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Real-time electrical resistance monitoring for quality control in titanium Grade2-Aluminum 7075 dissimilar joints through electrically assisted joining
Dieser Artikel befasst sich mit dem innovativen Einsatz von Echtzeit-Widerstandsüberwachung zur Qualitätskontrolle in ungleichen Titan-Aluminium-Verbindungen, einem kritischen Bereich für die Luft- und Raumfahrtindustrie und die Automobilindustrie. Die Studie untersucht die Grenzen aktueller Fügeprozesse, wie die Bildung spröder intermetallischer Verbindungen und das Fehlen von Echtzeit-Methoden zur Qualitätsbewertung. Es untersucht das Potenzial elektrisch unterstützter Verbindungen, die schnelle Heizzyklen und geringere thermische Verzerrungen bieten. Die Forschung stellt quantitative Korrelationen zwischen der Entwicklung des elektrischen Widerstands und den gemeinsamen mechanischen Eigenschaften her und identifiziert charakteristische Wendepunkte, die optimalen Grenzflächenbedingungen entsprechen. Die Studie hebt auch die einzigartigen elektrischen Signaturen hervor, die während des Fügeprozesses entstehen, was eine Grundlage für die Implementierung widerstandsbasierter, geschlossener Prozessregelungssysteme bildet. Die Ergebnisse deuten darauf hin, dass diese Methode zerstörerische Nachprozesstests überflüssig machen könnte, was eine Optimierung der Prozesse in Echtzeit ermöglicht und die umfassendere digitale Fertigungstransformation in hochzuverlässigen Anwendungen unterstützt.
KI-Generiert
Diese Zusammenfassung des Fachinhalts wurde mit Hilfe von KI generiert.
Abstract
This study establishes electrical resistance monitoring as a real-time quality control method for titanium-aluminum dissimilar joints produced via Electrically Assisted Joining (EAJ). Experiments systematically varied power (5–7 kW) and heating duration (0.1–1.4 s) to correlate electrical resistance signatures with joint mechanical properties. At 5 kW, maximum shear force, elongation at rupture, bonded area, and electrical resistance all increased approximately linearly with heating time, indicating steady joint development. At 7 kW, both maximum shear force and electrical resistance exhibited distinctive bilinear behavior with a critical inflection point at 0.3 s—corresponding to peak shear strength of 85.5 MPa. Resistance decreased sharply until 0.3 s, then exhibited a knee followed by more gradual decline. Continued heating beyond this inflection point increased total force (reaching 5.5 kN at 1.4 s) but decreased normalized shear strength due to material expulsion and defect formation. Strong quantitative correlation between electrical resistance evolution and joint quality (r = − 0.935, R² = 0.874) establishes a reliable foundation for real-time quality prediction and intelligent closed-loop process control, eliminating destructive post-process testing while ensuring consistent joint integrity in production environments.
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1 Introduction
The aerospace and automotive industries face mounting pressure to develop lightweight, high-performance components that combine the exceptional properties of dissimilar metals [1]. Titanium-aluminum joints represent a particularly strategic material combination, offering the corrosion resistance [2, 3] and high-temperature performance of titanium alongside the cost-effectiveness and weight advantages of aluminum [4]. However, current manufacturing approaches for these dissimilar joints suffer from fundamental limitations that impede widespread industrial adoption.
The primary challenge lies not merely in achieving adequate joint strength, but in developing reliable, non-destructive quality assessment methods suitable for high-volume production environments [5]. Existing joining processes rely heavily on destructive post-process testing, creating bottlenecks in production flow and providing only statistical rather than comprehensive quality assurance. This limitation becomes particularly critical in aerospace applications where joint failure can have catastrophic consequences, necessitating extensive testing protocols that significantly increase manufacturing costs and lead times [6, 7].
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Conventional welding and brazing processes for titanium-aluminum joints, including brazing [8, 9], plasma arc brazing [10], laser welding [11‐16], diffusion bonding [17], friction stir lap joining [18, 19], and friction stir welding [20, 21], face a dual challenge. First, these processes typically result in the formation of brittle intermetallic compounds, particularly TiAl₃, at the joint interface, which significantly compromises mechanical performance [22‐24]. While research indicates that limiting intermetallic thickness to approximately 5 μm can yield acceptable joint strength [25], achieving this control consistently remains problematic.
The above processes suffer of different limitations, e.g. brazing and plasma arc brazing produce joints with limited mechanical behavior [10], diffusion bonding require relatively long time to develop, friction stir lap welding and friction stir welding require relatively expensive machine and involve high stress during the process, while laser welding may produce beam reflections. Furthermore, and equally important, these methods provide no real-time feedback regarding joint quality during processing. Quality assessment relies entirely on post-process evaluation through destructive testing, optical inspection, or expensive non-destructive testing methods such as ultrasonic or radiographic examination. This approach is fundamentally incompatible with modern manufacturing requirements for 100% inspection capability and real-time process control.
While electrical resistance monitoring has been successfully implemented in resistance spot welding (RSW) of similar materials for quality control [26], its application to dissimilar metal joining presents unique challenges and opportunities that remain largely unexplored. Recently the authors have investigated the possibility to adopt electrically assisted joining process to produce dissimilar joints between aluminum and titanium substrates [27]. The results indicated potential improvements as compared to other available process, including high mechanical behavior, short cycle time, low equipment costs. The fundamental differences between similar and dissimilar material joining create distinct electrical signatures that require new understanding and interpretation.
In conventional RSW of similar materials, electrical resistance patterns follow predictable trends based on uniform material properties and symmetric heating behavior. However, dissimilar metal joining involves asymmetric heating due to different melting points, thermal conductivities, and electrical resistivities. The substantial melting point differential between titanium (1665 °C) and aluminum (635 °C) creates unique current distribution patterns and interface evolution dynamics that are not present in similar-material RSW.
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Furthermore, the failure mechanisms in dissimilar joints differ fundamentally from those in similar materials. While similar-material RSW joints typically fail through nugget pull-out, Ti-Al joints fail primarily through intermetallic brittleness or mechanical interlock failure. This distinction necessitates new approaches to electrical signature interpretation and quality correlation.
While resistance-assisted joining offers inherent advantages for dissimilar metal processing—including rapid heating cycles, short processing times, and reduced thermal distortion—current implementations lack the real-time quality assurance mechanisms essential for aerospace and high-reliability applications. The fundamental challenge stems from the absence of quantitative relationships between in-process electrical signatures and joint quality characteristics in dissimilar systems. Conventional resistance spot welding of similar materials benefits from well-established electrical monitoring protocols, yet these approaches cannot be directly applied to dissimilar metal combinations due to asymmetric heating profiles, disparate material properties, and distinct failure mechanisms. Consequently, dissimilar metal joining processes remain dependent on post-process destructive testing or expensive non-destructive evaluation techniques, creating significant production bottlenecks and cost inefficiencies. This gap represents a critical barrier to implementing Industry 4.0 principles—particularly 100% inline quality assurance and closed-loop process control—in dissimilar metal joining operations.
This study tests the hypothesis that distinctive electrical resistance signatures emerge during electrically assisted joining of titanium-aluminum dissimilar joints and that these signatures correlate quantitatively with mechanical performance, enabling real-time quality prediction without destructive testing. To address this research gap, we pursue three integrated objectives: (1) establish quantitative correlations between electrical resistance evolution and joint mechanical properties across systematically varied processing parameters; (2) identify characteristic inflection points and resistance patterns that correspond to optimal interfacial conditions and peak mechanical performance; and (3) develop the scientific foundation necessary for implementing resistance-based closed-loop process control systems suitable for production environments.
Beyond these direct benefits, this research advances fundamental understanding of how dissimilar material interactions—asymmetric heating, differential thermal softening, and mechanical interlocking mechanisms—manifest in electrical domain signals, providing generalizable principles applicable to other dissimilar material combinations (Ti-steel, Al-Mg, etc.). Implementation of resistance-based quality monitoring would eliminate statistical quality assurance limitations, enable real-time process optimization responsive to material variations and electrode wear, and support the broader digital manufacturing transformation in aerospace and automotive sectors.
2 Materials and methods
2.1 Materials selection and characterization
The joining experiments utilized commercially pure titanium (Grade 2) and AA7075 aluminum alloy sheets.
The chemical composition of the two materials, measured through XRF spectrometry (using SPECTRO X-LabPro) is reported in Table 1.
Table 1
Chemical composition of the involved materials as determined through XRF spectrometry
Titanium [%]
Aluminum [%]
Silicon [%]
Magnesium [%]
Copper [%]
Zinc [%]
Others
Titanium Grade 2
99.58
0.11
0.13
-
-
-
0.18
Aluminum 7075
-
85.85
-
4.35
1.72
6.33
1.75
The titanium sheets, with a thickness of 2 mm, were selected for their excellent corrosion resistance and moderate strength properties. The aluminum alloy sheets, measuring 3 mm in thickness, were chosen for their high strength-to-weight ratio and widespread use in aerospace applications. All specimens were precision-cut from their respective parent sheets using an abrasive waterjet cutting system to ensure dimensional accuracy and minimize heat-affected zones. The relevant mechanical properties of both materials, including yield strength, ultimate tensile strength, and elongation at break, along with their characteristic melting points, are presented in Table 2.
Table 2
Main mechanical properties of the adopted materials
2.2 Research instrumentation and measurement systems
The experimental investigation employed advanced instrumentation systems to ensure precise process control and comprehensive data acquisition.
A Q-switched fiber laser (IPG model YLP-RA30-1–50-20-20) with maximum power output of 30 W was utilized for titanium surface preparation. Key specifications include pulse duration of 50 ns, wavelength of 1064 nm, beam quality factor M² < 1.3, and maximum pulse frequency of 30 kHz. The laser system was integrated with a galvanometer scanning head providing positioning accuracy of ± 5 μm and maximum scanning speed of 7000 mm/s.
The joining process was performed using a modified resistance spot welding machine (Telwin PTE 18 LCD) equipped with integrated current and voltage sensors. Technical specifications include maximum power output of 18 kW, current measurement range of 0–25 kA with accuracy of ± 1% full scale, voltage measurement range of 0–50 V with accuracy of ± 0.5% full scale, and water-cooled copper electrodes with 5 mm diameter contact surface. The machine incorporates digital power control with 0.1 kW resolution and programmable cycle timing based on 50 Hz AC supply frequency.
To maximize experimental reproducibility and minimize positioning-related variations, a custom-designed clamping fixture was engineered and fabricated in-house. This apparatus, illustrated in Fig. 1, incorporated precise alignment features and robust clamping mechanisms, thereby ensuring consistent specimen positioning and applying uniform pressure distribution throughout the joining process.
Fig. 1
Schematic of the clamping equipment used during the joining tests
Process monitoring was achieved using a National Instruments USB6009 multifunction I/O device operating at 2.0 kHz sampling rate with 16-bit resolution. This configuration enabled real-time measurement of voltage (V), current (I), instantaneous power (Pi = V×I), average power (P), and cumulative energy input (E = ∫Pidt). Data acquisition software was developed using LabVIEW 2020 to provide synchronized measurement and storage of all electrical parameters.
Joint mechanical properties were evaluated using an MTS model 43.50 universal testing machine equipped with a 50 kN load cell (accuracy ± 0.5% of reading) and digital displacement measurement system (resolution 0.001 mm). The testing system incorporates servo-hydraulic actuators enabling precise displacement control and load application rates. All tests were executed under displacement control at a crosshead velocity of 2 mm/min. To establish statistical significance and quantify experimental variability, a set of four specimens was evaluated for each joining condition. The resultant force-displacement data were analyzed to extract key performance metrics, particularly the maximum shear force sustained by each joint and the elongation at rupture. Statistical analysis encompassing mean values and standard deviations was performed to characterize the mechanical performance distribution and assess process reliability. The geometric configuration of the test specimens, which was designed to comply with relevant testing standards while ensuring uniform load distribution across the joint interface, is detailed in Fig. 2. This specimen design was instrumental in achieving consistent stress states and minimizing the influence of secondary bending moments during testing.
Fig. 2
Schematic of the specimen used in single-lap shear tests
Microstructural characterization employed a Leica M205 stereomicroscope for macroscopic examination (magnification range 7.8x to 160x) and a Zeiss GeminiSEM 500 scanning electron microscope for detailed microstructural analysis.
2.3 Experimental design and statistical methodology
The experimental investigation followed a systematic full factorial design to ensure comprehensive parameter space coverage and statistical validity. Two power levels (5 kW and 7 kW) were combined with eight heating durations corresponding to 5, 10, 15, 20, 25, 30, 50, and 70 cycles (equivalent to 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 1.0, and 1.4 s respectively, based on 50 Hz supply frequency).
Each experimental condition was replicated five times (n = 5) to ensure statistical significance and enable calculation of confidence intervals. This replication strategy provided four specimens for mechanical testing and fracture surface analysis, while preserving one specimen for detailed metallographic cross-sectional examination. The total experimental matrix comprised 80 individual joining trials (2 power levels × 8 heating durations × 5 replications).
Critical process parameters were maintained constant throughout the experimental campaign to isolate the effects of the primary variables. These included cooling period (5 s), electrode pressure (standardized through custom fixture design), and electrode geometry (5 mm diameter round-tip configuration). The cooling period was determined through preliminary optimization trials conducted under maximum power and heating duration conditions.
2.4 Correlation analysis methodology
Comprehensive correlation analysis was performed to quantify relationships between electrical resistance evolution and joint mechanical properties. The analysis employed Pearson correlation coefficients (r) to assess linear relationships, with coefficient of determination (R²) values calculated to quantify the proportion of variance explained by the correlations.
For the bilinear behavior observed in 7 kW conditions, segmented correlation analysis was performed by dividing the data into pre-inflection (0.1–0.3 s) and post-inflection (0.3–1.4 s) phases. Statistical significance was evaluated using p-values with α = 0.05 significance level.
Data normalization was performed using z-score standardization: z = (x - µ)/σ, where x represents individual measurements, µ is the sample mean, and σ is the sample standard deviation. This normalization enables direct comparison between electrical resistance and mechanical property trends by eliminating unit dependencies and scaling differences.
3 Results
3.1 Electrical signals evolution and process signatures
Figure 3 presents the comprehensive electrical parameter measurements during the heating phase for the 7 kW power condition. The current and voltage signals exhibit characteristic periodic oscillations reflecting the alternating voltage supply, with peak values initially increasing before reaching maximum values, followed by slight decrease and plateau formation. Near the heating phase conclusion, both parameters decline linearly, reflecting the machine’s proportional-integral-derivative (PID) control system response as it achieves target current values and maintains steady-state operation.
Fig. 3
Variation of the (a) Current and Voltage and (b) Energy supplied during joining using P = 7.0 kW (average power)
The energy input evolution, calculated through numerical integration of instantaneous power over time, demonstrates nearly linear accumulation patterns for both power conditions. The 7.0 kW process delivers approximately 11,000 J after 1.5 s compared to 8,000 J for the 5.0 kW process, with the steeper slope of the 7 kW curve indicating faster energy delivery rate while maintaining consistent input rates throughout the heating phase as shown in Fig. 4b.
Fig. 4
(a) Variation of the supplied energy during heating phase using different power levels and heating times.; (b) Evolution of instant power and peaks of power (red dots)
The electrical resistance evolution during the heating phase reveals fundamentally different behaviors between the two power conditions, as illustrated in Fig. 5. Both conditions initially exhibit resistance increase due to temperature-dependent rise in bulk material resistance, followed by resistance peak and subsequent decrease as joint formation progresses. At 5.0 kW power, the resistance decreases gradually in an approximately linear manner throughout the heating phase (R² = 0.94 for linear regression fit). This behavior indicates steady, progressive heating and interface development with consistent material softening rates. In contrast, at 7.0 kW power, the resistance demonstrates a distinctive bilinear pattern characterized by sharp resistance drop until approximately 0.3 s, followed by more gradual decline. This behavioral difference suggests more rapid joint formation and accelerated material softening at higher power, with the inflection point at 0.3 s representing a critical transition in the joining mechanism.
Fig. 5
Variation of the electrical resistance during the heating phase: (a) P = 5 kW and (b) P = 7 kW
Figure 6 shows representative load-displacement curves obtained during tensile testing of specimens produced using mean powers of P = 5 kW and P = 7 kW at various heating times. The curves exhibit an initial linear region, followed by a distinct knee (particularly evident in stronger joints). This corresponds to a transition in loading configuration and stress state at higher forces, where the joint loading shifts from pure shear to combined shear and peeling. Upon reaching peak load, the joints fail catastrophically. Maximum force and displacement at failure were determined for each processing condition (power level and heating time) and are summarized in Fig. 7; Table 3.
Fig. 6
Force-displacement curves of some reference joints
The mechanical testing results show how processing parameters affect joint performance. Figure 7 demonstrates the influence of heating duration on joint strength and displacement capacity across different power levels. For specimens produced at 5 kW, maximum force and displacement at rupture increase linearly with joining time, exhibiting consistent, steady growth throughout the investigated time range. This behavior reflects gradual joint development consistent with the linear electrical resistance patterns observed for this condition. Specimens produced at 7 kW exhibit bilinear behavior: a steep increase in force (and displacement at rupture) up to approximately 0.3 s of heating time, followed by slower force increase with extended heating.
The energy-force relationship analysis reveals particularly significant findings. At equivalent energy inputs, joints produced with higher power (7 kW) demonstrate substantially superior load-bearing capacity compared to those produced at 5 kW. At the inflection point of the 7 kW curve (t = 0.3 s), maximum force reaches approximately 4 kN using only 0.31 kJ energy input, while achieving comparable strength at 5 kW requires 0.8 kJ input, representing 61% energy reduction. Furthermore, this latter condition provides lower displacement at rupture (as reported in Fig. 7a) 0.8 mm vs. 1.2 mm.
Table 3
Experimental desing with average values of key mechanical responses
Heating Time [s]
Power = 5 kW
Power = 7 kW
Max Force [kN]
Max Displacement [mm]
Max Force [kN]
Max Displacement [mm]
0.1
1.15
0.26
1.64
0.38
0.2
2.62
0.53
0.3
1.98
0.46
3.99
0.80
0.4
4.17
0.86
0.5
2.80
0.58
4.53
1.03
0.6
4.79
1.17
1.0
3.20
0.68
5.13
1.34
1.4
4.07
0.84
5.52
1.34
3.3 Microstructural analysis and interface characterization
Comprehensive microstructural analysis provides critical insights into the physical mechanisms underlying the observed electrical-mechanical correlations. Fracture surface (Fig. 8) examination reveals that titanium substrates display bright aluminum residue over the textured surface, transferred from the counterpart sheet during joining. The amount of transferred aluminum increases with heating time regardless of power level, confirming joined area expansion with extended processing duration. For specimens produced with 7 kW power and heating times exceeding 0.3 s, the joined area becomes surrounded by circular regions of resolidified aluminum. The morphology of these regions exhibits smooth surface characteristics, indicating minimal contribution to joint load-bearing capacity. This material results from aluminum expulsion from the central region due to extended high-current flow.
Fig. 8
Fracture surfaces of titanium and aluminum substrates of joints made under different joining conditions
The effective joined area measurements (shown in Fig. 9) are summarized in Table 4 to quantify this phenomenon. At 7 kW power, joined area increases more rapidly than at 5 kW conditions, particularly after 0.3 s heating time. However, the formation of non-load-bearing peripheral regions at extended heating times explains the mechanical property deterioration observed beyond the 0.3-second point.
Fig. 9
SEM image of a reference joints showing the morphology of the central region and surrounding region
Effective joined area (A_i) for different joining times and power levels
Joining time [s]
A_i 5 kW [mm²]
A_i 7 kW [mm²]
0.1
31.43
25.85
0.3
37.01
46.69
0.6
35.81
58.41
1.0
43.07
76.18
1.4
45.66
87.06
The effective joined area (A_i) for different processing conditions was reported in Fig. 10. The joined area increases with heating time for both power levels, but with significantly different rates. At P = 7 kW, the area increases more rapidly, especially after 0.3 s of heating time.
Fig. 10
Variation of the internal and external area using different joining conditions (a) 5.0 kW and (b) 7.0 kW
The highlighted areas in Fig. 9 represent the internal regions used to calculate the shear stresses at the fracture location of the joint. This analysis reveals that a significant portion of the energy supplied during the process is used to melt the aluminum and expel it from the joining area, particularly at higher power levels and longer heating times. This phenomenon directly impacts the mechanical properties, as the smooth regions surrounding the primary joint area do not contribute to mechanical strength.
Normalizing the peak loads with respect to the joined surface areas reveals a decrease in shear strength following a trend like that observed in the electrical resistance analysis, as shown in Fig. 11. Joints produced with an average power of 5 kW exhibit an approximately linear trend in shear fracture stresses. Conversely, the shear fracture stresses of samples welded at 7 kW show a bilinear trend. The first segment follows a monotonically increasing pattern, reaching a peak at t = 0.3 s. In contrast, the subsequent segment displays a decreasing trend as the joining time increases. This bilinear behavior in the mechanical properties at 7 kW directly correlates with the electrical resistance patterns observed in Fig. 5(b), further supporting the potential use of electrical resistance monitoring for process control.
Fig. 11
Variation of the force and shear strength with joining conditions
Figure 11 illustrates an important relationship between joining time and shear strength for the EAJ process at different power levels. For P = 5 kW (pink squares), we observe an initial steep increase in shear strength that follows a logarithmic trend (R² = 0.93), with maximum shear force of 90 MPa at 1.4 s (heating time). This behavior suggests that at lower power, joint formation progresses steadily but requires longer processing times to reach maximum strength. In contrast, for P = 7 kW (blue circles), the shear strength shows a distinctly different pattern. It increases rapidly to reach a peak of approximately 85 MPa at t = 0.3–0.4 s, then notably declines as joining time extends beyond this point. This decline corresponds precisely to the inflection point (“knee”) observed in the force-time graphs from the mechanical testing data. This deterioration in shear strength with extended joining times at higher power likely results from growing development of defects in the joint area. As revealed in the morphological analysis, prolonged heating at 7 kW causes excessive aluminum melting and expulsion from the joint interface, promoting the formation of non-load-bearing regions around the periphery and potentially compromising the effective joint strength. At 7.0 kW and 0.3 s heating time, energy efficiency is maximized (high Force/Joining Energy ratio) as this represents an optimal balance between shear force and strength. Beyond 0.3 s at this power level, excess energy is dissipated through material ejection from the joint, which reduces shear strength, as shown in Fig. 11b.
3.4 Correlation analysis
Detailed correlation analysis quantifies the relationships between electrical resistance evolution and joint quality, providing the statistical foundation necessary for implementing resistance-based quality control. As shown in Fig. 12, for the 5 kW condition, both time-series analysis and correlation evaluation demonstrate consistent, predictable relationships. Strong negative linear correlation exists throughout the entire process (r = −0.974, R² = 0.949, p < 0.001), enabling modeling through the relationship: Strength = −1.12 × Resistance + 1.53. This strong linear correlation indicates that electrical resistance provides reliable joint quality indication throughout the process without the complexities observed in higher power conditions. The 7 kW condition exhibits more complex but highly informative correlation patterns. Time-series analysis reveals electrical resistance exhibiting sharp decrease until approximately 0.3 s, followed by gradual decline, while shear strength increases rapidly until 0.3 s, then shows slight decrease before stabilizing. Segmented correlation analysis reveals bimodal relationships that provide crucial insights. During the early phase (0.1–0.3 s), strong negative correlation exists (r = −0.935, R² = 0.874, p < 0.001), while the late phase (0.3–1.4 s) demonstrates strong positive correlation (r = 0.977, R² = 0.954, p < 0.001). This behavior confirms that 0.3 s represents a critical transition point in the joining process at 7 kW power.
Fig. 12
Correlation analysis between shear strength and electrical resistance
The statistical analysis establishes that before the transition point, decreasing resistance strongly correlates with increasing joint strength. After the transition point, both electrical resistance and strength decrease together, indicating less efficient joining with energy waste through aluminum expulsion and excessive intermetallic formation.
4 Discussion
4.1 Mechanistic Understanding of electrical signatures in dissimilar metal joining
The distinctive electrical resistance patterns observed in Ti-Al EAJ differ fundamentally from conventional resistance spot welding of similar materials due to unique physical phenomena arising from dissimilar material interactions. The substantial melting point differential (1030 °C) between titanium and aluminum creates asymmetric heating responses that generate characteristic electrical signatures not observed in similar-material applications. During the initial heating phase, the high electrical resistivity of titanium (55 µΩ·cm) compared to aluminum (4.0 µΩ·cm) creates preferential heating in the titanium substrate. However, the significantly lower melting point of aluminum results in rapid softening and flow of this material into the laser-textured titanium surface features. This asymmetric material response creates the distinctive bilinear resistance pattern observed at 7 kW power levels. The sharp resistance drop until 0.3 s corresponds to the transition from surface contact resistance dominance to bulk material resistance dominance.
The resistance monitoring approach builds upon established principles from resistance spot welding [26], where the total circuit resistance comprises several component resistances in series, as illustrated in Fig. 4:
Electrode-sheet contact resistances (R1 and R6): formed at the interface between the electrodes and the outer surfaces of the sheets.
Bulk electrical resistances of the sheets (R2 and R5): representing the inherent resistances of the sheet materials.
Sheet-to-sheet interfacial contact resistance (R3): formed at the contact zone between the two sheets, typically the highest resistance where most heat is generated.
Bulk electrode resistances (R4 and R7): representing the inherent resistances of the electrode materials.
The interfacial contact resistance (R3 in Fig. 13) decreases dramatically as temperature rises and aluminum softens, while the textured titanium features penetrate the softened aluminum matrix. The subsequent gradual resistance decline indicates continued heating with diminishing returns in terms of joint quality improvement. This phase corresponds to excessive aluminum flow, intermetallic growth beyond optimal thickness, and formation of non-load-bearing peripheral regions that compromise joint efficiency.
Fig. 13
Schematic of the electrical resistances involved during joining process
4.2 Process efficiency and industrial implementation advantages
Direct mechanical comparison with literature values requires careful interpretation due to fundamental methodological and configurational differences. Table 5 summarizes the main mechanical behavior of hybrid joints made by titanium and aluminum sheets achieved in this work and reported in literature: The present study employs spot welding with joint dimensions substantially smaller than the test sample width, whereas most literature processes utilize continuous line welding spanning the full sample width. This geometric distinction significantly influences load distribution and stress concentration patterns. While competing processes achieve tensile strengths approaching 200 MPa (for butt configurations), normalization of the present study’s maximum force (5.5 kN) by the effective joint area (83 mm²) yields lower shear strength values. However, strength optimization remains beyond the scope of this investigation. Notably, the use of alternating current in the present study introduces inherent process limitations, as AC supply involves extremely high peak currents that generates defects such as internal voids and porosities that compromise mechanical performance [27]. Future implementations utilizing direct current or continuous voltage supply would substantially enhance joint quality while maintaining the electrical monitoring framework established herein.
Table 5
Comparison of different processes used for joining titanium and aluminium sheets
The EAJ process with online resistance monitoring provides substantial advantages over conventional approaches in both joint quality and manufacturing efficiency. The process demonstrates remarkable efficiency, achieving joint strengths comparable to conventional methods while requiring 80% less processing time and 67% less energy consumption than equivalent strength production at lower power levels. This efficiency advantage extends beyond simple time and energy savings to fundamental improvements in manufacturing capability. The rapid heating and cooling cycles minimize heat-affected zone formation, preserving base material properties while creating strong interfacial bonds. The concentrated energy delivery prevents excessive thermal diffusion that can compromise surrounding material integrity in conventional fusion welding processes. The correlation between electrical resistance patterns and joint quality provides the foundation for implementing intelligent manufacturing systems that can adapt to material variations, electrode wear, and surface condition changes. Unlike conventional approaches requiring extensive post-process testing, the resistance monitoring method enables real-time quality assessment with immediate feedback for process adjustment.
4.3 Scientific foundation for intelligent process control
The quantitative relationships established between electrical signatures and joint performance provide the necessary scientific foundation for developing closed-loop process control systems. The strong correlations observed (r > 0.93) exceed typical industrial requirements for process control implementation. The bilinear behavior observed at 7 kW power levels provides a clear process optimization strategy and preventing the process to produce excess of material ejection. Automatic detection of the resistance inflection point can trigger process termination, ensure consistent joint quality while minimizing energy consumption and processing time. This approach would compensate for material property variations, surface condition changes, and electrode wear that typically require empirical parameter adjustments. Implementation of such systems would require development of real-time signal processing algorithms capable of detecting resistance inflection points within the process time scale. The sampling rates and measurement precision demonstrated in this study (2.0 kHz, 16-bit resolution) are readily achievable with current industrial control systems, making practical implementation feasible.
4.4 Novelty and advancement beyond conventional RSW applications
While electrical resistance monitoring is established in conventional resistance spot welding, its application to dissimilar metal joining represents a significant advancement requiring new scientific understanding. The unique electrical signatures arising from asymmetric heating, different material properties, and distinct failure mechanisms create fundamentally different monitoring requirements compared to similar-material RSW. The bilinear resistance patterns observed in Ti-Al joining differ qualitatively from conventional RSW signatures, which typically exhibit monotonic trends during melting and solidification. The critical inflection point at 0.3 s corresponds to optimal mechanical interlocking rather than nugget formation, representing a distinctly different physical phenomenon requiring new interpretation approaches. Furthermore, the correlation between resistance signatures and intermetallic formation control provides new opportunities for quality optimization not available in similar-material applications. The ability to monitor and control intermetallic thickness through electrical measurements represents a significant advancement in dissimilar metal joining technology.
4.5 Industrial implementation framework and future development
The established resistance-quality relationships enable practical industrial implementation through several interconnected capabilities. Automated process termination at optimal resistance inflection points would ensure consistent quality while minimizing energy consumption and processing time. Real-time quality classification based on resistance signatures would enable immediate assessment without destructive testing requirements.
Statistical process control implementation using resistance measurements would provide quantitative feedback for continuous process improvement and parameter optimization. Integration with Industry 4.0 manufacturing systems would enable data-driven quality assurance and predictive maintenance capabilities.
The scientific foundation established in this study supports extension to other dissimilar metal combinations and joining configurations. The fundamental principles of asymmetric heating signature interpretation and quality correlation could be adapted to Ti-steel, Al-Mg, and other dissimilar material systems with appropriate calibration and validation.
5 Conclusions
This study establishes that electrical resistance monitoring during Electrically Assisted Joining enables real-time quality control of titanium-aluminum dissimilar joints. The optimal joining condition (7 kW, 0.3 s) produces joints with maximum force of 4 kN and corresponding peak displacement while achieving 80% reduction in processing time and 67% reduction in energy consumption compared to equivalent strength at lower power. Mechanical testing reveals contrasting trends between power levels. At 5 kW, maximum force and displacement increase linearly with heating duration throughout the investigated time range. Conversely, at 7 kW, both maximum force and displacement exhibit rapid initial increase until 0.3 s, followed by reduced rate of increase, establishing a characteristic bilinear trend with inflection point at 0.3 s. The distinctive bilinear resistance pattern observed at 7 kW, with a critical inflection point at 0.3 s, provides a reliable electrical signature for joint quality assessment (r = − 0.935, R² = 0.874). This strong correlation between electrical resistance evolution and mechanical performance reduces the need for destructive post-process testing and enables implementation of intelligent, closed-loop process control systems suitable for high-volume manufacturing.
Acknowledgements
The authors would like to thank prof. Edoardo Fiorucci from Dept. of Industrial Engineering for his precious contribution concerning electrical measurements.
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Declarations
Competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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Real-time electrical resistance monitoring for quality control in titanium Grade2-Aluminum 7075 dissimilar joints through electrically assisted joining
Verfasst von
Francesco Lambiase
Francesco Pace
Elena Andreucci
Alfonso Paoletti
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Die im Laufe eines Jahres in der „adhäsion“ veröffentlichten Marktübersichten helfen Anwendern verschiedenster Branchen, sich einen gezielten Überblick über Lieferantenangebote zu verschaffen.
