# A Review of the Development Status of Wire Arc Additive Manufacturing Technology

Authors: Xiaoxuan Chen; Xin Shang; Zirong Zhou; Sheng-Gui Chen
Journal: Advances in Materials Science and Engineering
 (2022)

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## Abstract

The WAAM molding process is complex, and the morphology, microstructure, and mechanical properties of the printed parts are affected by multiple factors. In this study, the heat input in the process of WAAM research has made the comprehensive elaboration. The numerical simulation method is used to simulate the molten pool in the process of WAAM, and the force, heat transfer, and velocity of the molten pool are analyzed. Path planning is also an important part of WAAM research, and there are many methods for additive manufacturing path planning, all of which are aimed at improving the dimensional accuracy and performance of the printed parts. Reasonable path planning can also be used for additive components with complex shapes and structures. This study comprehensively expounds on the research on WAAM path planning by scholars in recent years.


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## Body

## 1. Introduction

Additive manufacturing is nowadays one of the hot topics in the manufacturing and engineering worlds. The ability to create three-dimensional, complex, and near-net shape parts in a layer-by-layer deposition process is currently a major driving force for major breakthroughs. [1] Wire arc additive manufacturing (WAAM) is a metal additive manufacturing technology that uses an arc as a heat source to melt metal wires and manufacture metal components layer by layer on a substrate through a set additive path. The technology has the advantages of simple device, short production cycle, high material utilization rate, and low manufacturing cost. Moreover, larger parts can be made from common materials, so it has been widely used in aerospace, automotive, defense, and other fields. Electron beam powder bed deposition rates are 0.26 ∼ 0.36 kg/h, while WAAM deposition rates are 0.5 ∼ 4 kg/h. Although the accuracy is not high, and secondary processing is required after printing, the material utilization rate of WAAM can reach 90–100%. WAAM can be divided into gas metal arc welding (GMAW), gas tungsten arc welding (GTAW), and plasma arc welding (PAW) according to the process. The required welding process is determined by the user based on the requirement. [2–8] The WAAM process melts the metal wire for layer-by-layer deposition. The more layers are printed, the greater the heat accumulation of the printed parts. It has a great influence on the mechanical properties of the printed parts. How to reduce the influence of heat accumulation in the WAAM process is one of the current research hotspots. By changing the process method and then analyzing it through a series of characterization methods, the process parameters suitable for reducing heat accumulation are summed up. This is the research method of most scholars at present. Similarly, due to the complex physical process of WAAM, it is difficult to explore its physical process through in situ observation. WAAM is evolved on the basis of welding. In the research of welding technology, the research on the physical process and numerical simulation of arc welding has been very mature, and a lot of scholars have carried out numerical simulation analysis of WAAM process on this basis. Using the preestablished model to carry out simulation calculation with CFD software, the force of the molten pool and the speed and temperature of the molten pool can be quantitatively analyzed, which has a positive effect on the process optimization of the subsequent WAAM process. Path planning has always been a research hotspot of 3D printing technology. For WAAM, reasonable path planning is very important. WAAM is very prone to defects when printing irregular-shaped parts. Printing parts with complex shapes and overhangs also require unique path planning methods. Therefore, how to reduce printing defects and path planning for complex parts is currently a research hotspot in the direction of WAAM. This study will focus on the current research on the influence of heat input on the performance of parts in the additive manufacturing process of arc fuse, the numerical simulation of molten pool morphology, and the research and development direction of path planning for geometrically complex parts.


## 2. Related Research on Heat Input

The heat input during the arc fuse additive process has a great influence on the macrostructure, microstructure, and mechanical properties of the printed parts.


### 2.1. Experimentally Test the Effect of Heat Input on Part Performance

Wang et al. [9] studied the effect of heat input on the microstructure and mechanical properties of Al-Cu-Sn alloy by the CMT process. The study shows that as the heat input gradually increases, the printed layer thickness increases, and the number and size of pores in the deposits also improved. Li C et al. [10] studied the effect of heat input on the formability, microstructure, and properties of WAAM aluminum alloys. The printed samples were characterized by scanning electron microscopy and energy dispersive analysis. Studies have shown that Al-7Si-0.6 Mg alloys fabricated under the CMT process can form well under a wide range of heat inputs. Wu et al. [11] based on the GTAW process for the addition of Ti6Al4V alloy studied the effect of heat accumulation in the WAAM process on the microstructure and mechanical properties of the parts by means of OM, XRD, SEM, EDS, and tensile experiments. Studies have shown that due to the influence of heat accumulation during printing, the microstructure evolution, grain size, and crystal phase vary along the building direction of the as-fabricated wall, which creates variations in mechanical properties and fracture. The microstructures in the horizontal plane and the vertical plane display a similar tendency along the building direction: lathlike matrix structure, lamellar structures, basket-wave structures, and colony α structures. The fracture morphology and high-magnification fracture of the tensile specimen are shown in Figure 1.Figure 1
(a) Optical micrographs of selected samples in different horizontal planes: (A) schematic of the sample locations; (B) base metal extracted from a region outside of the heat affect zone; (C) M1; (D) M2; (E) M3; (F) M4. (b) The fracture appearance of the tensile specimens. (c) High-magnification fractographs of corresponding tensile samples: (A) S1; (B) S2; (C) S3; (D) S4; (E) S5; (F) S6; (G) S7; (H) S8.Most of the current research on thermal accumulation of WAAM uses some conventional characterization methods, such as SEM, EBSD, and XRD. In the future, we may see researchers use more characterization methods, such as synchrotron radiation and neutron diffraction, to further explore the impact of thermal accumulation on the performance of printed samples.


### 2.2. Reduce the Impact of Heat Input through Different Methods

Su et al. [12] used the CMT process to adjust the wire feeding speed and the welding torch travel speed to prepare Al-Mg alloys with different heat inputs. Li et al. [13] used the hot-wire arc additive manufacturing method to reduce the arc heat input, prepared Ti-6.5Al-3.5Mo-1.5Zr-0.3Si alloy, and refined the columnar crystals. The principle of the device is shown in Figure 2. In addition to the WAAM universal module, a new module for resistive heat generation has been added, which is used to produce the resistance heat through a power source and this heat can be assisted to melt the moving wire, thereby reducing the heat generated by the arc, to achieve low arc heat input conditions. Wang et al. [14] used the CMT process to make single-pass single-layer weld bead characteristics to select appropriate process parameters for additive, fabrication of AZ31 magnesium alloy, and thin-walled components with 50 layers deposited. To reduce heat accumulation, the waiting time for each layer of printing is 180s. Aldalur et al. [15] used the equipment shown in Figure 3 for additive material using the GMAW process. A geometric laser scanner and pyrometer are added inside the device to monitor the geometry and surface temperature of the printed parts. Overlapping additive and oscillatory additive methods are used to print test samples. Due to different thermal cycles created in both walls because of the different deposition sequences and paths, the microstructure in the oscillated wall and the overlapped wall is totally different. The oscillated wall grain size is larger than the overlapped wall due to the heat accumulation suffered. The overlapped wall hardness values are greater than the oscillated one because of the heterogeneity in the microstructure. The paths of overlapping and oscillating strategies and the microstructure of the oscillated wall and the overlapped wall in different planes are shown in Figure 3. Ding J et al. [16] used finite element simulation to calculate thermal field prediction using steady-state thermal models, and a simplified mechanical model applies peak temperature to the region that defines the effective plastic zone. It provides accurate residual stress and deformation predictions during WAAM processes to help optimize the process. The stress comparison of different deposition layers in different directions of the substrate is shown in Figure 4. The magnitude of the welding current in the WAAM process determines the heat input during the additive process, and different welding processes lead to different forming properties. Wu et al. [17] compared the forming and performance of the 316L additive process with the improved S-A and S-C processes based on the MIG process. S-A welding is a high-speed DC MIG welding process. Compared with other welding current processes, it has large voltage with the same current. S-C welding is an improved short-circuit transition mode with internal current controlling the current which is not excessively high in the short-circuit stage and the droplet spatter is reduced. The microstructures of samples were mainly austenitic (A) and small amount of peristaltic ferrite (F). Due to the high welding energy of sample S-A and higher temperature from heat accumulation, some ferrite was dissolved in austenite and the dendrite spacing was bigger than that of sample S-C. The macrographs and microstructures of samples S-C and S-A are shown in Figure 5. Artaza et al. [18] investigated cooling strategies in two different additive processes. ICS and COS control the cooling strategy between two adjacent weld passes and the cooling strategy between two adjacent deposition layers, respectively, and control the temperature of the next build. Its microstructure and dendrite structure are shown in Figure 6. Chen et al. [19] use an interlayer cooling process and control the heat input process to attempt to reduce the influence of thermal history on alloy deposits during the additive process. The results showed that interlayer cooling can optimize the molding quality of alloy deposits. The microstructure and morphology of the deposited specimens were observed and analyzed by microscope and electron back-scatter diffraction (EBSD). The process of controlled heat input results in a higher deposition rate, less side-wall roughness, minimum average grain size, and less coarse recrystallization. In addition, different thermal histories lead to different texture types in the interlayer cooling process. The corresponding parameter table for changing heat input conditions and its corresponding printing samples are shown in Figure 7. Wang et al. [20] introduced a fast water cooling system made of vortex tubes in the arc additive process, using four different ways to cool the parts during the additive process, interlayer cooling (IC), substrate cooling (SC), online cooling (OL), and natural cooling (NC).The experiments show that the mechanical properties of the parts printed by the OL cooling process are the best. The schematic diagram of the cooling system is shown in Figure 8. In the WAAM process, contact tube to workpiece distance (CTWD) will also have a certain influence on the heat input. Then, it is necessary to find a suitable distance to study the effect of arc heat input on WAAM. Henckell et al. [21] based on the GMAW process studied the effect of systematically adjusting CTWD during the WAAM process on the printed sample. Figure 9 shows WAAM of wall structures with 70 layers and differing CTWD.Figure 2
(a) Schematic of the hot-wire arc additive manufacturing system. (b) Overview of the hot-wire arc additive manufacturing system. (c) Schematic principle to procedure the resistance heat.Figure 3
(a) 3 + 2-axis ADDILAN machine and the welding torch. (b) Paths of overlapping and oscillating strategies. (c) (left) Microstructure of the center parts of the oscillated wall in YZ and XY planes; (right) microstructure of the overlapped wall in YZ and XY planes.
(a)(b)(c)Figure 4
Stress comparison in the longitudinal (LD), transverse (TD), and normal (ND) directions along a transverse line halfway along the base plate, 2 mm below the top surface for single (a), two (b), and three layers (c).
(a)(b)(c)Figure 5
(a) Macrographs and microstructures of transverse cross section of samples S-C and S-A. (b) Micrographs of longitudinal section of samples S-C and S-A: (a) micrographs of the upper part on two samples; (b) micrographs of the middle part on two samples; (c) micrographs of the bottom part on two samples. (d) Micrograph of the first layer of sample S-A.
(a)(b)Figure 6
Optical microscopic images. Microstructure of substrate (base plate) in cross-sectional view and transition grain structure of the IN718 wall for ICS and COS using both processing parameters. Micrograph of dendritic structure for the ICS and COS samples.Figure 7
Process parameter table for changing heat input and interlayer cooling. Macromorphology of deposited specimens with different processing methods.Figure 8
Vortex cooling system: (a) physical diagram and (b) schematic diagram.
(a)(b)Figure 9
WAAM of wall structures with 70 layers and differing CTWD.At the current stage, reducing the effect of heat accumulation in the WAAM process by changing the process parameters has certain limitations. Due to the difference in experimental equipment and materials, it is difficult to explore the most suitable process parameters to reduce heat accumulation. Only certain specific process parameters can change the heat accumulation phenomenon in the WAAM process, such as wire feeding speed, interlayer cooling time, and current and voltage. It seems to be a relatively direct and effective method to directly cool the printed sample by modifying the WAAM equipment to increase the cooling device, but it will also increase the maintenance cost of the equipment.


## 2.1. Experimentally Test the Effect of Heat Input on Part Performance

Wang et al. [9] studied the effect of heat input on the microstructure and mechanical properties of Al-Cu-Sn alloy by the CMT process. The study shows that as the heat input gradually increases, the printed layer thickness increases, and the number and size of pores in the deposits also improved. Li C et al. [10] studied the effect of heat input on the formability, microstructure, and properties of WAAM aluminum alloys. The printed samples were characterized by scanning electron microscopy and energy dispersive analysis. Studies have shown that Al-7Si-0.6 Mg alloys fabricated under the CMT process can form well under a wide range of heat inputs. Wu et al. [11] based on the GTAW process for the addition of Ti6Al4V alloy studied the effect of heat accumulation in the WAAM process on the microstructure and mechanical properties of the parts by means of OM, XRD, SEM, EDS, and tensile experiments. Studies have shown that due to the influence of heat accumulation during printing, the microstructure evolution, grain size, and crystal phase vary along the building direction of the as-fabricated wall, which creates variations in mechanical properties and fracture. The microstructures in the horizontal plane and the vertical plane display a similar tendency along the building direction: lathlike matrix structure, lamellar structures, basket-wave structures, and colony α structures. The fracture morphology and high-magnification fracture of the tensile specimen are shown in Figure 1.Figure 1
(a) Optical micrographs of selected samples in different horizontal planes: (A) schematic of the sample locations; (B) base metal extracted from a region outside of the heat affect zone; (C) M1; (D) M2; (E) M3; (F) M4. (b) The fracture appearance of the tensile specimens. (c) High-magnification fractographs of corresponding tensile samples: (A) S1; (B) S2; (C) S3; (D) S4; (E) S5; (F) S6; (G) S7; (H) S8.Most of the current research on thermal accumulation of WAAM uses some conventional characterization methods, such as SEM, EBSD, and XRD. In the future, we may see researchers use more characterization methods, such as synchrotron radiation and neutron diffraction, to further explore the impact of thermal accumulation on the performance of printed samples.


## 2.2. Reduce the Impact of Heat Input through Different Methods

Su et al. [12] used the CMT process to adjust the wire feeding speed and the welding torch travel speed to prepare Al-Mg alloys with different heat inputs. Li et al. [13] used the hot-wire arc additive manufacturing method to reduce the arc heat input, prepared Ti-6.5Al-3.5Mo-1.5Zr-0.3Si alloy, and refined the columnar crystals. The principle of the device is shown in Figure 2. In addition to the WAAM universal module, a new module for resistive heat generation has been added, which is used to produce the resistance heat through a power source and this heat can be assisted to melt the moving wire, thereby reducing the heat generated by the arc, to achieve low arc heat input conditions. Wang et al. [14] used the CMT process to make single-pass single-layer weld bead characteristics to select appropriate process parameters for additive, fabrication of AZ31 magnesium alloy, and thin-walled components with 50 layers deposited. To reduce heat accumulation, the waiting time for each layer of printing is 180s. Aldalur et al. [15] used the equipment shown in Figure 3 for additive material using the GMAW process. A geometric laser scanner and pyrometer are added inside the device to monitor the geometry and surface temperature of the printed parts. Overlapping additive and oscillatory additive methods are used to print test samples. Due to different thermal cycles created in both walls because of the different deposition sequences and paths, the microstructure in the oscillated wall and the overlapped wall is totally different. The oscillated wall grain size is larger than the overlapped wall due to the heat accumulation suffered. The overlapped wall hardness values are greater than the oscillated one because of the heterogeneity in the microstructure. The paths of overlapping and oscillating strategies and the microstructure of the oscillated wall and the overlapped wall in different planes are shown in Figure 3. Ding J et al. [16] used finite element simulation to calculate thermal field prediction using steady-state thermal models, and a simplified mechanical model applies peak temperature to the region that defines the effective plastic zone. It provides accurate residual stress and deformation predictions during WAAM processes to help optimize the process. The stress comparison of different deposition layers in different directions of the substrate is shown in Figure 4. The magnitude of the welding current in the WAAM process determines the heat input during the additive process, and different welding processes lead to different forming properties. Wu et al. [17] compared the forming and performance of the 316L additive process with the improved S-A and S-C processes based on the MIG process. S-A welding is a high-speed DC MIG welding process. Compared with other welding current processes, it has large voltage with the same current. S-C welding is an improved short-circuit transition mode with internal current controlling the current which is not excessively high in the short-circuit stage and the droplet spatter is reduced. The microstructures of samples were mainly austenitic (A) and small amount of peristaltic ferrite (F). Due to the high welding energy of sample S-A and higher temperature from heat accumulation, some ferrite was dissolved in austenite and the dendrite spacing was bigger than that of sample S-C. The macrographs and microstructures of samples S-C and S-A are shown in Figure 5. Artaza et al. [18] investigated cooling strategies in two different additive processes. ICS and COS control the cooling strategy between two adjacent weld passes and the cooling strategy between two adjacent deposition layers, respectively, and control the temperature of the next build. Its microstructure and dendrite structure are shown in Figure 6. Chen et al. [19] use an interlayer cooling process and control the heat input process to attempt to reduce the influence of thermal history on alloy deposits during the additive process. The results showed that interlayer cooling can optimize the molding quality of alloy deposits. The microstructure and morphology of the deposited specimens were observed and analyzed by microscope and electron back-scatter diffraction (EBSD). The process of controlled heat input results in a higher deposition rate, less side-wall roughness, minimum average grain size, and less coarse recrystallization. In addition, different thermal histories lead to different texture types in the interlayer cooling process. The corresponding parameter table for changing heat input conditions and its corresponding printing samples are shown in Figure 7. Wang et al. [20] introduced a fast water cooling system made of vortex tubes in the arc additive process, using four different ways to cool the parts during the additive process, interlayer cooling (IC), substrate cooling (SC), online cooling (OL), and natural cooling (NC).The experiments show that the mechanical properties of the parts printed by the OL cooling process are the best. The schematic diagram of the cooling system is shown in Figure 8. In the WAAM process, contact tube to workpiece distance (CTWD) will also have a certain influence on the heat input. Then, it is necessary to find a suitable distance to study the effect of arc heat input on WAAM. Henckell et al. [21] based on the GMAW process studied the effect of systematically adjusting CTWD during the WAAM process on the printed sample. Figure 9 shows WAAM of wall structures with 70 layers and differing CTWD.Figure 2
(a) Schematic of the hot-wire arc additive manufacturing system. (b) Overview of the hot-wire arc additive manufacturing system. (c) Schematic principle to procedure the resistance heat.Figure 3
(a) 3 + 2-axis ADDILAN machine and the welding torch. (b) Paths of overlapping and oscillating strategies. (c) (left) Microstructure of the center parts of the oscillated wall in YZ and XY planes; (right) microstructure of the overlapped wall in YZ and XY planes.
(a)(b)(c)Figure 4
Stress comparison in the longitudinal (LD), transverse (TD), and normal (ND) directions along a transverse line halfway along the base plate, 2 mm below the top surface for single (a), two (b), and three layers (c).
(a)(b)(c)Figure 5
(a) Macrographs and microstructures of transverse cross section of samples S-C and S-A. (b) Micrographs of longitudinal section of samples S-C and S-A: (a) micrographs of the upper part on two samples; (b) micrographs of the middle part on two samples; (c) micrographs of the bottom part on two samples. (d) Micrograph of the first layer of sample S-A.
(a)(b)Figure 6
Optical microscopic images. Microstructure of substrate (base plate) in cross-sectional view and transition grain structure of the IN718 wall for ICS and COS using both processing parameters. Micrograph of dendritic structure for the ICS and COS samples.Figure 7
Process parameter table for changing heat input and interlayer cooling. Macromorphology of deposited specimens with different processing methods.Figure 8
Vortex cooling system: (a) physical diagram and (b) schematic diagram.
(a)(b)Figure 9
WAAM of wall structures with 70 layers and differing CTWD.At the current stage, reducing the effect of heat accumulation in the WAAM process by changing the process parameters has certain limitations. Due to the difference in experimental equipment and materials, it is difficult to explore the most suitable process parameters to reduce heat accumulation. Only certain specific process parameters can change the heat accumulation phenomenon in the WAAM process, such as wire feeding speed, interlayer cooling time, and current and voltage. It seems to be a relatively direct and effective method to directly cool the printed sample by modifying the WAAM equipment to increase the cooling device, but it will also increase the maintenance cost of the equipment.


## 3. Research on Numerical Simulation of WAAM

The WAAM process is extremely complex, and the research on the analysis of the flow behavior of the molten pool is based on the arc welding. The previous simulation of arc welding molten pool is to study the droplet transfer of different welding processes, weld bead formation of single layer and single pass, and force and velocity vector analysis of molten pool. WAAM involves single-layer multipass and multilayer multipass welding simulation, and the research on its heat transfer is more prominent. Simulation software is used to simulate and analyze the flow of the molten pool and the formation of the weld bead, and the flow of the molten pool and the causes of defects after forming are analyzed, which is helpful for the optimization of the WAAM process.Cadiou et al. [22] used a numerical model to obtain the geometry of the part and its temperature field from the operating parameters. This predictive model takes into account electromagnetism, fluid flow, and heat transfer in the arc and the melt pool. The Lorentz forces, shear stress, arc pressure, and Joule effect are calculated. The droplet generation, its transfer and impingement onto the melt pool, and the melt pool dynamics were calculated. Its droplet transfer simulation analysis diagram is shown in Figure 10, which includes the simulation analysis of surface tension, electromagnetic force, and velocity. Furthermore, Cadiou et al. [23] proposed a 3D model based on this 2D axisymmetric model [22] to simulate the CMT-WAAM process. The model simulates the forward and backward motion of the filler wire. It also predicts its impingement into the melt pool, the growth of the melt pool, and the formation of the deposited bead. It is based on the computation of magnetothermal-hydrodynamic equations in all domains (wire, arc, melt pool, substrate) with the level set method for the interface tracking. The simulation results are shown in Figure 11. Based on the GTAW process, Pan et al. [24] used the numerical simulation method to simulate the weld pool and weld bead. The temperature and velocity distribution of the free surface of the molten pool, the deformation of the free surface of the molten pool, and the shape of the weld are systematically discussed, and the formation mechanism of weld defects is analyzed. Its simulation diagram is shown in Figure 12. Zhao et al. [25] studied the thermal stress evolution and residual stress distribution during the single-pass multilayer forming process in the WAAM process and studied the effect of the deposition direction on the residual stress and residual strain by numerical simulation. The study shows that the deposition of the last layer determines the residual stress of the entire component due to the influence of the poststress-releasing layer on the previous layer. The deposition direction has a significant effect on the residual stress and equivalent plastic strain distribution of the component, and the reverse deposition effect between two adjacent layers is better. The stress evolution simulation diagram is shown in Figure 13. Bai et al. [26] conducted three-dimensional numerical model simulation of fluid flow and heat transfer behavior in multilayer deposition process of plasma arc welding in WAAM. The transient simulation of the deposits in the first, second, and 21st layers is carried out. The shape of weld pass and weld pool is predicted and compared with the actual additive condition. The simulation results are shown in Figure 14. Zhou et al. [27] simulated the droplet transfer and molten pool flow of single-pass single-layer welding and double-pass single-layer welding in the arc additive process and compared the simulation results with the actual additive process. As shown in Figure 15, Wang et al. [28] added an external magnetic field to the arc, established a unified model including filler metal, arc, molten pool, and compound electromotive force, and studied the effect of compound electromotive force on the welding process. The simulation model of the applied magnetic field is shown in Figure 16. Jian et al. [29] established a unified fluid flow and heat transfer model. The interface between the arc plasma and the molten pool was processed by the local thermodynamic equilibrium-diffusion approximation method, and the dynamic change law of the surface current density, plasma arc pressure, and heat flux density of the molten pool with the evolution of the hole boundary was quantitatively analyzed. The evolution process of its plasma arc at different times is shown in Figure 17. Zhao et al. [30] proposed an arc-droplet-molten pool full-integrated mathematical model to investigate the complicated physical phenomena during the aluminum alloy GMAW process and reveal the influence mechanism of the external magnetic field (EMF) on behaviors of heat transfer and fluid flow of arc plasma and molten metal. A moderate magnetic flux density of EMF will not only compress the arc plasma and decrease the average temperature of droplet metal but also enhance the peak velocity of plasma and expand the high-velocity plasma zone. The simulation results are shown in Figure 18. Montevecchi et al. [31] proposed the use of jet impingement cooling technology to prevent the effect of heat accumulation during the WAAM additive process. The goal is to increase the convective heat transfer between the workpiece and the environment using impinging air jets. The effectiveness of this method is evaluated by means of numerical simulation. The manufacturing of a 15 layers wall is simulated. The results show that jet impingement cooling prevents heat build-up during the additive process. The simulation results are shown in Figure 19, but it has not been further verified in the actual additive process. Simulation of WAAM process is based on GMAW process by Ogino et al. [32]. Firstly, the temperature effect between two adjacent deposition layers is simulated, and the simulation shows that setting a reasonable cooling time between two adjacent deposition layers during the additive process can obtain better morphology. Secondly, the welding direction in the additive process is simulated. When the welding direction of each layer is opposite, the layer height change in each layer is small. Controlling the temperature of the molten pool and the motion of the welding torch is of great significance for controlling the shape of the molten pool. The simulation process and actual additive experiment diagram are shown in Figure 20.Figure 10
A sequence of the distribution of calculated process variables showing different moments during a pulse cycle.Figure 11
(a) Sequence of temperature fields (in (K)) during the detachment of the first two droplets (fromt = 0 to 0.4 (s)) and the growth of the melt pool during the build-up of the wall (from t = 1.5 to 4.5 (s)). The black arrow indicates the direction of the wire, and the white one indicates the relative motion of the substrate. (b) Longitudinal cross-sectional view (xz plane) of the fluid velocity field during metal transfer.
(a)(b)Figure 12
Simulation results of Pan et al.Figure 13
Stress evolution of ten-layer deposition component when heat source locates at (a) starting arc point of the 1st layer; (b) ending arc point of the 1st layer; (c) ending arc point of the 2nd layer; (d) ending arc point of the 5th layer; (e) ending arc point of the 10th layer; (f) when component cooling to room temperature.
(a)(b)(c)(d)(e)(f)Figure 14
3D views of molten pool and deposited bead during the 2nd layer deposition at different time: (a)t = 2.0 s; (b) t = 4.0 s; (c) t = 8.0 s; (d) t = 10.0 s. Comparison of cross-sectional profiles on x = 30 mm plane: experimental profiles of (e) 1st layer, (f) 2nd layer, and (g) 21st layer; simulated profiles at 8.5 s of (h) 1st layer, (i) 2nd layer, and (j) 21st layer.Figure 15
Molten pool morphology of single-bead and overlapping deposition.Figure 16
Simulated temperature distributions of the arc, droplet, and weld pool with EMF: (a)t2 = 0.100 (s) xoz section; (b) t2 = 0.100 (s) yoz section; (c) t3 = 0.133 (s) xoz section; (d) t3 = 0.133 (s) yoz section.
(a)(b)(c)(d)Figure 17
Evolution process of plasma arc-weld pool-keyhole at different times. (a)t = 0.001 (s),  (b) t = 0.5, (s) (c) t = 1.0 s, and (d) t = 1.48 s.
(a)(b)(c)(d)Figure 18
(a) Arc plasma flow characteristics atY = 0 section at t = 14 ms under different magnetic flux densities: (A) 0 mT; (B) 1 mT; (C) 2 mT; (D) 4 mT. (b) The arc plasma flow characteristics at Z = 7.5 mm section at t = 14 ms under different magnetic flux densities: (A) 0 mT; (B) 1 mT; (C) 2 mT; (D) 4 mT.
(a)(b)Figure 19
Comparison of temperature field (K) of reference and jet impingement simulations: contour plot during the deposition of 4th and 10th layers (a); temperature-time histories of two nodes at different distances from the base plate (b).
(a)(b)Figure 20
Numerical results of the deposition shape with different welding directions. (a) Pattern L-A. (b) Pattern L-B. Schematic images of welding tor\sition shape with different welding directions. (c) Pattern L-A. (d) Pattern L-B. Experimental results of the deposition shape with different welding directions. (e) Pattern L-A. (f) Pattern L-B.
(a)(b)(c)(d)(e)(f)With the continuous development of numerical simulation technology, WAAM numerical simulation has changed from single physical quantity simulation to multiphysics coupled simulation, for example, simultaneous calculation of molten pool temperature, speed, internal force, external electromotive force, and arc distribution. At present, CFD software packages such as Fluent and COMSOL have been matched in the supercomputer system, which will greatly reduce the time of numerical simulation calculation. In the future, more complex physical models will be proposed for WAAM. This greatly promotes the subsequent optimization of WAAM process parameters by means of numerical simulation.


## 4. Path Planning for WAAM

Since the shape and structure of the parts added by WAAM are different, the additive path planning methods for different parts are also different. In recent years, researchers have made many efforts for additive path planning of parts with different structural shapes, and the purpose is to find the most suitable additive path, improve the shape quality of printed parts as much as possible, and reduce the generation of printing defects.


### 4.1. Optimize Path Planning to Reduce Printing Defects

Liu et al. [2] proposed a compound path planning method and an acute angle correction strategy. The composite path planning method consists of the zigzag path and the contour offsetting path. Combining the outer contour offset filling path and the zigzag filling path eliminates pore formation in the geometric center, which often occurs with traditional path planning methods. Using Experiments to verify the feasibility of the composition path filling method and the sharp angle correction strategy. As shown in Figure 21, Schmitz et al. [33] introduced robot kinematics into path planning algorithm. It ensures the accessibility of the robot pose in complex path states and smoothens the generated path, thereby improving operability. Zhang et al. [34] proposed a WAAM path planning strategy combining zigzag and equidistant migration methods, obtaining a single WAAM deposit with optimal profile accuracy and residual stress state. The path planning diagram and XRD residual stress test diagram are shown in Figure 22. Wang et al. [35] proposed a water-pouring path planning method to ensure high density of the internal structure of the printed parts. The proposed path planning method can transfer all intersecting regions of the path to the outer contour, thus guaranteeing uniform compactness of the inner region. The innovative method of generating paths sequentially disperses the computational cost of generating all paths at one time and improves the real time and dynamic nature of path planning. The schematic diagram of the water-pouring path planning is shown in Figure 23. Michel F et al. [36] introduced a modular path planning solution. Each layer is divided into separate sedimentary segments. The speed and path of the torch travel are different for each individual deposition section. In this way, parts with complex shapes can be produced and printing defects can be reduced. The principle of its modular additive path and its comparison with the traditional path planning method are shown in Figure 24. To solve the control of weld bead height in the additive process, Zhao Y et al. [37] proposed a process planning strategy based on unit blocks. The goal of the strategy is to cut complex parts into parallel layers with uneven layer thickness. A unit block is defined as a block deposited per unit time, the size of which can be controlled by changing process parameters. This improves the accuracy of printed parts. An example of its unit block workflow and printing part block is shown in Figure 25. In recent years, more and more researchers have introduced deep learning neural network in WAAM path planning, by which to improve the production efficiency and accuracy of WAAM. Ding D et al. [38] established the relationship between the geometry of a single weld bead and the welding process parameters through an artificial neural network (ANN) model. Through the experimental deposition of two metal components, the adaptive layup path planning strategy and the established weld bead model were verified. The results show that high-quality, high-precision printed components can be produced by introducing this adaptive path planning algorithm. Its neural network structure diagram and printed part path optimization diagram are shown in Figure 26. Li et al. [39] used a deep learning neural network to predict the offset distance of the weld during the WAAM process. A reasoning algorithm was implemented to calculate the optimal distance between the centers of adjacent deposition paths in order to achieve a planned center distance between adjacent beads. This enables the control of the actual center distance of the adjacent beads according to an expected value. By introducing a neural network, Nguyen et al. [40] realized a new strategy for generating the optimal path of WAAM with a light rib network structure and mitigated disadvantages of discontinuous weld paths such as weld defects and uneven overlays. Paths of any rib mesh geometry can be generated, increasing productivity. Its neural network structure diagram and path optimization comparison diagram are shown in Figure 27.Figure 21
Experimental comparison of layers produced by proposed composite path filling strategy and contour offset path filling strategy: (a), (b), and (c) are the first, third, and fifth layers, respectively, produced using the composite strategy; (d), (e), and (f) are the first, third, and fifth layers, respectively, produced using the contour offset strategy.Figure 22
(a) XRD residual stress measurement point setup (taking Sample 4 as the example): (A) testing point depth location in the cross-sectional view; (B) point location in the top view. (b) XRD residual stress measurement results of Sample 1, Sample 2, Sample 3, and Sample 4 in a function of testing points from inner contour to outer contour.
(a)(b)Figure 23
Planned paths of the (a) sequential water-pouring path planning method in multilayer variable direction and (b) multilayer zigzag path planning method.
(a)(b)Figure 24
Circles indicate keyhole defects; the bounding boxes indicate the profile of the target geometry.Figure 25
(a) Workflow for decomposing unit blocks in single layer. (b) A typical model with different dimensional attributes and surface quality.
(a)(b)Figure 26
Neural network architecture for predicting bead geometry. Illustrations of different deposition paths. External lines represent the boundary of the geometry; internal lines represent the deposition paths with the numbers representing the order of the deposition paths; grey regions are deposited area by the relevant paths. (a) Contour path patterns; (b) the predicted high accuracy deposition but with internal gaps; (c) MAT path patterns; (d) the predicted void-free deposition but with extra material deposited along the boundary; (e) adaptive MAT path patterns with varying step-over distance; (f) the predicted void-free deposition with high accuracy at the boundary through using adaptive MAT path.Figure 27
Schematic architecture of the ANN. Performance of an example: (a) tool path pattern without correction; (b) tool path pattern with correction.


### 4.2. Exploration of Printing Paths for Complex Structural Parts

Wang et al. [41] proposed a cylindrical slice manufacturing method. The generated slice layers are located on the cylindrical surface, and these spatial contours are expanded onto the plane using the cylindrical coordinate system. Sweep parallel paths and contour offset paths are not suitable for direct machining of blades. A blend of the two paths and a blend of the skeleton and profile offset paths can result in a gapless blade. Symmetrical deposition can reduce the deformation of the propeller. Its different path planning schematics are shown in Figure 28. Dai et al. [42] introduced the definition of overhang point, overhang distance, and overhang vector in WAAM path planning and proposed a PCA-based path planning approach. The best scanning directions of slicing contours are computed for the generation of filling paths, including parallel lines, zigzag paths, and parallel skeleton paths. An overhanging structure is fabricated without supports. Its structure and path planning are shown in Figure 29. Dai et al. [43] proposed an integrated slicing, tool path planning, and welding process planning method and investigated the adaptive CSW model to improve the surface appearances of the welding beads on curved surfaces. The path-planning method and CSW model can be applied in the five-axis fabrication of the prototype of an underwater thruster. Its path planning method and actual additive situation are shown in Figure 30. Wang et al. [44]proposed a point-by-point surfacing strategy. A droplet force analysis model is established, the welding torch posture is adjusted, and the collapse problem during the WAAM process of the truss structure is solved. A collision detection model is established, the interference size between the truss structure and the welding torch is calculated, and it is used to control the offset of the welding torch. The ant colony algorithm is used to optimize the motion path of the welding torch between the trusses, and the collision-free rapid prototyping of the complex truss structure is realized, as shown in Figure 31.Figure 28
Different path planning methods.Figure 29
Path planning strategy and actual deposition process.Figure 30
(a) Path planning on conical surfaces. (b) The final WAAM part (left) and its finished part (right).
(a)(b)Figure 31
WAAM process of chair truss structure and the stability-enhancing parts.There are many optimization methods for WAAM path planning. For parts with different shapes, the path planning methods used are also different, which makes it difficult to widely apply and promote. With the increase in data samples and the continuous advancement of research, the use of deep learning neural network methods for path planning will be a major development trend in the future.


## 4.1. Optimize Path Planning to Reduce Printing Defects

Liu et al. [2] proposed a compound path planning method and an acute angle correction strategy. The composite path planning method consists of the zigzag path and the contour offsetting path. Combining the outer contour offset filling path and the zigzag filling path eliminates pore formation in the geometric center, which often occurs with traditional path planning methods. Using Experiments to verify the feasibility of the composition path filling method and the sharp angle correction strategy. As shown in Figure 21, Schmitz et al. [33] introduced robot kinematics into path planning algorithm. It ensures the accessibility of the robot pose in complex path states and smoothens the generated path, thereby improving operability. Zhang et al. [34] proposed a WAAM path planning strategy combining zigzag and equidistant migration methods, obtaining a single WAAM deposit with optimal profile accuracy and residual stress state. The path planning diagram and XRD residual stress test diagram are shown in Figure 22. Wang et al. [35] proposed a water-pouring path planning method to ensure high density of the internal structure of the printed parts. The proposed path planning method can transfer all intersecting regions of the path to the outer contour, thus guaranteeing uniform compactness of the inner region. The innovative method of generating paths sequentially disperses the computational cost of generating all paths at one time and improves the real time and dynamic nature of path planning. The schematic diagram of the water-pouring path planning is shown in Figure 23. Michel F et al. [36] introduced a modular path planning solution. Each layer is divided into separate sedimentary segments. The speed and path of the torch travel are different for each individual deposition section. In this way, parts with complex shapes can be produced and printing defects can be reduced. The principle of its modular additive path and its comparison with the traditional path planning method are shown in Figure 24. To solve the control of weld bead height in the additive process, Zhao Y et al. [37] proposed a process planning strategy based on unit blocks. The goal of the strategy is to cut complex parts into parallel layers with uneven layer thickness. A unit block is defined as a block deposited per unit time, the size of which can be controlled by changing process parameters. This improves the accuracy of printed parts. An example of its unit block workflow and printing part block is shown in Figure 25. In recent years, more and more researchers have introduced deep learning neural network in WAAM path planning, by which to improve the production efficiency and accuracy of WAAM. Ding D et al. [38] established the relationship between the geometry of a single weld bead and the welding process parameters through an artificial neural network (ANN) model. Through the experimental deposition of two metal components, the adaptive layup path planning strategy and the established weld bead model were verified. The results show that high-quality, high-precision printed components can be produced by introducing this adaptive path planning algorithm. Its neural network structure diagram and printed part path optimization diagram are shown in Figure 26. Li et al. [39] used a deep learning neural network to predict the offset distance of the weld during the WAAM process. A reasoning algorithm was implemented to calculate the optimal distance between the centers of adjacent deposition paths in order to achieve a planned center distance between adjacent beads. This enables the control of the actual center distance of the adjacent beads according to an expected value. By introducing a neural network, Nguyen et al. [40] realized a new strategy for generating the optimal path of WAAM with a light rib network structure and mitigated disadvantages of discontinuous weld paths such as weld defects and uneven overlays. Paths of any rib mesh geometry can be generated, increasing productivity. Its neural network structure diagram and path optimization comparison diagram are shown in Figure 27.Figure 21
Experimental comparison of layers produced by proposed composite path filling strategy and contour offset path filling strategy: (a), (b), and (c) are the first, third, and fifth layers, respectively, produced using the composite strategy; (d), (e), and (f) are the first, third, and fifth layers, respectively, produced using the contour offset strategy.Figure 22
(a) XRD residual stress measurement point setup (taking Sample 4 as the example): (A) testing point depth location in the cross-sectional view; (B) point location in the top view. (b) XRD residual stress measurement results of Sample 1, Sample 2, Sample 3, and Sample 4 in a function of testing points from inner contour to outer contour.
(a)(b)Figure 23
Planned paths of the (a) sequential water-pouring path planning method in multilayer variable direction and (b) multilayer zigzag path planning method.
(a)(b)Figure 24
Circles indicate keyhole defects; the bounding boxes indicate the profile of the target geometry.Figure 25
(a) Workflow for decomposing unit blocks in single layer. (b) A typical model with different dimensional attributes and surface quality.
(a)(b)Figure 26
Neural network architecture for predicting bead geometry. Illustrations of different deposition paths. External lines represent the boundary of the geometry; internal lines represent the deposition paths with the numbers representing the order of the deposition paths; grey regions are deposited area by the relevant paths. (a) Contour path patterns; (b) the predicted high accuracy deposition but with internal gaps; (c) MAT path patterns; (d) the predicted void-free deposition but with extra material deposited along the boundary; (e) adaptive MAT path patterns with varying step-over distance; (f) the predicted void-free deposition with high accuracy at the boundary through using adaptive MAT path.Figure 27
Schematic architecture of the ANN. Performance of an example: (a) tool path pattern without correction; (b) tool path pattern with correction.


## 4.2. Exploration of Printing Paths for Complex Structural Parts

Wang et al. [41] proposed a cylindrical slice manufacturing method. The generated slice layers are located on the cylindrical surface, and these spatial contours are expanded onto the plane using the cylindrical coordinate system. Sweep parallel paths and contour offset paths are not suitable for direct machining of blades. A blend of the two paths and a blend of the skeleton and profile offset paths can result in a gapless blade. Symmetrical deposition can reduce the deformation of the propeller. Its different path planning schematics are shown in Figure 28. Dai et al. [42] introduced the definition of overhang point, overhang distance, and overhang vector in WAAM path planning and proposed a PCA-based path planning approach. The best scanning directions of slicing contours are computed for the generation of filling paths, including parallel lines, zigzag paths, and parallel skeleton paths. An overhanging structure is fabricated without supports. Its structure and path planning are shown in Figure 29. Dai et al. [43] proposed an integrated slicing, tool path planning, and welding process planning method and investigated the adaptive CSW model to improve the surface appearances of the welding beads on curved surfaces. The path-planning method and CSW model can be applied in the five-axis fabrication of the prototype of an underwater thruster. Its path planning method and actual additive situation are shown in Figure 30. Wang et al. [44]proposed a point-by-point surfacing strategy. A droplet force analysis model is established, the welding torch posture is adjusted, and the collapse problem during the WAAM process of the truss structure is solved. A collision detection model is established, the interference size between the truss structure and the welding torch is calculated, and it is used to control the offset of the welding torch. The ant colony algorithm is used to optimize the motion path of the welding torch between the trusses, and the collision-free rapid prototyping of the complex truss structure is realized, as shown in Figure 31.Figure 28
Different path planning methods.Figure 29
Path planning strategy and actual deposition process.Figure 30
(a) Path planning on conical surfaces. (b) The final WAAM part (left) and its finished part (right).
(a)(b)Figure 31
WAAM process of chair truss structure and the stability-enhancing parts.There are many optimization methods for WAAM path planning. For parts with different shapes, the path planning methods used are also different, which makes it difficult to widely apply and promote. With the increase in data samples and the continuous advancement of research, the use of deep learning neural network methods for path planning will be a major development trend in the future.


## 5. Conclusion

The current problem of WAAM technology is that the dimensional accuracy of the printed parts is not high, and the surface quality of the parts is difficult to control. Heat accumulation can greatly affect the mechanical properties of WAAM prints. The main direction of current WAAM research is to explore the relationship between process parameters and part grain morphology, size, and orientation. Active control technology for grain morphology and crystal orientation of metal components is established. WAAM holds great promise, and researchers are exploring the technology in a variety of materials, including titanium, aluminum, and nickel. Through different means to optimize the WAAM process, the performance of its printed parts becomes better.(1)
Much effort has been made to study the effect of heat input on the macrostructure, microstructure, and mechanical properties of printed parts, especially the effect of heat input on the microstructure, which can lead to the occurrence of grain structure, grain size, and pore area percentage significant changes. Research on heat input will continue to increase in the future.(2)
Numerical simulation is also a very important means to analyze the molten pool flow behavior in WAAM process. The simulation analysis of molten pool temperature field, velocity field, and droplet transition morphology, as well as the simulation of process optimization scheme, is beneficial to improve the performance of printed parts.(3)
There are many optimization methods in path planning, and the introduction of modular block planning or deep learning neural network algorithm can effectively reduce printing defects. In terms of artificial neural networks, with the increase in data samples, more and more people may devote themselves to studying WAAM path planning using deep learning methods in the future.



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*Source: 5757484-2022-09-17.xml*