Development Status of Production Purification and Casting and Rolling Technology of Electrical Aluminum Rod

Abstract

As the demand for lightweight and high-performance conductive materials grows in power transmission systems, aluminum alloy rods have emerged as a cost-effective and scalable alternative to copper conductors. This review systematically examines the development status and technological progress in the purification and casting–rolling processes used in the production of Electrical Round Aluminum Rods (ERARs). It explores current challenges in improving electrical conductivity and mechanical strength while addressing issues such as hydrogen and oxide inclusion removal, grain refinement, and impurity segregation. Key purification techniques—including flux refining, gas treatment, filtration, and rotary injection—are compared in terms of performance, cost, and environmental impact. The paper also analyzes different casting–rolling methods, including continuous casting and rolling, twin-roll casting, and extrusion processes, with attention to process optimization and equipment design. Furthermore, emerging applications of artificial intelligence (AI) in predictive modeling, defect detection, and process parameter optimization are highlighted, offering a novel perspective on intelligent and sustainable ERAR production. This paper aims to provide insights for facilitating the industrial-scale production and performance enhancement of ERAR materials.

1. Introduction

Aluminum is one of the most abundant elements in Earth’s crust and the most widely used non-ferrous metal [1], owing to its combination of low density, low cost, high reflectivity, workability, relatively high alloying strength, and high electrical conductivity [2,3,4,5]. Its electrical conductivity is 64.94% IACS, while its thermal conductivity is approximately 237 W·m⁻¹·K⁻¹ at room temperature [2,3]. Although copper is traditionally regarded as the standard conductor, aluminum offers several key advantages. Per unit weight, aluminum provides nearly twice the conductivity of copper while being significantly more cost-effective [6,7]. Applications requiring high electrical conductivity account for approximately 20% of total aluminum usage now [8]. Moreover, only 0.01% of Earth’s crust is made up of copper, and its availability is vulnerable to geopolitical tensions [9].

With the expansion of industrial scale and the growth of long-distance transmission demand, reducing line losses and improving transmission stability and capacity have become the core goals of power system optimization. In this context, aluminum alloy wire, with its excellent electrical conductivity and cost advantages [10], has become the core material carrier of the strategy of “saving copper with aluminum”. ERAR is a kind of raw material used to make aluminum alloy cables. The preparation of round aluminum rods for electricians can roughly be summarized into two steps: purification process and casting and rolling process. The purification process improves conductivity by increasing the component ratio and purity of ERAR and optimizing the microcrystalline structure. The casting and rolling process, on the other hand, largely influences mechanical properties such as elongation and tensile strength, as well as service life [11,12,13]. This paper reviews the development achievements of these two processes, providing a reference basis for scientific research. Therefore, this review aims to provide a comprehensive overview of the current state of purification and cast-rolling technologies for ERAR, to identify key challenges in improving electrical and mechanical properties, and to explore how AI-driven methodologies can contribute to future process optimization.

The literature review was primarily conducted using the Web of Science (WoS) Core Collection and the China National Knowledge Infrastructure (CNKI) databases. The search covered publications from 2010 to 2024, using keywords such as “electrical round aluminum rod”, “aluminum alloy”, “electrical conductivity”, “continuous casting and rolling”, “material microstructure and properties”, and “process optimization”.

2. Aluminum Alloy Cable Market Size and Classification

Due to their favorable properties, including low density and high electrical conductivity, aluminum alloy conductors have seen rapid development, particularly in the production of electrical aluminum rods. With the expansion of the aluminum alloy cable market and the ongoing structural upgrading of the electrical aluminum rods industry, the range of products used in electrical applications has become increasingly diverse. Consequently, the establishment of detailed specifications and standardized constraints for electrical aluminum rods models used in electrical systems has become a pressing necessity.

2.1. Aluminum Alloy Cable Market Size

Compared with traditional pure copper and aluminum core cables, aluminum alloy cables show significant comprehensive performance advantages. Its density (2.7 g/cm³) is only 30% of that of copper. Based on maintaining a 61.8% IACS conductivity, the conductor cross-sectional area can be optimized to 1.6 times that of a copper core cable, and the weight per unit length can be reduced by 50% [14,15]. By alloying with elements such as Fe, Mg, and Si, aluminum alloy cables achieve a tensile strength of 113.8 MPa and an elongation of 30%, effectively overcoming the defects of the insufficient mechanical properties of pure aluminum materials (see

Table 1. Performance comparison of different electrical conductors Reprinted from refs. [14,15].

Type of Electrical Conductor	Density /(g/mm3)	Melting Point /°C	Conductivity /(%IACS)	RTC/(°C−1)	TS/MPa	YS/MPa	E/%
8030	2.7	660	61.8	4.03	113.8	53.9	30
Cu	8.89	1083	100	3.93	220–270	60–80	30–45
Al	2.7	660	62	4.03	70–110	20–30	23–25

Note: RTC = Resistance temperature coefficient; TS = Tensile strength; YS = Yield strength; E = Elongation; 8030 = 8030 aluminum alloy electrical conductor; Cu = Copper electrical conductor; Al = Aluminum electrical conductor.

). This improved corrosion resistance and creep resistance make them a compelling alternative in the cable industry.

According to market data, China’s total cable industry output reached RMB 228.4 billion in 2020, representing a 13.3% year-on-year increase, and accounting for 37.8% of power transmission sector’s total output. Among this, aluminum alloy cables contributed RMB 72.3 billion, representing 31.6% of the cable industry’s total output. Driven by the ongoing “aluminum replacing copper” strategy, the replacement rate of aluminum alloy cables has approached the international averages. Consequently, the Chinese market continues to expand (Figure 1), and the recent investments of the State Grid Corporation of China have been increasing continuously (Figure 2). The application share of aluminum alloy cables in China in new energy grid connection and ultra-high-voltage (UHV) power transmission exceeds 45% [16].

2.2. Classification and Performance Requirements of Round Aluminum Rods for Electricians

ERAR is extensively used in cables, wires, transformers, reactors, generators, and busbars in the aluminum electrolysis industry. With the rapid development of ultra-high-voltage (UHV) grids, long-distance transmission systems, new energy infrastructure, electric vehicles, and the communication industry, the demand for ERAR has grown significantly, presenting strong market potential. Figure 3 [18] show the average market price of a 9.5 mm round aluminum pole for electrical engineering. China’s power grid investment reached RMB 520 billion in 2023 and exceeded RMB 600 billion in 2024. It is projected to surpass RMB 650 billion in 2025, indicating a consistent year-on-year growth trend in grid investment. It can be seen through the SMM website; we can clearly see the price of 9.5 mm electrician round aluminum rods peaked in 2022 at 20,860 RMB/t. In 2023, the average price declined to 19,588 RMB/t. It is projected to rise again to 20,278 RMB/t in 2024. Overall, the price has remained around 20,000 RMB/t over the past three years [18].

High-strength and high-conductivity ERAR are essential to ensure the safety, reliability, and energy efficiency of downstream applications. However, owing to the impurities and rolling process conditions, it remains challenging to simultaneously achieve optimal electrical and mechanical properties. As one of the products of aluminum deep processing, ERAR offers low weight, corrosion resistance, and excellent electrical conductivity and thermal conductivity, making it widely applied in wires and cables, electronics, communications, and automobiles. With the development of new energy and aerospace, its potential applications are also expanding.

In order to meet the expanding market demand for aluminum alloy cables, the development of ERAR products has progressed rapidly, and many ERAR grades have been put into the market (see

Table 2. Mechanical and electrical properties required by GB/T 3954-2022 Reprinted from ref. [19].

Brand Number	Status	T/MPa	E/%	Resistivity/nΩ·m
1B90, 1B93, 1B95, 1B97	O	35~65	35	27.15
	H14	60~90	15	27.25
1B85	H14	105~130	5	27.95
1370	O	60~95	25	27.90
	H12	85~115	11	28.01
	H13	105~135	8	28.03
	H14	115~150	6	28.05
	H16	130~160	5	28.08
1A60, 1R50	O	60~90	25	27.55
	H12	80~110	13	27.85
	H13	95~115	11	28.01
	H14	110~130	8	28.01
	H16	120~150	6	28.01
1R60	O	95~130	8	28.03
	H15	100~135	7	28.34
1350	O	60~95	25	27.90
	H12	85~115	12	28.03
	H14	105~135	10	28.08
	H16	120~150	8	28.12
6101	T4	150~200	10	34.50
6201	T4	160~220	10	34.50
8A07	H15	95~135	7	28.64
	H17	120~160	6	31.25
8030	H14	105~155	10	29.73
8E76	O	60~100	25	28.00
	H12	85~125	13	28.45
8R76	H13	95~120	11	28.78
	H14	105~140	8	28.89
	H16	115~150	6	29.00

Note: T = Tensile strength under room temperature testing; E = Elongation after breaking under room temperature testing; GB/T 3954-2022 = the latest version of the National Standard of the People’s Republic of China, “Electrical Aluminum alloy Rod”.

). Different countries employ distinct systems for standardizing aluminum conductor grades. However, the same type of aluminum alloy can be found with corresponding grades in the standards of various countries. As a developed country, the United States has a mature system for aluminum alloy grades, which adopts the naming convention of AA (representing the Aluminum Association) followed by four digits. It classifies aluminum alloys into eight series, and each series has a similar composition. The naming rule in Europe is to use “EN AW-” as a prefix, with the numerical part corresponding to the American grade (

Table 3. Comparison of common aluminum conductor standards in Europe, America.

Compare Dimensions	European Aluminum Conductor Grades	American Aluminum Conductor Grades
Main ingredients	Basically consistent	Basically consistent
Applicable market	European project	Worldwide
Testing key point	Dimensional accuracy of wire	Annealing Process

compares the specific differences between European and American standards corresponding to the same brand ERAR). Notably, Europe places greater emphasis on processing states in its standards, directly incorporating them into grades, and imposes stricter limits on impurity elements while allowing slightly broader ranges for other components than the American system.

3. Development of the Purification Process

Purification is essential for producing high-purity ERAR, directly affecting downstream casting and rolling. Hydrogen and oxide inclusions, formed by the reaction between liquid aluminum and water vapor, significantly reduce ERAR’s mechanical properties. Therefore, purification—via physical and chemical methods—is required to eliminate hydrogen and other impurities from the melt (Typical impurities in aluminum conductors are shown on

Table 4. Typical impurities in aluminum conductors Reprinted from refs. [21,22].

Impurity Type	Ingredients	Morphology	Density/(g/cm3)	Size/(μm)	Mass Fraction	Sources
Gas	H2	Atom			0.00002~0.00006	Raw materials, melting environment, chemical reactions
Oxides	Al2O3	Granules, membranes	3.97	Granules: 0.2~30 Membrane: 10–5000	0.0006~0.0016	Oxidation reaction High-temperature melting Casting Refractory
	MgO	Granules, membranes	3.58	Pellets: 0.1~5.0 Membrane: 10~5000
	MgAl2O4	Granules, membranes	3.60	Pellets: 0.1~5.0 Membrane: 10~5000
	SiO2	Pellets	2.66	0.5~5.0
Salt	Fluoride, chloride	Particle	1.98–2.16	0.1~5.0	<0.0001	Chloride, cryolite electrolyte, etc.
Carbides	Al4C3	Pellets	2.36	0.5~25.0	0.0002~0.0012	High-temperature reaction
	SiC	Pellets	3.22			High-temperature reaction
Nitrides	AlN	Granules, membranes	3.25	10~50	0.0003~0.0012	High-temperature reaction
Borides	TiB2	Particle	4.50	1~30	<0.0001	Grain refiner
	AlB2	Pellets	3.19	0.1~3.0	0.0001~0.0100	Grain refiner used incorrectly
Intermetallic compounds	Al (FeMnCr) Si	Granules	>4.00	1~50		Melting process tool

). Common techniques include fluxing, gas purification, filtration, and rotary injection, and are classified as adsorptive, non-adsorptive, or composite purification [20].

3.1. Fluxing Method

The fluxing method uses molten salts to remove impurities through chemical reactions and physical adsorption, improving melt cleanliness and fluidity. Fluxes are broadly categorized into refining agents for impurity removal and intermediate alloys for alloying and grain refinement.

3.1.1. Refining Agent

Fluxing is a key method for purifying aluminum melts, removing impurities through chemical reactions and physical adsorption between molten salts and contaminants. The impurity removal mechanism is illustrated in Figure 4 [23] and Figure 5 [24]. Early refining agents were mainly chloride based (e.g., KCl, NaCl, MgCl₂), which reacted with Al₂O₃ to form low-melting-point compounds, aiding inclusion flotation.

Kulikov et al. [25] conducted a systematic comparative study of five widely used aluminum refining fluxes (SF1–SF5), employing XRD, DTA, and TGA analyses alongside direct chill casting experiments under near-industrial conditions. Their research examined the chemical composition of flux, moisture content, melting points, particle size distributions, and refining efficiency.

The experiments revealed that each flux contained crystalline hydrates, which decomposed at high temperatures to release water vapor. This vapor reacted with molten aluminum to produce hydrogen and oxide inclusions, further worsening melt contamination. Figure 6 and Figure 7 show that the refining agents were composed entirely of chlorides, highlighting the direct link between flux function and chemical makeup. Meanwhile, Figure 8 presents DTA–TGA thermal analysis curves that precisely identified the temperature ranges and weight losses associated with moisture release, confirming that water content is a critical negative factor in refining performance. Due to poor wettability with aluminum, these agents achieved only 30% impurity removal and were mainly used as surface cover agents to prevent oxidation.

Incorporating fluoride compounds into refining agents markedly improves impurity removal efficiency and overall refining performance. Adding fluorides such as Na₃AlF₆ and MgF₂ to the KCl–NaCl system reduces interfacial tension and wetting angle, forms a protective surface layer, and increases impurity removal efficiency to 70–90% [24]. Figure 9 [23] illustrates the impact of various fluorides on interfacial tension with molten aluminum. Experimental evaluations identified KF combined with K₃AlF₆ or KAlF₄ as the most effective formulations, as shown in Figure 10 [23], which depicts aluminum droplet coalescence in KAlF₄ solutions. Shi et al. [26] reported that chloride–fluoride synergy enhances alumina dissolution and improves flux resistance to melt corrosion. This composite system effectively removes impurities and hydrogen from A356 alloy, promotes grain refinement, and improves mechanical properties. Consequently, refining agent design has evolved beyond traditional chloride systems toward more advanced, multifunctional formulations.

Composite refining agents have gradually replaced traditional chloride-based salts due to their superior performance. These agents integrate multiple functions through synergistic components: chlorides offer surface coverage, fluorides aid slag-crystal separation, carbonates release gas bubbles that promote flotation, and nitrates improve melt fluidity via exothermic reactions. For example, the Mg₃N₂ flux developed by Chen [27] refines and floats impurities by releasing nitrogen gas. GFLUX-J100 [28] reduces aluminum loss during refining, while AJ401K [29] lowers oxidation loss and enhances purification efficiency.

Further innovations include potassium cryolite-based fluxes, which showed excellent performance in aluminum alloy refining (Tkacheva et al. [30]), and a Na₂SO₄–NaCl system that effectively removes oxide inclusions (Widyantoro et al. [31]). Rare-earth elements (REEs) offer promising potential: they form stable compounds with hydrogen and oxygen; reduce surface tension; and enrich at grain boundaries, inhibiting defect formation [32]. For instance, La and Ce improve bonding strength and mechanical properties, while the LaF₃-containing flux developed by Guan [21] enables efficient melt purification by releasing atomic-state RE species.

From a theoretical standpoint, Zhang et al. [33] optimized a NaF–Na₂CO₃–CaF₂–Na₃AlF₆ composite using thermodynamic analysis, achieving low wetting angles with Al₂O₃ and excellent refining performance. Current research emphasizes optimizing flux composition ratios and developing multifunctional agents capable of degassing, impurity removal, and grain refinement.

In summary, as shown in

Table 5. Comparison of different types of aluminum alloy refining agents Reprinted from refs. [24,26,27,29,30,33].

Refining Agent Type	Hydrogen Removal Capacity/(mL/100 g Al)	Impurity Removal Capacity	Cost Estimation	Environmental Friendly	Basis
Traditional chlorinated salt refining agent	0.15~0.20	40%~60%	Low	Large amounts of smoke and slag	NaCl, KCl, KF, KAlF4
New type of compound refining agent	≤0.12	70%~90%	Medium	The amount of flue gas and slag is significantly reduced	K2SO4, NaNO3, KF, K2CO3, RE
Nano-composite refining agent	≤0.10	>85%	High	Low slag output, high energy consumption	C2Cl6, Na2CO3, nano-Al2O3

, refining agents vary in cost, efficiency, and environmental impact. While chloride salts remain widely used due to their low cost and simplicity, they produce excessive smoke and slag, posing environmental and recovery challenges. By incorporating thermally stable additives like carbonates, sulfates, and nitrates, composite agents significantly reduce emissions and slag—by over 50% and 30%, respectively—while improving refining efficiency. The inclusion of rare-earth elements has become a leading research direction, though it also increases costs. Balancing performance and economic feasibility remains a key challenge. Nano-composite refining agents, which utilize nanoparticle-loaded salt carriers for deep impurity removal, show great promise; but high costs and nanoparticle instability currently limit their practical application.

3.1.2. Intermediate Alloy

Intermediate alloys play a vital role in aluminum melt purification by pre-combining active or easily oxidized elements with the aluminum matrix, thereby avoiding challenges associated with direct elemental addition. They help offset compositional losses from flux reactions and enhance product stability.

Moreover, these alloys serve as heterogeneous nucleation sites, promoting grain refinement and reducing melt viscosity, which in turn mitigates casting defects such as shrinkage porosity, segregation, and thermal cracking. Compared to direct refining agents, intermediate alloys offer superior compatibility and efficiency. Common industrial systems include Al–Ti–B, Al–Ti–C, and rare-earth-based alloys.

Titanium is widely used for grain refinement through the formation of TiAl₃ intermetallic compounds. When the melt temperature exceeds 665 °C and titanium content is above 0.15%, TiAl₃ acts as nucleation sites, significantly enhancing alloy solidification. Yang et al. [34] confirmed the segregation phenomenon of titanium during solidification, with higher concentrations within the boundary of grains than the matrix.

Adding boron improves both impurity control and nucleation. It forms stable high-melting borides (e.g., B4V₃, MnB₂, FeB₂) with trace impurities. Chen et al. [35] reported that Al–B master alloys reduced grain size by over 40% in Al–Si systems, though their effectiveness in Al–Cu and Al–Mg alloys is limited [36].

Table 6. Electrical conductivity of the alloys with boron treatment (%IACS) Reprinted with permission from ref. [37]. 2017 Elsevier.

Sample	B Addition/%
	0	0.03	0.06	0.09	0.12	0.24
Al-0.5Mg-0.35Si	52.2	57.0	54.3	53.9	55.0	58.7
Al-0.5Fe-0.2Si	53.0	54.1	56.6	56.9	58.3	57.4
Al-0.8Fe-0.2Cu	56.2	57.0	58.0	58.3	59.8	58.7

demonstrates the impact of boron on the electrical conductivity of aluminum alloys [37].

Rare-earth elements (REEs) provide new possibilities for intermediate alloy design. Elements such as La and Ce form stable compounds with oxygen and hydrogen, enabling deep melt purification. Their preferential segregation at grain boundaries—5 to 8 times higher than in the matrix—can inhibit harmful FeSi phases. Liu et al. [38] demonstrated that 0.20% scandium reduces grain size by over 50%, while Zeng et al. [39] showed that 0.15% lanthanum increases tensile strength by 18% and boosts conductivity by 5%.

Table 7. Electrical conductivity, ultimate tensile strength, yield strength, and elongation of different chemical compositions in the as-annealed state and comparison of their properties with those of other works Reprinted with permission from ref. [40]. 2024 Elsevier.

Alloy Composition/wt.%	Electrical Conductivity/%IACS	Ultimate Tensile Strength/MPa	Yield Strength/MPa	Elongation/%
Pure Al	59.9–60.3	91.4	85.4	13.6
Al-0.2Ce	60.3–61.2	100.3	94.7	14.2
Al-0.2Ce-0.1Y	62.9–63.3	106.4	101.3	14.3
Al-0.25Zr-0.03Y	~57.2
Al-0.2Y-0.05Sc	60.8	144 ± 2		12.6 ± 0.2
Al-0.2Y-0.2Sc	60.2	194 ± 1		12.2 ± 0.4
Al-0.2Y-0.2Sc-0.3Er	59.7	207		14
Al-0.2Ce-0.2Sc-0.1Y	61.77 ± 0.11	198 ± 2		8.5 ± 0.2
Al-0.2Y-0.2Sc-0.3Yb	54.9	228 ± 2		11.5 ± 0.8

visually showcases the impact of adding various rare-earth elements on the electrical conductivity and mechanical properties of aluminum alloys [40].

To meet the demands of advanced aluminum alloys, future development of master alloys will focus on optimizing compositions through REE additions, designing dispersion-strengthened systems, and improving processing methods. Although Al-Ti-B and Al-Ti-C alloys remain effective refiners, challenges such as impurity introduction, particle agglomeration, and high additive cost must be addressed through continued innovation.

3.2. Gas Purification Method

Gas purification technology removes hydrogen and non-metallic inclusions from molten aluminum by injecting fine gas bubbles into the melt. The purification mechanism involves the adsorption of hydrogen atoms and micron-sized inclusions onto the rising bubbles, which then carry these impurities to the melt surface for removal [41].

Depending on the type of gas used, the technique can be classified into inert gas refining (N₂, Ar), reactive gas refining (Cl₂, F₂), and mixed-gas systems. Based on the injection method, gas purification is typically categorized into static and rotary injection techniques.

Inert gas refining typically employs nitrogen as the primary medium, with hydrogen-free bubbles introduced into the aluminum melt via a graphite nozzle. According to Sievert’s law, dissolved hydrogen atoms diffuse into the bubbles and rise through the melt, while oxidized inclusions adhere to the bubble surfaces and are subsequently removed.

Nitrogen is widely used in industrial applications due to its low cost and easy availability. However, at temperatures of 700–720 °C, it can react with aluminum to form aluminum nitride (AlN) inclusions, thereby compromising melt cleanliness. To address this issue, researchers have developed argon-based refining systems.

Experimental results [42] indicate that increasing the argon flow rate leads to smaller bubble sizes, higher hydrogen removal efficiency, and improved preservation of the alumina surface layer. Nevertheless, uneven bubble size distribution continues to limit purification efficiency, and issues such as compositional segregation remain significant.

While reactive gas refining improves hydrogen removal efficiency, it is highly corrosive, reducing equipment lifespan and posing occupational health risks. To balance purification performance with environmental and safety concerns, mixed-gas refining has been developed. Typical gas formulations include 15 vol.% Cl₂ + 11 vol.% CO + 74 vol.% N₂ and 2 vol.% CCl₂F₂ + 23 vol.% CO₂ + 75 vol.% N₂ [43].

Qin Yiming et al. [44] experimentally measured the inclusion content following nitrogen and mixed-gas refining and concluded that the mixed-gas approach achieved superior impurity removal. Despite being both efficient and environmentally friendly, mixed-gas refining suffers from inconsistent performance and fails to achieve optimal purification. To address these limitations, researchers have developed the rotary gas injection method.

3.3. Filtration Method

The filtration method achieves the separation of inclusions by passing molten aluminum through a porous medium, effectively removing secondary-phase particles in the size range of 1–30 μm. Common industrial filter materials include glass fiber filters, packed bed filters, and ceramic foam filters. The purification mechanism involves a synergistic combination of mechanical interception and surface adsorption processes [45].

Glass fiber filters employ a braided structure, offering advantages such as low cost, ease of operation, and moderate effectiveness in reducing oxidized inclusion content. However, they are primarily suitable for removing large-particle inclusions and require frequent replacement.

In comparison, packed bed filters consist of 2–8 mm Al₂O₃ spheres stacked to form a porous medium with enhanced adsorption capacity due to their increased specific surface area [45]. When integrated into in-line purification systems directly connected to melting furnaces, these filters can operate under argon protection to prevent secondary oxidation. Moreover, the filter beds are typically reusable [46].

Despite these advantages, bed filter systems still face limitations, including difficulty in temperature control, challenges in replacing the filter medium, and the formation of dead zones that lead to uneven filtration.

Ceramic foam filters utilize a three-dimensional mesh structure that enables finer purification of molten aluminum. Compared with the SNIF and MCF filtering methods, Wu [42] indicates that CFF can achieve a better impurity removal effect (see Figure 11). Research by Qiu et al. [21] indicates that, under identical functional pore sizes, filters with higher surface roughness exhibit greater filtration efficiency. This improvement is attributed to increased flow resistance caused by the rough interface, which enhances particle adsorption.

Comparative experiments conducted by Schoß et al. [48] demonstrated that ceramic foam filters made of Al₂O₃ and ZrO₂ exhibit superior inclusion removal performance compared to those made of SiC. Although ceramic foam filters are widely applied in the production of standard aluminum alloy wires, secondary refining of ultra-fine wires still requires the use of deep-bed filters.

The filtration method requires regular replacement of filters, which incurs certain cost burdens. Moreover, it is difficult to remove tiny bubbles and impurities. Therefore, it is usually combined with other purification methods to improve overall performance. Its effect is relatively poor when used alone. Currently, the ERAR purification process is gradually evolving towards a mixed purification process that combines multiple purification methods. In the future, the filtration method will also find considerable application in ERAR production.

3.4. Rotary Injection Method

With the development of gas injection technology from the fixed tube type to rotary type, the rotary injection method breaks large bubbles into micro-bubbles with smaller diameter through shearing force generated by a high-speed rotating nozzle. With its large specific surface area, high interface energy, and low buoyancy characteristics, it significantly improves the bubbles’ stability, adsorption capacity, and melt residence time, thus enhancing the purification effect. In industrial applications, researchers have formed diversified and improved processes such as SILF, ALPUR, FILD, RDU, GBF, MINT, the Alcoa segregation technique, Heprojet, and LARS^TM by combining with other aluminum alloy purification technologies. On the basis of inheriting the core principle of rotary injection, these methods achieve a breakthrough in performance through structural innovation and process combination, forming a unique technical system.

The SNIF method (Spinning Nozzle Inert Gas Flotation) is a common online processing technique for aluminum melts. It includes configurations such as single-chamber single-rotor, dual-chamber dual-rotor, and triple-chamber triple-rotor systems [49]. Figure 12 and Figure 13 [49] show the diagram of the dual-chamber dual-rotor SNIF device and a schematic of the rotating nozzle, which is the core component of the SNIF system. Its unique structure allows the rotating nozzle to maximize the dispersion of the purging medium through effective stirring. Its principle involves using graphite rotating nozzles to mix inert gas with trace chlorine gas and break it into microbubbles. With melt stirring to promote the uniform diffusion of bubbles, the degassing rate can reach 70% [49].

With the ALPUR method through the special nozzle structure to strengthen the agitation of the melt, so that the bubble and the metal liquid have full contact, degassing efficiency reached 60–75%. The FILD law integrates inert gas refining and flux filtration technology; adopting a two-chamber structure for gas refining and alumina ball filtration in turn, the processing speed can reach three times that of the ALPUR method, and the filter layer has the dual functions of mechanical interception and chemisorption.

As an online processing device developed by ALcoa, although the Alcoa segregation technique requires preheating of the filter bed and periodic replacement of alumina balls (see Figure 14 and Figure 15), it can reduce the concentration of Na to 0.00001% at a processing rate of 23 t/h through double filtration and the injection of refining gas.

Table 8. Typical obtained aluminum purity grade and its impurity content—in ppm, data from Reprinted from ref. [50].

Al	Si	Fe	Cu	Mn	Mg	Cr	Ni	Zn	Ti	V	B	Ga	Zr
3N6	135	165	10	13	4	2	10	10	10	10	6	70	10
3N8	69	60	3	9	4	2	3	5	6	13	4	42	10
4N3	22	18	2	5	1	1	0	5	0	2	2	11	3
4N7	7	4	3	0	1	1	0	1	1	1	1	2	1
5N5	0.7	0.2	0.3	0.1	0.5	0.1	0.005	0.05	0.05	0.01	0.1	0.005	0.01

summarizes the typical purity levels achieved during this process, along with the corresponding impurity contents present at each purity grade. RDU method uses a graphite rotor to produce spiral-rising microbubbles (diameter 38 mm), achieving a 95% inclusion removal rate and 0.07 mL/100 g gas impurity control level. The GBF method uses a 700 r/min high-speed nozzle to generate uniform microbubbles, and the degassing rate is 69–73%. Compared to SNIF, which focuses more on the uniformity of bubble distribution, GBF emphasizes the efficient generation of small bubbles. The Heprojet method combined with rotary injection and flux refining can achieve grain refinement while achieving a relatively high degassing rate, and has the advantages of less flux consumption and no pollution. The LARS^TM rule, by preheating gas to maintain the melt temperature, in the process of flotation refining controls the hydrogen content in the range of 0.18–0.26 mL/100 g and reduces more than 50% slag content.

Each method continues to innovate in the aspects of bubble control, reaction interface optimization, and equipment structure design, forming a complete technical spectrum covering different purity needs, production conditions, and environmental requirements. Among them, the SNIF and RDU methods are outstanding in the control of ultra-low hydrogen content, FILD and ALPUR rules have advantages in the processing capacity per unit time, while the Heprojet and LARS^TM methods focus on environmental protection and energy saving and melt insulation and other specific needs, reflecting the differentiated path of technological development. At present, the rotary jetting technology has been widely applied in the production of high-end aluminum alloys both at home and abroad. Compared with the traditional chlorinated salt flux refining method, the dehydrogenation rate of the former can only reach about 30%, while the rotary jetting technology can achieve over 70%, effectively meeting the purity requirements of high-end aluminum alloys and offering a more environmentally friendly production process. However, in the production process of ERAR, the rotary jetting method has not yet become the mainstream purification method. Nevertheless, in the high-end ERAR field in China, the combined refining process of the rotary jetting and flux methods has begun to be piloted in some areas. It is believed that in the future, the combined refining process of flux and jetting will be more widely used.

3.5. Other Auxiliary Methods

Vacuum degassing refining is based on Sievert’s law and promotes the escape of dissolved gases from aluminum melt by reducing system pressure. Combined with mechanical stirring, the degassing efficiency can be improved. Experimental results [50] indicate that when the rotational speed is maintained below 50 rpm, the adsorption between oxide inclusions and hydrogen bubbles is maximized. However, at speeds exceeding this threshold, increased melt turbulence leads to higher inclusion retention rates. The study by Zhou et al. [51] demonstrated that vacuum stirring exhibits superior purification performance compared to non-vacuum treatment, effectively reducing pore defects and enhancing matrix continuity. Figure 16 [51] illustrates the gas flow behavior during the vacuum refining process.

Ultrasonic refining employs high-frequency elastic waves to generate cavitation effects within the aluminum melt. The resulting cavitation bubbles absorb dissolved hydrogen and promote dendrite fragmentation, thereby enhancing grain refinement. A trace amount of titanium from the transducer dissolves into the melt, forming second-phase intermetallic compounds that further contribute to grain refinement [52]. Through parameter optimization, Zhou et al. [53] demonstrated that ultrasonic treatment at 2500 W and 20 kHz minimizes both hydrogen and oxide content in aluminum alloys. This technology is advancing toward intelligent monitoring, with real-time feedback improving processing accuracy. When optimized in terms of process parameters and technical integration, ultrasonic and vacuum refining can significantly enhance impurity removal efficiency. These techniques may be effectively combined with other purification processes, such as rotary injection and flux treatment, to achieve deep purification of aluminum melts.

Zone refining relies on the differential solubility of impurities in the solid and liquid phases, by which directional separation is achieved through localized melting and resolidification. High-frequency induction heating combined with a moving temperature field can maintain the intrinsic properties of the material, but it needs to be combined with inert gas protection to prevent oxidation. The numerical simulation of the Spim model [54] provides a theoretical basis for equipment improvement. Although effective impurity removal can be achieved under laboratory conditions, its industrial application is still limited by high energy consumption, intermittent operation, and challenges in controlling oxidation, and it is still in the exploratory stage.

4. Cast-Rolling Process

Following purification, the high-purity aluminum melt must still undergo casting and rolling to produce the final ERAR product. While purification is essential for quality control, the subsequent solidification and deformation stages are equally critical. With advances in manufacturing technology, previously distinct casting and rolling stages have increasingly merged into an integrated continuous casting and rolling process. This evolution not only enhances production efficiency but also improves the mechanical and electrical performance of ERAR products.

The aluminum alloy casting and rolling process is a continuous process in which aluminum melt is directly cast and rolled simultaneously. The core principle is that the molten aluminum liquid is rapidly solidified and rolled by a pair of high-speed rotating cooling rolls for aluminum alloy used as wire and cable. There are four general processing and casting methods: the continuous casting and rolling method, the two-roll casting and rolling method, the direct water-cooled semi-continuous casting and extrusion method, and the continuous casting and extrusion process. Each process has its own unique advantages (see

Table 9. Comparison of different casting—rolling processes.

	Equipment Investment	Energy Consumption	Advantage
continuous casting and rolling process	medium	medium	continuous production, high efficiency
two-roll cast-rolling process	low	low	short process
DEP	high	high	better mechanical properties
Continuous Casting and Extrusion Process	medium	medium	high flexibility

Note: DEP = Direct water-cooled semi-continuous casting and extrusion process.

), which will be introduced in turn.

4.1. Continuous Casting and Rolling Process

The continuous casting and rolling process, as a core technology for producing aluminum alloy wire, has become the global mainstream manufacturing method since the launch of the Properzi production line in Italy in 1948. In China, aluminum alloy wire rod is also mainly produced by this method. The typical process includes refining and purifying the electrolytic aluminum melt, continuous casting, and hot continuous rolling, resulting in is an aluminum wire with a diameter of 9.8 mm. Despite the huge scale of domestic application, there is still a gap between the equipment accuracy and process control and the international advanced level. At present, the main equipment types include five-wheel continuous casting machine and horizontal continuous casting machine. The five-wheel continuous casting machine is equipped with a diameter of 1600 mm crystallization wheel and tension wheel system to achieve wheel-belt casting [55]. The horizontal continuous casting machine integrates a tundish, crystallizer, intelligent control system, and other modules to ensure process stability.

At present, the most widely used casting and rolling process is the continuous casting and continuous rolling process. However, the mechanical properties of aluminum alloys are significantly affected by their final heat treatment conditions. Heat treatment is usually carried out after this process to obtain electrical aluminum alloy rods with stronger mechanical properties. Thus, understanding the basic mechanical properties of aluminum alloys under various heat treatment conditions has become a key area of current research [56]. Liu et al. [57] selected a common precipitation hardening alloy, 7075 aluminum alloy (the components are shown in

Table 10. Chemical composition of base metal and welding wire (wt.%) Reprinted from ref. [57].

Element	Si	Fe	Cu	Mn	Mg	Cr	Zn	Ti	Other Elements	Al
7075/T6 Base Metal	0.08	0.20	1.41	0.06	2.55	0.20	5.60	0.02	≤0.05	Bal.
7075Wire	<0.01	0.03	2.18	0.16	1.96	<0.01	5.80	<0.01	≤0.05	Bal.

), for heat treatment research. Analysis of the microstructures presented in Figure 17 and Figure 18 indicates that a higher heat treatment temperature facilitates grain growth, the dissolution of secondary phases, and a more advanced state of recrystallization. Reference [58] also analyzed and studied the heat treatment of 7075 aluminum alloy. It further concluded that for 7075 aluminum alloy plates, a solution treatment at 510 °C for 30 min followed by artificial aging at 120 °C for 24 h can achieve the best stamping effect.

The 6xxx series of aluminum alloys have excellent corrosion resistance and are widely used in many fields. However, compared with 7075 and 2219 aluminum alloys, 6061 aluminum alloy mainly contains Mg and Si(the components are shown in

Table 11. Chemical composition table of 6061 aluminum alloy (wt.%) Reprinted from ref. [57].

Mg	Si	Mn	Fe	Cu	Cr	Zn	Ti	Al
0.97	0.13	0.12	0.56	0.27	0.12	0.08	0.023	Bal.

), while 7050 aluminum alloy mainly consists of Zn and Mg, and 2219 alloy is mainly Cu. Therefore, 6061 aluminum alloy mainly achieves strengthening by forming Mg₂Si precipitates, which is different from the strengthening of 7075 aluminum alloy by the η phase (MgZn₂) and the θ phase strengthening (Al₂Cu) of 2219 aluminum alloy. Thus, there are significant differences in the heat treatment parameters between 6061 and 7075 or 2219 alloys. To study the specific effects of different heat treatment parameters on the properties of 6061 aluminum alloy, Jia et al. [57] designed an orthogonal experiment. The experimental results indicate that increasing the solution temperature within the range of 510 °C to 540 °C enhances the plasticity of the alloy. However, when the temperature reaches 570 °C, significant grain coarsening occurs and grain boundary brittleness increases, resulting in a notable reduction in plasticity. Furthermore, it was observed that prolonging the aging time leads to a decrease in elongation initially, followed by a subsequent increase. The mechanical properties of the aluminum alloy after heat treatment are shown in

Table 12. Mechanical and electrical performance parameters of 6061 aluminum alloy tensile test specimens Reprinted from ref. [57].

Strain Rate/(S−1)	Direction	Yield Strength/MPa	Tensile Strength/MPa	Elongation Rate/%
0.05	RD	347	395	9.0
	ST	306	355	7.9
	LT	321	378	9.8
0.01	RD	346	395	10.9
	ST	312	357	7.4
	LT	334	378	10.5
0.001	RD	345	391	10.4
	ST	345	355	7.4
	LT	310	367	8.2

. Therefore, heat treatment can effectively improve the mechanical properties of the material.

All the above-mentioned studies have focused on discussing the impact of heat treatment on the strength of aluminum alloys. However, as ERAR materials, they not only require high strength but also have extremely high demands for high electrical conductivity. It is an established fact that the 1xxx, 5xxx, 6xxx, and 8xxx aluminum series have been extensively utilized for electrical applications. In recent decades, in the field of high voltage transmission lines, the 6201 Al alloy as a high-performance all-aluminum alloy conductor (AAAC) is even expected to replace the traditional Al conductor steel rein-forced (ACSR), which usually consists of commercially pure Al alloys, such as 1350 Al alloy, and an iron core. Therefore, Mao et al. [59] conducted experimental research on the 6201aluminum alloy within the 6xxx series of aluminum alloys. The research results show that the process route of under-aged (UA) + RS + re-aged at 160 °C (RA160) can achieve a conductivity of 50.6% IACS and a tensile strength of 363 MPa. The combination of peak aging (PA) + RS + RA160 can achieve an excellent combination of 51.7% IACS and 352 MPa (the tensile properties of the 6201Al alloy in each state are detailed in

Table 13. Tensile properties of 6201 Al alloy in different processing condition. Error values are standard deviations Reprinted from ref. [58].

Sample	Yield Strength/MPa	Tensile Strength/MPa	Uniform Elongation/%	Elongation at Failure/%	Electrical Conductivity/%IACS
ST	121 ± 11	210 ± 9	21 ± 3	32 ± 4	45.2 ± 0.2
UA	153 ± 10	235 ± 14	16 ± 2	29 ± 2	48.6 ± 0.6
PA	226 ± 17	274 ± 14	11 ± 1	19 ± 2	50.5 ± 0.2
ST + RS	328 ± 13	347 ± 16	2.0 ± 0.3	6.8 ± 0.5	43.9 ± 0.3
UA + RS	367 ± 10	371 ± 9	1.7 ± 0.1	6.5 ± 0.3	45.5 ± 0.4
PA + RS	384 ± 8	387 ± 14	0.7 ± 0.1	6.1 ± 0.2	50.3 ± 0.7
ST + RS + RA160	341 ± 21	360 ± 16	5.1 ± 0.2	12.8 ± 0.3	47.0 ± 0.2
UA + RS + RA160	350 ± 15	363 ± 14	4.2 ± 0.3	11.8 ± 0.4	50.6 ± 0.3
PA + RS + RA160	348 ± 17	352 ± 13	4.0 ± 0.2	11.5 ± 0.3	51.7 ± 0.2
ST + RS + RA180	319 ± 14	330 ± 10	4.2 ± 0.1	11.3 ± 0.3	49.8 ± 0.2
UA + RS + RA180	313 ± 10	320 ± 9	3.5 ± 0.2	10.6 ± 0.5	51.5 ± 0.4
PA + RS + RA180	308 ± 7	312 ± 10	2.3 ± 0.1	10.2 ± 0.4	53.0 ± 0.5
ST + RS + RA200	258 ± 11	278 ± 8	2.5 ± 0.1	11.0 ± 0.2	52.8 ± 0.3
UA + RS + RA200	269 ± 13	277 ± 11	2.8 ± 0.1	10.8 ± 0.6	52.9 ± 0.2
PA + RS + RA200	268 ± 9	275 ± 8	3.0 ± 0.1	11.4 ± 0.4	53.5 ± 0.3

Note: ST = solution treatment; UA = under-aged; PA = peak-aged; RS = rotary swaging; RA = re-aging.

). This research has significantly broken through the trade-off relationship between strength and conductivity in traditional processes. Although both studies focused on the 6xxx series of aluminum alloys, Mao’s research was more in-depth compared to that of Jia et al. Mao was not confined to experiments on heat treatment only. By combining rotary swaging (RS) deformation with aging heat treatment, the comprehensive performance of 6201 aluminum alloy wire in terms of strength, electrical conductivity, and ductility was systematically optimized. This research also provides theoretical and technological basis for improving the mechanical and electrical properties of metallic materials through microstructure design in a coordinated manner.

In recent years, most research has focused on short-time heat treatment of aluminum alloys. This study [60] explored the effect of short-time T6 heat treatment (ST6) on the microstructure and mechanical properties of semi-solid formed graphene nanoplatelet (GNP) reinforced A356 aluminum alloy composites. The results showed that compared with conventional T6 treatment, ST6 treatment could more effectively refine the eutectic silicon phase, promote its spheroidization, and inhibit grain growth, while forming more intermetallic compounds. This treatment enabled the composites to achieve higher yield strength and tensile strength, which were 28% and 36% higher than those of T6 treatment, respectively, with slightly better elongation at break. Short-time heat treatment significantly saves energy and time while enhancing strength, making it suitable for industrial fields that require high-strength and lightweight materials. This research achievement provides ideas for the innovative development of new aluminum-based cable materials.

In general, this process has the advantages of moderate equipment investment, high production efficiency, and stable yield [61], but problems such as macro segregation and coarse grains still restrict the improvement of product performance. International studies have shown that increasing the casting speed to 1.2 m/min and increasing the cooling water flow rate by 30% can increase the material elongation by 15% [62]. The process monitoring system developed by Wang Yanan et al. [63] captures real-time parameters such as casting temperature and rolling pressure. With a defect prediction accuracy exceeding 85%, it provides robust data support for process optimization. Future technological development will focus on the development of intelligent control systems and the application of environmentally friendly lubricants, and deeper research on the mechanisms of multi-field coupling in casting and rolling to improve process stability.

4.2. Two-Roll Cast-Rolling Process

By pouring molten aluminum into counter-rotating rolls to achieve rapid solidification, the twin-roll casting and rolling process reduces the number of production steps from 25 to 11, significantly shortening the overall production cycle. This technology offers several advantages, including a highly flexible production line adaptable to multi-specification products, as well as reduced energy consumption and capital investment. The 6082 aluminum alloy rods produced via this method meet the mechanical property requirements specified in EN754-2 [64], and after-heat treatment, comply with EN755-2 [65] standards (The detailed data are presented in

Table 14. Mechanical properties of rods with diameter 9 mm from alloy 6082 Reprinted with permission from ref. [66]. 2018 Materials Science Forum.

Condition	Bar Length Selection Zone/mm	Ultimate Tensile Strength/MPa	Yield Strength/MPa	Elongation to Failure/%
hot-extruded	output	285	165	22.2
	middle	213	133	23.8
	in the end	210	126	25.3
heat-treated	output	397	294	15.6
		369	258	14.0
	middle	386	326	15.6
		373	305	15.8
	In the end	371	307	10.7
		378	315	12.4
Requirements for EN 755-2 for extruded rods to 25 mm from alloy 6082 in condition T6		≥295	≥250	≥8

and

Table 15. Mechanical properties of a drawn rod with a diameter of 8 mm from alloy 6082 Reprinted with permission from ref. [66]. 2018 Materials Science Forum.

Condition	Ultimate Tensile Strength/MPa	Yield Strength/MPa	Elongation to Failure/%
hot-extruded	290	268	12.0
	284	260	12.3
	269	251	12.0
heat-treated	331	268	16.0
	327	264	18.5
	335	276	16.5
	343	287	19.5
	343	280	17.5
	331	276	18.7
Requirements for EN 755-2 for extruded rods to 25 mm from alloy 6082 in condition T6	≥310	≥255	≥10

) [66]. Furthermore, compared with the traditional direct extrusion process (the detailed data are presented in

Table 16. Comparative technical and economic indicators of traditional and new technology of twin roll casting–extruding Reprinted with permission from ref. [66]. 2018 Materials Science Forum.

Indicators		Traditional Extruding Technology	TRCR Technology
The yield/%	melting	77	96
	extruding	63	97
Electricity/(kW·h on 1 ton)	melting	832	95
	extruding	5303	3073
Cost price at variable costs/(RUR/ton)		193,754	163,692
Reducing the cost price/(RUR/ton)			30,062

Note: TRCR = Two-Roll Cast-Rolling.

), the Two-Roll Cast-Rolling (TRCR) process significantly enhances metal yield (the product yield can reach 95–97%) and reduces energy consumption (the electricity consumption for smelting has been reduced by approximately 89%). Despite these benefits, central segregation remains the primary technical bottleneck limiting broader adoption. As illustrated in Figure 19 [67], the mechanism involves the formation of columnar crystals under the rapid cooling action of the rolls. During this process, low-melting-point alloying elements tend to migrate toward the center due to dendritic growth, resulting in severe compositional segregation. To address this challenge, researchers both in China and abroad have conducted systematic investigations combining numerical simulations with experimental validation. As a result, the solidification model developed by Yu Wei’s team [67] can predict the critical thresholds for surface and linear segregation successfully.

In addition, the surface quality of aluminum materials produced by traditional twin-roll casting is affected by various process parameters. Cho [68] analyzes from a novel perspective that the spontaneously formed Al coating during the twin-roll casting process can effectively enhance interfacial heat transfer, thereby improving the surface quality of the finished aluminum products. As shown in the SEM image in Figure 20 and Figure 21 [68], the surface quality of the product is significantly improved when covered with an aluminum coating.

In terms of process improvement, domestic studies have confirmed that the application of ultrasonic and electromagnetic composite fields can effectively inhibit segregation [61]. Current technological development focuses on process parameter optimization and intelligent model development, combined with computer simulation to promote the process in the direction of green automation. The twin-roll casting and rolling process can significantly shorten the process cycle and effectively reduce the capital investment of the ERAR production line. Once the segregation prediction and prevention capabilities of the twin-roll casting and rolling process are further strengthened, this process also has the potential for widespread application.

4.3. Direct Water-Cooled Semi-Continuous Casting and Extrusion Process

The production process of direct water-cooled semi-continuous casting—the extrusion process is as follows: electrolytic aluminum liquid → semi-continuous casting → sawing → homogenization treatment → extrusion → φ9.5 mm aluminum alloy wire. This process seamlessly combines semi-continuous casting with hot extrusion and eliminates the intermediate cooling and reheating links in the traditional process, effectively shortening the process cycle [69].

4.3.1. Direct Water-Cooled Semi-Continuous Casting

In direct water-cooled semi-continuous casting (DC casting), the refined aluminum melt is poured into a crystallizer, where partial solidification occurs. The partially solidified ingot is then withdrawn from the crystallization zone by a pulling mechanism and further cooled using a water spray system, enabling continuous production. The equipment configuration is illustrated in Figure 22 [70]. When the cooling rate of the ingot reaches a balance with the traction speed, aluminum with uniform composition can be produced stably. Compared with the traditional casting process, the DC method has the advantages of high production efficiency and a high degree of automation. Engler et al. [71] systematically investigated the microstructural evolution of AA3105 aluminum alloy during direct chill (DC) casting and subsequent homogenization, using a combination of microstructural characterization techniques and numerical simulations. However, this process still suffers from significant drawbacks: large cross-section ingots tend to develop coarse columnar grains due to thermal gradients between the core and surface during solidification, leading to pronounced anisotropy in mechanical properties. To address this challenge, the research team successfully refined the grain structure by introducing physical field intervention methods such as ultrasonic field and electromagnetic stirring [72].

As the core equipment of DC casting, the design optimization of crystallizer plays a decisive role in product quality. At present, the domestic mold research and development is still mainly imitation, and there are technical bottlenecks in heat flow balance control and surface microstructure design. With the advancement of the Internet of Things and big data technologies, intelligent casting systems are now capable of real-time acquisition of process parameters via multisource sensors. By integrating machine learning algorithms, these systems can perform defect prediction, issue early warnings, and autonomously optimize process parameters. The particle method 3D model based on boundary conditions developed in the literature [73] can simulate the influence of shunt geometry and ingot speed on melt flow field. Building upon the thermal-fluid–solid coupling framework, the model proposed by [74] innovatively incorporates an ingot rupture prediction module and employs the discrete particle method (DPM) to address complex boundary conditions. This enhanced model offers a powerful tool for optimizing casting processes under extreme operational conditions.

Compared with traditional casting, DC casting has the advantage of a high degree of automation. However, due to the difficulty in achieving continuous production completely, it has gradually been phased out by the ERAR production process, which is currently technologically complete and mature, in contrast to the continuous casting and continuous rolling process.

4.3.2. Extrusion Process

The aluminum ingots obtained by DC casting need to be homogenized by annealing to eliminate dendrite segregation, then sawn and heated to a specific temperature for extrusion molding. In contrast to rolling, which shapes material through compressive deformation between rotating rolls, extrusion applies unidirectional pressure within a closed chamber to force the aluminum alloy through a die. This process allows full utilization of the alloy’s plasticity and enables the production of high-strength, complex-shaped profiles. As shown in Figure 23 [75], experimental results indicate that when the extrusion deformation reaches 70%, the coarse grains in the as-cast microstructure are fragmented and the grain boundaries become significantly denser. When deformation exceeds 80%, a fibrous structure becomes dominant, resulting in more than an 80% reduction in grain boundary defects and a substantial enhancement in material strength. However, limited by equipment, the extrusion process has segregation problems caused by a low degree of automation and uneven melt flow. The literature [76] confirms that the tensile strength of alloys treated with severe plastic deformation (SPD) technology increases by 40% (see

Table 17. Performance evolution of 6xxx series aluminum alloys under varying strain conditions Reprinted from ref. [76].

Indicators	Initial Peak Aging State	Rate of Area Reduction (19%)	Rate of Area Reduction (91%)
average grain size/μm	184.47	81.22	8.80
yield strength/MPa	289.61	351.00	410.72
Elongation/%		9.44	6.71
Conductivity/%IACS	52.85		52.78
dislocation density/m−2			4.4 × 1014

), showing better mechanical properties. After SPD treatment, the grains are significantly refined and take on a fibrous shape, with a substantial increase in the density of low-angle grain boundaries and dislocations. A large number of dislocations cause the precipitated phase to break and dissolve, significantly enhancing the tensile strength. References [77,78] further found that SPD increased the volume fraction of strengthening phases (the phase composition is Solute clusters with extremely high number density) through severe plastic deformation and formed a submicron grain structure.

Equal Channel Angular Pressing (ECAP), the core technology of Severe Plastic Deformation (SPD), has become a hot topic in the research of ERAR extrusion process due to its unique shear deformation mechanism. Wang et al. [79] reported that, after heat treatment, nanoscale precipitates formed in ECAP-processed aluminum alloy contributed to the inhibition of grain growth. In addition, the increased dislocation density enhanced the diffusion of solute atoms, which led to a 25% reduction in the degree of segregation. Murashkin’s study further showed that the application of ECAP above 100 °C could increase the yield strength of alloy by 50% [80]. Traditional models are usually simple mathematical fittings, lacking connections with micro-mechanisms. Moreover, they can only accurately predict peak stress but fail to take strain into account to precisely predict the rheological stress under complex working conditions, optimize the parameters of thermal processing, provide methodological references for modeling new materials, and explain macroscopic behaviors through microscopic analysis. Eckert [81] established a constitutive equation for hot deformation of X153CrMoV12 steel through the Zener–Hollomon parameter framework and strain compensation technique, which can accurately predict the hot deformation behavior over a wide range with high precision. The core equations are:

$$\dot{\mathsf{\epsilon }}=\mathrm{A}{\left[\sin \mathrm{h}\right(\mathsf{\alpha }\mathsf{\sigma }\left)\right]}^{\mathrm{n}}\exp \left(-{\displaystyle \frac{Q}{\mathrm{R}T}}\right)$$

(1)

$$\mathrm{Z}=\dot{\mathsf{\epsilon }}\exp \left({\displaystyle \frac{Q}{\mathrm{R}T}}\right)$$

(2)

where $\dot{\mathsf{\epsilon }}$ is the strain rate; Q is the deformation activation energy (the energy barrier reflecting material deformation); R and T are the gas constant and temperature, respectively; and α, n, A are the material constant.

This model can also be applied to the ERAR extrusion production process, providing a theoretical tool for it. The Z-parameter model can correlate temperature with the strain rate of aluminum alloys, thereby determining the optimal temperature range for recrystallization, achieving the purpose of refining grains and enhancing the homogeneity of ERAR. Meanwhile, the polynomial model for strain compensation in reference [81] can be extrapolated and extended to the large strain range of SPD to predict the work hardening behavior and guide the controllable preparation of ultrafine-grained structures.

Future technology development will focus on the continuous development of SPD devices, combined with ultrasonic assistance and intelligent temperature control systems, to break the traditional strain limits. Combined with the application of digital twin models, real-time optimization of process parameters can be achieved, promoting the upgrading of extrusion processes towards high precision and low energy consumption. It can be expected that the extrusion process combined with SPD technology and big data models will become a popular research direction in the future, and may even replace the existing mature continuous casting and continuous rolling process.

4.4. Continuous Casting and Extrusion Process

Aluminum alloy continuous casting and extrusion process is an advanced metal integration technology [82], integrating the advantages of casting and extrusion. As a short process technology—compared with the traditional semi-continuous casting—extrusion technology, continuous casting, and the extrusion process are an integrated continuous production; it has significant advantages, such as to shorten the processing cycle and significantly enhance the mechanical properties of the alloy [39,83]. Research findings [83] indicate that both Al-3Fe alloy and pure aluminum exhibit the uncommon phenomenon of simultaneous enhancement in strength and elongation when the rolling deformation exceeds 60%. At 90% deformation, the alloy demonstrates optimal comprehensive mechanical properties (see in Figure 24 [84]). The continuous casting and extrusion process flow chart is shown in Figure 25 [85]. In this process, the melt is injected into the extrusion mold under the condition of solid–liquid coexistence, and the plastic deformation is completed under the action of high pressure and shear force. The casting cooling, sawing, heating, and multiple extrusion processes in the traditional process are omitted, and the energy consumption is reduced by 40–60%. The wire grains obtained by continuous casting and extrusion are fine equiaxed crystals. It is pointed out in the literature [84] that Al-Mg-Si-Cu alloy, under the double action of rapid cooling and frictional shear deformation, forms an equiaxed grain with an average grain size of 43.3 μm after continuous casting and extrusion, which has excellent ductility and good mechanical strength.

With the progress of semi-solid forming technology and intelligent control, the future continuous casting and extrusion process will be combined with integrated digital twin technology to achieve adaptive optimization of process parameters, and promote the development of green and high-end aluminum alloy processing. As an efficient short-process casting and rolling technology, it further enhances the mechanical properties of ERAR on the basis of accelerating the production cycle, demonstrating significant research potential. Once the technology matures in the future, it can greatly reduce the construction cost of ERAR production lines. Compared with traditional continuous casting and continuous rolling lines, this process is more likely to be favored by small metallurgical enterprises.

5. The Prospects of AI-Driven Process Innovation

Innovations and optimizations in purification and continuous casting–rolling processes are clear. However, several limitations remain in the field.

From the perspective of purification, although methods such as flux treatment, master alloy addition, filtration, gas refining, and composite purification have yielded notable results, the experimental process still relies heavily on repetitive trials. Outcomes often exhibit a degree of randomness, and the optimization of process parameters—such as the formulation of new fluxes or master alloys and the control of gas injection rates—typically requires either complex mathematical modeling or lengthy, costly, high-precision experiments.

Similarly, in the casting–rolling process, improvements to equipment dimensions and the optimization of parameters such as cooling rate and rolling speed also demand substantial financial and time investments. Integrating artificial intelligence (AI)—particularly machine learning (ML) and deep learning (DL)–into ERAR design and manufacturing could significantly shorten development timelines, reduce experimental workloads, and lower labor costs.

AI, and especially ML/DL, is reshaping the landscape of functional materials research. As shown in Figure 26 [86], the influence of ML/DL in materials science—from superconducting compounds to high-entropy alloys—is rapidly expanding by revealing composition–structure–property relationships, thereby accelerating materials discovery Current research on electrical round aluminum rod (ERAR) production primarily focuses on [87]. In materials synthesis and processing, AI has evolved from predicting single-point performance to enabling integrated, closed-loop innovation that combines data-driven design, intelligent control, and performance prediction.

Although dedicated AI applications in ERAR remain limited, advances in related aluminum alloy systems provide transferable strategies that can be adapted for ERAR manufacturing.

5.1. AI Innovations in Purification Processes

The purification of ERAR aims to achieve grain refinement and precise compositional adjustment through physicochemical means. ML/DL approaches enable accurate compositional homogenization control, eliminating the need for prolonged computation and experimentation.

For example, in melt purification, Yang et al. [88] employed transfer learning to automate nanoparticle size distribution analysis, greatly improving the efficiency of traditional electron microscopy and paving the way for data-driven optimization of grain structures—an important factor in enhancing ERAR conductivity. Hao et al. [89] proposed a hybrid MTGPR-PSO model to optimize hydrogen removal efficiency under multivariable conditions (e.g., rotor speed, flux addition rate), reducing prediction error to 3.8% and offering a promising path for improving melt treatment consistency and quality.

Reference [90] proposed an explainable AI framework from a novel perspective, achieving precise mapping and mechanism analysis of components, processes, and performance, and breaking through the strong plasticity trade-off. It has broken the traditional 7xxx aluminum alloy design reliance on trial-and-error experiments and local empirical models. The research team conducted 225 sets of experiments, covering 25 alloys and 5 processes. And the strengthening phase and strengthening mechanism were confirmed by TEM. The evolution process of grain structure was analyzed using EBSD. Develop a new large model of artificial intelligence DNN, and at the same time use the LIME algorithm to explain the DNN prediction logic and quantify the contribution of each parameter to YS/TS/EL. The average error MAPE of the test set is 9.93%, and the coefficient of determination R² is 0.926. This paper provides efficient design tools, promotes the application of high-strength aluminum alloys, and also offers novel ideas for the composition design of aluminum alloys in the ERAR field.

ML can extract valuable insights from large datasets and predict future trends. In the ERAR context, it could be used to forecast material properties, optimize microstructures, and design alloy and refining agent compositions. Such models can rapidly screen potential nanomaterials and predict their properties, significantly shortening R&D cycles and lowering experimental costs.

5.2. AI Applications in Casting-Rolling Processes

The properties of ERAR materials following the casting–rolling process are significantly influenced by factors like porosity [91,92,93]. Being able to accurately forecast these microstructural attributes based on chemical composition and processing conditions—such as cooling rate and initial hydrogen levels—is vital for enhancing process efficiency [94,95,96].

In the context of managing porosity and secondary dendrite arm spacing (SDAS), Chen et al. [97] proposed a cellular automaton (CA) framework to model dendritic solidification in both binary Al-Si and ternary Al-7Si-Mg alloys. The SDAS results obtained from simulated cross-sections were found to align closely with empirical estimates. Nevertheless, as castings grow larger and more complex, these simulation techniques demand greater computational resources and become increasingly expensive. Liu et al. [98] developed a U-Net convolutional neural network to dynamically monitor grain refinement in aluminum alloys, demonstrating that adding yttrium to AlTiB refiners significantly reduced SDAS by 38.9%. This highlights the potential of ML-based monitoring for real-time process control and microstructure optimization of ERAR conductors.

Hou [99] proposed an AI-based framework to predict porosity and SDAS in aluminum alloy castings (the experimental methodology is illustrated in Figure 27). Using 3D CA simulations, a dataset of 472 porosity samples was generated for A356 alloy, covering cooling rates from 0 to 10 /s and initial hydrogen contents from 0.15 to 0.40 mL/g. From these reconstructions, five porosity indicators were extracted. Additionally, a dataset of 310 SDAS samples was compiled from the literature, including alloy composition and cooling rate, with strict quality control.

All data were standardized and split into training and testing sets (80–90% for training). Seven AI models—XGBoost, CatBoost, gradient boosting regression (GBR), TabNet, Transformer, random forest regression (RFR), and support vector regression (SVR)—were evaluated. Hyperparameters were optimized via grid search with five-fold cross-validation. Model performance was assessed using the coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and relative error analysis. The SHAP (Shapley additive explanation) method was used to interpret the influence of input features (e.g., cooling rate, hydrogen content, alloying elements).

Results showed that XGBoost achieved the highest prediction accuracy for most porosity indicators (R² up to 0.89) and for SDAS (R² = 0.95), with prediction errors within 10–15% in experimental validation. CatBoost also performed competitively for porosity percentage prediction. Overall, ensemble learning models outperformed deep learning and traditional regression models for small structured datasets, with inference times of only a few seconds, compared with several hours for CA simulations. SHAP analysis identified cooling rate and initial hydrogen content as the most influential factors for porosity, and cooling rate, strontium (Sr), and silicon (Si) content as most influential for SDAS.

The experimental results mentioned above provide a new perspective for optimizing the ERAR casting and rolling process. The application of artificial intelligence models addresses the most critical shortcomings of traditional trial-and-error experiments. Artificial intelligence can accurately identify intrinsic relationships from the provided data resources, thereby enabling the prediction and optimization of product performance. This significantly reduces human resource requirements, shortens experimental time, and lowers input costs.

5.3. AI Applications in ERAR Product Inspection

Tiwari [100] demonstrated the value of AI in alloy design by combining principal component analysis (PCA) and K-means clustering to classify 50 commercial 6xxx series aluminum alloys based on performance features. This interpretable analysis enables reverse derivation of optimal compositions based on precipitation-hardening theory. Since ERAR often uses related alloy grades, such classification methods can guide intelligent selection of alloying elements to balance conductivity and strength.

AI also offers inherent advantages in product quality inspection [101]. One study developed an automated inspection system based on a Faster R-CNN deep learning model to detect shape defects in aluminum smelter anode rods. The system replaces subjective manual inspection with an automated, near real-time, multi-class defect detection process.

Using SketchUp, the team generated a dataset of 600 images (720 × 1280 pixels) covering five defect classes and one normal class (100 images per class). Images were annotated with LabelImg, producing XML label files, converted into TFRecord format, and split into 80% training and 20% testing sets.

The TensorFlow Faster R-CNN Inception V2 model was configured for six classes with customized label mapping and training parameters [100]. Training began with an initial loss of around 1.6 and continued until the loss stabilized below 0.05 (at approximately 6265 steps).

The trained model achieved accurate multi-class defect detection with near real-time performance. Its modular architecture supports future feature expansion, while its Python (Python 3.7)-based implementation ensures flexibility for integration with other classifiers. This approach shows strong potential for reducing subjectivity and improving cost-effective decision-making in anode rod maintenance.

In summary, the AI research on ERAR will significantly drive the production transformation of ERAR. Opportunities exist in refiner optimization, melt purification, alloy composition control, and casting–rolling process management. Integrating ML models into these domains can enhance process precision, reduce trial-and-error costs, and accelerate the development of high-performance electrical conductor materials.

5.4. Summary and Prospects

Artificial intelligence (AI) has demonstrated transformative potential in overcoming longstanding bottlenecks in the manufacturing of aluminum conductors, particularly in the production of electrical round aluminum rods (ERAR). Conventional purification processes often rely on repetitive experimentation and are characterized by high variability in outcomes. The optimization of refining agent formulations, gas purging parameters, and other process variables typically demands complex modeling or costly experimental procedures. Similarly, controlling cooling rates and rolling speeds during the casting–rolling stage poses challenges related to high capital investment and extended development cycles.

AI technologies, leveraging machine learning (ML) and deep learning (DL), offer a fundamental shift in this landscape. In purification processes, AI enables intelligent and precise control to achieve grain refinement and compositional homogenization, thereby significantly enhancing the electrical conductivity of the resulting conductors. During casting and rolling, AI-based ensemble learning models can accurately predict microstructural features such as porosity in real time-reducing computation times from hours to mere seconds and guide the optimization of cooling rates and alloying strategies. In quality inspection, vision systems based on Faster R-CNN enable real-time automated detection of multiple defect types, replacing subjective manual assessments.

While AI applications specifically tailored to ERAR remain in their early stages, the integration of data-driven design, intelligent process control, and performance prediction has already shown promise in dramatically shortening development cycles, reducing experimental costs, and minimizing human labor. Collectively, AI is poised to revolutionize the entire ERAR production chain—from alloy design and melting purification to casting–rolling and quality inspection. The increasing adoption of ML and DL across these stages will further accelerate the development of high-performance, reliable aluminum conductor materials, enabling the industry to meet the growing technological demands of power transmission and other critical applications.

6. Conclusions

With increasing performance demands in the power transmission industry, driven by technological advancement, optimizing the production process of aluminum alloy rods has become essential for achieving high-quality products. As manufacturing technologies continue to evolve, various production methods and equipment have emerged. Therefore, it is crucial to analyze the advantages and limitations of different processing steps. This paper provides a comparative summary of two representative production processes.

As the most widely used purification method for aluminum alloys, the flux purification process is undergoing significant innovation. Traditional fluoride-based fluxes are being rapidly replaced by new composite flux systems. Currently, the mainstream high-efficiency fluxes are based on fluorine–chlorine systems enhanced with rare-earth additives. Researchers are actively exploring the incorporation of additional thermodynamic components to further improve purification efficiency. Future research will continue to focus on the quantitative optimization of flux compositions and interface reactions. In particular, more attention will be paid to understanding the release mechanisms of atomic-scale rare-earth elements and developing cost-effective industrial production routes. However, the usage of refining agents at this stage will inevitably lead to changes in ERAR’s composition and increase the cost burden. Therefore, in the future, it is necessary to explore more appropriate ways of adding refining agent and balance the relationship between refining effect and cost.

Master alloys have effectively addressed the challenge of directly incorporating refractory and easily oxidized elements during the aluminum casting process. With the usage of master alloy, they contribute to grain refinement and enhance the mechanical strength and toughness of aluminum alloy products. Master alloys also reduce elemental loss and improve the compositional uniformity of the molten aluminum. However, several technical challenges remain. The addition of master alloys may introduce unintended impurity elements, promote the agglomeration of TiB2 particles, and increase production costs by 10–20%. These drawbacks can reduce the electrical conductivity of the final product and lead to defects such as perforation or wire breakage. In addition, extensive industrial usage is still subject to economic restrictions, and is usually used together with refining agents.

Other refining technologies are expected to evolve toward environmentally friendly, energy-efficient, and multifunctional composite purification systems, particularly with advancements in gas purification, rotary gas injection, and filtration techniques. Other emerging techniques such as vacuum refining, ultrasonic treatment, and zone refining also show considerable potential. However, their widespread application is limited by high energy consumption and poor compatibility with continuous production processes. Therefore, it can be foreseen that the future purification strategy may involve hybrid methods and integrate a variety of technologies to achieve efficient impurity removal.

Within the realm of casting–rolling technologies, continuous casting and rolling have reached a highly mature stage and are widely applied throughout the industry. Looking forward, future advancements are expected to focus on integrating physical fields, intelligent control systems, and automated monitoring tools to enhance both product quality and production efficiency.

AI technology has been widely used to guide the production of iron and steel industry. For ERAR aluminum processing industry, the combination of AI can also promote the rapid development of the industry. By optimizing the composition of refining agent through AI design, we can obtain multifunctional and efficient composite refining agent to achieve deep impurity removal. AI can also automatically monitor the production process, simulate production through big data modeling, optimize process parameters, and prevent defects such as segregation.