Key words: Laser welding, Dissimilar materials, Copper alloy, Aluminum alloy, Battery, Deep learning, Intermetallic compound

1. Introduction

The development of environmentally friendly vehicles to replace conventional internal combustion engine vehicles has been spurred by recent carbon emission regulations. The market share of electric vehicles (EV), among those vehicles, is rapidly increasing, and secondary batteries in EV play the same role as engines in internal combustion engine vehicles. In secondary batteries, Al or Cu materials with good electrical conductivity are used for power. They are assembled at the cell, module, and package levels through a joining process. These joints are mainly joined through a welding process, and since the performance of the joints directly affects the efficiency of the secondary battery and the safety of the electric vehicle, it is crucially required to ensure the reliability of the joints in the secondary batteries for EV.

In the cell assembly process, ultrasonic welding and laser welding are widely used to join Al/Cu dissimilar materials. Ultrasonic welding is solid-phase welding and has the advantage of minimizing thermal deformation, but it is being replaced by laser welding due to the following reasons: tool wear, joint deformation, noise and dust generation, and design constraints¹⁾. Laser welding is a non-contact process with low thermal effects around the joint, and it facilitates high productivity because its welding speed is fast. However, unlike ultrasonic welding, laser welding is a fusion welding process that requires minimizing deformation caused by heat input, and since it is a non-contact process, it requires gap control of the joint.

Al/Cu alloys are typically challenging joining materials for laser welding. High-power lasers commonly used as welding lasers have a wavelength of about 1 ㎛, and in this region, Al/Cu alloys have very low absorption of the laser beam, resulting in high initial reflection. Moreover, high laser density and power are required for welding applications. Fig. 1 shows the absorption rate of Al and Cu alloys at room temperature. At a wavelength of 1 ㎛, Cu has a very low absorption rate of approximately 2.5% at room temperature. It increases to approximately 10% in the liquid state, and even higher in the keyhole state due to multiple reflections. Therefore, when welding Cu materials, it is critical to create a keyhole state instantaneously by focusing the laser¹⁾. Al, like Cu, has a very low absorption rate of approximately 4% at room temperature.

Fig. 1

Absorption rates of copper, copper alloys, aluminum and steel surfaces at room temperature over the wavelength¹⁾

This study presents the problems encountered when performing laser welding of Al/Cu alloys and introduces a technical approach to deal with them. It also introduces the laser welding technologies of Al/Cu dissimilar materials studied in recent years.

2. Laser Welding Characteristics of Al/Cu Alloys

In laser welding, material properties, along with the laser beam absorption rate of the material, affect the formation of the weld. The basic properties of Al/Cu alloys are introduced below.

Table 1 shows the physical characteristics of 1xxx-series Al and oxygen-free copper, respectively, which are commonly used in secondary batteries for EV²⁾.

Table 1

Physical characteristics of aluminum and copper

	Cu	Al
Density [g/cm3]	8.92	2.71
Melting Temperature [°C]	1,083	660
Thermal Conductivity [W/(m∙K)]	398	237
Thermal Expansion Coefficient [°C-1]	17∙10-6	24∙10-6
Specific Heat Capacity [J/(kg∙K)]	385	880
Absorptivity [λ: 1064 nm]	2–3%	5-7%

Compared to other Al alloys, 1xxx-series Al has low mechanical strength but high corrosion resistance, high electrical and thermal conductivity, and excellent machinability and weldability. 1xxx-series Al has high electrical conductivity, and although it is about two-thirds of that of Cu, it is four times higher than that of pure iron (Fe).

The melting point of Cu is 1,083℃ and that of Al has is 660℃, a difference of approximately 400℃. The thermal conductivity of Cu is more than eight times higher than that of mild steel and more than two times higher than that of Al. Moreover, Cu has a high thermal expansion coefficient, which makes it easy to crack due to stress caused by shrinkage during cooling after laser welding. Cu, which is mainly used as a secondary battery material for EV, is oxygen-free copper and is characterized by high electrical conductivity.

Since Al/Cu alloys have different melting points, thermal expansion coefficients, and thermal conductivity, as described above, it is difficult to weld between dissimilar materials.

3. Characteristics of Al/Cu Dissimilar Material Joints

3.1 Tensile Strength

3.1 Tensile Strength

Table 2 summarizes the maximum tensile strength of laser welds of Al/Cu dissimilar materials as a function of laser source, material combination, joint geometry, and modulation. The fracture load of the welded joints is 76 to 1,206 N, as shown in the table, but since the fracture load of the welded overlap joint is proportional to the weld strength, the weld interface width, and the weld length, we divided the fracture load by the product of the material thickness and the weld length, which is shown as the equivalent tensile strength. Considering that Al 1050-O has a minimum strength of 76 MPa in the specifications, it can be seen that in most cases the fusion strength of Al materials is achieved through process optimization.

Table 2

Tensile strength of laser-welded overlap joints according to laser source and material combination

No	Source/ wave length	Modulation	Upper metal/ thickness (mm)	Lower metal/ thickness (mm)	Fracture Load (N)	Welding Length (mm)	Equivalent Tensile Strength (MPa)	Year (Ref.)
1	Nd:YAG 1064 nm	Spot	E-Cu58 1.2	Al99.9 1.2	1,100	-	-	20093)
2	Disk 1030 nm	Linear Line	Al1050 0.2	Cu1020 1.0	≈490	70	127.4	20124)
3	Nd:YAG 1064 nm	Pulse Linear Line	Al1050 0.45	Cu1020 0.3	≈1,187	45	87.8	20195)
4	Fiber 1070 nm	Beam Oscillation	Al1050 0.45	Cu1020 0.3	≈1,206	45	89.2	20196)
5	Nd:YAG 1064 nm	Linear Line	Al1060 0.3	Cu-T2 0.3	≈539	20	89.9	20147)
6	Fiber 1070 nm	Linear Line	Al1050 0.3	Cu-OF 0.3	540	60	205	20148)
7	Fiber 1070 nm	Pulse Linear Line	Al1060 0.3	Cu-OF 0.3	≈1,078	45	79.8	20199)
8	Green 515 nm	Linear Line	Al6061 0.4	Cu1020 0.4	-	50	79.6	202110)
9	Green 515 nm	Beam Oscillation	Cu-OF 0.4	Al1050 0.4	-	50	79.6	202111)
10	Disk 1030 nm	Spiral Spot	Cu-OF 0.4	Al1050 0.4	≈350	40	21.8	202212)
11	Fiber 1070 nm	Linear Line	Al3003 0.3	Cu101 0.3	76	-	-	201613)

Since the late 2000s, studies on the application of interlayers in Al/Cu overlap joints have been published.

Weigl et al.³⁾ chose silver (Ag) as the interlayer material. They used a pulsed laser with a duration of 9 ms and chose the frequency as a variable. A frequency of 850 Hz and an Ag interlayer improved the tensile strength by 20% compared to the previous work.

Feng et al.⁴⁾ used Zn-Al-Ce as the interlayer material to compare the tensile strength. When the Ce content was 0.05 wt%, the strength increased by about 30%, resulting in a maximum tensile strength of 91.3 MPa.

Hailat et al.¹⁴⁾ used tin (Sn) as an interlayer material to analyze the tensile strength and fracture surface of welds. The tensile strength of the specimen with S-bond 220, an Sn sheet, as the interlayer was 20% higher than that of the specimen without the interlayer, and ductile fracture was observed in the specimen with the interlayer.

In recent years, many studies have been conducted to improve the strength by modulating the laser beam in terms of time or space.

Lerra et al.⁵⁾ evaluated the tensile strength of the laser welded part according to the pulse interval. They conducted the experiment by fixing the pulse energy at 13 J and changing the pulse interval within the range of 0.1 to 1.2 mm. The result showed the highest tensile strength at a pulse interval of 0.55 mm and the lowest tensile strength at 0.1 mm. They mentioned that the ductile fracture occurred under the pulse interval condition of 0.55 mm and said that the pulse interval of 0.1 mm was too narrow, resulting in high heat input and lower strength.

Dimatteo et al.⁶⁾ optimized the laser welding process variables by applying beam oscillation and obtained a maximum tensile strength of 87.1 MPa, an improvement of 20% over the previous work.

Zhu et al.¹⁵⁾ conducted experiments by creating different weld paths using laser beam scanning on a 0.2 mm thick material. The highest fracture load of approximately 200 N was achieved in the beam path formed in a spiral shape from the outside to the inside.

Kraezch et al.¹⁶⁾ achieved a bond strength of 80% compared to the strength of the Al base material by applying the shape of the infinity symbol through laser beam scanning.

Mathivanan et al.¹⁷⁾ selected several pulse shapes to compare the tensile strength. In the results, the pulse shape that included the preheating section showed the maximum tensile strength.

3.2 Intermetallic Compound Formation and Effects

3.2 Intermetallic Compound Formation and Effects

Table 3 shows a summary of the element content, electrical resistance, and hardness of the intermetallic compound formed in the Al/Cu laser welds. In the case of laser welding, γ, ζ, η, and θ phases are formed according to the Al/Cu element content. If they exist in the weld, the electrical resistance increases, and so does the hardness, resulting in a weak structure¹⁸⁾. Therefore, in the case of Al/Cu laser welding, heat input should be controlled to minimize the intermetallic compound phase formed in the weld¹⁹⁾.

Table 3

Properties of aluminum and copper intermetallic compounds²³⁾

Phase	Nominal Composition	% at Al	Electrical Resistance ρ at 20°C [μΩ cm]	Hardness HV (10 g)
Cu	-	0–19.7	2.0	75
γ	Al4Cu9	31–37.5	14.2	770
ζ	Al3Cu4	43.7–44.8	12.2	930
η	AlCu	47.6–50.2	11.4	905
θ	Al2Cu	67–68.1	8.0	630
Al	-	97.52–100	2.4	36

Zuo et al.⁷⁾ examined the phase formed on the Al/Cu weld at each point through line scanning of SEM-EDX. In Fig. 2, Zone 1 is composed of the γ phase and formed near the Cu side. Zone 2 has a structure in which the eutectic phase and the hypoeutectic phase are mixed. Zone 3 consists of α and θ phases. Zone 4 consists of Al and the α phase with a dendritic structure. Their study pointed out that it is important to control the Al/Cu mixing through optimized process variables and techniques in Al/Cu laser welding.

Fig. 2

Morphology of four zones in intermediate layer⁷⁾

Lee et al.⁸⁾ evaluated the distribution of intermetallic compounds in the weld zone as a function of the Al/Cu laser welding speed and the upper/lower plate material combination. As shown in Fig. 3, intermetallic compounds Al₂Cu(θ), Al₄Cu₉(γ), AlCu(η) phases were detected more at a welding speed of 10 m/min than at a welding speed of 50 m/min. When Cu was the upper plate (Fig. 3c, 3d), the Al₄Cu₉(γ) phase was detected more.

Fig. 3

Micro-XRD analytical results of laser weld fusion zone produced in dissimilar Al (upper)-Cu (lower) lap sheets at welding speeds of (a) 10 m/min and (b) 50 m/min and Cu (upper)-Al (lower) lap sheets at welding speeds of (c) 10 m/min and (d) 50 m/min⁸⁾

Yan et al.²⁰⁾ changed the laser power to evaluate the weld composition, Al/Cu diffusion mechanism, and fracture pattern. Fig. 4 shows the melting and diffusion mechanisms of Al/Cu. In the initial section, as shown in Fig. 4a and Fig. 4b, Cu diffuses into the molten pool at the joint interface, whereas Al diffuses toward the Cu side due to the stirring and convection effect of Al/Cu during melting. In Fig. 4c, Al solid solution and Al₂Cu(θ) are formed along the joint interface due to rapid cooling. Then, in Fig. 4d, the additional cooling leads to the settlement of the Al/Cu process phase and solid solution at the joint interface. Figs. 5a and 5b show the fracture surface of Al/Cu and Fig. 5c shows the fracture path. The crack initiation is in the Al+Al-Cu process phase and is fractured along the Al₂Cu(θ) phase. The fracture of the Al/Cu weld proceeds along the Al₂Cu(θ) phase. Furthermore, it is clearly visible that the thickness of the IMC layer of Al/Cu increases with the increase of laser power. Therefore, they mentioned that the formation of the IMC layer should be minimized by selecting an appropriate laser power and controlling the dilution amount of Al/Cu.

Fig. 4

Schematic of interface reaction of Al/Cu joints²⁰⁾

Fig. 5

Cross-section fracture image of Al/Cu lap joint; (a) & (b) Cross-section fracture image, (c) Schematic of the location of the fracture surface20)

3.3 Electrical Resistance

3.3 Electrical Resistance

Table 4 shows a summary of the electrical resistance measurement results according to the material and measurement method for the laser-welded Al/Cu joints. The measured electrical resistance of the weld is in the range of 10.5 to 520 μΩ, and there are large differences depending on the material thickness, material type, and measurement method. Fig. 6 shows the path of the current flowing in the cross-section of the overlap-welded Al/Cu joint, and the difference in the measured electrical resistance is large depending on the joint interface (w) and the weld penetration depth (d). The above has been reviewed in detail by Jarwitz et al.²¹⁾ Therefore, when measuring the electrical resistance value, it should be noted that it changes if the cross-sectional shape of the weld, measurement method, and material combination change. In the following, we will review the electrical resistance measurement methods that have been used in recent years and explain the control of Al/Cu dilution rate using laser welding technology and the change in resistance value due to the minimization of intermetallic compound growth.

Table 4

Measured electrical resistance of overlap-welded Al/Cu joints

No	Upper metal/ thickness (mm)	Lower metal/ thickness (mm)	Method	Electrical resistance (μΩ)	Year (Ref.)
1	Al5754 1.0	Cu-OF 1.0	4 Point Probe Measurement	≈32	201211)
2	Al1050 0.2	E-Cu 0.5	Direct Electric Measurement	204	201623)
3	Al1050 0.6	Cu-OF 0.6	Sequential Resistance Measurement	520	201724)
4	Al1050 2.0	Cu-OF 2.0	4 Point Probe Measurement	≈10.5	201925)

Fig. 6

Sketch of a cross-section of the weld seam with indication of the path of an electrical current21)

The 4-point probe measurement method can be used to measure the electric resistance of the laser-welded joint, as shown in Fig. 7²²⁾.

Fig. 7

Sketch of the setup for electrical resistance measurement22)

Schmalen et al.^23,24) introduced an electrical resistance test method using the electrodes of resistance spot welding. As shown in Fig. 8, they mentioned that the smaller the value of d(S₁+S₂), the higher the accuracy of electrical resistance measurement. In the electrical resistance test using the electrodes of resistance spot welding, as shown in Fig. 9, it is advantageous to measure the electrical resistance by minimizing the d value to reduce the existing path of the current and applying the current directly to the weld. By using the electrodes in direct contact with the weld, it is possible to achieve an effect of minimizing the influence of external factors on the electrical resistance measurement and improve the accuracy.

Fig. 8

Serial resistance of an overlap welded sample24)

Fig. 9

Setup for a sequential resistance measurement with electrodes on bottom of the weld seam24)

Jarwitz et al.²²⁾ controlled the formation of intermetallic compounds by minimizing the Al/Cu dilution rate of the weld through spatial modulation. The electrical resistance was 1 μΩ at the weld where spatial modulation was applied and 1.5 μΩ at the weld where spatial modulation was not applied.

Schmalen et al.²⁴⁾ evaluated the electrical resistance as a function of the growth of the intermetallic compound generated during Al/Cu welding (Fig. 10). Comparing the electrical resistance between the sound weld (520 μ) and the weld with excessive heat input, it was found that the electrical resistance of the weld with excessive heat input increased to 800 μΩ due to the growth of the intermetallic compound.

Fig. 10

Electric resistance of laser overlap welds of 0.6 mm Al and 0.6 mm Cu. Three process phases were observed24)

Reisgen et al.²⁵⁾ used the 4-point probe measurement method to compare the electrical resistance of laser welds with and without beam oscillation and offset in a vacuum. When laser beam offset and beam oscillation were applied, the intermetallic compounds grew in the weld, resulting in an increase in electrical resistance from 10.5 μΩ to a maximum of 23 μΩ.

4. Laser Welding Technology

This section introduces the effects of spatial and temporal modulation in laser welding of Al/Cu alloy dissimilar materials, welding characteristics according to laser wavelengths, and monitoring techniques.

Modulation is a technology that controls the characteristics of laser beams in terms of space or time, and is largely divided into spatial modulation and temporal modulation. Typical spatial modulation techniques include ring-core beam composition, beam oscillation, and wobbling, and temporal modulation techniques include time-based beam profile configuration and pulse implementation.

4.1 Spatial Modulation

4.1 Spatial Modulation

Beam oscillation, a type of spatial modulation, is a technique that uses a motor or galvanometer to control the path of a laser beam in one or more dimensions, and more uniform weld quality can be obtained by increasing the overlap rate of the beam by oscillation²⁶⁾.

Dimatteo et al.⁶⁾ used spatial modulation to apply a circular laser beam in a direction parallel to the weld line, and proposed a welding condition to increase the heat input to overcome the reflection rate problem of the laser beam when Cu is the upper plate in the combination of the upper and lower plates. It was explained that Cu has a high reflection rate, so the application of modulation is helpful to ensure the absorption rate of the laser beam.

Kraetzsch et al.¹⁶⁾ applied beam oscillation and 1D and 2D laser scanning patterns to butt welds and overlap welds. 1D scanning is a method of forming a weld area along a single axis (horizontal or vertical), and 2D scanning means that the laser beam forms a weld area along two axes. 1D scanning forms straight welds, and 2D scanning forms various welding patterns to create and use curves, circles, infinity symbols, etc. The defects found in the butt and overlap welds when conventional beam oscillation and 1D and 2D laser scanning patterns were not used were not found when these techniques were applied. It was mentioned that this was because the thickness formation range of the IMC layer was minimized to 5 to 10 μm by controlling the heat input by increasing the area of the laser beam focused on the weld.

Jarwitz et al.²¹⁾ used spatial modulation on the Al/Cu weld joint to apply a laser beam perpendicular to the welding direction and proposed the correlation between the welding depth and width on the lower Cu plate as a geometric feature. In particular, the results of applying and not applying beam oscillation were visually analyzed, as shown in Fig. 11, and it was found that the bead width of the interface increased when beam oscillation was applied.

Fig. 11

Seam width at interface over welding depth into Cu for welds produced with spatial beam oscillation (blue) and welding process without beam oscillation (red). The error bars indicate the standard deviation21)

Jarwitz et al.²²⁾ applied spatial modulation of a laser beam perpendicular to the direction of the weld line and evaluated the cross-section of Al/Cu welds. They compared the cross-section of the straight weld with that of the spatial modulation-applied weld and confirmed that Al/Cu was uniformly mixed in the cross-section of the weld when spatial modulation was applied.

Holatz et al.²⁷⁾ increased the joint area effectively by implementing circularly overlapping welds in a direction parallel to the weld line and compared the cross-section of the weld between different combinations of upper and lower Al/Cu plates. As shown in Fig. 12, when Al is the upper plate, the cross-sectional shape of the weld is an inverted triangle, in which the bottom side is narrower. When Cu is the upper plate, a narrow and long weld shape is formed. Since Cu has higher thermal conductivity than Al, the welding heat is quickly dissipated and cooled along the upper Cu plate, resulting in a narrow weld.

Fig. 12

Cross sections with different laser power and sheet both arrangements27)

Fetzer et al.²⁸⁾ used spatial modulation to apply a laser beam perpendicular to the weld line direction. In the experiment, the amplitude was used as the main variable to control the Al/Cu weld penetration depth, and the fine adjustment of the weld penetration depth was implemented by controlling the frequency. In this case, it was possible to adjust the weld penetration depth using amplitude and frequency without controlling the laser power and welding speed.

Some studies have been conducted to experiment with the overlapping of laser beam patterns in a circular shape.

Bono et al.²⁹⁾ stacked Al/Cu foil sheets of different thicknesses and applied laser beam patterns in wobbling and infinite symbol shapes. Wobbling is a technique in which the welding is performed by periodically moving the center position of the laser beam in a circular or elliptical pattern, and has the effect of minimizing the weld penetration depth and increasing the fusion area³⁰⁾. They applied the wobbling method to the combination of stacking 30 Al sheets with a thickness of 20 μm and found that the joint area at both ends of the weld tended to decrease or separate. This was attributed to the micro-gaps that occur when the foil sheets are stacked, and the use of an appropriate fixture was recommended. The combination of the stacking of 10 sheets of Al 100 μm foil, to which the laser beam pattern of infinite symbol shape was applied, showed a sound weld.

4.2 Temporal Modulation

4.2 Temporal Modulation

Temporal modulation welding is a technique in which the power of a laser beam changes over time²²⁾, and the change in laser beam power can be useful for materials with different laser absorption rates in the solid and liquid phases.

Lerra et al.⁵⁾ evaluated the cross-section in straight welds of Al/Cu alloy for different pulse types and pulse intervals. Fig. 13 shows the weld cross-section by pulse shape. In the case of B, C and F, there is a preheating effect as a result of using the maximum peak power for a short time. In the case of D and E, on the other hand, the power is high at the beginning but gradually decreases. Sound welds have been observed in the case of B, C, and F pulse shapes, which can produce a preheating effect before welding.

Fig. 13

Seam morphology with E = 13 J and d = 0.32 mm for various pulse shapes, (a) square pulse, (b) increasing step pulse, (c) increasing ramp pulse, (d) decreasing step pulse, (e) decreasing ramp pulse, (f) central peak pulse5)

Ascari et al.⁹⁾ used a long pulse (0.5-20 ms) and a short pulse (17-320 ns) to compare the cross-section between the straight welds produced. The long pulse- applied welds showed a sound formation of the weld bead surface except for the high heat input conditions, and no porosity or cracking was found. For the short pulse-applied welds. The wobbling technique was applied, and there was no significant difference between the long pulse and the short pulse, except for the high heat input conditions.

Zhu et al.¹⁵⁾ used a nanosecond pulsed laser to simultaneously apply both spatial and temporal modulation to the spot weld. In Fig. 14, Model 1 has a spiral shape starting from the outside and ending at the inside, and Model 2 has a spiral shape starting from the inside and ending at the outside. In Model 3, laser scanning is performed in the shape of filling the circular section with straight lines. In the case of Model 1, there is no crack in the cross-sectional shape, but in Model 2, there are wave-shaped defects. Model 3 shows fewer wave-shaped defects and pores compared to Model 2, but Model 1 shows a more sound weld by minimizing the formation of intermetallic compounds under the condition of lower heat input.

Fig. 14

Schematic diagrams for scanning paths during the nano-second pulse laser welding of copper to Al: (a) model 1 (outer spiral), (b) model 2 (concentric circle, inner to outer), (c) model 3 (straight)15)

Ascari et al.³¹⁾ applied a nanosecond pulsed laser to thin plates of Al/Cu to form line and spiral welds and evaluate their cross-sections. For line welds, they used wobbling to control the weld penetration depth and ensure a sound bead appearance. For spiral welds, they used a method similar to that shown in Fig. 14 but arranged the laser beam in a spiral path instead of a concentric circle to reduce the dilution rate of Al/Cu and prevent weld deformation.

Mathivanan et al.¹⁷⁾ used both spatial and temporal modulation and arranged Cu as the upper plate. They applied spatial modulation in the shape of the infinite symbol “∞” and pulse modulation, which is temporal modulation, to the process. They produced and experimented with four types of pulse shapes based on the preheating, main welding, and cooling sections and confirmed that the pulse shape with preheating conditions was advantageous for deep weld penetration.

4.3 Laser Characteristics by Wavelength

4.3 Laser Characteristics by Wavelength

When a 1 μm wavelength IR laser used for general welding is applied to laser welding of Al or Cu alloys, the initial beam absorption rate is very low, and this section introduces studies using short-wavelength lasers (green laser and blue laser) that have been developed to address this problem.

The green laser is a laser that is green in the visible light region and is half the wavelength of 1 μm. Essentially, the wavelengths of fiber (1,070 nm), disk (1,030 nm), and Nd:YAG (1,064 nm) lasers are modulated at twice the frequency and converted to wavelengths of 535 nm, 515 nm, and 532 nm, respectively, for use. The green laser has a great advantage in the laser beam absorption rate of Cu material. As shown in Fig. 15, the beam absorption rate of the Cu material for the green laser is approximately 40%, which is about eight times higher than the conventional IR laser absorption rate.

Fig. 15

Laser absorption on C1020P10)

Kim et al.¹⁰⁾ used a green laser with a wavelength of 515 nm to weld 6xxx-series Al and oxygen-free copper C1020P. Using a laser power in the range of 800 to 1,200 W and a welding speed in the range of 180 to 220 mm/s, they obtained a sound weld with no spatter in the entire section.

Stritt et al.³²⁾ analyzed the melting behavior by simultaneously applying an infrared pulsed laser and a green pulsed laser, respectively, with Cu as the upper plate. In their study, the high beam absorption rate of the green pulsed laser facilitated preheating, and the weld was rapidly melted by the infrared pulsed laser. Moreover, the formation of intermetallic compounds was minimized by rapid melting.

Mathivanan et al.³³⁾ produced a sound Al/Cu weld using a green laser with a wavelength of 515 nm. The sound weld was formed with a laser power of 700 to 750 W, which is low compared to the infrared laser power. When Cu was positioned as the upper plate, the formation of intermetallic compounds was suppressed, and Al solid solution and Cu solid solution were mainly formed.

4.4 Monitoring for Laser Welding

4.4 Monitoring for Laser Welding

Laser welding monitoring technology has been developed and improved to predict welding quality in real-time. Since lasers have the property of light, non- contact sensors such as photodiodes, spectrometers, and CCD cameras are mainly preferred as monitoring equipment. The following is a description of studies that evaluated the weld based on the data collected using non-contact sensors for monitoring during the laser welding process.

Mathivanan et al.¹²⁾ used a photodiode to collect the spectral signals of Al produced by the generation of the molten Al plume and predicted the weld penetration phenomenon in the lower Al plate. The melting of Al started in the zone where the measured intensity of the Al vapor signal was high, and it was possible to predict the weld penetration phenomenon using the collected data.

Schmalen et al.³⁴⁾ used a photodiode, an optical filter, and a spectrometer to measure the spectral signals of Al and Cu in the vapor generated during laser welding. The signal strength was greatest at 394 nm and 396 nm for Al and at 578 nm for Cu. It was confirmed that the zone where Al/Cu begins to melt can be distinguished by the sensor signal.

Simonds et al.³⁵⁾ used laser-induced fluorescence to investigate the applicability of real-time in-situ monitoring. Laser-induced fluorescence is a type of spectral analysis, and in the study, it was used to predict Cu penetration. The Cu spectral signal was confirmed in the laser power range of 3,460 to 7,910 W, indicating that the Cu penetration phenomenon was reflected.

Recently, studies have been conducted using deep learning to classify the quality of welds and predict the result values^10,36-40). Deep learning is used in various fields and is a type of machine learning, a kind of artificial intelligence. Machine learning is divided into supervised learning, unsupervised learning, and reinforcement learning based on the type of training data. Among them, supervised learning is mainly used, where input and labeled data are entered as training data. It is applied to classification and regression models³⁶⁾. The following is a description of studies that used artificial neural networks (ANN) and convolutional neural networks (CNN) to classify the appearance quality of laser weld beads and predict the weld penetration depth.

Kim et al.¹⁰⁾ captured images of laser weld beads and used a deep learning method to classify their weld quality. As for the image data by process variable, they collected about 12,000 data, and trained the deep learning model by classifying good and bad welds. Figure 16 shows the structure of the CNN used to train the deep learning model, and the CNN consists of 5 convolutional layers and 3 fully connected layers. As a result of training the model, the defects were discriminated with an accuracy of over 98% when images showing spatters were input. The accuracy of discriminating sound weld images was approximately 66%, which was relatively low. When capturing an image of the weld bead surface, the shadow caused by the illumination angle becomes a factor that reduces the accuracy of discrimination. Therefore, it is required to collect the image data in a consistent manner.

Fig. 16

Structure of deep learning by Alexnet Al-Cu weld quality monitoring10)

Mathivanan et al.³⁷⁾ measured the acoustic signal with a microphone during laser welding to collect data, and then classified the state of Al and Cu welds using an ANN. As shown in Fig. 17, they suggested that the amplitude in the acoustic signal represents the intensity of the acoustic emission during welding, and the total acoustic emission (sum of the square of the amplitude, ∑y²) represents the melting activity during laser welding. Based on 85% of the strength value of the Al material used in the experiment (i.e., 85% of 291 N), those exceeding 250 N in terms of the tensile shear strength were classified as sound welds, and those below 250 N were classified as poor welds. As a result of training the model, 100% training accuracy and 91% validation accuracy were achieved. It was suggested that by applying ANN, it is possible to predict the weld condition based on the acoustic emission signal for various laser powers and speeds.

Fig. 17

Flow chart showing the signal processing step to obtain the total sum of amplitude square as the acoustic emission value37)

Lee et al.³⁸⁾ collected photodiode signals during laser welding, used them as input, set the weld penetration depth as output, and used SVM (Support Vector Machine), FCN (Fully Connected Network), and CNN models to predict the penetration depth. The laser welds were classified as not good, transition, or good. All three models showed an accuracy of over 90%, with the CNN model showing the highest accuracy at 98%. The accuracy was also examined in a validation test where the actual and predicted values were compared while increasing the laser power.

Kang et al.³⁹⁾ trained a CNN model consisting of a CCD image and a spectrometer signal as input and a weld penetration depth as output for laser welding. The model was evaluated by dividing it into a single-sensor model and a multi-sensor model. The single-sensor model used only a CCD image as input, while the multi-sensor model used a CCD image and a spectrometer signal as input. On average, all models showed a coefficient of determination (R²) of 0.96 in the test results, which was close to 1.0. The R² of the multi-sensor model was 0.99, which was higher than that of the single-sensor model.

Kang et al.⁴⁰⁾ acquired CCD images and photodiode signals during laser welding and constructed a single-sensor model and a multi-sensor model to predict the weld penetration depth. The input of the single-sensor model was a CCD image, and that of the multi-sensor model was a CCD image and photodiode signal. In addition, the photodiode signal data were converted using Short-Time Fourier Transform (STFT) and Fast Fourier Transform (FFT). The R² was greater than 0.98 for all models. Among them, the multi-sensor model with STFT showed the highest R² of 0.99 in terms of predicting the weld penetration depth.

5. Conclusion

In this study, we introduced the trend of laser welding technology of Al/Cu for the production of secondary batteries and drew the following conclusions.

• 1) It is possible to ensure the bond strength at the level of the Al base material strength by optimizing the process variables of the Al/Cu dissimilar material welding, and the spatial and temporal modulation techniques are used to ensure the weld strength.

• 2) For the bond strength and electrical conductivity, it is necessary to select a welding process that can keep the formation of intermetallic compounds below a certain level.

• 3) Lasers with high absorption rates, such as green and blue lasers, have been developed to ensure the productivity and soundness of Al/Cu dissimilar material welds, and various deep learning methods can be applied to predict the quality.

Acknowledgments

This research was supported by the MOTIE (Ministry of Trade, Industry, and Energy) in Korea, under the Human Resource Development Program for Industrial Innovation (Global) (P0017303, Smart Manufacturing Global Talent Training Program) supervised by the Korea Institute for Advancement of Technology (KIAT)

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