Annals of “Dunarea de Jos” University of Galati. Fascicle XII, Welding Equipment and Technology

Microstructural Characterization of Recycled Al–Cu Chips Processed by Friction Stir Consolidation in a Cylindrical Die

2025-12-16T22:25:42+02:00

In this work, the microstructural evolution and hardness response of aluminum (AA2024) and high-purity copper machining chips consolidated by the friction stir consolidation (FSC) process using a cylindrical die and pinless tool configuration were studied. Microhardness tests and microstructural analysis were used to assess the effect of three essential processing parameters: tool rotation, preheating time, and chip weight. The hardness measurements showed a clear dependence on heat input and plastic deformation conditions, with regions rich in aluminum reaction showing the highest level of strengthening at optimal consolidation conditions, whereas zones rich in copper only show improvement to the hardness when a sufficient amount of thermal-mechanical energy has been put into the system. The intermediate hardness in the Al–Cu interfacial region indicated an order of magnitude change with a corresponding variation in material flow, chip breakage, and solid-state bonding. SEM observation demonstrated less porosity, better chip dispersion, and stronger microstructure at the higher rotating speed. EDS elemental maps exhibited localized Al–Cu diffusion, but no evidence of the continuous intermetallic layers was identified, while XRD patterns showed only FCC-Al and FCC-Cu, confirming that the FSC thermal cycle was still below the level needed for equilibrium phase formation of Al–Cu compounds. The consolidated disks obtained were dense, well-bonded, and fine-grained, indicating that the FSC has the potential to serve as an energy-efficient, eco-friendly approach for converting metallic machining waste into utility-based solid parts.

Comparative Analysis by Finite Element Method of Welding Paths in Aluminium Alloys T-Joints

2025-12-16T22:20:02+02:00

In recent decades, due to their great advantages, the aluminium alloys have been more and more used in the shipbuilding industry, particularly in the construction of yacht superstructures. Nevertheless, a significant disadvantage of aluminium alloys subjected to welding is their high deformation tendency, which represents an important drawback that needs additional remedial measures. In this study, a finite element analysis was performed, in order to investigate and optimize the design of welding paths used to carry out AA 5083 aluminium alloy T-joints of 6mm thick sheets by Metal Inert Gas (MIG) welding. The research methodology was focused on three case studies, in order to determine and comparatively analyse the Heat Affected Zone extend and the level of stress and displacement. In the first case, the welding beads were successively deposited, following the same welding direction. In the second case, the seams were successively welded, but the welding directions on one side and on the other side of the joint were opposite. In the third case, the seams were simultaneously welded, on both sides of the joint, the welding directions being opposite, as in the previous case study. Based on the research results, it was found that the larger Heat Affected Zone (HAZ) was identified in the third case study, while the total displacement was 25-30% lower, in comparison with the results obtained in the first two case studies. This phenomenon can be explained by the high amount of heat developed simultaneously by two electric arcs and transferred, by conduction, to the base materials. Due to the joining technology applied, that consisted in welding simultaneously on the both sides of the joint, an adequate balance of stress and strain was achieved, determining a lower total displacement.

Investigation of Single-Sided TIG Welding in Dissimilar AISI 316L and 304L Stainless Steel Joints

2025-12-16T22:20:03+02:00

This study investigates the Tungsten Inert Gas (TIG) welding of dissimilar austenitic stainless steels, AISI 304L and AISI 316L, each with a thickness of 3.2 mm. A single-pass butt joint was welded using a 100 A current with argon shielding gas. The joints underwent mechanical testing, including tensile, bend, impact, and hardness tests. The tensile test revealed a 25% reduction in the weld joint strength compared to the base metal, primarily due to differences in thermal expansion and mechanical properties, resulting in an overall joint efficiency of 75%. Macro and microstructural analyses indicated good fusion without defects, and typical weld metal microstructures were observed. The heat-affected zone (HAZ) of AISI 304L showed larger grains, while AISI 316L exhibited a flaky structure. The hardness test indicated the highest value in the weld zone (191 HV), compared to the HAZ (177 HV) and the parent metal (170 HV), which can be attributed to grain refinement and the use of the SS304 filler rod. Impact tests demonstrated good impact resistance (45 J), with the HAZ exhibiting higher toughness compared to the parent metal. Bend tests revealed no cracks on the weld face, whereas cracks were observed in the root bend tests. The study demonstrated sufficient strength, toughness, and hardness in the dissimilar TIG joint for engineering applications, despite the reduced tensile strength compared to the base metals.

Spot Welding Parameters Influence on the Assembled Structures Behavior

2025-12-17T19:37:44+02:00

Resistance spot welding (RSW) remains a critical joining technique in the automotive and aerospace industries due to its efficiency in assembling thin metallic sheets. However, the mechanical integrity of spot welds is often compromised by tensile stresses perpendicular to the weld plane, leading to premature failure. This study proposes a systematic experimental approach to quantify the influence of welding parameters, such as current intensity, electrode pressure, and welding time, on the tensile strength of welded joints. The novelty of this work lies in establishing a direct correlation between process parameters and joint performance to maximize weld strength without compromising microstructural integrity. Hardness tests confirmed that the hardness of the weld point depends on the initial microstructure of the base material and on the welding conditions. The results demonstrate that increasing current and welding time within controlled limits enhances nugget formation and joint resistance by up to 25% compared to conventional settings. These findings provide valuable insights for optimizing RSW processes to achieve reliable and high-quality assemblies.

Insights into Ultrasonic Welding of ABS-PC (Mychril) Polymers: Characterization and Performance Analysis

2025-12-17T19:49:13+02:00

This study looks at the characterization and performance of ultrasonic welding for ABS-PC (Mychril) polymer blends used in EV station applications. Experiments were conducted and a detailed characterization of the welded interfaces were performed using Scanning Electron Microscopy (SEM) to examine their structure. Fourier Transform Infrared Spectroscopy (FTIR) was deployed to identify chemical interactions and any potential degradation, and X-ray Diffraction (XRD) to evaluate changes in crystallinity caused by the welding process. SEM images showed consistent fusion and minimal voids, while FTIR and XRD tests showed that key functional groups remained intact with slight changes in crystallinity. Mechanical tests were also carried out on the welded samples that involved tensile, impact and fracture assessments. Pearson’s heat map coefficient analysis was performed to understand the influence of input process parameters on the mechanical strength outcomes. This research shows that ultrasonic welding is a suitable, sustainable, and effective method for assembling ABS-PC samples in EV applications, providing mechanical reliability and design flexibility.

Evaluation of Microstructure, Mechanical and Tribological Properties of AA6061 Reinforced with SiC and CuO Particles by Friction Stir Processing

2025-12-16T22:20:04+02:00

Friction Stir Processing (FSP) is a great solid-state surface modification method that can be employed to enhance the mechanical and tribological properties of metallic materials by using reinforcement particles. In the present study, AA6061 aluminium alloy plates with 6mm thickness were subjected to FSP process with constant parameters of 1000 rpm rotational speed and 50 mm/min traverse speed, by employing a cylindrical taper pin tool. Silicon carbide (SiC) and copper oxide (CuO) particles were incorporated in various quantities (1 to 4 wt %) to achieve surface metal matrix composites and to evaluate the impact of the reinforcement on the AA6061 alloy mechanical and wear properties. The microstructure investigation, made by optical microscopy method, demonstrated a significant change in grain structure and a uniform distribution of particles in the stir zone. Among four composites samples, the specimen reinforced with 1 wt % SiC and 3 wt % CuO (sample 3) exhibited excellent properties such as tensile strength of 190.7 MPa, elongation of 8.2%, hardness of 72.9 HV, and wear rate of 0.0526 mm³/Nm. The fracture surface analysis, performed by Scanning Electron Microscopy (SEM) technique, revealed reduced number of dimples and absence of visible cracks, as well as improved resistance to fracture and adequate structural integrity. Furthermore, the analysis of the worn surface indicated shallow grooves and minimal debris, confirming diminished abrasive wear. The results achieved by the methods presented above make the optimized FSP-treated AA6061 composite a promising material for industrial applications such as automotive components, aerospace structures, and tooling elements.

Sustainable Friction Stir Spot Joining Process for Aluminum Alloy AA 4045 Parameter Optimization and Mechanical Assessment

2025-12-16T22:20:04+02:00

This study aims to improve Friction Stir Spot Joining (FSSJ) parameters for AA4045 by examining their effects on energy use and mechanical strength. Also, it proposes a sustainable FSSJ strategy for AA4045 aluminum alloy through multi-parameter optimization and energy-based process evaluation. A Taguchi L9 experimental design was used to study how the rotational speed, plunge depth, and dwell time affect joint performance. Lap-shear tests showed that speeds between 900 and 1200 rpm with a plunge depth of 0.5 to 0.8 mm gave the best bonding. In contrast, a shallow plunge depth of 0.2 mm did not allow enough penetration, resulting in weak adhesion at the interface. Sample No. 9 (1200 rpm, 0.8 mm, 2 s) had the highest shear strength at 2.7 kN, while Sample No. 1 (600 rpm, 0.2 mm, 1 s) had the lowest. The sustainability assessment found that higher rotational speeds increased energy use from 0.06 to 0.11 MJ per weld, but 900 rpm gave the best strength-to-energy ratio. Life-Cycle Assessment (LCA) with ecoinvent data showed a Global Warming Potential (GWP) of 0.004–0.007 kg CO₂-eq per weld, confirming that FSSJ of AA4045 is a low-emission alternative to traditional spot welding. These findings show that FSSJ is an eco-efficient joining method for lightweight applications. This research offers a practical guide for using energy-efficient manufacturing in lightweight structures, which is important for reducing emissions in transport and supports SDG 9 (Industry, Innovation, and Infrastructure) and SDG 12 (Responsible Consumption and Production). Optimised parameters not only lower environmental impact but also improve joint strength.

White Layer Suppression and Surface Integrity Enhancement in Hard Turning of AISI D3 Steel Using CBN Inserts under Dry and Gas Mixture Cooling

2025-12-16T22:20:05+02:00

Superalloys and hardened steels are widely used across industries due to their superior mechanical properties, including high hardness, toughness, wear resistance, and fatigue strength. Because of these characteristics, they typically require grinding for final finishing; however, grinding operations demand expensive machinery, specialized equipment, high-cost abrasive wheels, lengthy setup procedures, and extended machining cycles. As a result, hard turning has emerged as a preferred alternative for finishing hard-to-cut materials, offering advantages such as reduced lead time, lower setup and tooling costs (no fixtures or form wheels), and decreased energy and coolant requirements. In addition, hard turning (HT) can achieve better part quality compared to conventional grinding. However, the process is often affected by the formation of a detrimental surface feature known as the white layer (WL), which significantly reduces fatigue life and undermines surface reliability due to dynamic phase transformations. In this study, super-hardened AISI D3 steel was machined using a semi-worn CBN insert under dry machining and gas-mixture cooling conditions. The results indicate that dry machining neither eliminated the WL nor improved surface finish, whereas gas-mixture cooling effectively suppressed WL formation and produced superior surface quality.

Accurate Prediction of Yield Strength and Welding Defects in FSW Joints of 2XXX and 6XXX Al Alloys Using Artificial Neural Network Based Correlation Analysis

2025-12-17T18:58:00+02:00

The accurate prediction of mechanical properties and defect formation in friction stir welding (FSW) of aluminium alloys is crucial for ensuring structural integrity and reliability in aerospace and automotive applications. In this study, an artificial neural network (ANN) was developed to predict yield strength (YS) and welding defects (WD) in FSW joints of AA2024, AA2219, and AA6061 alloys. A dataset of one hundred experimental cases obtained from peer-reviewed literature was employed, where process parameters rotational speed (RS), plate thickness (PT), shoulder radius (SR), axial pressure (AP), pin root radius (PR), pin tip radius (PS), and tool angle (TA) were served as inputs, and YS and WD were target outputs. The ANN was implemented in MATLAB R2016a and trained using backpropagation. Results showed strong predictive accuracy: for YS, correlation coefficients (R) of 0.96568, 0.99874, and 0.96278 were achieved for training, validation, and testing sets, with an overall R of 0.96764. The minimum validation error (MSE = 2.1901) occurred at epoch 11. For WD prediction, the overall R was 0.83229, with lowest validation error (MSE = 0.09041) at epoch 17. These findings highlight the potential of ANN-based models for real-time prediction and optimization of FSW quality. Future work will focus on expanding datasets, integrating hybrid AI techniques, and developing adaptive models for industrial-scale welding applications.

Experimental-Numerical Evaluation of Tensile-Shear Response in Resistance Spot Welding of AISI 304L Stainless Steel Joints

2025-12-16T22:42:36+02:00

This study investigated the influence of resistance spot welding (RSW) on the tensile-shear strength of stainless steel AISI 304L joints and was carried out using experimental and FE analyses. The mechanical properties were determined by a tensile shear test of RSW on AISI 304L sheet under different conditions (welding current, weld time, squeeze time, and hold time). The results showed that heat input, mainly controlled by welding current and welding time, was the main factor influencing nugget formation and the load-bearing capacity. A 3D FE model with a close-to-reality mesh of the nugget-HAZ interface was prepared to match the test conditions. The model demonstrated a ring-shaped stress zone along the edge of the nugget and was consistent with the pull-out failure observed in the experiment. The experimental results with errors (3.5–4.8%) and observations can be used to ensure that a properly calibrated FE model can predict RSW performance in AISI 304L and possibly aid in weld parameter optimization. The results provide an effective means of evaluating welds through experiments and simulations.

Multi-Objective Optimization Technique to Optimize the Process Parameters of EDM for Al Alloys Using Taguchi with GRA and TOPSIS Method

2025-12-16T22:20:05+02:00

Electrical Discharge Machining (EDM) is a non-traditional machining process which employed to make complex products from hard materials. The machining efficiency and surface integrity are influenced by the selection of parameters. In this research, datasets were collected from earlier experimental research works on EDM process of AL alloy to evaluate machining performance. The input parameters: pulse-on time, pulse-off time, discharge current, gap voltage, flushing pressure, and tool rotational speed and the output responses: material removal rate (MRR), tool wear rate (TWR), surface roughness (SRS), recast layer (RLR), and microhardness (MHS) were considered. MINITAB software was utilized to identify the most significant factors affecting responses through signal-to-noise analysis. For multi-objective optimization, the Taguchi method was integrated with Grey Relational Analysis (GRA) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). From the results, GRA identified optimal parameters of discharge current of 11 A, pulse-on time of 75 µs, pulse-off time of 25 µs, gap voltage of 50 V, flushing pressure of 0.4 MPa, and tool speed of 600 rpm, yielding the highest productivity with MRR of 4.76 g/min, moderate TWR of 0.478 g/min. In contrast, TOPSIS suggested a discharge current of 7 A, pulse-on time of 175 µs, pulse-off time of 90 µs, gap voltage of 40 V, a flushing pressure of 0.5 MPa, and a tool speed of 900 rpm, which produced superior surface quality with a lower SRS of 7.34 µm, reduced RLR of 19.6 µm, and higher MHS of 120.9 HV and a reduced MRR of 0.78 g/min. Both optimal results were validated using an Adaptive Neuro-Fuzzy Inference System (ANFIS) model, confirming accurate prediction of EDM responses. This study demonstrates that GRA is more suitable for productivity-focused applications, whereas TOPSIS is advantageous when surface integrity and hardness are critical, offering a robust decision-making framework for EDM optimization.

Comparative Analysis of Mechanical Properties of Inconel and Martensitic Stainless Steel Joints Achieved by Tungsten Inert Gas Welding

2025-12-16T22:20:07+02:00

This work provides a detailed comparative study on the mechanical performance and microstructural features of dissimilar joints produced by Tungsten Inert Gas (TIG) welding between Inconel 718 and AISI 410 martensitic stainless steel. Welding was performed on 2 mm thick sheets of both alloys using optimized conditions: 80 A current, 12.1 V voltage, 60 cm/min travel speed. Microstructural evaluation revealed contrasting solidification patterns, with Inconel 718 exhibiting Laves phase precipitation within dendritic networks, while AISI 410 displayed coarse dendrites interspersed with sulfur-based inclusions. Vickers microhardness analysis indicated a non-uniform distribution across the weldment. The highest hardness (410 HV) occurred in the AISI 410 heat-affected zone (HAZ) due to martensitic transformation, whereas the Inconel 718 base metal and fusion zone recorded 267±3 HV and 264±5 HV, respectively. Tensile testing demonstrated an ultimate tensile strength of 461±2 MPa, a yield strength of 273 MPa, and an elongation of 17.95±0.8% for the welded joint.

Machine Learning Models for Predicting Mechanical Properties in Friction Stir Welding of Al Alloys

2025-12-16T22:20:07+02:00

Friction Stir Welding (FSW) has developed as an extremely reliable solid-state joining technique for Al alloys, which possesses superior mechanical properties and minimal defects compared to conventional fusion welding. Accurate prediction of welding output parameters such as Ultimate Tensile Strength (UTS), Elongation (%), Hardness, and Wear Rate is crucial for ensuring weld quality and optimizing process conditions. In this study, four machine learning (ML) approaches were employed, such as Backpropagation Neural Network (BP), Extreme Gradient Boosting (XGB), Support Vector Machine (SVM), and Principal Component Analysis (PCA) to predict the output responses with the help of a dataset derived from experimental values. Initial correlation analysis highlighted D/d ratio and tilt angle as critical parameters, moderately correlated with UTS, elongation, and wear rate, but weld speed was inversely related to hardness. In the model performance, SVM achieved the highest prediction accuracy of more than 99.5% for UTS and elongation, followed by BP and XGB methods. PCA demonstrated stable performance, particularly for hardness prediction and BP achieved ~99.44% accuracy for wear rate. Error analysis demonstrated that SVM exhibited the lowest and most stable percentage errors, particularly for UTS and elongation, while PCA showed the least deviation for hardness predictions. The Mean Squared Error (MSE) values for the models were as follows: BP 0.6280, XGB 1.1681, SVM 0.4838, and PCA 0.9590. Overall, the comparative study validates the effectiveness of integrating ML algorithms for accurate welding output prediction, offering potential for real-time process monitoring and optimization in advanced manufacturing environments.

Influence of Surface Roughness on Fatigue Life of AISI D3 Component Hard Turned by CBN Insert under Dry Machining

2025-12-21T05:17:40+02:00

Hard turning has been broadly attributed as a surrogated process for customary grinding because it facilitates any intricate profiles with a single setting. It is a precise machining process, that could accomplish remarkable dimensional accuracy, geometrical tolerance and surface finish, and also ensures coolant-free machining for super hardened steels and alloys. Still, the parts produced are affected by the adverse surface integrity along with a surface pattern often called a White Layer. As a result, the parts produced are affected by fatigue life due to cyclic loads. This investigation was attempted to analyse surface roughness against the Fatigue life of AISI D3 component hard turned by semi-worn out CBN insert under the dry machining. L27 orthogonal array was designed with three cutting speeds, feed rates and depth of cuts. ANNOVA table was formed and found that the feed rate influenced around 62.4% on the surface roughness, subsequently depth of cut impacts nearly 26.6% and cutting speed implies 11%. In addition, it was revealed that the surface roughness influences the Fatigue life of the part. The lowest surface roughness has produced highest Fatigue life of 2.8×107 cycles.

Energy Analysis during Ultrasonic Welding of PP: EDS-ABS Polymer Blends – Experimental Investigation, Statistical Assessment and Mathematical Modelling for Energy and Power Signal Dynamics

2025-12-21T05:23:39+02:00

This research provides an intensive experimental investigation of the energy issues with respect to tensile strength and energy consumption during ultrasonic welding of thermoplastic PP and Electrostatic Discharge ABS polymers. A scheduled set of 27 experiments were performed through varying amplitude, weld pressure, and weld time that led to the identification of the effect of process parameters on joint quality and energy usage. Quantitative data were gathered and analysed to recognize patterns to improve efficiency. The Pearson correlation coefficient was applied to confirm relations between the input variables and the response variables. The analysis indicated a strong positive correlation of weld time with energy consumed, and with the tensile strength and amplitude of the weld. The findings can help create energy-efficient welding methods for polymer materials used in energy systems. Power signals and harmonics analysis is performed using MATLAB to understand its variation when the process is ON and it provides an insight into signals and control mechanism to be fine-tuned for ultrasonic welding process.