18th International Conference Metal Forming 2020 Project

2D thermal finite element analysis of laser cladding of 316L+WC

Composite coatings

Seifallah Fetnia , Tommaso Maurizi Enricib, Tobia Niccolinia, Son Hoang Trana, Olivier Dedryb, Ruben Jardina, Laurent Duchênea, Anne Mertensb, Anne Marie Habraken a,*

a University of Liege, MSM Unit, Allée de la Découverte, 9 B52/3, B 4000 Liège, Belgium

b University of Liege, MMS Unit, Allée de la Découverte, 9 B52/3, B 4000 Liège, Belgium

* Corresponding author. E-mail address: anne.habraken@uliege.be

Abstract

In this work, a 2D-thermal model of laser cladding (also called Directed Energy Deposition) of composite coating (316L stainless steel reinforced by hard WC carbide particles) was developed. The temperature field and its time evolution were computed by the Finite Element software Lagamine and then compared to experimental measurements. Indeed, in a related work, the effects of the high temperatures on the WC particles in contact with the molten metal and the resulting microstructure at the end of the fabrication were evaluated by means of different experimental techniques.

Thus, correlations between simulated thermal histories and microscopic analysis as well as thermocouple records were established. The temperature distribution in the substrate allows the prediction of the depths of the melt pool as well as the thermal histories of the different parts of the clad. Such a model is of great interest and can be applied in situ-calculations to offer quick data about the influences of the process parameters on the properties of the built part.

© 2020 The Authors. Published by Elsevier B.V.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Peer-review under responsibility of the scientific committee of the 18th International Conference Metal Forming 2020 Project.

Keywords: 316L+WC composite coating ; Laser Cladding; Thermal analysis ; Lagamine ; melt pool depth

Introduction

In recent years, researches on additive manufacturing computer aided-design have been extended to optimize microstructures of materials which are selected for advanced applications: aerospace, manufacturing, medical etc. [1, 2, 3]. Laser cladding is a promising additive manufacturing (AM) process that allows the building of 3D objects composed of multi-layer materials.

Comparing to traditional techniques, direct energy deposition (DED) has various advantages especially high heating and cooling rates, fast deposition speeds and high solidification velocities. These features allow the generation of fine microstructures and small heat affected zones (HAZ) [4, 5, 6].

Laser cladding processes have been applied on various alloys and substrates: aluminum and titanium alloys [5], high speed steels (HSS) [7, 8] and stainless steels like 316L and 420 [9, 10].

Austenitic steels, especially 316L, have been widely used thanks to their excellent mechanical and corrosion properties. Nevertheless, the associated wear resistance is a critical limiting factor to use them in advanced conditions and prolonged applications when good tribological properties are needed. Therefore, reinforcing the 316L metallic matrix constitutes a promising approach to obtain homogeneous microstructures and thus good mechanical behavior and long-term service. Different approaches have been followed. Zhang et al. [9] studied the reinforcing of the matrix by the molybdenum as a refractory material in order to enhance corrosion resistance. Other techniques have already been used to reinforce the metal matrix by adding hard carbides. The reinforcements must exhibit chemical affinity with the matrix, high hardness and high melting temperature [1, 2]. Tungsten carbides have been reported as good candidates to improve wear resistance of laser clad materials [11].

Researches of Metallic Materials Science Unit (MMS), of the University of Liège, are giving great attention to reinforcing 316L matrix by tungsten carbides (WC) which are very abrasion resistant and have optimized properties at room and high temperatures. That constitutes a novel approach [6]. Different WC concentrations have been applied up to 20% wt.

DED process, as well as similar techniques like LENS (Laser Engineered Net Shaping) or EBM (electron beam melting), are still under improvement in front of several challenges: possibilities of appearance of cracks, heterogeneous microstructures, dilutions etc. This is due to complex phenomena occurring during deposition depending on various processing parameters (laser power, scanning speed, preheating conditions, powder quality, laser-powder interactions etc.) [12, 13]. Studying the influence of these various parameters in laboratory means high cost and too much time. Therefore, modelling is a key to optimize the manufacturing process parameters and allow predicting clad properties like formed microstructures, HAZ, stressing distribution, clad geometry, melting pool etc. In literature, a number of 2D and 3D numerical models have been developed to apply them on various materials such as aluminum/titanium alloys [14], HSS [4, 15, 16] and stainless steels [9].

In this study, a particular attention is paid to the application

of DED process on composite coatings. The heat accumulation resulting in high thermal gradient, as well as high cooling rate, has a direct impact on the evolution of material properties. Therefore, modelling is potentially required to reach a better control of processing parameters. As the thermal history is the key to predict the solidified microstructure, a 2D model of DED of 316L stainless steel and WC carbides is here developed. Correlations between predicted thermal histories and experimental analyses are established in order to validate the model.

Experimental set-up and measurements

DED process was performed using a 5-axis Irepa Laser Cladding (LC) machine, from Sirris Research Center, equipped with a Nd-Yag laser source. The maximum operating power is up to 2000 W. The nozzle angle is 90° while the angle of powder injection is between 38 and 45°. For DED deposition of 316L-20%WC composite coatings, a laser power of 570 W was chosen. The laser beam radius was 0.75 mm. The scanning speed was set between 270 and 290 mm/min. The obtained deposit consisted in 19 layers with a dimension of 35 x 35 x 12.37 mm (Fig.1). Each layer is composed of 26 tracks. The process parameters were selected in the aim to get dense deposits and are summarized in Table 1.

Table 1. Summarize of the DED parameters used to build 316L-WC samples.

Parameter	Value
Laser power [W]	570
Laser beam radius rL [mm]	0.75
Scanning speed [mm/min] Number of tracks/layer Building time [s]	270-290 26 3819.47

The thermo-physical properties of the material used for substrate (316L) were measured by MMS (see Table 2), while those of the clad are mentioned in a related work [17]. They show a strong variation compared to pure 316L. These properties were established using dilatometry, differential scanning calorimetry (DSC) and laser flash diffusivimetry.

In order to record the evolution of the temperature in the substrate, 4 thermocouples were used during deposition as illustrated in Fig. 1c (by red color). The whole diameter of each thermocouple is 2 mm. The recorded curves (temperature versus time) were useful for model validation.

a

b	c

Fig. 1. Illustration of the manufactured sample by DED process.

Model of DED process

Finite element code for modelling

The thermal field of LC process was computed by the nonlinear finite element (FE) code Lagamine, developed at the University of Liège. Authors of this paper have total control of the nonlinear computing, constitutive laws and thermal elements. In addition to that, numerical scripts have been developed and updated to ensure death and birth techniques and post-processing analysis.

Table 2. Thermal Properties of 316L used for substrate.

Temperature [°C]	Thermal conductivity [W/m.K]	Heat capacity [J/g.K]	Density [g.cm-3]
30	15.24	0.45	8.03
100	17.16	0.46	8
204	19.61	0.48	7.96
302	21.15	0.49	7.91
404	23.02	0.51	7.87
509	24.74	0.52	7.82
609	26.71	0.55	7.77
709	28.09	0.55	7.72
806	28.1	0.55	7.68
905	29.45	0.56	7.63
1003	30.02	0.56	7.58
1102	30.92	0.56	7.54
1201	34.43	0.58	7.48
1301	34.98	0.55	7.43

Heat transfer equations

The temperature distribution (T) in the clad and substrate (316L) was computed based on the classical formulation of
heat transfer:

(1)

Here k is the thermal conductivity (W/m.K), Qint (W/m3) the density of power generated in the workpiece, ρ the density [g.cm-3] and Cp the apparent heat capacity [J/g.K].

Surface heat exchange by convection and radiation was defined by the following equation:

(2)

Where h is the heat transfer coefficient in W/m-2K-1. Ta is the room temperature, ɛ the emissivity coefficient Stefan-Boltzmann constant (W/m-2.K-4). Qr is the laser heat flux expressed as:

(3)

here 𝜷 is the absorption factor, P the laser power and rL the laser beam radius. This coefficient loses his physical meaning in 2D modelling, so it needs to be correctly adjusted. It was fitted by comparing the experimental and numerical curves of substrate temperature (Thermocouple T3 records).

Assumption in 2D modelling: one track represents one layer

For 2D modelling (plane ZY), the assumption that the middle track of each layer can be representative for all tracks is made. The influence of the 25 other tracks on the middle one as well as the out of plane thermal flow has to be compensated by adjusted boundary conditions. The physic meaning of the absorption coefficient on the laser power is lost, while heat convection and radiation emissivity are adjusted by inverse modelling (see further discussion about 2D models in section 4.3). The thermal field of the clad would be inferred from those of the middle section. The section (YZ) in Fig.1 was considered for the model. The records of thermocouple T3 (in the bottom of substrate) are selected because it is the nearest one to the middle track. The records during clad deposition are illustrated in Fig. 2. As mentioned, the thermocouple T3 is in the bottom of the middle track, that’s why the maximum peaks correspond to the middle tracks of each of the 19 layers. In the real process, the laser performs one layer by making several tracks. Then, it moves along the (Z) direction with a shift to start the following layer. However, in 2D modelling, there is only one track which is performed for each layer. The time obtained in the simulation to make the 2D sample would be much lower than the real time for sample manufacturing (3819.47s) as we keep the real laser velocity for the track simulation. In order to compare experimental and computed substrate temperature evolution in one single curve, the experimental curve was scaled to the total simulation time. Note that a virtual idle time was also optimized to recover both melt pool depth and thermal history recorded at thermocouples. As already demonstrated in [4], a calibrated 2D model can help to understand microstructure genesis through the computed thermal history of material points.

Fig. 2. T3 temperature substrate during all deposition process.

Mesh procedure

The mesh used to predict thermal field evolution during DED is shown in Fig. 3. Three zones can be distinguished as follows. First, a refined mesh is applied on the clad. Here, an element width of 0.7 mm is selected so that laser heat flux can be applied on two elements. It should be noted that, even if the laser beam radius is 0.75 mm, the power density is considered constant for only a radius of 0.7mm. Then, a transition mesh zone within the substrate can be observed; here mesh is irregular to ensure good link between fine mesh of the clad and coarse substrate mesh.

Fig. 3. Illustration of the 2D mesh used to predict the thermal field during DED process.

Finally, the mesh of the substrate is coarse to reduce computing time. The mesh is composed of two kinds of elements. Indeed, quadrilateral elements are attributed for solid parts (thermo-mechanical), while interface linear elements are selected to consider radiation and convection with the environment. Only associated thermal degrees of freedom are activated. When laser advances, a group of 2 elements (1 solid elements and 1 interface convection/radiation elements or CONRA elements) under the laser beam diameter of 1.4 mm are switched on (Fig. 4.). Before the deposition of each layer, 2 adjacent solid elements and associated CONRA elements are activated. In DED modelling, this technique is called the element birth and death technique.

Fig. 4. Illustration of the element birth and death technique during DED pro-cess.

Results and Discussion

Experimental and numerical substrate temperature evolution

The temperature history measured by the thermocouple T3 is compared to numerical predictions. This comparison is of great importance to validate numerical results. As shown in Fig. 5, there is a close agreement between experimental curve and numerical one. This result was found after some manual trials. The sensitivity analysis of the model to input parameters such as absorption parameter, heat convection and radiation emissivity, guided the calibration process. The laser power is the prime parameter affecting the computed temperature field then the emissivity and heat transfer coefficient of substrate and clad respectively. It should be noted that CONRA parameters of the substrate play a significant role at the beginning of the deposition, then, when clad height becomes important, CONRA of the clad starts to influence the thermocouple curve. For conductivity and specific latent heat, the measured values are put in the code using the enthalpy formulation of the heat problem (ρ.Cp).

The model is able to predict the complex thermal evolution in the substrate during clad deposition based on various input parameters: material properties, heat transfer coefficients and processing parameters. The chosen substrate dimensions put in the 2D model (real values) ensure the dissipation of the heat after track deposition.

Fig. 5. Comparison between the history of the substrate temperature (recorded by thermocouple T3) and the numerical prediction.

Sensitivity analysis

The sensitivity of the model to a variation of the laser heat flux in shown in Fig. 6. Here, the term ’Ref’ means the numerical curve without any perturbation of input parameters. Increasing the laser power by 15% leads to a maximum temperature fluctuation of 64.5°C. This result proves the sensitivity of the model to power variation. Indeed, this increase in laser power generates an overheating of the deposit, that cannot be consumed by the environment (convection and radiation) or substrate (conduction). It is reminded that thermal conductivity of the clad was measured in MMS laboratory using as techniques: DSC, dilatometry and laser flash diffusivimetry. That can result in a maximum fluctuation of 7% of the obtained temperature-depending curve of the conductivity.

Fig. 6. Sensitivity of the model to a slight variation of laser power.

The influence of a variation of the clad conductivity by an amount of 7% was then checked. As illustrated in Fig. 7, the curves are almost superimposed. Indeed, a maximum difference of 8.6°C could be identified. This result confirms the small effect of a slight variation of conductivity and it is justified by the high conductivity of the clad, reached by the addition of 20% of tungsten carbides to the 316L matrix.

Fig. 7. Sensitivity of the model to measurement errors of the thermal conductivity.

Similarly, a variation of 3.5% in the amount of the apparent heat capacity of the clad (possible measurement error) is shown in Fig. 8. It results in a maximum difference of 16.5°C. This impact of a slight perturbation could be due to two different reasons; the high conduction of the clad on the one hand, and the high volume of the substrate (compared to the clad) on the other hand.

Fig. 8. Sensitivity of the model to measurement errors of the heat capacity.

Melt pool analysis

The computed thermal field during the deposition of the 16th layer is illustrated in Fig. 9. In Fig. 10, we propose to follow the thermal history of three points of interest representing two adjacent elements at each time: in the beginning of deposition (point 1), in the medium (point 2) and towards the end of the deposition (point 3). When the laser arrives to the midpoint of the 4th layer, the temperature is about 1730°C, this value slightly increases when reaching midpoints of layers 10 and 16 (about 1800 °C). These values exceed the melting point of the 316L matrix (1400°C), but are below the melting point of WC carbides (2800°C). Indeed, partial or total dissolution of the WC carbides takes place in contact with the molten metal during deposition. The well distributed WC carbides observed after fabrication in the reinforced matrix correspond to the core of the original WC powders [6, 11].

Fig. 9. Numerical thermal field during the deposition of the layer number 16; black area corresponds to the melt pool, and location of 3 points of interests.

Fig. 10. Numerical curves illustrating the thermal histories within the three points of interest defined in Fig. 9.

Note that heat accumulation within the melt pool leads to the enhancement of the Marangoni convection [9]. For each point, an appreciable decrease in temperature is noticeable when laser moves away.

The second peak, near 1400°C for each point, corresponds to the melting of the subsequent layer and is calculated at the interfaces between the two layers.

In order to validate the model, comparison between the real

melt pool depth and the numerical obtained one should be carried out. For this purpose, we propose in Fig. 11 an illustration of the method of determination of the melt pool size using optical microscopy. Indeed, an area containing the 5 x 19 subsequent middle tracks is selected (Fig. 11a). For the accuracy of the measurements, we consider the 5 medium tracks for each layer and we calculate the average value of the melt pool depth at each time. The strategy to carry out an estimation of the melt pool depth is illustrated in Fig. 11b and explained as follows. Based on the form the HAZs, two circular surfaces are drawn. Each surface is associated with a diameter. The circle (D2) refers to the circle inscribed in the melt pool. That gives estimation about the limit of the melt pool depth. The shape of the melt pool can be determined by the circle whose diameter is (D1). Experimental analyses show that the minimum height of the 19th layer is higher than the previous ones; 1.03 mm which exceeds by 21% the average value of the 18 previous tracks. This trend is explained by the fact that each layer is modified by the subsequent one. Therefore, we make the assumption that the D2 for a track i has to be corrected in order to obtain the real value during deposition. So, we consider a new value D2* defined as: D2* = α.D2, where α is the ratio defined as follows. Considering the influence of the 18th track by the 19th one,
α is obtained by dividing the height of the 19th track by that of the previous one. Then, each computed melt pool depth should be compared to the correspond D2*.

a

b

Fig.11. Experimental determination of the melt pool width using light optical microscopy. (a) Medium zone containing 5x19 superimposed tracks; (b) Strategy to carry out melt pool depth.

A comparison between the real view and numerical prediction of the 4th layer is shown in Fig.12. The two melt pools are very close in term of dimension (in particular the width). The real melt pool exhibits a shape which is well simulated by the 2D numerical tool. Experimental and numerical melt pool widths for all tracks were compared and were found in the same range.

a	b

Fig.12. Evolution of the numerical melt pool depth and the diameter D2*

During the clad deposition.

For a better visualization of the evolution of the melt pool depths, predicted by simulation, Fig. 13 shows the associated trend. In the same graph, evolution of the D2* diameter is also plotted to ensure that the predicted depth of the melt pool is still near to D2*. The trend of the computed melt pool is to increase to get closer to the experimental values (D2 limit) during the calculation. It can be observed in Fig. 13, that the melt pool depth becomes in the range of height of the finite elements after 5 layers then it slightly increases. For the first layers, the relatively low depths of the predicted melt pool could be explained by the fact that the transient heat received from the laser beam and heat losses by the different heat transfer modes (conduction, convection and radiation) are not well captured by the 2D model. Then, this behavior can also be due to the fact that the 2D model does not take in account the preheating effect due to other track deposition. So insufficient energy is stored for first layers. Indeed, in real process, when laser is cladding tracks from 1 to 12 for each layer, that preheats the substrate or the previous layer. Moreover, in laboratory, laser power is slightly varied in the beginning to adjust the laser heat flux, while it is considered constant during all the computation. In the presented thermal model. Experimental results show that the melt pool size remains relatively constant during deposition.

In literature, few 2D models have been developed to simulate multi-layer deposition by laser cladding processes [18, 19]. Yin et al. [18] developed a two-dimensional FE model in order to simulate the thermal behavior during the multi-layer deposition of 410 stainless steel by LENS process. To check the reliability of their model, they compared their obtained results with those of the 3D model previously developed by Wang et al. [12] applied on the same steel and using the same process. The 2D model was able to capture the temperature distribution and cooling rates as well as changes in the scanning speed. Therefore, our study gives new proof of the reliability of the 2D modelling applied on additive manufacturing to offer rapid and accurate data about the deposited material. Fast computing offers the possibility of the dynamic control of the process and then provides cost and time reduction. However, some precautions should be taken in account when modelling. Indeed, the laser heat flux should be adapted from 3D to 2D models. Absorption factor for stainless steel materials is between 0.3 and 0.4 [20]. Although the laser absorptivity allows well describing the 3D configuration, it loses its meaning in 2D state, and then needs to be fitted. Thus, the calibration of laser power in 2D modelling is a great importance. In fact, it is assumed that there is no heat loss along the perpendicular direction of the considered section. In addition to that, specific care must be paid to heat dissipation through the substrate and clad. Indeed, a higher heat loss is ensured in 3D compared to the 2D one because of the noticeable difference in thickness.

Fig.13. Evolution of the numerical melt pool depth and the diameter D2*

During the clad deposition.

Conclusions

In this work, a new 2D FE thermal model, for DED of 316L+WC composite coating was developed and is still under validation. The possibility of the use of such a multi-layer model to capture thermal phenomena is of great interest, when adjusting processing parameters before sample building. The model relies on real parameter: laser power, scanning speed, geometry and especially materials properties which have been measured in laboratory. Once the laser heat input is reasonably calibrated and boundary conditions, the 2D model can simulate actual phenomena.

However, some limitations of the 2D have also to be highlighted. Indeed, due to the difference in size between the real geometry and the 2D section, the model cannot well capture thermal field evolution for first layers. Then, such a model is suggested to be applied only on multi-layers LC. For one-layer manufacturing, 3D models are strongly suggested. This fact can constitute a main draw back for 2D models.

After a first validation of the model, it is planned to simulate preheating effects when laser arrives to the considered elements, in order to better adjust experimental and computed curves as well as melt pool depth. Then, the model can be applied on analysis of solidification modes during sample manufacturing. More correlations should be carried out between numerical results and microstructural observations (carbides distribution).

Acknowledgements

As Research Director of FRS-FNRS, AM Habraken acknowledges the support of this institution.

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