid-state processing using ball milling plays a major role; this process can be used to manufacture nanomaterials on a large industrial scale [7]. The strength, hardness, and electrical conductivity of Al–Cu composites were significantly improved by a new production technique that involved ball milling and microwave sintering. The sintered samples were post-treated using friction stir process [8]. Based on the materials’ application requirements, the materials synthesized via ball milling displayed various structures, such as supersaturated, nanocrystalline [9], and homogeneous amorphous [10] forms. Milling maps were generated based on the milling conditions to define the boundaries of the formed phases, either crystalline or amorphous [11]. Powder metallurgy (P/M) is widely used for fabricating several metal-based products for many engineering applications [12]; fabricating net-shaped complex parts is an attractive feature of the P/M method [13]. Ball milling, however, requires careful selection of the processing variables that affect the synthesized materials’ structure and performance. These processing variables, which include the milling time (MT), ball-to-powder mass ratio (BPMR), milling speed (MS), grinding medium (GM), and milling atmosphere (MAT), influence the performance of the material. Optimum selection of these processing variables is critical to attain the desired structure/performance of the produced materials [1].
Fe–Mn–Cu alloys are used as biodegradable materials (BDMs). These materials are used for supporting a treated tissue during the healing period and, thereafter, they degrade progressively without leaving behind any implant material; consequently, the recovery course can be completed without additional medical action [14]. Fe–Mn–Cu alloys have been designed as BDMs for medical applications, including fracture fixation and temporary orthopedic devices [15], cardiovascular support and other devices [16], and stent implantation [17]. The basic requirements of BDMs are compatibility, rigidity, acceptable strength, and high rate of degradation [18]. Therefore, metallic materials, such as iron (Fe), magnesium (Mg), zinc (Zn), and their alloys, are used as BDMs [19]. Fe-based alloys are favorable BDMs, due to their high strength and stiffness; these alloys also display a low rate of corrosion [20]. The mechanical and corrosion properties of Fe-based BDMs have been investigated in several studies. The degradation rate of Fe scaffolds was found to be accelerated by the nitriding technique [21]. Fe–Mn alloys developed for cardiovascular uses exhibited improved mechanical strength, biocompatibility, and corrosion-resistant properties [22]. Fe–hydroxyapatite composites were developed with enhanced corrosion properties; however, the obtained mechanical properties were reported to be lower than those of Fe [23]. Numerous researchers have studied the effects of the synthesis techniques used and the alloying elements added on the mechanical behavior and degradability of Fe-based alloys. The corrosion behavior of Fe–Mn–C–Pd BDMs in virtual fluids of the human body was investigated [24]. The mechanical properties and degradation behavior of Fe–Mn–C–S BDMs produced by the casting method with rapid cooling rate were examined [25]. The properties of Fe30-Mn6-Si alloy produced by the melting route were evaluated for biodegradable applications [26]. Fe–(20, 25, 30, 35)–Mn were produced by P/M for biodegradable stents [27]. The influence of alloying elements such as Mn, Co, Al, W, B, C, S, and Sn on the bio-compatibility and biodegradability of Fe produced by the casting method was examined [28]. Ball milling/mechanical alloying was successfully used for synthesizing several BDMs, such as Fe–Mn–Cu [15], Fe–30Mn–(1–3)Ag [29], Fe–30 wt% Mn [30], and Fe–35 wt%Mn [31].
The study of compressibility and relative density (RD) of the processed powders before further processing is technologically significant, because the RD of the compacted samples affects not only the possibility of green sample handling for further processing but also the mechanical behavior of the final parts obtained by P/M. Several factors influence the compressibility of powders. The influence of the powder characteristics on the compressibility of Al-6061–TiO2 micro/nanocomposites produced by low-/high-energy ball milling has been studied [32]. The compressibility of Fe powders has been observed to depend on the powder characteristics such as size and morphology [33]. The applied load, powder composition, and annealing treatment also affect the compressibility of powders [34]; the MT of mechanically alloyed powders has a significant impact on the powder's compressibility [35]. The ball milling/mechanical alloying process is probabilistic in nature, and the performance of this process depends on the proper selection of the input and output process parameters [36]. Presently, designing of experiments for minimizing the number of experiments is widely used [37].
In the present study, the effect of ball milling input parameters on the microstructure and compactibility (RD) of processed [(Fe–35wt%Mn)100−
Table 1 displays the design input parameters for the present study, which includes 27 experiments conducted on the [(Fe–35wt%Mn)100−
Design input parameters (MT, MS, BPMR, and CC) and experimental results (RD, crystallite size, and lattice strain) obtained in the present study
1 | BD0-1 | 5.5 | 10 | 100 | 0 | 71.65 | 37.0 | 0.3120 |
2 | BD0-2 | 5.5 | 10 | 300 | 0 | 65.94 | 28.0 | 0.3340 |
3 | BD0-3 | 1.0 | 10 | 200 | 0 | 66.63 | 32.0 | 0.3030 |
4 | BD0-4 | 10.0 | 10 | 200 | 0 | 67.76 | 25.0 | 0.3730 |
5 | BD0-5 | 5.5 | 5 | 200 | 0 | 67.99 | 44.0 | 0.2850 |
6 | BD0-6 | 5.5 | 15 | 200 | 0 | 68.53 | 25.0 | 0.3760 |
7 | BD5-1 | 1.0 | 5 | 200 | 5 | 75.04 | 58.0 | 0.1650 |
8 | BD5-2 | 10.0 | 5 | 200 | 5 | 72.66 | 18.0 | 0.5070 |
9 | BD5-3 | 1.0 | 15 | 200 | 5 | 73.49 | 38.0 | 0.2480 |
10 | BD5-4 | 10.0 | 15 | 200 | 5 | 66.04 | 22.0 | 0.4180 |
11 | BD5-5 | 5.5 | 5 | 100 | 5 | 75.46 | 58.0 | 0.1650 |
12 | BD5-6 | 5.5 | 15 | 100 | 5 | 72.08 | 28.0 | 0.3340 |
13 | BD5-7 | 5.5 | 5 | 300 | 5 | 71.20 | 41.0 | 0.2740 |
14 | BD5-8 | 5.5 | 15 | 300 | 5 | 64.30 | 26.0 | 0.3550 |
15 | BD5-9 | 1.0 | 10 | 100 | 5 | 75.19 | 58.0 | 0.1670 |
16 | BD5-10 | 10.0 | 10 | 100 | 5 | 71.79 | 17.0 | 0.5560 |
17 | BD5-11 | 1.0 | 10 | 300 | 5 | 70.98 | 21.0 | 0.4610 |
18 | BD5-12 | 10.0 | 10 | 300 | 5 | 66.16 | 18.0 | 0.5050 |
19 | BD5-13 | 5.5 | 10 | 200 | 5 | 69.66 | 22.0 | 0.4180 |
20 | BD10-1 | 5.5 | 10 | 100 | 10 | 75.23 | 25.0 | 0.3740 |
21 | BD10-2 | 5.5 | 10 | 300 | 10 | 67.80 | 23.0 | 0.4080 |
22 | BD10-3 | 1.0 | 10 | 200 | 10 | 75.08 | 33.0 | 0.3360 |
23 | BD10-4 | 10.0 | 10 | 200 | 10 | 67.32 | 28.0 | 0.3340 |
24 | BD10-5 | 5.5 | 5 | 200 | 10 | 75.38 | 23.0 | 0.4080 |
25 | BD10-6 | 5.5 | 15 | 200 | 10 | 72.19 | 24.0 | 0.4580 |
26 | BD5-13R | 5.5 | 10 | 200 | 5 | 68.99 | 22.0 | 0.4180 |
27 | BD5-13R | 5.5 | 10 | 200 | 5 | 69.12 | 22.0 | 0.4180 |
BPMR, ball-to-powder mass ratio; CC, Cu content; Exp. No., Experiment Number; MT, milling time; MS, milling speed; RD, relative density.
All processed samples were examined by X-ray diffraction (XRD) to identify the formed phases and to calculate the crystallite size and lattice strain under various processing conditions. An XRD machine (Empyrean, Malvern Panalytical) was used with a radiation source of Cu-
A field-emission gun high-resolution scanning electron microscope (FEG-HR-SEM; Apreo; ThermoScientific) coupled with an energy-dispersive spectrometer (EDS) was used for investigating the microstructure of the selected samples under varying processing conditions. The size and morphology of the as-received and processed powders and the elemental mapping of the processed powders and green samples were examined. Additionally, particle size analysis was performed on selected samples of the processed alloys by Zetasizer.
Compaction experiments were conducted to study the effect of the processing variables (shown in Table 1) on the densification. The compaction experiment was performed using an MTS universal testing machine (MTS Systems Corporation, USA) and a hardened H13 tool steel die with an inner diameter of 15 mm. All compaction experiments were performed at room temperature by applying CP ranging from 25 MPa to 1,100 MPa at a loading rate of 1 mm/min. RD–CP curves were generated from the compaction experiment.
RSM, which is a tool for the design of experiments, was used to analyze the effect of the input parameters on the compressibility/densification, in terms of RD response of the studied green samples, by identifying the most significant variables affecting the densification response. The central composite design was used to perform the experimentations. Twenty-seven experiments were carried out as given in Table 1. The repeated conditions in Table 1 (Experiments 19, 26, and 27) are essential for the design of the experiment by the central composite design. These repeated experiments are necessary for the accurate determination of the center of the space in the central composite design. The input parameters in the present study include MT, BPMR, MS, and alloy composition (CC). The measured response in this study was the RD. It should be highlighted that the RD listed in Table 1 was obtained at the maximum applied load (1,100 MPa) during compaction. The design of the experiments and the analysis of the results obtained in the present study were performed using Minitab software (version 15). ANOVA test was used to determine the most significant input parameters.
Figure 1 shows the secondary electron images (SEIs) of the as-received Fe, Mn, and Cu powders used in the present study. Figure 1A illustrates the spherical particles of Fe powders with size <10 μm. Figure 1B displays Mn powders with facet shape and particle size <80 μm. Figure 1C illustrates the Cu powders’ dendritic shape with an average size <120 μm.
Figure 2 shows the SEIs of three selected samples to illustrate the effect of BPMR and MT, as examples, on the size and morphology of the processed alloy powders. Figure 2A displays the BD5-1 alloy with processing conditions of 5 wt%Cu, 1 h MT, 200 rev/min MS, and 5:1 BPMR. Figure 2B exhibits the processed powders of BD5-3 alloy with input parameters of 5 wt% Cu, 1 h MT, 200 rev/min MS, and 15:1 BPMR. Figure 2C reveals the processed powders of BD5-4 alloy with conditions of 5 wt% Cu, 10 h MT, 200 rev/min MS, and 15:1 BPMR. It is obvious from these images that on increasing the BPMR (BD5-1
Figure 3 presents the results of the EDS analysis of the same selected alloys (BD5-1, BD5-3, and BD5-4). Figures 3A, 3D, and 3G show the SEIs for the BD5-1, BD5-3, and BD5-4 alloys, respectively. Figures 3B, 3E, and 3H show the elemental maps for the BD5-1, BD5-3, and BD5-4 alloys, respectively; these maps are in full agreement with the results shown in Figure 2. The EDS spectra in Figures 3C, 3F, and 3I confirm the alloys’ chemical composition. Increasing BPMR from 5:1 in the BD5-1 alloy – shown in Figures 3A and 3B – to 15:1 in the BD5-3 alloy – shown in Figures 3D and 3E – was observed to improve the elemental dispersion, with a notable refining in the metal particles. Moreover, increasing the MT from 1 h in BD5-3 alloy (illustrated in Figures 3D and 3E) to 10 h in BD5-4 alloy (shown in Figures 3G and 3H) resulted in an additional refining of the powder particles and improved the elemental dispersion with a notable flattening/deformation of Fe particles due to milling action.
The alloys selected for particle size analysis include the following: BD5-1
The results of particle size analysis for selected alloys
BD5-1 | 1,477.00 | 480.50 | 96.50 | 0.33 |
BD5-2 | 655.40 | 220.30 | 91.20 | 0.49 |
BD5-4 | 327.10 | 53.28 | 100.00 | 0.52 |
BD5-9 | 1,274.00 | 315.90 | 96.20 | 0.42 |
BD5-10 | 638.70 | 214.10 | 91.10 | 0.41 |
BD5-11 | 499.50 | 129.40 | 99.10 | 0.35 |
PDI, polydispersity index.
XRD analysis was performed on all samples to illustrate the effect of the input parameters of ball milling on the crystallite size and homogeneous lattice strain of the processed powders. The results of the selected samples (from Table 1) are discussed in this section. The selected samples include BD5-1
The compaction experiments were performed according to the input parameters listed in Table 1. The RDs at the maximum pressure applied at 1,100 MPa are shown in Table 1. Figure 6 displays the RD versus compaction load for selected samples of [(Fe–35 wt%Mn)95–Cu5]. From Figure 6, it is obvious that the RD for all samples revealed a continuous improvement with increase in the CP. However, the improvement rate in RD varied according to the controlling mechanisms of powder compressibility [32]. From Figure 6, a sharp increase in the RD of the samples up to ∼ 60 MPa is seen due to the particle ordering and rapid reduction in the spacing among the powders. Figure 6 also shows that in the CP ranging from ∼ 60 MPa to ∼ 200 MPa, the RD is still increasing but with a reduced slope due to the predominant mechanism of powder deformation, which led to extra contact areas among the powders and reduced pores [34]. Pressures >200 MPa were observed to produce more improvements in the compressibility until the maximum values of RD were reached at 1,100 MPa; however, the curve in this region revealed a lower slope due to the increased strain hardening of the powders, which resist further deformation and impede additional increase in the contact areas among powders [35]. With regard to the behavior of the selected samples presented in Figure 6, these 13 samples displayed the same general behavior of RD versus CP; however, these samples did not reveal the same RD at the applied loads, specifically, at the maximum CP of 1,100 MPa. The BD5-5 alloy displayed the maximum RD (75.46%), while the BD5-8 alloy exhibited the minimum RD (64.30%). The remaining 11 samples presented in Figure 6 were observed to display various RD values with CP between the maximum and minimum behaviors according to the various input parameters (Table 1). The sample with exceedingly high energy of ball milling involving the highest BPMR (15:1) and MS (300 rev/min) displayed the least value of RD due to the large stored energy, increased lattice strain, and great amount of work hardening in the processed powders. Therefore, the processed powders resist the compaction process and the final RD was reduced. On the other hand, the highest RD in Figure 6 was attained with a moderate energy of ball milling, which included BPMR (5:1), and MS (100 rev/min); in this case, the processed powders were still deformable with minimum strain hardening effect, which permitted more compressibility and enhanced the RD of the powders.
Figures 7 and 8 show the effect of the input parameters (MT, BPMR, and MS) on the microstructure of these compacted green samples on investigation of the selected green samples by EDS analysis. Figures 7, 8A, 8D, and 8G reveal the SEI of the green compacts collected from the selected samples. The chemical composition of the selected samples was confirmed by the EDS spectra in Figures 7, 8C, 8F, and 8I. Figures 7, 8B, 8E, and 8H show the elemental mapping accomplished on the outlined regions of the selected samples. From the maps presented in Figures 7B, 7E, and 7F, it can be observed that increasing the BPMR from 5:1 (BD5-1 alloy) to 15:1 in (BD5-3 alloy) resulted in improvement in the elemental dispersion with a notable refining in the microstructure constituents, as shown in Figures 7B and 7E, respectively. Additionally, increasing the MT from 1 h (BD5-3 alloy) to 10 h (BD5-4 alloy) resulted in an additional refining of the microstructure constituents and enhanced dispersion of the elements, as revealed in Figures 7E and 7H, respectively. From the maps presented in Figures 8B, 8E, and 8F, it can be noticed that a similar effect was observed when increasing the MT from 1 h (BD5-9 alloy) to 10 h (BD5-10 alloy). Increasing the MS from 100 rev/min (BD5-9 alloy) to 300 rev/min (BD5-11 alloy) resulted in an extra refining of the microstructure constituents and an additional enhancement in the dispersion of the elements, as revealed in Figures 8E and 8H, respectively.
EDS analysis was used in the present study for qualitative and quantitative analyses of the microstructure of the powders and green samples obtained under different processing conditions. With regard to the accuracy of the quantitative EDS analysis, it depends on the structural homogeneity and density of the examined alloys. In general, quantitative EDS analysis of standard dense alloys with a homogeneous structure provides high accuracy and low scattering. On the other hand, a scattering in EDS results is attained for experimental samples that reveal low density and a relatively segregated microstructure. In the present study, the examined samples were in powder and green forms and were subjected to different processing variables (MT, MS, and BPMR), as shown in Table 1. Accordingly, these examined samples displayed different homogeneity levels and densities. In this case, qualitative analysis was used to perfectly illustrate elemental dispersion and other features such as microstructure refinement. Chemical analysis of the examined samples confirmed the presence of the alloying elements and their dispersion profiles for each sample; however, there was scattering in the quantitative chemical analysis values, which could be attributed to variations in the processing variables, the differences in sample homogeneity levels, and the low densities for the examined samples. The same reasons are responsible for the considerable influence observed on the compaction behavior of the samples, as shown in Figure 6 and the RD values listed in Table 1.
With regard to the input parameters and the experimental results displayed in Table 1, the ANOVA test was performed to identify the input parameters having significant and nonsignificant influence on the crystallite size, lattice strain, and compactibility/densification of the processed alloys in terms of RD. The results of the ANOVA test for all responses are shown in Tables 3–5 for the crystallite size, lattice strain, and RD, respectively. Tables 3–5 list the estimated statistical terms such as sum of squares, degree of freedom (DF), mean square,
The results of the ANOVA test performed for the crystallite size
Model | 2.696 × 105 | 10 | 26,963.56 | 3.89 | 0.0079 |
MT | 1.025 × 105 | 1 | 1.025 × 105 | 14.78 | 0.0014 |
BPMR | 56,033.33 | 1 | 56,033.33 | 8.08 | 0.0118 |
MS | 33,920.33 | 1 | 33,920.33 | 4.89 | 0.0419 |
CC | 12,610.08 | 1 | 12,610.08 | 1.82 | 0.1963 |
(MT) × (BPMR) | 14,520.25 | 1 | 14,520.25 | 2.09 | 0.1672 |
(MT) × (MS) | 36,100.00 | 1 | 36,100.00 | 5.20 | 0.0366 |
(MT) × (CC) | 144.00 | 1 | 144.00 | 0.0208 | 0.8872 |
(BPMR) × (MS) | 5,402.25 | 1 | 5,402.25 | 0.7789 | 0.3906 |
(BPMR) × (CC) | 7,225.00 | 1 | 7,225.00 | 1.04 | 0.3226 |
(MS) × (CC) | 1,190.25 | 1 | 1,190.25 | 0.1716 | 0.6842 |
Residual | 1.110E × 105 | 16 | 6,936.07 | ||
Lack of Fit | 1.109E × 105 | 14 | 7,923.37 | 316.93 | 0.0031 |
Pure Error | 50.00 | 2 | 25.00 | ||
Total | 3.806 × 105 | 26 |
ANOVA, analysis of variance; BPMR, ball-to-powder mass ratio; CC, Cu content; DF, degree of freedom; MS, milling speed; MT, milling time.
The results of the ANOVA test performed for the lattice strain
Model | 0.1630 | 10 | 0.0163 | 2.49 | 0.0499 |
MT | 0.0855 | 1 | 0.0855 | 13.08 | 0.0023 |
BPMR | 0.0128 | 1 | 0.0128 | 1.96 | 0.1807 |
MS | 0.0147 | 1 | 0.0147 | 2.25 | 0.1532 |
CC | 0.0092 | 1 | 0.0092 | 1.41 | 0.2518 |
(MT) × (BPMR) | 0.0074 | 1 | 0.0074 | 1.13 | 0.3032 |
(MT) × (MS) | 0.0298 | 1 | 0.0298 | 4.55 | 0.0487 |
(MT) × (CC) | 0.0013 | 1 | 0.0013 | 0.1983 | 0.6621 |
(BPMR) × (MS) | 0.0019 | 1 | 0.0019 | 0.2962 | 0.5938 |
(BPMR) × (CC) | 0.0003 | 1 | 0.0003 | 0.0442 | 0.8361 |
(MS) × (CC) | 0.0001 | 1 | 0.0001 | 0.0169 | 0.8983 |
Residual | 0.1046 | 16 | 0.0065 | ||
Lack of fit | 0.1045 | 14 | 0.0075 | 355.54 | 0.0028 |
Pure error | 0.0000 | 2 | 0.0000 | ||
Total | 0.2676 | 26 |
ANOVA, analysis of variance; BPMR, ball-to-powder mass ratio; CC, Cu content; DF, degree of freedom; MS, milling speed; MT, milling time.
The results of ANOVA test performed for the RD
Model | 274.0800 | 10 | 27.4100 | 12.2300 | <0.0001 |
MT | 50.7600 | 1 | 50.7600 | 22.6400 | 0.0002 |
BPMR | 37.1000 | 1 | 37.1000 | 16.5500 | 0.0009 |
MS | 102.2000 | 1 | 102.2000 | 45.5900 | <0.0001 |
CC | 50.0200 | 1 | 50.0200 | 22.3100 | 0.0002 |
(MT) × (BPMR) | 6.4300 | 1 | 6.4300 | 2.8700 | 0.1098 |
(MT) × (MS) | 0.5041 | 1 | 0.5041 | 0.2249 | 0.6418 |
(MT) × (CC) | 19.7600 | 1 | 19.7600 | 8.8100 | 0.0090 |
(BPMR) × (MS) | 3.1000 | 1 | 3.1000 | 1.3800 | 0.2570 |
(BPMR) × (CC) | 3.4800 | 1 | 3.4800 | 1.5500 | 0.2308 |
(MS) × (CC) | 0.7396 | 1 | 0.7396 | 0.3299 | 0.5737 |
Residual | 35.8700 | 16 | 2.2400 | ||
Lack of fit | 35.6100 | 14 | 2.5400 | 20.1700 | 0.0482 |
Pure error | 0.2522 | 2 | 0.1261 | ||
Total | 309.9500 | 26 |
ANOVA, analysis of variance; BPMR, ball-to-powder mass ratio; CC, Cu content; DF, degree of freedom; MS, milling speed; MT, milling time; RD, relative density.
As an example, the correlation of the experimental and predicted results for RD with the corresponding normal probability of residuals is presented in Figure 9. Figure 9A reveals the actual RD experimental values versus the predicted values by the statistical analysis performed in the present study. Figure 9B displays the normal %probability versus residual, which assesses the model validity. Figures 9A and 9B show that almost all data points are adjacent to the straight line, which confirms the accuracy of the statistical analysis results obtained in the present study and the robust relationship between the predicted RD and the experimental results.
The impact of the input variables on the compactibility/RD of the developed alloy systems is presented in the form of 3D surface plots and 2D contour plots, as shown in Figure 10. Figure 10 (A–L) can be used for forecasting the RDs of the studied alloys. Figures 10A and 10B reveal the effect of the MT and BPMR on the RD. From the surface plot in Figure 10A and the contour plot in Figure 10B, it can be observed that the optimum RD and compressibility can be attained by decreasing the MT and BPMR. This behavior is due to the fact that increasing the MT and the BPMR results in increasing the energy of milling, which leads to increased lattice strain (as discussed in Section 3.3) and excessive plastic deformation of the powders [32]. The increased lattice strain and severe deformation of the powders impede the compressibility of the powders and decrease the RD [35]. Figures 10C and 10D show the impact of the MT and MS on the RD. From Figures 10C and 10D, it can be noticed that the RD decreased on increasing the MT and MS. Increasing the MT and speed led to high energy input during milling, which produced extra work-hardened powders with a substantial amount of lattice strain, which resists the compression and reduces the RD [34]. Figures 10E and 10F reveal the impact of MT and CC on the RD. Concerning Figures 10E and 10F, it can be seen that the RD improved by decreasing the MT and increasing the CC. Increasing the CC led to an improved RD because copper is a ductile element and increasing its content promoted the mechanism of compaction process through providing more deformability of the powders. Figures 10G and 10H illustrate the influence of MS and BPMR on the RD; it can be observed that the powder's compactibility and RD were enhanced on reducing the MS and/or BPMR due to the decreased power input during milling. Figures 10I and 10J show the influence of BPMR and CC on the RD, and it can be concluded that the powder's compactibility and RD were enhanced on increasing the CC and/or reducing the BPMR. Figures 10K and 10L display the influence of MS and CC on the RD; it can be perceived that the RD was enhanced on reducing the MS and/or increasing the CC.
The abovementioned sections covered the investigation into the effect of the input parameters on the microstructure and compactibility/RD of the developed alloys. Based on this analysis, selecting the input parameters to attain the desired microstructure features and improved densification behavior is a challenge. It was found that increasing the ball milling parameters (MT, MS, and BPMR) resulted in significant enhancements in the microstructure, such as uniform elemental dispersion and reduced segregation of the developed alloys (e.g., Figures 7 and 8). Additionally, increases in the input parameters produced refined particles with a substantial decrease in their crystallite size (see Tables 2 and 3). On the other hand, increasing these parameters exhibited an undesirable influence on the powder compactibility and RD of the developed alloys, as displayed in Figure 10. Increasing MT, MS, and/or BPMR resulted in high amounts of stored energy, increased lattice strain, and large amount of work hardening in the processed powders. Therefore, the processed powders resist the compaction process, and the attained RD was negatively affected. Accordingly, a recommended combination of the milling parameters was selected to provide the optimum compromise among the microstructure features and powder compressibility/RD of the developed alloys. Based on the surface and contour plots shown in Figure 10, the following conditions would provide a reasonable combination of milling conditions to attain an acceptable compromise of enhanced microstructure features and improved compactibility and RD: MT – 5 h; MS – 150 rev/min; and BPMR – 10:1. In addition, as discussed earlier, increasing the CC resulted in a substantial improvement in the RD of the developed alloys; hence, 10 wt% Cu is recommended.
(Fe–35wt%Mn)100−
ANOVA test results confirmed that the input parameters MT (1–10 h), BPMR (5:1–15:1), MS (100–300 rev/min), and CC (0–10 wt%) are statistically significant.
Increasing the milling power during ball milling by raising the MT to 10 h, the MS to 300 rev/min, and/or BPMR up to 15:1 resulted in significant enhancements in the microstructure, such as uniform elemental dispersion, reduced segregation, refinement of particles, and a substantial decrease in the crystallite size to 18 nm, e.g., in the BD5-12 sample.
Raising the energy input during ball milling, by increasing MT, MS, and/or BPMR, led to a detrimental influence on the compressibility and RD of the developed alloys, with the minimum RD of the BD5 alloys being 64.30% for the BD5-8 alloy, while the BD5-5 alloy displayed the maximum RD of 75.46%.
Increasing the CC resulted in a substantial improvement in the compressibility and RD of the studied alloys, whereby the BD0-5 alloy with 0 wt% Cu displayed 67.99% RD, while the BD10-5 alloy with 10 wt% Cu with the same milling variables showed 75.38% RD.
The combination of the studied variables recommended to attain an acceptable compromise of enhanced microstructure features and improved compaction behavior and RD includes the following: MT for 5.5 h, MS for 200 rev/min, BPMR of 10:1, and CC of 10 wt%.