Abstract

Given the recent trend toward hybrid processing involving the integration of wire arc additive manufacturing (WAAM) and machining capabilities, this paper aims to identify and correlate microstructural variations observed in wire arc additively manufactured aluminum alloy 4043 workpieces to their specific micromilling responses. This is done with the explicit goal of assessing the feasibility of using micromilling responses to detect microstructural variations in WAAM workpieces. As part of this effort, variations in the interlayer cooling time are used to induce changes in the microstructure of a thin-wall WAAM workpiece. The microstructures are first characterized using in-process thermographic imaging, optical microscopy, polarized light microscopy, and indentation. Micromilling slotting experiments are then conducted on different regions within the workpiece. The findings suggest that cutting force signals are the premier candidate for in situ extraction of information regarding microstructural variations within WAAM workpieces. In particular, in situ analysis of the cutting force frequency spectrum can provide critical information regarding dominant failure mechanisms related to the underlying microstructure. Other key micromilling responses such as surface roughness, burr formation, and tool wear also correlate well with the underlying microstructural variations. While these early stage findings hold promise, future research efforts spanning multiple metal alloys systems and micromachining processes are needed to mature the proposed concept.

1 Introduction

Wire arc additive manufacturing (WAAM) is a direct-energy deposition process that involves the use of a metal inert gas (MIG) welding head to perform layer-by-layer deposition of metal filaments [14]. This process is particularly applicable toward the creation of near-net shape parts for a variety of large-scale industrial applications, such as aerospace, marine, and construction manufacturing [58]. Traditionally, expensive and time-consuming imaging techniques have been used to gain insight into microstructural variations present within WAAM workpieces. However, the recent push toward hybrid processing involving the integration of both machining and WAAM capabilities [911] opens up an interesting possibility of using machining processes to probe for microstructural changes in WAAM workpieces.

Macroscale machining processes, involving tool diameters greater than 1 mm, have been used on various metallic workpieces (e.g., AISI-H13, Ti-6Al-4V, and SS316L) produced using direct-energy deposition processes [1214]. While the primary goal of these machining studies has been to improve the surface finish of the additively manufactured workpiece, the size-scale of these processes makes them insensitive to variations observed in the microstructure. Micromachining processes, involving tool diameters between 50 and 500 μm [15,16], provide a feasible alternative since they are sensitive to microstructural variations seen in diverse material systems [1723]. However, there is a lack of studies exploring the micromachining of additively manufactured metallic workpieces. A recent 2017 publication [24] took a step in this direction by studying the micromilling responses of Ti-6Al-4V workpieces produced using laser engineered net shaping (LENS). However, it did not effectively map the micromachining responses to the underlying microstructure.

In light of the current state of literature, this paper aims to correlate microstructural variations observed in wire arc additively manufactured aluminum alloy 4043 (Al-4043) workpieces to their specific micromilling responses. This is done with the explicit goal of assessing the feasibility of using micromachining responses to detect microstructural variations in WAAM workpieces. As part of this effort, variations in the interlayer cooling time are used to induce changes in the microstructure of a thin-wall WAAM workpiece. The workpiece microstructure is characterized using in-process thermographic imaging, optical microscopy, polarized light microscopy, and indentation. Micromilling experiments are then conducted on these workpieces, and the variations in the cutting force, surface roughness, burr formation, and tool wear are correlated with corresponding microstructural features.

The remainder of this paper is organized as follows. Section 2 outlines both the WAAM process used to create the thin-wall workpieces and the associated workpiece characterization efforts. Section 3 deals with the micromilling experimental setup and cutting parameters. Section 4 presents the trends seen in key micromilling responses and discusses their correlation to the underlying microstructures. Finally, Sec. 5 presents the specific conclusions that can be drawn from this work.

2 Wire Arc Additive Manufacturing

2.1 Experimental Setup.

The experimental setup for the WAAM process consisted of a six degrees-of-freedom robotic arm (Yaskawa MA2010) and a welding unit (Fronius TPS 500i) operated in the cold metal transfer (CMT) mode [25] (Fig. 1(a)). The workpieces were made from aluminum alloy 4043 (Al-4043) wire (Haun Welding Supply, Albany, NY). Al-4043 is an aluminum–silicon (Al–Si) alloy, which nominally has a 4.5–6.0 wt% of Si, in addition to other trace elements (iron, copper, manganese, magnesium, zinc, titanium, and beryllium). All thin-wall workpieces (Fig. 1(b)) were printed on an aluminum substrate using a single pass of the welding head, per layer. The thin-wall workpieces were nominally 100 mm long, with a width of ∼ 4 mm corresponding to the size of the weld-bead. A bidirectional (zig–zag) toolpath was implemented to provide an even heat accumulation at the ends of the workpiece, which also improves the overall part quality [26].

Fig. 1
Wire arc additive manufacturing (WAAM) experimental setup (a) system including infrared camera for thermographic characterization (b) characteristic WAAM thin-wall workpiece with Regions A–E identified (c) and (d) characteristic in-process thermographic signatures obtained while depositing Regions B and E
Fig. 1
Wire arc additive manufacturing (WAAM) experimental setup (a) system including infrared camera for thermographic characterization (b) characteristic WAAM thin-wall workpiece with Regions A–E identified (c) and (d) characteristic in-process thermographic signatures obtained while depositing Regions B and E
Close modal

Table 1 summarizes the process parameters used to produce each WAAM workpiece. The voltage and current parameters were obtained from the materials database of the welding controller, whereas the values for welding head speed, wire feed rate, and shielding gas flow rate were based on early stage experiments aimed at achieving uniform layer height and minimal porosity in the ensuing WAAM workpieces. The interlayer cooling time (ILCT), which is the time delay between consecutive printed layers, was the key variable that was varied to induce microstructural changes. All workpieces were printed in ambient, natural convection environments. The ILCT values were chosen at 5 s and 120 s to distinctly affect the thermal history of the workpiece, while also maintaining reasonable build times. In order to monitor the temperature of the workpiece during the WAAM process, a thermographic infrared camera (FLIR A320, FLIR Systems, Wilsonville, OR) was used. A frame rate of 30 frames per second was used on the thermographic camera. Figures 1(c) and 1(d) depict two instances of the characteristic in-process thermographic data obtained with a resolution of 4 pixels per millimeter.

Table 1

Process parameters to manufacture Al-4043 WAAM workpieces

Voltage10.7 V
Current42 A
Shielding gasArgon
Shielding gas flow rate15.34 L/min
Welding head speed5 mm/s
Wire feed rate2.54 m/min
Number of layers40
Interlayer cooling time (ILCT)5 s and 120 s
Voltage10.7 V
Current42 A
Shielding gasArgon
Shielding gas flow rate15.34 L/min
Welding head speed5 mm/s
Wire feed rate2.54 m/min
Number of layers40
Interlayer cooling time (ILCT)5 s and 120 s

2.2 Workpiece Characterization.

As part of this effort, the workpieces were characterized for their part height, microstructure, and hardness characteristics.

2.2.1 Part Height.

The WAAM workpieces were nominally 100 mm long and comprised of a total of 40 layers. The workpieces made using an ILCT of 5 s and 120 s had a final height of 65.1 ± 0.2 mm and 71.1 ± 0.3 mm, respectively. This difference in the total height of the part is attributed to the thermal gradients that influence the build-up of the deposited metal within each layer [27]. With the shorter ILCT of 5 s, the deposition of the new layer is on a relatively hotter surface (refer to Sec. 2.3 for details). This residual heat results in greater side flow and slower solidification of the metal on the previously deposited layers, thereby resulting in a smaller overall workpiece height. Given the conservation of volume, this reduction in layer height (∼150 μm per layer) over the course of the build of the 5 s ILCT workpiece should also correspond to an increase in its width over that of the 120 s ILCT workpiece. However, since the micromilling experiments first involved a face-milling process to remove the surface undulations seen in Fig. 1(b), the width characterization effort was not undertaken in this study.

To account for the mismatch in thermal boundary conditions between the workpiece and substrate, the bottom 25–30 mm of each workpiece was discarded for this study. The remaining portion of the workpiece was divided into five regions (Fig. 1(b)). These will be referred as Regions A–E and will be denoted with the color schema presented in Fig. 1(b), for the remainder of this paper, with Region E being at the top of the workpiece.

2.2.2 Microscopy Characterization.

Both optical microscopy (OM) and polarized light microscopy (PLM) were used to characterize the workpiece microstructure. The microscopy samples were prepared in accordance with the procedures reported by Wang et al. [28] and Slámová et al. [29]. A 20 s immersion in Keller's reagent was performed before OM at 20× magnification (Olympus PMG3, USA) in order to acquire the secondary dendrite arm spacing (SDAS) of the Si boundaries for Regions A–E. In order to measure the grain size using PLM, a 20 s immersion in Keller's reagent, followed by a 200 s anodization in Barker's reagent was performed on the workpiece. These workpieces were imaged at 10× magnification (Olympus PMG3, Minneapolis, MN).

Optical microscopy and PLM images from the 5 s ILCT and 120 s ILCT workpiece are shown in Figs. 2(a)2(d), respectively. It should be noted that these images are taken at a depth of ∼500 μm into the surface of the workpiece visible in Fig. 1(b). The microstructure for Al-4043 consists of both Al and Si phases, with the harder Si phase segregating in eutectic boundaries. Within the OM images, the white regions represent the Al dendritic structures, while the black regions represent the segregated Si eutectic boundary. Image analysis using the mean lineal intercept method [30] was conducted to measure the average SDAS and grain size in each workpiece region. Forty line measurements were taken on the OM images to obtain the average SDAS. Fifteen linear measurements were taken on the PLM images to obtain the average grain size. It should also be noted that the grain size and SDAS are correlated to each other, as reported in previous literature [31]. Their values for the 5 s and 120 s ILCT workpieces are reported in Figs. 2(e) and 2(f), respectively. For the 5 s ILCT workpiece, the average SDAS increases by ∼40% between Regions A and E, whereas the SDAS values are fairly uniform throughout those same regions in the 120 s ILCT workpiece.

Fig. 2
Microstructure characterization of WAAM workpieces (a) and (b) Region A and Region E of 5 s ILCT workpiece; (c) and (d) Region A and Region E of 120 s ILCT workpiece; (e) and (f) Grain size and secondary dendrite arm spacing (SDAS) measurements across all workpieces regions for 5 s ILCT and 120 s ILCT workpiece. (Note: OM = optical microscopy and PLM = polarized light microscopy.)
Fig. 2
Microstructure characterization of WAAM workpieces (a) and (b) Region A and Region E of 5 s ILCT workpiece; (c) and (d) Region A and Region E of 120 s ILCT workpiece; (e) and (f) Grain size and secondary dendrite arm spacing (SDAS) measurements across all workpieces regions for 5 s ILCT and 120 s ILCT workpiece. (Note: OM = optical microscopy and PLM = polarized light microscopy.)
Close modal

The grain size and SDAS are two microstructural features within the Al-4043 alloy that have characteristic length scales relevant to micromilling. The grain size (measured using the PLM) represents a relatively larger microstructural feature (>100 μm) that is of the order of typical axial depth-of-cuts and tool diameters encountered in micromilling. The SDAS is a microstructural feature (< 30 μm) that is expected to be more sensitive to the feed-per-tooth values that are typically between 1 and 10 μm. In addition to these two Al-related features, the Si eutectic boundaries (surrounding the dendritic Al structures) are observed to form an interconnected network within the WAAM workpiece. Despite having the same weight percentage of Si within the alloy (4.5–6 wt%), the spatial distribution of the eutectic Si network varies as a function of the ILCTs. Pixel-based measurement performed on multiple OM images taken over a 900 μm × 900 μm area reveal that the 5 s and 120 s ICLT workpieces have a two-dimensional Si areal coverage of 45.3 ± 3.7% and 62.0 ± 5.4%, respectively. Given that the Si eutectic boundaries are present even within the Al grain boundaries (refer to PLM images in Figs. 2(a)2(d)), the SDAS coupled with the local concentration gradients of Si in those eutectic boundaries can be expected to influence microscale phenomenon, such as crack propagation and Si debonding that come into play during micromilling.

2.2.3 Hardness Characterization.

Vickers hardness test was performed according to the ASTM Standard E92 [32]. A load of 200 g was applied for the test. The hardness value for each workpiece region was measured based on ten indentation measurements. The average Vickers hardness measurements for both the 5 s ILCT workpiece and the 120 s ILCT workpiece are reported in Fig. 3. Two observations can be made from these hardness measurements. First, the 120 s ILCT workpiece is harder than the 5 s ILCT workpiece across all Regions A–E. Second, the 5 s ILCT workpiece shows an 8.3% increase in hardness along the build direction spanning Regions A–E. However, no statistical change is observed in the hardness measured along the build direction for the 120 s ILCT workpiece.

Fig. 3
Vickers hardness measurement in WAAM workpieces produced at 5 s ILCT and 120 s ILCT (n = 10 indentations for each Regions A–E)
Fig. 3
Vickers hardness measurement in WAAM workpieces produced at 5 s ILCT and 120 s ILCT (n = 10 indentations for each Regions A–E)
Close modal

2.3 Thermographic Process Characterization.

The temperature of the workpiece during the WAAM process has an effect on the resulting microstructure. In order to understand the thermal history of the workpiece during the WAAM process, in situ thermographic imaging was performed using a FLIR A320 camera (Fig. 1(a)). Prior to deploying this camera, offline radiation characterization tests were conducted to obtain the emissivity (ε) of the Al-4043 workpiece. As part of this effort, a thermocouple was first attached to the center of the workpiece. The workpiece–substrate combination was then heated to 300 °C in a high-temperature furnace, followed by imaging using the FLIR A320 camera. The emissivity value was then tuned iteratively so that the thermographic camera readout at the thermocouple location, matched the measurement made by the thermocouple. Based on these offline experiments, the emissivity of the workpiece was set to 0.13 during the in situ thermographic imaging.

Previous WAAM studies have used pixel-based thermographic imaging techniques to characterize the temperature trends seen during the build process [33]. Figures 4(a) and 4(b) show such in-build temperature measurements taken at the center of each of the Regions A–E, for both the 5 s ILCT and 120 s ILCT workpieces, respectively. The data show that the peak temperatures measured for both of the workpieces are comparable at ∼1274 ± 44 °C. However, there are two key differences between the plots in Figs. 4(a) and 4(b). First, as expected, the finer microstructure observed in the 120 s ILCT workpiece (Fig. 2) comes at the price of a ∼5X increase in the build time, over that for the 5 s ILCT workpiece (horizontal axes in Figs. 4(a) and 4(b)). Second, given the differences in the interlayer cooling times, the heat accumulation within each region of the 5 s ILCT workpiece is greater than that seen in the 120 s ILCT workpiece. This is confirmed by the steady-state temperature values in the postdeposition region (i.e., postpeak region) that are seen to be ∼ 285 °C and 130 °C, respectively in Figs. 4(a) and 4(b). Given its longer wait time between subsequent layer depositions, it is suspected that the 120 s ILCT workpiece dissipates more heat through natural convection, conduction, and radiative heat transfer, which explains the decrease in the temperature measured during the WAAM build.

Fig. 4
Thermographic measurements during WAAM build process: (a) 5 s ILCT workpiece, (b) 120 s ILCT workpiece, and (c) time spent above solidus temperature (Ts) of 577 °C
Fig. 4
Thermographic measurements during WAAM build process: (a) 5 s ILCT workpiece, (b) 120 s ILCT workpiece, and (c) time spent above solidus temperature (Ts) of 577 °C
Close modal

In order to explain the microstructural variation present in the WAAM workpieces (Fig. 2), the temperature data were correlated with the Al-Si phase diagram for a 5 wt% Si composition [34,35]. For this weight percentage, the Al-Si phase diagram has three distinct phases. The liquid phase (L) lies above the liquidus temperature (TL) of 617 °C. The solid phase (Al+Si) lies below the solidus temperature (TS) of 577 °C. Finally, a hypoeutectic region (L + α-Al) exists between the solidus and liquidus temperatures (between 577 and 617 °C).

The temperatures associated with these three phases are plotted with dashed lines in Figs. 4(a) and 4(b). Figure 4(c) reports the time duration for which the measured pixel, within each region, was above the TS threshold. As the part builds, the microstructure of the 5 s ILCT workpiece spends more time above the solidus temperature, as compared to the microstructure of the 120 s ILCT workpiece. This increase in the time duration spent above the solidus temperature results in greater amounts of Si segregation, which explains the increase in SDAS, as reported in Fig. 2.

3 Micromilling Experimental Setup

The micromilling experiments were carried out on a 3-axis micromachining center (Mikrotools DT-110, Singapore), using a high-precision electric spindle (NSK, Japan) with a rated speed of 80,000 RPM. The surface waviness of the “as-built” WAAM workpieces was first removed using a face milling operation. After this, the samples were mounted onto a three-axis dynamometer (Kistler 9256C1, Switzerland, 30 kHz sampling rate) as shown in Fig. 5.

Fig. 5
Micromilling setup
Fig. 5
Micromilling setup
Close modal

Two-fluted, tungsten carbide micro-endmills of 500 μm diameter from Performance Micro Tools (USA) were used for the micromilling tests. Five-millimeter-long, full-immersion, slot milling tests were conducted at an axial depth-of-cut of 100 μm by using a cutting speed of 80 m/min and a feed-per-tooth value of 1 μm. These cutting parameters were chosen based on their similarity to other micromilling studies involving aluminum alloys [36]. For both the 5 s and 120 s ILCT workpieces, three replicates were conducted for each of the Regions A–E. To eliminate confounding effects due to tool wear, a new micro-endmill was used for each of the Regions A–E (i.e., a total of 10 micro-endmills used across the two ILCT workpieces). As seen in Fig. 5, the slots were micromilled along the Y-direction, which coincides with the vertical WAAM build direction shown in Fig. 1(b). This specific slot milling direction was chosen since it corresponds to the direction of maximum heat flux responsible for microstructural heterogeneity in the WAAM workpieces. The cutting parameters are summarized in Table 2.

Table 2

Process parameters for micromilling experiments

Workpiece
  • Al-4043—built using WAAM with interlayer cooling times (ILCT) of 5 s and 120 s

  • Five Regions A–E (as indicated in Fig. 1(b))

Tool
  • Performance Micro-Tool (USA), Tool Number: TR-2-0200-S

  • 500 μm diameter, two-fluted, tungsten carbide endmill, edge radius- 2 μm, flute length—1.5 mm, helix angle—30 deg

Cutting Conditions
  • Full-immersion, slot milling

  • Cutting speed: 80 m/min

  • Feed-per-tooth: 1 μm

  • Axial depth of cut: 100 μm

  • Length of cut: 5 mm

Workpiece
  • Al-4043—built using WAAM with interlayer cooling times (ILCT) of 5 s and 120 s

  • Five Regions A–E (as indicated in Fig. 1(b))

Tool
  • Performance Micro-Tool (USA), Tool Number: TR-2-0200-S

  • 500 μm diameter, two-fluted, tungsten carbide endmill, edge radius- 2 μm, flute length—1.5 mm, helix angle—30 deg

Cutting Conditions
  • Full-immersion, slot milling

  • Cutting speed: 80 m/min

  • Feed-per-tooth: 1 μm

  • Axial depth of cut: 100 μm

  • Length of cut: 5 mm

4 Micromilling Responses

This section will discuss the mapping between the microstructural variations and the trends seen in cutting forces, surface roughness, burr formation, and tool wear. This specific sequence for discussing the micromachining responses is based on their viability for in situ deployment in WAAM environments.

4.1 Cutting Force.

Micromilling cutting forces have been shown to be sensitive to microstructural variations [1719]. This is expected to hold true even for the WAAM workpieces in this study given the size-range of the microstructural features present within the Al-4043 microstructure (see Sec. 2.2.2). For a 5-mm-long slot with an axial depth-of-cut of 100 μm, the tool is expected to encounter multiple aluminum grains. Furthermore, during a single revolution of the cutting tool, the cutting edge will encounter innumerable dendrites representing Al–Si boundaries present within the cutting path. As seen in the OM images in Fig. 2, both the spatial frequency of these Al–Si boundaries and the concentration of segregated Si within those boundaries vary as a function of the ILCTs and regional location within the workpiece (i.e., Regions A–E). The combinatorial effect of both the grain size and Al–Si boundaries is expected to influence the ensuing fluctuations within the cutting force signal.

4.1.1 Power Spectrum Analysis.

Figure 6(a) shows a characteristic resultant cutting force signal plotted against the number of revolutions of the tool. While these data are for Region E of the 5 s ILCT workpiece, it is characteristic of all Regions A–E across both the 5 s and 120 s ILCT workpieces. As seen, the signal can be broadly segregated into three zones based on the cutting force fluctuations. Zone-1, as identified in Fig. 6(a), is indicative of the cutting tool being fully engaged with regions of the workpiece containing dendritic Al-rich regions with low Si content. In Zone-1, the characteristic two-fluted milling signature is observed in both the cutting force signal (Fig. 6(b-i)), and the frequency spectrum (Fig. 6(b-ii)).

Fig. 6
Cutting force characteristics (a) resultant cutting force signal over 50 tool revolutions, (b) cutting force and associated power spectrum during fully engaged cutting in Zone-1, (c) cutting force and associated power spectrum during workpiece fracture in Zone-2, and (d) power trends at tooth passing frequency over entire length of cut. (Note: All data are from Region E, and data in (a)–(c) are from the 5 s ILCT workpiece.)
Fig. 6
Cutting force characteristics (a) resultant cutting force signal over 50 tool revolutions, (b) cutting force and associated power spectrum during fully engaged cutting in Zone-1, (c) cutting force and associated power spectrum during workpiece fracture in Zone-2, and (d) power trends at tooth passing frequency over entire length of cut. (Note: All data are from Region E, and data in (a)–(c) are from the 5 s ILCT workpiece.)
Close modal

Zone-2 as identified in Fig. 6(a) involves regions where one observes an abrupt drop in the cutting force signal. This signature is indicative of the tool edge encountering dense eutectic Si boundaries that result in fracture dominated failure in the workpiece. The power spectrum of the Zone-2 cutting force signal (Fig. 6(c-ii)) still shows the characteristic frequency associated with the spindle RPM. However, there is a noticeable drop in the power associated with the tooth passing frequency when compared to the Zone-1 signature (Fig. 6(b-ii)). This indicates that while the tool continues to rub against the workpiece surface, the localized fracture dominated failure is removing portions of the workpiece significantly larger in size than the feed-per-tooth value (1 μm). Finally, Zone-3 deals with a gradual reentry of the cutting edge into the workpiece, as it transitions out of Zone-2 (Fig. 6(a)).

Figures 6(d-i) and 6(d-ii) show representative moving-average plots (window of 10 revolutions) from Region E for the power concentrated at the tooth-passing frequency. The 5 s ILCT workpiece (Fig. 6(d-i)) shows a relatively steady power over the entire length of cut, which indicates constant engagement of both cutting edges during the micromilling process. However, the corresponding plot for the 120 s ILCT workpiece (Fig. 6(d-ii)) shows a lower average power value when compared to the 5 s ILCT workpiece. More interestingly, there are 11 locations where the power concentrated at the tooth passing frequency drops to 0 (as indicated by the markers on the horizontal axis in Fig. 6(d-ii)). As discussed in Fig. 6(c), this indicates that in those regions the two cutting edges are not fully engaged in the cutting process. Based on such discrete instances where the cutting power concentrated at the tooth passing frequency drops to zero, on an average ∼12–20 fracture events are observed per slot for the 120 ILCT workpiece. The corresponding number for the 5 s ILCT workpiece is that of ∼0–4 fracture events per slot. This finding implies that the power concentrated at the tooth passing frequency is a strong indicator of the tool encountering Si-rich zones.

4.1.2 Resultant Cutting Force Trends.

Figures 7(a) and 7(b) presents the average maximum resultant cutting force across three micromilled slots measured in Regions A-E of the 5 s ILCT and 120 s ILCT workpiece, respectively. Only fully engaged Zone-1 portions of the signal (Fig. 6(a)) were considered for this calculation. For the 5 s ILCT workpiece, the resultant cutting force shows an increasing trend across the regions (from A to E), whereas the 120 s ILCT workpiece displays a near constant value across all those same regions. These trends map to the trends in the SDAS and grain size (Figs. 2(e) and 2(f)).

Fig. 7
Average resultant cutting force: (a) 5 s ILCT workpiece and (b) 120 s ILCT workpiece. (Note: (i) the three vertical bars per Region A–E represent the three replicates in the cutting experiment and (ii) the relative trend seen in the cutting forces across Regions A–E is comparable to the microstructural trends seen in Figs. 2(e) and 2(f).)
Fig. 7
Average resultant cutting force: (a) 5 s ILCT workpiece and (b) 120 s ILCT workpiece. (Note: (i) the three vertical bars per Region A–E represent the three replicates in the cutting experiment and (ii) the relative trend seen in the cutting forces across Regions A–E is comparable to the microstructural trends seen in Figs. 2(e) and 2(f).)
Close modal

The combined results presented in Figs. 2, 6, and 7 provide insight into the cutting mechanics of these WAAM workpieces. The large number of brittle Si boundaries present within the 120 s ILCT workpiece promotes fracture-based cutting. This fracture-based cutting, combined with minimal variations in the grain size and SDAS (Fig. 2(f)), lead to a relatively constant cutting force across the workpiece regions that is lower than those seen for the 5 s ILCT workpiece. The increase in cutting force across Regions A–E in the 5 s ILCT workpiece likely has two possible reasons. First, the increased Al grain size/SDAS between Regions A–E indicates increased possibility of rubbing/ploughing due to elastic recovery in the Al-phase. Second, larger Si segregation areas are observed within Region E, as opposed to Region A (Fig. 2(b)). This implies a fewer number of interfaces for crack propagation, thereby minimizing the occurrence of fracture within Region E. It should be noted here that since cutting forces are affected by the mechanics of material failure, their trends are not necessarily in line with the quasi-static hardness measurements, where the indenter measures a lower hardness value for the 5 s ILCT workpieces due to its higher volume percentage of Al dendrites.

4.2 Surface Roughness.

The surface roughness of the floor of the micromilled slots was measured at three locations along the 5 mm length of the slot using an optical profilometer (Zeta 20, Zeta Instruments, USA). Table 3 shows the calculated average surface roughness (Sa) value in each region. It is clear that the 5 s ILCT workpiece has an increasing trend of surface roughness along the build direction. Specifically, the average Sa value of the 5 s ILCT workpiece is 0.70 ± 0.13 μm in Region A, and 1.23 ± 0.12 μm in Region E. The surface roughness of micromilled slots in the 120 s ILCT workpiece stays relatively uniform at an average of ∼0.77 ± 0.07 μm. Similar to the cutting force trends reported in Sec. 4.1, the data for the surface roughness also reflect the microstructural trends in Figs. 2(e) and 2(f).

Table 3

Surface roughness values (Sa)

RegionABCDE
5 s ILCT slot floor Sa (μm)0.70 ± 0.130.80 ± 0.080.83 ± 0.070.95 ± 0.141.23 ± 0.12
120 s ILCT slot floor Sa (μm)0.79 ± 0.060.76 ± 0.070.78 ± 0.010.76 ± 0.120.77 ± 0.13
RegionABCDE
5 s ILCT slot floor Sa (μm)0.70 ± 0.130.80 ± 0.080.83 ± 0.070.95 ± 0.141.23 ± 0.12
120 s ILCT slot floor Sa (μm)0.79 ± 0.060.76 ± 0.070.78 ± 0.010.76 ± 0.120.77 ± 0.13

Note: The relative trend seen in the Sa values across Regions A–E is comparable to the microstructural trends seen in Figs. 2(e) and 2(f) and cutting force trends in Fig. 7.

Figures 8(a) and 8(b) present the 3D surface scans of the floor of the micromilled slots in Regions A and E of the 5 s ILCT workpiece. Larger voids from Si fracture and debonding are seen in Region E, which explains the Sa trends. These voids are ∼ 2X as deep and ∼2X as wide, compared to the voids seen in Region A. The difference in size of these voids, and in turn, the surface roughness, follows suit with the differences in the size of the dendrites reported in Fig. 2. While the surface scans for the 120 s ILCT workpiece are not presented here for brevity, the voids on the floor of the slot are (i) smaller in size when compared to the 5 s ILCT workpiece and (ii) comparable in size across Regions A–E. This observation is also in line with the microstructural homogeneity observed across Regions A–E, for the 120 s ILCT workpiece.

Fig. 8
3D surface scans of micromilled slot floor, showing voids from fractured and debonded Si for (a) Region A and (b) Region E of the 5 s ILCT workpiece
Fig. 8
3D surface scans of micromilled slot floor, showing voids from fractured and debonded Si for (a) Region A and (b) Region E of the 5 s ILCT workpiece
Close modal

4.3 Burr Formation.

The formation of burrs on the top surface of a micromilled slot is another indicator of the microstructural variations in the 5 s and 120 s workpieces. Figure 9(a) presents two separate optical micrographs of the top surface of the micromilled slots (i.e., X-Y plane, represented in Fig. 5) produced in Regions A and E. In this image, the width of the slot is indicated by the overlaid cutter inset, whereas the black-colored regions indicate the projected two-dimensional area of the top-burr seen along the edges of the micromilled slot. As indicated by the arrows in Fig. 9(a), the burr formation is more prominent along the down-milling side of the slot. Figure 9(b) shows the top burr-area values measured across Regions A–E, for both the 5 s and 120 s ILCT workpieces. These values were calculated using a digital area characterization of images similar to those in Fig. 9(a).

Fig. 9
Average top burr area: (a) optical images from Regions A and E in the 5 ILCT workpiece (coordinate axes identified from Fig. 5) and (b) burr measurements across all workpieces and regions (pooled data across all replicates)
Fig. 9
Average top burr area: (a) optical images from Regions A and E in the 5 ILCT workpiece (coordinate axes identified from Fig. 5) and (b) burr measurements across all workpieces and regions (pooled data across all replicates)
Close modal

Similar to the cutting force and surface roughness trends, the measred burr-area results between the Regions A–E are also in line with the microstructural trends. The data show that in general, the burrs seen in the 5 s ILCT workpiece are larger than those seen for the 120 s ILCT workpiece. These trends correlate directly with the size of the corresponding Al dendrites between the two ILCT workpieces as burrs are formed primarily due to the plastic side-flow of the softer Al-phase. The fact that the burr values do not change much for the 120 s workpiece across Regions A–E is also indicative of a homogenous microstructure between Regions A and E (Fig. 2(d)). The only trend that is surprising here is that of the apparent increase in the burrs seen in Region A of the 5 s ILCT workpieces. A possible increase in the coefficient of friction resulting from the dislodged Si particles may explain this observation (Fig. 8(a)) but it needs further tribological investigation.

4.4 Tool Wear.

A tool wear study was conducted with the same cutting parameters as listed in Table 2. Only Regions A and E from both the 5 s and 120 s ILCT workpieces were chosen for this study that comprised of a total of 40, 5 mm long full-immersion cuts. A total of four additional pristine cutting tools were used for this independent tool wear study. Images of the tool rake face were taken after every 5 micromilled slots (i.e., after every ∼ 1.25 mm3 of material removal), using a 10X objective lens and high-resolution camera (Infinity 2, USA). The in-process deployment of the camera allowed for the tool to remain in the spindle during the imaging step. This eliminated the possibility of introducing varying tool offset lengths into the tool wear study through intermittent removal of the tool from the spindle.

Figures 10(a) and 10(b) depict representative raw optical images of the tools and Fig. 10(c) shows the rake-face of a pristine tool overlaid with the wear progression seen in Region E of the 5 s ILCT workpiece. The numerical change in wear area of the rake-face is reported in Fig. 11. For both Regions A and E, the 5 s ILCT workpiece experiences more severe tool wear when compared to the 120 s ILCT workpiece. This trend is in line with the data in Secs. 4.14.3. The combination of a low feed-per-tooth value and the presence of larger Al dendrites within the 5 s ILCT workpiece (Fig. 2) leads to elastic recovery of the workpiece material during micromilling. This elastic recovery coupled with the higher cutting forces (Fig. 7) is responsible for the 5 s ILCT workpiece having a greater tool wear. The fracture-dominated failure due to the Si-rich regions in the 120 s ILCT workpiece is responsible for its lower tool wear. For the 5 s ILCT workpiece, the relative tool wear trends between Regions A and E are also not surprising since they are in line with the trend seen in the microstructural heterogeneity. However, for the 120 s ILCT workpiece, in spite of the microstructural homogeneity between Regions A and E, one observes a higher tool wear in Region E. This warrants a more careful tribological investigation in the future.

Fig. 10
Progressive wear of the micro-endmill, including (a) optical image of (a) pristine tool, (b) tool after 35 micromilled slots, and (c) overlay of profiles of cutting edge during tool wear study (Note: Data are from Region E of the 5 s ILCT workpiece)
Fig. 10
Progressive wear of the micro-endmill, including (a) optical image of (a) pristine tool, (b) tool after 35 micromilled slots, and (c) overlay of profiles of cutting edge during tool wear study (Note: Data are from Region E of the 5 s ILCT workpiece)
Close modal
Fig. 11
Progression of tool wear as a function of micromilled slots within Region A and Region E of the (a) 5 s ILCT workpiece and (b) 120 s ILCT workpiece
Fig. 11
Progression of tool wear as a function of micromilled slots within Region A and Region E of the (a) 5 s ILCT workpiece and (b) 120 s ILCT workpiece
Close modal

5 Conclusions

This paper was aimed at exploring the feasibility of using micromilling techniques to detect microstructural variations in WAAM structures. The following specific conclusions can be drawn from this work:

  • The data reveal that when WAAM processing parameters clearly affect the underlying microstructure (in this case the interlayer cooling time variation being the key parameter), micromachining responses are indeed capable of detecting these changes.

  • Cutting force signals are the premier candidate for in situ extraction of information regarding microstructural variations during WAAM. In particular, in situ analysis of the cutting force frequency spectrum can provide critical information regarding dominant failure mechanisms that are related to the underlying microstructure. The magnitude of the resultant cutting force is another parameter that also correlates closely with the microstructural attributes.

  • Other key micromilling responses such as surface roughness, burr formation, and tool wear also correlate well with underlying microstructural variations. However, these may serve as complementary responses to the cutting force signal due to the difficulty in implementing them for in situ measurements.

  • Given the complicated nature of machining induced failure in materials, the results should be interpreted carefully since it may not correlate strongly with the conventional measures such as hardness characterization.

While the work was focused on Al-4043, it highlights the critical building blocks for implementing this strategy in metal alloys where harder eutectic phases get precipitated along dendritic boundaries during direct-energy deposition processes, such as WAAM. However, future research efforts spanning multiple metal alloys systems and micromachining processes (milling/drilling, etc.) are needed to mature the proposed concept.

Acknowledgment

The authors acknowledge the support from U.S. National Science Foundation (NSF) CAREER Award CMMI 13-51275. J.F. Nowak acknowledges support from the U.S. National Science Foundation Graduate Research Fellowship Program (NSF GRFP) under Grant No. DGE-1247271. Glenn Saunders (Sr. Research Scientist, Manufacturing Innovation Center at Rensselaer Polytechnic Institute) is acknowledged for setup of the WAAM system and initial project discussions. Professor Shankar Narayanan (Mechanical, Aerospace, and Nuclear Eng. Dept., Rensselaer Polytechnic Institute) is acknowledged for aiding with the offline emissivity calibration experiments.

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