materials Article
Quantification of Microstructural Features and Prediction of Mechanical Properties of a Dual-Phase Ti-6Al-4V Alloy Dong Yang 1,2 and Zhanqiang Liu 1,2, * 1 2
*
School of Mechanical Engineering, Shandong University, Jinan 250061, China;
[email protected] Key Laboratory of High Efficiency and Clean Mechanical Manufacture, Ministry of Education, Shandong University, Jinan 250061, China Correspondence:
[email protected]; Tel.: +86-531-8839-3206; Fax: +86-531-8839-2045
Academic Editor: Mark T. Whittaker Received: 16 June 2016; Accepted: 22 July 2016; Published: 28 July 2016
Abstract: Ti-6Al-4V titanium alloy milling has been frequently used in aviation/aerospace industries. Application environments put forward high requirements to create a desired proportion of the constituent phases and fine grain size for optimum mechanical properties of the machined workpiece. However, quantifying microstructural features of dual-phase (α + β) Ti-6Al-4V titanium alloy is difficult due to its irregular geometry and large dimension span. In this paper, a novel scanning electron microscope (SEM) image processing method was proposed to identify the content of constituent phases of materials. The new approach is based on the fact that the constituent phases of Ti-6Al-4V titanium alloy show different gray levels in digital images. On the basis of the processed image, distribution and average values of grain sizes were calculated directly using Image-Pro Plus software. By the proposed method, sensitivity of microstructural changes to milling parameters is analyzed and the stress-strain behavior for two ductile phase alloys is developed. Main conclusions are drawn that Ti-6Al-4V titanium alloy milling induces a high content of β phase and small grain size on the machined surface. The maximum measured values of change rate of β phase, grain refinement rate at the machined surface, and thickness of the deformation layer are 141.1%, 47.2%, and 12.3 µm, respectively. Thickness of the deformed layer and grain refinement rate decreased distinctly with the increase of cutting speed, but increased with the increase of the feed rate. The parameter of the depth of cut played a positive role in increasing the thickness of the deformed layer, while opposite to the grain refinement rate. For the variation of the change rate of the β phase at the machined surface, depth of cut is the foremost factor among the three studied parameters. Values of yield strength varied from 889–921 MPa with the change of content of the β phase from 30%–45%. Keywords: microstructure quantification; mechanical property optimization; Ti-6Al-4V; milling
1. Introduction Milling of Ti-6Al-4V titanium alloy is the primary operating process for aviation/aerospace manufacturing industry. However, Ti-6Al-4V is known to be difficult to machine for its high chemical reactivity and low thermal conductivity, which gives rise to short tool life and poor machining quality [1]. Surface integrity, with particular regard to microstructure, determines the mechanical properties and performance of the product achieved by final machining [2,3]. Fan [4] investigated the role of microstructure on fatigue properties of Ti-6Al-4V. The result was drawn that the fatigue strength ranking from high to low is as follows: equiaxed, bimodal, Widmanstätten, and acicular α’ martensite microstructure. Chan [5] found that variation of mean stress distribution in individual microstructural units can lead to fatigue life variability in Ti-6Al-4V with a duplex microstructure. Previous studies
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have shown that the lamellar microstructure exhibits lower strength, lower ductility, and better fatigue propagation resistance compared with an equiaxed microstructure. Equiaxed microstructure provides better fatigue initiation resistance, but poorer propagation resistance than a lamellar microstructure. Bimodal microstructures exhibit a well-balanced fatigue properties profile, since they combine the advantages of both lamellar microstructure and equiaxed microstructures. For the different mechanical properties exhibited by various microstructural features, it is important to identify the machining-induced microstructures, and to obtain favorable microstructures. Digital micrographs taken by scanning electron microscope (SEM) or optical metallurgical microscope are common used to identify the variation of microstructures in and beneath the machined surface. In Che-Haron’s [6] study on surface integrity of machining Ti-6Al-4V, it was found that a thin deformed layer was formed underneath the machined surface under the dry cutting condition with uncoated carbide cutting tools. A dull tool caused severe microstructure alteration when the thickness of the deformed layer was less than 10 µm. Hughes [7] showed that the thickness of the deformed layer increased with the depth of the cut, but the effect of cutting speed and feed rate was not distinct. The phenomenon of the β phase decreased at the vicinity of the machined surface was also found when dry drilling Ti-6Al-4V [8]. Different from the above findings, neither phase transformation, nor deformed layer, did Velasquez observe when high-speed dry turning Ti-6Al-4V in the high cutting speed range [9]. The main reason for these inconsistent results or doubtful conclusions is that visual inspection of microstructure micrographs is insufficient to reveal the changes in the microstructure. To study the correlation between mechanical properties of materials and microstructural features induced by machining, quantitative microstructural information (such as grain size, which is the key parameter in Hall-Petch relation [10,11]) should be extracted from digital micrographs. Several standard test methods for determining the distribution gradient of constituent phases and average grain size have been developed [12,13]. Quantifying microstructural features in dual-phase (α + β) Ti-6Al-4V titanium alloy are still difficult. Irregular geometry and large dimension span caused by different thermo-mechanical processing methods are the two primary reasons. Alborz Shokrani [14] investigated the microstructural features in end milling of Ti-6Al-4V under dry, wet, and cryogenic conditions. Average white pixel concentration below the machined surface was identified on the basis of various image processing techniques using Matlab. Collins [15] demonstrated the possibility of developing automated or semi-automated stereological procedures to determine the average values of the grain size and the volume fraction of the constituent phases. Tiley [16] measured the grain size and volume fraction of the constituent phases using random line segments and grid of cycloids methods. Moreover, automated or semi-automated tools for image analysis of α/β Ti-alloy type microstructures were developed to determine average values of the complex microstructural features, such as the thickness of the Widmanstätten α-laths, the colony scale factor, the prior β grain factor, and the volume fraction of Widmanstätten α [17,18]. However, statistical characteristics of microstructures, such as frequency of the grain size and distribution of the volume fraction were not obtained in these studies. In the current study, a novel method of image recognition is developed to quantify microstructural features in milling Ti-6Al-4V titanium alloy. The new approach is based on the fact that the constituent phases of Ti-6Al-4V titanium alloy show vastly different gray levels in digital images. By the proposed method, the distribution gradient of constituent phases on and beneath the machined surface are identified. Meanwhile, size distribution and mean diameter of α grains were calculated using Image-Pro Plus software on the basis of the processed images. Sensitivity of microstructural changes (change rate of the constituent phases, depth of the deformation layer, and grain refinement rate) to milling parameters (cutting speed, feed rate, and depth of cut) are also investigated. On the basis of the recognized microstructural features, mechanical properties of the machined surface are controlled.
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2. Experimental Procedure 2. Experimental Procedure 2.1. Materials 2.1. Materials The workpiece material studied was α + β two‐phase Ti‐6Al‐4V titanium alloy, which was
formed the free material forging studied process. Microstructural features photographed by which scanning Theby workpiece was α + β two-phase Ti-6Al-4V titanium alloy, waselectron formed microscope (SEM), process. energy Microstructural spectrum, and chemical compositions by identified energy microscope dispersive by the free forging features photographed scanningby electron spectroscopy (EDS) are shown in Figure 1. As shown in Figure 1, the original microstructures of the (SEM), energy spectrum, and chemical compositions identified by energy dispersive spectroscopy material, primary α grains shown in dark gray and lamellar α + β colonies shown in bright gray, are (EDS) are shown in Figure 1. As shown in Figure 1, the original microstructures of the material, included. primary α grains shown in dark gray and lamellar α + β colonies shown in bright gray, are included.
(a)
(b) Figure 1. (a) Microstructural feature; and (b) energy spectrum and chemical compositions of the Ti‐ Figure 1. (a) Microstructural feature; and (b) energy spectrum and chemical compositions of the 6Al‐4V alloy studied. Ti-6Al-4V alloy studied.
2.2. Cutting Experiments 2.2. Cutting Experiments The machining machining experiments experiments were out on on aa vertical-type vertical‐type machining machining center center The were carried carried out (DAEWOOACE‐V500). The workpiece dimensions were 50 mm × 30 mm × 5 mm. The cutting length (DAEWOOACE-V500). The workpiece dimensions were 50 mm ˆ 30 mm ˆ 5 mm. The cutting was set to 50 mm. Materials for the milling cutter with four‐flute and variable helix angles (38° and length was set to 50 mm. Materials for the milling cutter with four-flute and variable helix angles 41°) were cemented carbides. The diameter of the milling cutter was 6 mm. A sketch of the cutting (38˝ and 41˝ ) were cemented carbides. The diameter of the milling cutter was 6 mm. A sketch of tool is shown in Figure 2. The operation mode was down‐milling. Each test sample was machined the cutting tool is shown in Figure 2. The operation mode was down-milling. Each test sample with a new tool. The aim is to assess the influence of cutting conditions on the microstructure change was machined with a new tool. The aim is to assess the influence of cutting conditions on the independently of tool wear. Machining without the use of any cutting fluid (dry or green machining) microstructure change independently of tool wear. Machining without the use of any cutting fluid is becoming increasingly more popular due to concerns regarding the safety of the environment. Most (dry or green machining) is becoming increasingly more popular due to concerns regarding the safety industries apply cutting fluids/coolants when their use is not necessary. The coolants and lubricants of the environment. Most industries apply cutting fluids/coolants when their use is not necessary. used for machining represents 16%–20% of the manufacturing costs, hence, the extravagant use of The coolants and lubricants used for machining represents 16%–20% of the manufacturing costs, these fluids should be restricted [19]. Based on the above considerations, the tests were performed hence, the extravagant use of these fluids should be restricted [19]. Based on the above considerations, with dry machining. the tests were performed with dry machining.
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Figure 2. Sketch of the milling tool with 4-flute and variable helix angles. Figure 2. Sketch of the milling tool with 4‐flute and variable helix angles. Figure 2. Sketch of the milling tool with 4‐flute and variable helix angles. 3(4 Taguchi’s L16 L16 orthogonal experimental design method was applied applied to investigate investigate the Taguchi’s (4(4 )33orthogonal experimental design method was applied to investigate the response Taguchi’s L16 ) ) orthogonal experimental design method was to the response of the microstructure change to cutting parameters. By reference to the cutting parameters of the microstructure change to cutting parameters. By reference to the cutting parameters of a certain response of the microstructure change to cutting parameters. By reference to the cutting parameters of a certain type of aero engine casing, levels of experimental factors are selected as shown in Table 1. type of aero engine casing, levels of experimental factors are selected as shown in Table 1. of a certain type of aero engine casing, levels of experimental factors are selected as shown in Table 1.
Table 1. Orthogonal experimental parameters. Table 1. Orthogonal experimental parameters. Table 1. Orthogonal experimental parameters.
Levels/Factors Cutting Speed v Cutting Speed vcc (m/min) (m/min) Levels/Factors Levels/Factors Cutting Speed vc (m/min) Level 1 20 Level 1 20 Level 1 20 Level 2 50 Level 2 50 Level 2 50 Level 3 80 Level 3 80 Level 3 80 Level 4 110 Level 4 110 Level 4 110
Feed Rate fzz (mm/z) (mm/z) Radial Depth of Cut aee (mm) (mm) Feed Rate f Feed Rate fz (mm/z) Radial Depth of Cut a Radial Depth of Cut ae (mm) 0.02 0.5 0.02 0.5 0.02 0.5 0.03 1.0 0.03 1.0 0.03 1.0 0.04 1.5 0.04 1.5 0.04 1.5 0.05 2.0 0.05 2.0 0.05 2.0
2.3. Samples Preparation 2.3. Samples Preparation 2.3. Samples Preparation As shown in Figure 3, a slice of material was extracted from the center position of the machined As shown in Figure 3, a slice of material was extracted from the center position of the machined As shown in Figure 3, a slice of material was extracted from the center position of the machined workpiece. Two mosaic blocks were made to identify the microstructural features on the machined workpiece. Two mosaic blocks were made to identify the microstructural features on the machined workpiece. Two mosaic blocks were made to identify the microstructural features on the machined surface and the cross section. Samples were polished, and then etched in 5 mL HNO (65% conc.) + 3 surface and the cross section. Samples were polished, and then etched in 5 mL HNO 33 (65% conc.) + 3 surface and the cross section. Samples were polished, and then etched in 5 mL HNO 3 (65% conc.) mL HF (40% conc.) + 100 mL H 2 O at room temperature for 10 s (removal rate 25 nm/s) [20]. [20]. mL HF (40% conc.) + 100 mL H 2 O at room temperature for 10 s (removal rate 25 + 3 mL HF (40% conc.) + 100 mL H2 O at room temperature for 10 s (removal rate 25 nm/s) nm/s) [20]. Approximately 250 nm materials were removed. A SH‐3000 Mini‐SEM ( HIROX, Tokyo, Japan) was Approximately 250 nm materials were removed. A SH‐3000 Mini‐SEM ( HIROX, Tokyo, Japan) was Approximately 250 nm materials were removed. A SH-3000 Mini-SEM ( HIROX, Tokyo, Japan) was utilized to image the microstructures of the samples. utilized to image the microstructures of the samples. utilized to image the microstructures of the samples.
Figure 3. Diagram of sample preparation. Figure 3. Diagram of sample preparation. Figure 3. Diagram of sample preparation.
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3. Inspection of the Microstructural Features 3. Inspection of the Microstructural Features 3.1. Qualitative Description 3.1. Qualitative Description Figure 4 shows the microstructures taken from the cross‐section and machined surface of the Figure 4 shows the microstructures taken from the cross-section and machined surface of workpiece at v , fz = 0.05 mm/z and ae = 2 mm. In milling operations, strain aging, and the workpiecec = 50 m/min at vc = 50 m/min, fz = 0.05 mm/z and ae = 2 mm. In milling operations, strain recrystallization occurred on and beneath the machined surface under the combined effect of thermal, aging, and recrystallization occurred on and beneath the machined surface under the combined mechanical, and chemical energy. As a consequence, plastic deformation, phase transformation, and effect of thermal, mechanical, and chemical energy. As a consequence, plastic deformation, phase microstructure alteration will inevitably happen. It is especially for machining Ti‐6Al‐4V titanium transformation, and microstructure alteration will inevitably happen. It is especially for machining alloy, due to its high chemical reactivity and low thermal conductivity, while, in the cross‐section of Ti-6Al-4V titanium alloy, due to its high chemical reactivity and low thermal conductivity, while, the sample, no remarkable microstructural alterations were observed by visual inspection. Contrary in the cross-section of the sample, no remarkable microstructural alterations were observed by visual to the founding in the cross‐section, obvious changes of the grain sizes and the evolution of textures inspection. Contrary to the founding in the cross-section, obvious changes of the grain sizes and the were observed in the machined surface. All of the above phenomena were also found in other milling evolution of textures were observed in the machined surface. All of the above phenomena were also operations by visual inspection. found in other milling operations by visual inspection.
Figure 4. Microstructures taken from cross-section and machined surfaces of the workpiece. Figure 4. Microstructures taken from cross‐section and machined surfaces of the workpiece.
3.2. Quantitative Identification 3.2. Quantitative Identification 3.2.1. Proposed Method 3.2.1. Proposed Method In aa SEM ofof thethe two-phase Ti-6Al-4V titanium alloy, alloy, the constituent phases showed In SEM image image two‐phase Ti‐6Al‐4V titanium the constituent phases different showed gray levels, which are determined by the gray values of each image pixel. As such, contents of the different gray levels, which are determined by the gray values of each image pixel. As such, contents constituent phases can be obtained by calculating the distribution of the gray values of image pixels. of the constituent phases can be obtained by calculating the distribution of the gray values of image Meanwhile, some processing techniques should be applied to eliminate the influence of other factors, pixels. Meanwhile, some processing techniques should be applied to eliminate the influence of other such as noise and uneven light. factors, such as noise and uneven light. A digital image processing program was developed in Matlab. As shown in Figure 5, a flowchart A digital image processing program was developed in Matlab. As shown in Figure 5, a flowchart of the image processing shows seven main steps, including: of the image processing shows seven main steps, including:
(1) (1) The original image was entered into Matlab software with an uncompressed tagged image file The original image was entered into Matlab software with an uncompressed tagged image file format. Cropping, smoothing, and sharpening were then carried out to eliminate image defects, format. Cropping, smoothing, and sharpening were then carried out to eliminate image defects, such as uneven brightness; such as uneven brightness; (2) Noise reduction of the digital image using a Gaussian low‐pass filter; (2) Noise reduction of the digital image using a Gaussian low-pass filter; (3) (3) Implement the Canny edge detection algorithm after executing image graying; Implement the Canny edge detection algorithm after executing image graying; (4) Superposition of the the images, images, which processed by step (1) step and (3), step (3), contrast to add contrast (4) Superposition of which areare processed by step (1) and to add between between the α and β phases; the α and β phases; (5) (5) The gray image was transformed into a binary image. The image matrix of the binary image only The gray image was transformed into a binary image. The image matrix of the binary image only consists of pixel values “0” and “1”, where “1” represents the α phase, and “0” represent the β consists of pixel values “0” and “1”, where “1” represents the α phase, and “0” represent the phase; β phase; (6) Extract the density of the constituent phases per square micron. The distribution gradients of the constituent phases beneath the machined surface are then obtained; and
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of the constituent phases per square micron. The distribution gradients 6 of 14 of the constituent phases beneath the machined surface are then obtained; and (7) (7) Lamellar α + β colonies were regarded as a single β phase, and then profiles of the α phase were Lamellar α + β colonies were regarded as a single β phase, and then profiles of the α phase picked up. The and and mean diameter of α were were picked up.size The distribution size distribution mean diameter of grains α grains werethen thencalculated calculatedusing using Image‐P Plus software. The statistics cover the whole SEM image with a size of 244 μm × 167μm. Image-P Plus software. The statistics cover the whole SEM image with a size of 244 µm ˆ 167 µm. The diameter isis the the average length of diameters measured at 2 degree intervals and The mean mean diameter average length of diameters measured at 2 degree intervals and passing passing through the profile centroid of the α phase. through the profile centroid of the α phase.
Figure 5. Flowchart of the image processing. Figure 5. Flowchart of the image processing.
3.2.2. Identification Results 3.2.2. Identification Results Figure 6 shows the distribution gradient of the β phase beneath the machined surface. The content Figure 6 shows the distribution gradient of the β phase beneath the machined surface. The of the β phase (fβ ) in the matrix is ranged from 13%–30%, and the average value is 22.1%. A significant content of the β phase (fβ) in the matrix is ranged from 13%–30%, and the average value is 22.1%. A increase of the content of the β phase at the vicinity of the machined surface was observed. significant increase of the content of the β phase at the vicinity of the machined surface was observed. The distribution gradient of the β phase beneath the machined surface is consistent with micro The distribution gradient of the β phase beneath the machined surface is consistent with micro hardness gradient beneath the machined surface. This stems from the fact that the β phase is much hardness gradient beneath the machined surface. This stems from the fact that the β phase is much harder than the α phase [21]. Two key features, including the change rate of the β phase at the harder than the α phase [21]. Two key features, including the change rate of the β phase at the machined surface and the settle distance of the distribution gradient of the β phase, could be identified machined surface and the settle distance of the distribution gradient of the β phase, could be from the distribution gradient of the β phase. Figure 7 shows that the settle distance of the distribution identified from the distribution gradient of the β phase. Figure 7 shows that the settle distance of the gradient of the β phase can be used to reflect the deformation layer depth. According to Figure 6, distribution gradient of the β phase can be used to reflect the deformation layer depth. According to values of the change rate of the β phase at the machined surface and deformation layer depth are Figure 6, values of the change rate of the β phase at the machined surface and deformation layer 100.1%, 4 µm and 125.5%, 6 µm at vc = 50 m/min, fz = 0.02 mm/z, ae = 1.5 mm and vc = 110 m/min, depth are 100.1%, 4 μm and 125.5%, 6 μm at vc = 50 m/min, fz = 0.02 mm/z, ae = 1.5 mm and vc = 110 fz = 0.02 mm/z, ae = 0.5 mm, respectively. m/min, fz = 0.02 mm/z, ae = 0.5 mm, respectively. Figure 8 shows the microstructures and grain size distributions of the original material and the Figure 8 shows the microstructures and grain size distributions of the original material and the machined surface under different cutting condition. Grain diameters of the original material ranged machined surface under different cutting condition. Grain diameters of the original material ranged from 6 µm to 24 µm, and the average value is 11.7 µm. Grain refinement was noticed at the machined from 6 μm to 24 μm, and the average value is 11.7 μm. Grain refinement was noticed at the machined surface under different cutting condition. The grain refinement rates under the cutting condition of (b), surface under different cutting condition. The grain refinement rates under the cutting condition of (c), and (d) in Figure 7 were 30.34%, 29.1%, and 27.35%, respectively. (b), (c), and (d) in Figure 7 were 30.34%, 29.1%, and 27.35%, respectively. Under the cutting conditions of the present study, ranges of measured values of the change rate Under the cutting conditions of the present study, ranges of measured values of the change rate of the β phase, the grain refinement rate at the machined surface, and the thickness of the deformation of the β phase, the grain refinement rate at the machined surface, and the thickness of the deformation layer are 46.7%–141.1%, 18.6%–47.2%, and 3.7–12.3 µm, respectively. layer are 46.7%–141.1%, 18.6%–47.2%, and 3.7–12.3 μm, respectively. Microstructural parameters, such as the phase distribution gradient and the grain size frequency, Microstructural parameters, such as the phase distribution gradient and the grain size are the key factors in understanding the relationship between microstructural features and mechanical frequency, are the key factors in understanding the relationship between microstructural features properties of the machined surface. Quantification of these microstructural parameters allows cutting and mechanical properties of the machined surface. Quantification of these microstructural parameters optimization to obtain the desired mechanical properties. parameters allows cutting parameters optimization to obtain the desired mechanical properties.
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Content Contentofofββphase phase
50% 50% 40% 40% 30% 30% 20% 20% 10% 10% 0% 0%
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Depth below the machined surface (μm) Depth below the machined surface (μm)
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Content Contentofofββphase phase
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Figure 6. Distribution gradients of the β phase beneath the machined surface. (a) vc = 50 m/min, fz = Figure 6. Distribution gradients of the β phase beneath the machined surface. (a) v z = Figure 6. Distribution gradients of the β phase beneath the machined surface. (a)c = 50 m/min, f vc = 50 m/min, 0.02 mm/z, ae = 1.5 mm; and (b) vc = 110 m/min, fz = 0.02 mm/z, ae = 0.5 mm. e = 1.5 mm; and (b) vc = 110 m/min, fz = 0.02 mm/z, ae = 0.5 mm. 0.02 mm/z, a fz = 0.02 mm/z, ae = 1.5 mm; and (b) vc = 110 m/min, fz = 0.02 mm/z, ae = 0.5 mm.
Figure 7. Similarity between the settle distance of the distribution gradient of the β phase and the Figure 7. Similarity between the settle distance of the distribution gradient of the β phase and the deformed layer thickness. Figure 7. Similarity between the settle distance of the distribution gradient of the β phase and the deformed layer thickness. deformed layer thickness.
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(a)
(b)
(c)
(d) Figure 8. Microstructure and grain size distribution of the original material and the machined surface. Figure 8. Microstructure and grain size distribution of the original material and the machined surface. (a) Original microstructure and frequency of grain sizes; (b) machined surface (vc = 20 m/min, fz = 0.03 (a) Original microstructure and frequency of grain sizes; (b) machined surface (vc = 20 m/min, mm/z, ae = 1.5 mm); (c) machined surface (vc = 50 m/min, fz = 0.05 mm/z, ae = 2.0 mm); and (d) machined fz = 0.03 mm/z, ae = 1.5 mm); (c) machined surface (vc = 50 m/min, fz = 0.05 mm/z, ae = 2.0 mm); surface (vc = 80 m/min, fz = 0.04 mm/z, ae = 1.0 mm). and (d) machined surface (vc = 80 m/min, fz = 0.04 mm/z, ae = 1.0 mm).
3.3. Milling Parameter Sensitivity Analysis 3.3. Milling Parameter Sensitivity Analysis Different microstructural features are generated in milling Ti‐6Al‐4V titanium alloy under Different microstructural features are generated in milling Ti-6Al-4V titanium alloy under different cutting conditions. As a consequence, the final machined workpiece will express discrepant different cutting conditions. As a consequence, the of final workpiece will express discrepant mechanical properties. A quantitative description the machined sensitivity of microstructural features to mechanical properties. A quantitative description of the sensitivity of microstructural features to
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milling parameters can play a vital role in controlling the performance of the workpiece. Three main features, including the change rate of the β phase on the machined surface, the thickness of the milling parameters can play a vital role in controlling the performance of the workpiece. Three main milling parameters a vital in controlling thethe performance the workpiece. Threeof main deformed layer, and the grain refinement rate were investigated in this research. features, including can the play change rate role of the β phase on machined of surface, the thickness the features, including the change rate of the β phase on the machined surface, the thickness of the Variations of microstructural features with milling parameters are given in Figures 9–11. Cutting deformed layer, and the grain refinement rate were investigated in this research. deformed layer, and the grain refinement rate were investigated in this research. speed, feed rate, and depth of cut have been approved to affect microstructural features distinctly. In Variations of microstructural features with milling parameters are given in Figures 9–11. Cutting Variations of microstructural features with milling parameters are given in Figures 9–11. the milling process, variations of microstructural features depend on various factors, such as cutting speed, feed rate, and depth of cut have been approved to affect microstructural features distinctly. In Cutting speed, feed rate, and depth of cut have been approved to affect microstructural features temperature, strain, and strain rate, etc. With the increase of the cutting speed, cutting temperature the milling process, variations of microstructural features depend on various factors, such as cutting distinctly. In the milling process, variations of microstructural features depend on various factors, increased in the primary shear zone, which is in favor of recovery, recrystallization, and grain growth. temperature, strain, and strain rate, etc. With the increase of the cutting speed, cutting temperature such as cutting temperature, strain, and strain rate, etc. With the increase of the cutting speed, cutting On the contrary, contact duration decreased with the increase of the cutting speed. As a consequence, increased in the primary shear zone, which is in favor of recovery, recrystallization, and grain growth. temperature increased primary shear zone, which is in recrystallization, the deformation degree inin the the tool‐workpiece contact zone is favor small. of A recovery, greater cutting force and On the contrary, contact duration decreased with the increase of the cutting speed. As a consequence, and grain growth. On the contrary, contact duration decreased with the increase of the cutting speed. deformation can degree be developed by increasing the feed zone rate and the depth of cut. Therefore, the the deformation in the tool‐workpiece contact is small. A greater cutting force and As a consequence, the deformation degree in the tool-workpiece contact zone is small. A greater mechanical work and energy, which would the be converted to the energy of the phase deformation can be developed by increasing feed rate and the driving depth of cut. Therefore, the cutting forcework and deformation be developed increasing rate energy and the of depth cut. transformation and grain growth, went up. All of these reasons are responsible for the variation of mechanical and energy, can which would be by converted to the the feed driving the of phase Therefore, the mechanical work and energy, which would be converted to the driving energy of the microstructural features in milling Ti‐6Al‐4V titanium alloy. In the range of experimental parameters, transformation and grain growth, went up. All of these reasons are responsible for the variation of phase transformation and grain growth, went up. All of these reasons are responsible for the variation the thickness of the deformed layer and grain refinement rate decreased distinctly with the increase microstructural features in milling Ti‐6Al‐4V titanium alloy. In the range of experimental parameters, of microstructural features in milling Ti-6Al-4V titanium alloy. In the range of experimental parameters, of the cutting speed, but increased with the increase of the feed rate. The parameter of the depth of the thickness of the deformed layer and grain refinement rate decreased distinctly with the increase the thickness of the deformed layer and grain refinement rate decreased distinctly with the increase of cut played a positive role in increasing the thickness of the deformed layer, while opposite to the of the cutting speed, but increased with the increase of the feed rate. The parameter of the depth of the cutting speed, but increased with the increase of the feed rate. The parameter of the depth of cut grain refinement rate. For the variation of the change rate of the β phase at the machined surface, the cut played a positive role in increasing the thickness of the deformed layer, while opposite to the played a positive role in increasing the thickness of the deformed layer, while opposite to the grain depth of cut is the foremost factor among the three studied parameters. grain refinement rate. For the variation of the change rate of the β phase at the machined surface, the refinement rate. For the variation of the change rate of the β phase at the machined surface, the depth depth of cut is the foremost factor among the three studied parameters. of cut is the foremost factor among the three studied parameters.
Figure 9. Variations of microstructural features with the cutting speed. Figure 9. Variations of microstructural features with the cutting speed. Figure 9. Variations of microstructural features with the cutting speed.
Figure 10. Variations of microstructural features with the feed rate. Figure 10. Variations of microstructural features with the feed rate. Figure 10. Variations of microstructural features with the feed rate.
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Figure 11. Variations of microstructural features with the depth of cut. Figure 11. Variations of microstructural features with the depth of cut.
4. Mechanical Property Optimization 4. Mechanical Property Optimization Hall‐Petch relation [22] expressed the dependency of the lower yield point or the fracture stress Hall-Petch relation [22] expressed the dependency of the lower yield point or the fracture stress of metals on the grain size. It was also suggested for the effect of the grain size on other mechanical of metals on the grain size. It was also suggested for the effect of the grain size on other mechanical properties of polycrystalline metals and alloys [23]. As shown in Equation 1, the flow stress has a properties of polycrystalline metals and alloys [23]. As shown in Equation (1), the flow stress has a linear relationship with the reciprocal square root of grain size: linear relationship with the reciprocal square root of grain size: . (1) σ σ0 “ k1 ` k2 d´0.5 (1) where , are material constants, and σ and are flow stress and grain size. where k1 , k2 are material constants, and σ0 and d are flow stress and grain size. For the reason that the Hall‐Petch relation was proposed for the single phase material, originally, For the reason that the Hall-Petch relation was proposed for the single phase material, originally, this relationship was not reliable for α + β two‐phase Ti‐6Al‐4V titanium alloy. Furthermore, grain this relationship not reliable forto α the + β complex two-phasemicrostructural Ti-6Al-4V titanium alloy. of Furthermore, size size is difficult was to calculate due features Ti‐6Al‐4V, grain which is is difficult to calculate due to the complex microstructural features of Ti-6Al-4V, which is comprised comprised with α phase and the lamellar α + β colonies, although the Hall‐Petch relation was occasionally with α phase and the lamellar α + β colonies, although the Hall-Petch relation was occasionally used. used. For these reasons, a constituent phase content‐based finite element model was proposed. For these reasons, a constituent phase content-based finite element model was proposed. The underlying idea in modeling flow stress of the α + β two phase Ti‐6Al‐4V titanium alloy is The underlying idea in modeling flow stress of the α + β two phase Ti-6Al-4V titanium alloy is to to simulate a uniaxial tensile experiment. The finite element analyses were performed under constant simulate a uniaxial tensile experiment. The finite element analyses were performed under constant stress increments and in the plane stress condition. stressAs shown in Figure 12, is the schematic diagram of geometry modeling. Micrographs of the two‐ increments and in the plane stress condition. As shown in Figure 12, is the schematic diagram of geometry modeling. Micrographs of the phase microstructure were first obtained, where a lamellar α + β colony was regarded as a single β two-phase microstructure first obtained, where ausing lamellar α + β colony was regarded a phase. Edges of the two were phases were then mapped Solidworks software. α phases as are single β phase. Edges of the two phases were then mapped using Solidworks software. α phases are represented in yellow and β phases are in blue. In Solidworks/Simulation, the Ramberg–Osgood (R‐ represented in yellow and β phases are in blue. In Solidworks/Simulation, the Ramberg–Osgood O) constitutive model, shown in Equation 2, was applied. Material constants of the constituent phases (R-O) constitutive model, shown in Equation (2), was applied. Material constants of the constituent are listed in Table 2. phases are listed in Table 2. σ σ σ ε σ a1 σy σ N (2) ε“ ` pσ q (2) E E σy where σ , N, ′, E are yield stress, inverse of the strain hardening exponent, empirical constant, and where σy , N, a1 , E are yield stress, inverse of the strain hardening exponent, empirical constant, Young’s modulus, respectively. The Ramberg–Osgood constitutive equation was created on the basis and Young’s modulus, respectively. The Ramberg–Osgood constitutive equation was created on of rate‐independent plastic flow. Hardening behavior of the material depends on the material the basis of rate-independent plastic flow. Hardening behavior of the material depends on the constants and N. 1 Due to the power‐law relationship between stress and plastic strain, the material constants a and N. Due to the power-law relationship between stress and plastic strain, Ramberg–Osgood model implies that plastic strain is present even for very low levels of stress. the Ramberg–Osgood model implies that plastic strain is present even for very low levels of stress. Nevertheless, for low applied stresses and for the commonly used values of the material constants 1′ Nevertheless, for low applied stresses and for the commonly used values of the material constants a and N, the plastic strain remains negligible compared to the elastic strain. On the other hand, for and N, the plastic strain remains negligible compared to the elastic strain. On the other hand, for stress stress levels higher than σ , plastic strain becomes progressively larger than elastic strain. levels higher than σy , plastic strain becomes progressively larger than elastic strain.
Table 2. Material constants of the constituent phases for Ti‐6Al‐4V [5].
Phase σy (MPa) E (GPa) N ′ α 345 140 17.959 3.484 β 1000 170 8.246 5.305
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Table 2. Material constants of the constituent phases for Ti-6Al-4V [5].
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Phase
σy (MPa)
E (GPa)
a1
N
α β
345 1000
140 170
17.959 8.246
3.484 5.305
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Figure 12. Schematic diagram of geometry modeling (fβββ = 22.1%). = 22.1%). Figure 12. Schematic diagram of geometry modeling (f = 22.1%). Figure 12. Schematic diagram of geometry modeling (f
Contact condition is set to global contact, where the constituent phases are bonded. Therefore, Contact condition is set to global contact, where the constituent phases are bonded. Therefore, Contact condition is set to global contact, where the constituent phases are bonded. Therefore, no no contact surface separation separation will will be be produced produced in in the the process process of of the the simulation. simulation. Moreover, Moreover, no contact contact surfacesurface separation will be produced in the process of the simulation. Moreover, incompatible incompatible mesh technology and the plane triangle element with three nodes were applied in the incompatible mesh technology and the plane triangle element with three nodes were applied in the mesh technology and the plane triangle element with three nodes were applied in the simulation. simulation. simulation. Figure 13 shows the stress and strain contour figures under the condition that the tensile stress is Figure 13 shows the stress and strain contour figures under the condition that the tensile stress Figure 13 shows the stress and strain contour figures under the condition that the tensile stress 600 Mpa. The average stress and strain of each state can be calculated using Equations (3) and (4): is 600 Mpa. The average stress and strain of each state can be calculated using Equations (3) and (4): is 600 Mpa. The average stress and strain of each state can be calculated using Equations (3) and (4): ż 111 σ“ σ VV (3) (3) σσdV (3) V V
ż 111 ε “̅ ̅ ε VV εεdV V V
(4) (4) (4)
where, σ and εand are the average stress and the average V isstrain, the total volume of the microstructure Where, and are the average average stress and the the strain, average strain, V is is the total total volume of the the Where, ̅ ̅ are the stress and average V the volume of model, and σ and ε are the stress computed at every Gauss point in the model. Based on the average microstructure model, and σ and ε are the stress computed at every Gauss point in the model. Based are the stress computed at every Gauss point in the model. Based microstructure model, and σ and ε stress and strain of each state, stress-strain curve was drawn. Seven microstructures were simulated on the average stress and strain of each state, stress‐strain curve was drawn. Seven microstructures on the average stress and strain of each state, stress‐strain curve was drawn. Seven microstructures were simulated with volume fractions of primary a grains ranging from 0–100%. They are all shown with volume fractions of primary a grains ranging from 0–100%. They are all shown in Figure 14. As a were simulated with volume fractions of primary a grains ranging from 0–100%. They are all shown in Figure 14. As a contrast, a stress‐strain curve (red in Figure 14) reported in [24] was chosen (strain‐ in Figure 14. As a contrast, a stress‐strain curve (red in Figure 14) reported in [24] was chosen (strain‐ contrast, a stress-strain curve (red in Figure 14) reported in [24] was chosen (strain-rate hardening and rate hardening and thermal softening were not considered in the present study). rate hardening and thermal softening were not considered in the present study). thermal softening were not considered in the present study).
(a) (a) Figure 13. Cont. Figure 13. Cont. Figure 13. Cont.
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(b) (b)
Figure 13. (a) Stress and (b) strain contour figures under the condition that the tensile stress is 600 Figure 13. (a) Stress and (b) strain contour figures under the condition that the tensile stress is 600 Figure 13. (a) Stress and (b) strain contour figures under the condition that the tensile stress is 600 Mpa Mpa (f β = 22.1%). β = 22.1%). (fβMpa (f = 22.1%).
Figure 14. Computed stress‐strain curves for various contents of β phase. Figure 14. Computed stress‐strain curves for various contents of β phase. Figure 14. Computed stress-strain curves for various contents of β phase.
The correlation coefficient (R), as described by Equation 5, was applied to illustrate the statistical The correlation coefficient (R), as described by Equation 5, was applied to illustrate the statistical relationship between the reference curve and the predicted one (Brown in Figure 14): The correlation coefficient (R), as described by Equation (5), was applied to illustrate the statistical relationship between the reference curve and the predicted one (Brown in Figure 14): ∑ the predicted ̅ relationship between the reference curve and one (Brown in Figure 14): (5) ∑ ̅ ∑ ̅ řn (5) ∑i“1 pxi ´ xq ̅ pyi ´ yq R “ bř where xi and yi are the data point of reference and predicted curves. ̅ and are average values of (5) n 2 2 data points of reference and predicted curves. where x i and yi are the data point of reference and predicted curves. ̅ and are average values of i“1 pxi ´ xq pyi ´ yq The calculated value of R was 0.893, which indicated a high degree of consistency between data points of reference and predicted curves. where xThe yi are the value data point reference and predicted curves. and y are average values of data i and reference and predicted stress‐strain curves. Conclusion can also be drawn from Figure 14 that a high calculated of R of was 0.893, which indicated a high xdegree of consistency between points of reference and predicted curves. content of β phase represented a high strength of material. reference and predicted stress‐strain curves. Conclusion can also be drawn from Figure 14 that a high TheYield strength, which is measured by 0.2% offset strain method, was identified from Figure 14. calculated value of R was 0.893, which indicated a high degree of consistency between content of β phase represented a high strength of material. According to Figure 15, values of yield strength varied from 889–921 MPa with the change of content reference and predicted stress-strain curves. Conclusion can also be drawn from Figure 14 that a high Yield strength, which is measured by 0.2% offset strain method, was identified from Figure 14. of β phase from 30% to 45%. content of β phase represented a high strength of material. According to Figure 15, values of yield strength varied from 889–921 MPa with the change of content
Yield strength, which is measured by 0.2% offset strain method, was identified from Figure 14. of β phase from 30% to 45%. According to Figure 15, values of yield strength varied from 889–921 MPa with the change of content of β phase from 30% to 45%. The most outstanding advantage of the proposed method of characterizing microstructural features is its available in presenting gradient distribution of microstructural features beneath the machined surface. These studied microstructural features were closely related to the mechanical property as represented by the stress-strain behavior. As a consequence, the proposed method will serve as a powerful tool in developing machining parameters—mechanical property models of the dual-phase Ti-6Al-4V titanium alloy.
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Figure 15. Yield strength variation of the material with content of the β phase. Figure 15. Yield strength variation of the material with content of the β phase.
5. Conclusions The most outstanding advantage of the proposed method of characterizing microstructural features is its microstructural available in presenting gradient distribution of microstructural features beneath the To quantify features and control the machining-induced mechanical properties machined surface. These studied microstructural features were closely related to the mechanical in milling titanium alloy Ti-6Al-4V, a novel method of image recognition is developed to identify the property as represented by the stress‐strain behavior. As a consequence, the proposed method will microstructural changes at the vicinity of the machined surface. According to this investigation’s serve as a powerful tool in developing machining parameters—mechanical property models of the results, the conclusions can be summarized as follows: dual‐phase Ti‐6Al‐4V titanium alloy.
1.
2.
3.
A digital image processing method was proposed. The new approach is based on the fact that 5. Conclusions the constituent phases of Ti-6Al-4V titanium alloy show different gray levels in digital images. To quantify microstructural features and control the machining‐induced mechanical properties By the proposed method, microstructural features, including the content of constituent phases in milling titanium alloy Ti‐6Al‐4V, a novel method of image recognition is developed to identify the and grain size, were identified. A high content of the β phase and small grain size were found microstructural changes at the vicinity of the machined surface. According to this investigation’s at the machined surface. The maximum measured values of change rate of β phase, grain results, the conclusions can be summarized as follows: refinement rate at the machined surface, and thickness of the deformation layer are 141.1%, 47.2%, 1. A digital image processing method was proposed. The new approach is based on the fact that and 12.3 µm, respectively. the constituent phases of Ti‐6Al‐4V titanium alloy show different gray levels in digital images. Sensitivity of microstructural changes to milling parameters was investigated. The thickness By the proposed method, microstructural features, including the content of constituent phases of the deformed layer and grain refinement rate decreased distinctly with the increase of the and grain size, were identified. A high content of the β phase and small grain size were found cutting speed, but increased withmaximum the increase of the feed rate. parameter ofphase, the depth of cut at the machined surface. The measured values of The change rate of β grain refinement rate role at the and thickness the deformation layer are 141.1%, played a positive in machined increasingsurface, the thickness of theof deformed layer, while opposite to the grain47.2%, and 12.3 μm, respectively. refinement rate. For the variation of the change rate of the β phase at the machined surface, 2. Sensitivity of microstructural changes to milling parameters was investigated. The thickness of the depth of cut is the foremost factor among the three studied parameters. the deformed layer and grain refinement rate decreased distinctly with the increase of the cutting Stress-strain behavior of two ductile phase alloys was developed using the finite element method. speed, but increased with the increase of the feed rate. The parameter of the depth of cut played A high content of the β phase was found to have a high strength of materials. Values of yield a positive role in increasing the thickness of the deformed layer, while opposite to the grain strength varied from 889–921 MPa with the change of content of the β phase from 30%–45%. refinement rate. For the variation of the change rate of the β phase at the machined surface, the
depth of cut is the foremost factor among the three studied parameters. 3. Stress‐strain behavior of supported two ductile alloys Natural was developed using the finite element Acknowledgments: This work was by phase the National Science Foundation of China (51425503, method. A high content of the β phase was found to have a high strength of materials. Values of 51375272, U1201245), Major Science and Technology Program of High-End CNC Machine Tools and Basic Manufacturing Equipment (2014ZX04012014) and grants from Taishan Scholar Foundation (TS20130922). yield strength varied from 889–921 MPa with the change of content of the β phase from 30%–45%. Author Contributions: Dong Yang and Zhanqiang Liu conceived and designed the study; Dong Yang performed Acknowledgments: This work was supported by the National Natural Science Foundation of China (51425503, the experiments and analyzed the data; Dong Yang wrote theof paper; Zhanqiang Liu reviewed edited 51375272, U1201245), Major Science and Technology Program High‐End CNC Machine Tools and and Basic the manuscript. Manufacturing Equipment (2014ZX04012014) and grants from Taishan Scholar Foundation (TS20130922). Conflicts of Interest: The authors no Zhanqiang conflict of interest. Author Contributions: Dong declare Yang and Liu conceived and designed the study; Dong Yang performed the experiments and analyzed the data; Dong Yang wrote the paper; Zhanqiang Liu reviewed and edited the manuscript. References
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