3D-Finite element simulation and image processing based prediction of width and height of single layer deposition by micro-plasma transferred arc process

Sagar H. Nikam1, Neelesh Kumar Jain2 @

1 Research Scholar; 2 Professor, Discipline of Mechanical Engineering

Indian Institute of Technology Indore, Simrol 453 552 (MP) India

@ Corresponding author: nkjain@iiti.ac.in; Phone: +91 7324 306 989; Fax: +91 731 2438 721

Abstract

This paper reports on prediction of width and height of single layer deposition by micro-plasma transferred arc (µ-PTA) deposition process. It involved three-dimensional finite element simulation (3D-FES) of the melt pool using specific power of the micro-plasma, travel rate of worktable, deposition material feed rate, temperature dependent properties of the substrate material, calculation of its dimensions using image processing technique and prediction of deposition width and height. The proposed approach was validated by comparing the predicted values with the corresponding experimental values for single layer deposition of AISI P20 tool steel using different combinations of the µ-PTA deposition process parameters. Values of average errors as 6.11% and 7.15% for width and height of the single layer deposition validates the simulation predicted results. Study of influence of µ-PTA process parameters on deposition geometry revealed that micro-plasma power and travel rate of worktable significantly affect the width and height of the deposition layer. The proposed approach will be of great help in selecting the optimum values of deposition process parameters for any combination of substrate and deposition material thus improving accuracy and productivity of the additive layer manufactured parts.

Keywords: Layered manufacturing; metallic deposition; micro-plasma; finite element simulation; image processing; P20 tool steel.

Nomenclature

A1

Deposition area (mm2)

A2

Diluted area (mm2)

Ad

Cross-section area of the deposition material (mm2)

Ae

Area of deposition geometry having elliptical shape (mm2)

C

Correction factor

Cps

Specific heat of the substrate material (J/Kg K)

D

Dilution (%)

E

Electric field of micro-plasma arc (V/m)

Ees

Electric field for the substrate material (V/m)

fd

Feed rate of the deposition material (mm/s)

fw

Travel rate of worktable (mm/s)

h

Height of the deposition (mm)

hconv

Convective heat transfer coefficient (W/m2 K)

J

Current density of the micro-plasma (A/m2 )

Ks

Thermal conductivity of the substrate material (W/m K)

Ks*

Modified thermal conductivity of the substrate material after taking into account the Marangoni effect (W/m K)

L

Stand-off distance between the substrate material and micro-plasma torch (m)

n

p

Unit vector normal to the surface of the melt pool

Specific power of the micro-plasma (= J E ) (W/m3)

q(x,y)

Theoretical volumetric heat flux density at a point having coordinates x and y (W/m3)

q

Actual volumetric heat flux density (W/m3)

ro

Radius of the micro-plasma arc (m)

T

Instantaneous temperature in the melt pool (K)

Ti

Ambient temperature (K)

Tms

Melting temperature of the substrate material (K)

t

Time at time at which micro-plasma arc strikes the substrate material (s)

V

Applied voltage to micro-plasma (volts)

Vd

Volumetric deposition rate (= f Aw) (mm3/s)

wm

Width of the melt pool (mm)

w

Width of the deposition (mm)

εs

Emissivity of the substrate material

Ƞ

Thermal efficiency of micro-plasma transferred arc (%)

ρs

Density of the substrate material (Kg/m3)

σ

Electrical conductivity of the micro-plasma channel (ohm-1 m-1)

σes

Electrical conductivity of the substrate material (ohm-1 m-1)

σsbc

Stefan-Boltzmann constant (5.67 x 10-8 W/m2 K4)

1. Introduction

Additive layer manufacturing (ALM) is a bottom-up approach for manufacturing the components having complex geometry, to change geometry of the existing products by adding delicate features and to repair or remanufacture or recondition the damaged components [1, 2]. It is the fastest emerging technology in manufacturing industries. ALM processes generally uses electron beam, laser beam or arc for deposition of metals and alloys. Electron and laser beam based deposition processes yield higher accuracy of deposition but give low deposition rate and are very costly. This restricts application of these processes for micro and meso scale deposition of metallic materials. Whereas, arc-based deposition processes have high deposition rate and are cheaper but, yield lower accuracy of deposition. Therefore, arc-based deposition processes are mainly applicable for bulk deposition of metallic materials. Consequently, there exists a wide gap between capabilities of the beam-based and arc-based deposition processes in terms of deposition quality, deposition rate, energy consumption and equipment cost which is presented in Table 1. Some researchers have tried to bridge these gaps either by (i) exploiting applicability of the existing processes for manufacturing complex parts made of functionally graded materials [3] and porous material [4]; or (ii) using a novel heat source such as micro-plasma transferred arc (µ-PTA) [5]. It can be observed from Table 1 that capabilities of µ-PTA deposition process significantly bridges the gap in capabilities of laser-based and arc-based deposition processes. Moreover, it is energy efficient, material efficient and highly economical then existing deposition processes.

Table 1 Comparison of process capabilities of lased-based, arc-based and µ-PTA based deposition process for metallic deposition [5-7].

Criteria

Laser-based deposition process

Arc-based deposition process

µ-PTA deposition process

Deposition rate

754 mm/min

2880 mm/min

1275 mm/min

Quality of deposition

Very good

Poor

Medium

Energy consumption per unit travel length of substrate material

48-75 J/mm

1490 J/mm

270 J/mm

Energy consumptions per unit length of deposition material

30-48 J/mm

50 J/mm

20 J/mm

Equipment cost

Very high

Medium

Low

Prediction of deposition geometry of any additive layer manufactured components greatly helps in analyzing and improving their accuracy, selection of optimized process parameters, and reduction in extensive and expensive experimentations. Consequently, this requires accurate prediction of melt pool size and estimation of the deposition geometry parameters which can be accomplished accurately and precisely using finite element simulation. Peyre et al. [8] did numerical and analytical modeling to predict the dimensions of the melt-pool and temperature for multi-layer deposition using laser based direct metal deposition process. The results were validated and compared with experimental results carried out for depositing a wall consisting of 25 layers. Vasquez et al. [9] proposed a finite element simulation to predict the shape and size of the melt pool. The influence of deposition parameters on the melt pool was simulated and validated experimentally for laser based powder deposition process. Toyserkani et al. [10] proposed a model that can predict the deposition geometry in terms of deposition process parameters. Influence of travel speed and feed rate of deposition material on shape and size of the molten pool were simulated and validated with experimental results. Lalas et al. [11] proposed an analytical model to estimate clad geometry parameters in terms of speed of deposition process and feed rate of deposition material for laser based deposition process. It was capable of predicting the clad width and depth with good accuracy. Zaho et al. [12] performed transient heat transfer simulation for weld-based rapid prototyping to study the distribution of temperature within the melt pool. The influence of deposition direction on temperature and temperature gradient was simulated and validated experimentally. Gan et al. [13] did a finite element simulation for prediction of size of the melt pool and thermal stresses generated during plasma-sprayed coating process. Results obtained from simulation were in reasonable agreement with the experimental results. Abid and Siddique [14] performed the non-linear transient thermal simulation of single track deposited by metal inert gas deposition process. The simulation results were validated experimentally to study the effect of different angular deposition positions on temperature distribution within melt pool and residual stresses in deposited material. Goyal et al. [15] developed analytical model to predict the geometry parameters and temperature of the weld pool for pulsed gas metal arc welding process. Traidia and Roger [16] did finite element simulation of pulsed gas tungsten arc welding process. They numerically investigated the weld pool dynamics for various deposition process parameters and compared with experimental results. Wu et al. [17] carried out three-dimensional simulation to predict the weld pool behavior and keyhole formation mechanism during plasma arc welding process. Lee [18] developed finite element based model to study temperature distribution in the substrate and deposition material for low pressure plasma deposition process. Temperature distribution profile was simulated and experimentally verified for deposition of copper on nickel substrate.

From the past work it can be concluded that the geometry of single layer deposition by an ALM processes mainly depends upon deposition process parameters, temperature distribution within the melt pool, and properties of substrate and deposition materials. Influence of these on the melt pool and deposition geometry need to be studied by developing a three-dimensional finite element simulation. Consequently, objectives of the work reported in this paper are: (i) three dimensional finite element simulation (3D-FES) of the melt pool of single layer deposition by µ-PTA deposition process using specific power of the micro-plasma (p), feed rate of the deposition material (fd), travel rate of worktable (fw), and temperature dependent properties of the substrate material; (ii) measuring the dimensions of the melt pool using image processing technique; (iii) prediction of deposition height and width. Outcome of the present work will be of great help in analysis and improvement of accuracy and productivity of the parts manufactured by µ-PTA deposition process and optimization of its process parameters to reduce extensive and expensive experimentations.

2. Three-dimensional finite element simulation of geometry of single layer deposition

Geometry of deposition obtained during µ-PTA process mainly depends on different parameters such as specific power of the micro-plasma (p), feed rate of the deposition material (fd), travel rate of worktable (fw), and temperature dependent properties of the substrate material. Figure 1 illustrates the geometry of the single layer deposition which is characterized by deposition width (w), deposition height (h), bead root angle (θ) and percentage dilution which is ratio of diluted area (A2) to summation of the deposited area (A1) and diluted area (i.e. A2/[A1 + A2]).

Fig.1 Characterizing parameters of geometry of single layer deposition [19].

Quality of deposition can be improved by predicting optimum deposition geometry parameters values in terms of deposition process parameters. In the present work, melt pool in the substrate material has been simulated by 3D finite element simulation using heat conduction governing equation along with appropriate boundary conditions through the ANSYS (version 13.0) software [20] and by making the following assumptions:

· Micro-plasma torch travels perpendicular to the substrate material with constant value of stand-off distance.

· The substrate material is at ambient temperature Ti (= 298 K) at the start of deposition process and the boundary conditions are applied to the substrate material.

· Width of deposition ‘w’ is approximately equal to predicted width of the melt pool ‘wm’ which is calculated in the absence of the deposition material [10].

· Deposition height ‘h’ is calculated using the predicted width of the melt pool.

The size of melt pool depends upon the amount of heat transferred from heat source to substrate material and amount of heat loss primarily through conduction from melt pool to the substrate material though solid-liquid interface as shown in Fig. 2.

Fig. 2 Top view of the melt pool in substrate material [19].

Therefore, the governing equation for heat conduction has been used in the present work to predict the temperature distribution within the melt pool and it is expressed as,

In which, ρs is the density of the substrate material (Kg/m3); Cps is the specific heat of the substrate material (J/Kg K); t is the time at which micro-plasma arc strikes the substrate material (s); q is the actual volumetric heat flux density (W/m3); Ks* is the modified thermal conductivity of the substrate material considering the Marangoni effect (W/m K); and T is instantaneous temperature in the melt pool (K). Following are the boundary conditions used in 3D finite element simulation of single layer deposition by µ-PTA process:

· Heat source: Gaussian distribution for the heat flux density of the micro-plasma arc as proposed by Nikam and Jain [21] has been used in present study. Theoretical value of volumetric heat flux density q(x,y) (W/m3) having Gaussian distribution at a point having coordinates ‘x’ and ‘y’ with respect to the center of the heat source of radius ‘ro’ (m), is given by

Here, p is the specific power of the micro-plasma (W/m3) which is the product of micro-plasma current density ‘J’ (A/m2) and electric field for µ-plasma arc ‘E’ (i.e. p =JE). Micro-plasma density can be approximated as product of electric field in the substrate material ‘Ees’ (V/m) and electrical conductivity of the substrate material ‘’ (i.e. J ≈ σes Ees) and its value as 1.5 x 104 ohm-1 m-1 has been used for the AISI P20 tool steel material [22]. Since, stand-off distance between the substrate material and the µ-plasma torch is small therefore electric field in the substrate material ‘Ees’ (V/m) has been approximated by the electric field of the µ-plasma arc ‘E’ which in turn can be approximately taken as V/L i.e.

Here, ‘V’ is the applied voltage to micro-plasma (volts) and ‘L’ is the stand-off distance between the substrate material and micro-plasma torch (m). Actual value of volumetric heat flux density ‘q’ (W/m3) is calculated after considering radiation losses to the atmosphere. This loss is taken into account by multiplying thermal efficiency ‘ƞ’ of micro-plasma arc with theoretical value of volumetric heat flux density ‘q(x,y)’ given by Eq. 2, i.e.

In the present work, 60% value of the thermal efficiency was used for 3D-FES. This value was obtained by Nikam and Jain [21] after running simulations in the range of 40% to 90% with an increment of 5%.

· Marangoni flow: This effect has been considered by modifying thermal conductivity of the substrate material as suggested by Alimardani et al. [23].

Where, Ks is the thermal conductivity of the substrate material (W/m K); C is the correction factor and its value of 2.5 has been used as mentioned by Lampa et al. [24].

· Radiation and convection losses from the substrate material are considered by combining them as follows

Here, εs is the emissivity of the substrate material; σsbc is the Stefan-Boltzmann constant (5.67 x 10-8 W/m2 K4); Tms is the melting temperature of the substrate material (K); Ti is the ambient temperature (298 K); hconv is the convective heat transfer coefficient (W/m2 K).

· Temperature dependent properties of the substrate material: Temperature dependent material properties for the AISI P20 tool steel substrate material namely thermal conductivity, density and specific heat (presented in Table 2) were used in the 3D-FE simulation. Figure 3 illustrates variation of thermal conductivity, density and specific heat of AISI P20 tool steel with respect to the temperature.

Table 2 Temperature dependent material properties of the substrate and deposition material (i.e. AISI P20 tool steel) [25].

Material properties

Substrate material

Temperature ‘T’ (K)

AISI P20 Tool steel

Thermal conductivity (W/m K)

273-1700

6.8354+0.0156T

Density (Kg/m3)

273-1700

8137.8–0.4997T

Specific heat (J/Kg K)

273-873

554.17+0.0176T

873-1700

401.3+0.1821T

(a) (b)

(c)

Fig. 3 Variation of temperature dependent properties of the substrate material AISI P20 tool steel with temperature (a) thermal conductivity; (b) density and (c) specific heat [25].

2.1 3D-finite element simulation of melt pool in the substrate material

The present study was mainly focused on distribution of temperature within the melt pool formed on substrate material during single layer deposition. Consequently, a 3D-finite element simulation was done on substrate material of AISI P20 tool steel having size of 100 mm × 180 mm × 9 mm. Accuracy of the 3D-FES and computation time required to predict temperature distribution within the melt pool is mainly dependent on its mesh shape and size. Therefore, in present work 150 simulations were done by varying micro-plasma power between 350-450 W, travel rate of worktable between 40-100 mm/sec and the mesh size between 1-0.1 mm (i.e. from coarser to finer meshing) with a decrement of 0.1 mm. Based on the obtained temperature distribution values, it was observed that simulations done using coarser mesh under predicted the temperature distribution. While simulations done using finer mesh predicated temperature distribution within the range of melting point of substrate material (i.e. 1900-2200K). Therefore, the geometry of the substrate material has been discretized with cubic 8-node elements having element edge length of 0.1 mm as shown in Fig. 4. The discretized geometry consists of 20,62,500 elements having total 21,70,648 nodes. To analyze the temperature distribution within the melt pool element type as SOLID70 has been used in ANSYS software. The movement of the micro-plasma torch was simulated along deposition direction i.e. along y-axis.

Fig. 4 Meshed geometry of substrate material used in 3D-FES to simulate temperature distribution within the melt pool (inset showing its magnified view).

2.2 Experimental validation of thermal cycles and temperature distribution

Figure 5 depicts the schematic of experimental apparatus used to measure thermal cycles in single layer deposition by µ-PTA wire deposition process. The deposition material as AISI P20 tool steel in wire form having 0.3 mm diameter has been deposited on rectangular geometry substrate of similar material having dimensions 100 mm × 180 mm × 9 mm. The process parameters such as micro-plasma power as 350 W and travel rate of worktable as 40 mm/min were used for experimental validation. Figure 6 depicts the location and orientation of K-type thermocouple used to measure thermal cycle by µ-PTA wire deposition process. Results of 3D-FES were validated by comparing simulated thermal cycles with the experimented thermal cycles recorded by three thermocouples fixed at different locations on the substrate material.

Fig. 5 Schematic of experimental apparatus used to measure thermal cycles for single deposition by µ-PTA wire deposition process.

Fig. 6 Location and orientation of three K-type thermocouples used to measure thermal cycles by µ-PTA wire deposition process.

Figure 7 shows comparison of the 3D-FE simulated and experimentally recorded thermal cycles which shows very good agreement between them. Figure 8 shows temperature distribution within the melt pool formed on surface of the substrate material. This confirms that the temperature in the melt pool is in the range of 1900-2200 K which is above melting point of the substrate material.

(a)(b)

(c)

Fig. 7 Comparison of 3D-FE simulated and experimented thermal cycles recorded by the three thermocouples located at (a) TC1; (b) TC2; and (c) TC3.

Fig. 8 Temperature distribution within the melt pool simulated by 3D-FES.

2.3 Prediction of width and height of deposition

Width of the melt pool formed in the substrate material was predicted by capturing its image obtained from the 3D-FES as shown in Fig. 9 and by exporting it to the MATLAB software [26] for image processing. Following steps were used in processing of the captured image of the melt pool:

1. Number of pixels within the known width of the substrate material is measured. For example, as shown in Figure 9, for 100 mm width of substrate 243.00 pixels were measured.

2. Number of pixels within unknown width of the melt pool is measured. For example, as depicted in the inset of Figure 9, for unknown width of the melt pool 4.97 pixels were measured. The width of the melt pool ‘wm’ (mm) was predicted as follows

Therefore, width of the melt pool ‘wm’ and consequently width of deposition were calculated as

Fig. 9 Photographs of the melt pool obtained by 3D-FES and image processing to predict the width of melt pool (inset showing magnified size and shape of the melt pool).

Nikam et al. [19] have proposed an equation to calculate the height of deposition ‘h’ in terms of travel rate of worktable ‘fw’, dilution ‘D’, width of deposition ‘w’ and feed rate of the deposition material ‘fd’ by equating the deposition area ‘A1’ to the area of deposition geometry having elliptical shape ‘Ae’. i.e.

By rearranging Eq. 9, gives following expression for height of deposition ‘h’ (mm)

Where, ‘D’ is dilution (%); ‘fw’ is travel rate of worktable (mm/s); and ‘Vd’ is the volumetric deposition rate (mm3/s) which is equal to product of feed rate of deposition material ‘fd’ (mm/sec) and area of cross-section of the deposition material ‘Aw’ (mm2) i.e. Vd = fd Aw. Using this relation giving following expression for height of deposition

Values of width of melt pool were measured by image processing technique using the concept demonstrated in Eq. (8) and the deposition height was calculated using Eq. (11) for all the 15 experiments for single layer deposited using µ-PTA deposition process. Processed image to predict the deposition width for experiment no. 1 is shown in Fig. 9 while images for the experiments 2-15 are presented in Fig. A1 as included in appendix A.

3. Results and discussion

The experimental results obtained by Jhavar et al. [5] for deposition process carried out on substrate material (size 100 mm × 180 mm × 9 mm) of AISI P20 tool steel using wire of 0.3 mm diameter of similar material was used in present work. The deposited single layer was cross-sectioned in perpendicular to deposition direction and then the samples were prepared using standard metallographic procedure to measure the deposition width and height under Leica DM IL compact inverted microscope. Table 3 presents the predicted and experimentally measured values of width and height of deposition for fifteen experimental runs along with corresponding percentage error between them. Figure 10 depicts the comparison of predicted and experimentally measured values of deposition width (Fig. 10a) and deposition height (Fig. 10b) for different experimental runs. The results revealed that almost all the predicted results are in close agreement with the experimental results. Minimum error between predicted and experimentally measured value of deposition width is 0.52% for experiment no. 8 and that of deposition height is 0.87% for experiment no. 7 (Table 3) whereas average values of error for deposition width is 6.11% and for deposition height is 7.15%. This error may be due to neglecting the influence of feed rate of the deposition material for calculating width of melt pool and assuming shape of deposition geometry as elliptical throughout deposition length for calculating height of deposition.

Table 3 Predicted and experimental values of width and height of deposition for different experimental runs.

Exp. no.

Process parameters

Width of deposition (mm)

% error

Height of deposition (mm)

% error

Micro-plasma power (W)

Travel rate of worktable (mm/min)

Deposition material feed rate (mm/min)

Experi-mental value

Predicted value

Experi-mental value

Predicted value

1

350

40

1700

1.9

2.05

7.32

1.8

1.68

-7.19

2

350

50

1.8

2.01

10.45

1.6

1.37

-16.78

3

350

63

1.5

1.83

18.03

1.3

1.19

-8.84

4

350

80

1.4

1.55

9.68

1.2

1.11

-8.06

5

350

100

1.2

1.21

0.83

1

1.14

12.13

6

400

40

2

2.06

2.91

1.6

1.67

4.26

7

400

50

2

1.95

-2.56

1.4

1.41

0.87

8

400

63

1.9

1.91

0.52

1.1

1.14

3.88

9

400

80

1.8

1.85

2.70

0.9

0.93

3.27

10

400

100

1.9

1.82

-4.40

0.8

0.76

-5.74

11

450

40

2.1

2.08

-0.96

1.5

1.66

9.37

12

450

50

2.1

1.95

-7.69

1.3

1.41

7.95

13

450

63

2

1.93

-3.63

1

1.13

11.70

14

450

80

1.9

1.85

-2.70

0.9

0.93

3.27

15

450

100

2.1

1.79

-17.32

0.8

0.77

-3.99

(a) (b)

Fig. 10 Comparison of predicted and experimentally measured (a) width of deposition; and (b) height of deposition for different experiments.

3.1 Influence of micro-plasma power on width and height of deposition

Figure 11 present the comparison of variation of the predicted and experimentally measured deposition width (Fig. 11a) and deposition height (Fig. 11b) with micro-plasma power for constant value of travel rate of worktable as 63 mm/min and deposition material feed rate as 1700 mm/min. It can be observed that as micro-plasma power increases, width of deposition increases (Fig. 11a) and height of deposition decreases as shown in Fig. 11b. This is due to the fact that increase in micro-plasma power increases the amount of heat transferred to substrate and deposition materials. It also influences the fluid properties of deposited layer which leads to increase in temperature of the melt pool and decrease in viscosity of the molten deposition material thus increasing width of deposition and decreasing height of deposition.

(a) (b)

Fig. 11 Variation of the predicted and experimentally measured (a) width of deposition; and (b) height of deposition with micro-plasma power for the constant values of travel rate of worktable and deposition material feed rate.

3.2 Influence of travel rate worktable on width and height of deposition

Figure 12 show comparison of variation of the predicted and experimentally measured deposition width (Fig. 12a) and deposition height (Fig. 12b) with travel rate of worktable for constant value of micro-plasma power (350 W) and deposition material feed rate (1700 mm/min). It can be observed that the width of deposition decreases as the travel rate of worktable increases (Fig. 12a). This is due to the fact that faster movement of the worktable reduces the interaction time between the substrate material and micro-plasma arc which reduces the amount heat available to form melt pool in the substrate material thus decreasing width of deposition. Also, the height of deposition reduces with increase in travel rate of worktable (Fig. 12b). But, this is due to reduction in interaction time between the deposition material and substrate material which reduces quantity of the deposition material available to form the deposition.

(a) (b)

Fig. 12 Variation of predicted and experimentally measured (a) width of deposition; and (b) height of deposition with travel rate of worktable for the constant values of micro-plasma power and deposition material feed rate.

4. Conclusions

This paper reported on 3D-FES of melt pool in single layer deposition of AISI P20 tool steel on similar substrate material by µ-PTA deposition process, use of image processing technique to predict width of deposition and subsequently calculation of height of deposition considering deposition. Very close agreement was observed between the predicted results and the earlier published experimental results thus validates the effectiveness of the proposed approach. Following main conclusions can be drawn from present work:

· Finite element simulation has ability to simulate the temperature distribution within the melt pool using specific micro-plasma power, travel rate of worktable, deposition material feed rate and temperature dependent material properties of the substrate and deposition materials.

· Image processing technique has ability to predict the size of the melt pool with good accuracy.

· The predicted values are in good agreement with the experimental values with average error of 6.11% and 7.15% for width and height of deposition respectively thus validated the accuracy of the 3D-FES.

· Some predicted results are not in close agreement with the experimental results. This may be due to neglecting the influence of feed rate of deposition material while predicting the width of deposition and assuming deposition geometry of elliptical shape while calculating height of deposition.

· Study of influence of µ-PTA deposition process parameters on width and height of deposition revealed that they are mainly influenced by micro-plasma power and travel rate of worktable.

· The proposed approach will be of great help in selection of optimum values of deposition process parameters for any combination of the substrate and deposition material thus improving accuracy and productivity of the additive layer manufactured parts.

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Appendix A

Exp. 2 Exp. 3

Exp. 4 Exp. 5

Exp. 6 Exp. 7

Exp. 8 Exp. 9

Exp. 10 Exp. 11

Exp. 12 Exp. 13

Exp. 14 Exp. 15

Fig. A1 Photographs of the melt pool obtained from image processing to predict the width of melt pool boundary along the deposition direction for different experimental runs.

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