Tauheed Shehbaz

Steel is widely used in structural applications, and wire laser additive manufacturing (WLAM) provides a promising approach for fabricating structural steel components. Understanding the relationship between microstructure and mechanical response is essential for optimizing the performance of WLAM-fabricated steels. In this study, ER70S-6 steel wall was fabricated using WLAM and examined in the as-printed condition and after post-annealing heat treatment at 600 °C and 700 °C. Microscopic analysis revealed a ferrite–pearlite microstructure in the as-printed sample. Following heat treatment, the pearlite fraction progressively decreased, the as-printed sample exhibited an average pearlite fraction of ∼6.5%, which reduced to 3.8% and 3.4% after annealing. Concurrently, the average grain size increased from 0.98 µm to 1.50 µm and 1.85 µm, confirming grain coarsening during heat treatment. SEM-EDS revealed a ferritic matrix containing pearlite along grain boundaries and EDS point analysis revealed minor variations in the concentration of Mo and Mn at analyzed locations. Moreover, XRD analysis confirmed α-Fe as the dominant phase. Mechanical testing demonstrated a clear trade-off between strength and ductility, with hardness and strength decreasing after heat treatment, whereas elongation increased. The as-printed wall exhibited a UTS of 447 MPa and 18.3% elongation, after annealing at 600 °C, UTS decreased by 2.4% and elongation increased by 11.9%, whereas at 700 °C, UTS decreased by 7% and elongation increased by 29%. These results demonstrate that ER70S-6 steel produced via WLAM, combined with post-annealing, allows tailoring of microstructure and mechanical properties, providing a pathway for designing high-performance structural components.

Electroless coatings mark a significant improvement in surface engineering by providing superior uniformity, precision, and functional properties compared with traditional plating processes. Electroless Ni-P-Al₂O₃ composite coatings were successfully deposited on Ti-6Al-4V, forming a Ni-Ti intermediate (~14–16 µm) that ensured strong interfacial bonding. Al₂O₃ concentration (0.2–1.4 g/L) strongly influenced microstructure and performance, with 0.4 g/L yielding the most refined coating. AFM revealed a dense, uniform surface with the lowest roughness (~23.4 nm). This composition achieved the highest hardness (464.6 HV_0.1), ~157% higher than the substrate and ~53% higher than Ni-P. It also showed superior tribological behavior, reducing wear volume to ~4.46 × 10⁻⁷ mm³ and friction coefficient to ~0.32 (~85% and ~50% reductions vs. substrate). Corrosion resistance was maximized at 0.4 g/L, with the lowest corrosion current (2.45 × 10⁻⁶ A/cm²) and rate (0.0053 mpy), outperforming both Ni-P and uncoated Ti-6Al-4V due to the compact composite matrix and stable passive film.

In the present study, AA-6061 and LF-06 aluminum alloys were subjected to pulsed Tungsten Inert Gas (P-TIG) welding using ER4043 (Al-Si) Filler alloy. A detailed microstructural examination revealed significant variations in the grain morphology, accompanied by the formation of intermetallic phases, notably Mg ₂ Si, Al ₃ Mg ₂ , and Al ₃ Fe, as well as the presence of both columnar and equiaxed grains. X-ray diffraction (XRD) and energy dispersive spectroscopy (EDS) analyses corroborated the identification of peaks corresponding to second-phase precipitates Mg ₂ Si, Al ₃ Mg ₂ , and Al ₃ Fe within the fusion zone (FZ). Notably, the welded joint exhibited no signs of porosity or hot cracking. Microhardness and nanohardness assessments demonstrated a significant increase in hardness, approximately 68% and 51% higher in the FZ compared to the AA-6061 base alloy, respectively. Furthermore, tensile test results indicated that the weldment strength of 132 MPa corresponds to ∼95% of the AA-6061 base alloy strength (139 MPa). AFM analysis demonstrated moderate pile-up behavior in the FZ compared to the base alloys, indicating localized plastic accommodation associated with microstructural heterogeneity and second-phase particles, without adversely affecting joint integrity.

Wire arc additive manufacturing (WAAM) has emerged as a promising technique for large-scale metal fabrication due to its high deposition rate and cost efficiency; however, its widespread adoption is hindered by the occurrence of process-induced defects such as porosity, spattering, and burn-through. This study presents an in-situ defect detection framework based on acoustic signal analysis and deep learning. Acoustic emissions generated during the welding process are captured using a low-cost electret microphone and transformed into time–frequency spectrograms via short-time Fourier transform (STFT). These spectrograms are utilized as inputs to a ResNet18 convolutional neural network with transfer learning to perform multi-class classification of welding conditions. The proposed framework achieves an overall accuracy of 93.67% across four classes, demonstrating that acoustic signals contain discriminative features associated with weld quality and defect formation mechanisms. Compared to conventional monitoring approaches that rely on complex sensing systems, the proposed method offers a simple, non-intrusive, and cost-effective solution. The findings highlight the potential of acoustic-based monitoring for real-time WAAM process control and its integration into intelligent and digital manufacturing systems.

A void closure analytical model for the diffusion bonding of titanium TC4 alloy is developed in this study, in which an FEA-based deformation mechanism was coupled with the numerical analysis for diffusion. The focus was on evaluating the effect of pressure and temperature on the bonded ratio. By increasing the value of bonding pressure or bonding temperature, the bonding time decreases. The dependence of the bonded ratio on time was modeled as an exponential curve. Furthermore, deep learning models were employed to convert the simulation videos of bonded ratio over time into their respective graphs using the encoder-decoder and U-Net models. The U-Net model predicted the value of bonded ratio with 95% accuracy, while the encoder-decoder model predicted it with an accuracy of 94%. The prediction of the material properties was done by a convolutional 2D model, and the loss and Mean Absolute Error (MAE) for the model were 0.0303 and 0.12, respectively, for the test results.

Titanium is widely used in aerospace and medical industries for its high strength-to-weight ratio and corrosion resistance, while Inconel 718 is favored in aerospace and power generation for its exceptional mechanical strength and oxidation resistance at high temperatures. It’s challenging to directly combine the Inconel 718 and the titanium, so the interlayer of vanadium is used which causes the strengthening of the bond by the formation of inter-metallics (TiaNib, NixVy). In this study, the RVE model was developed in order to examine the mechanical properties (i.e. Modulus, Poisson ratio) of the inter-metallics, by examining their microstructures. Furthermore, nanoindentation techniques are employed across different zones of the weldment to determine the modulus and hardness values. At the vanadium-Inconel interface, hardness and modulus values were observed to range from 2 to 8.5GPa and 130 to 205GPa respectively. The maximum error in hardness between the experimental and simulation was 3.75%. The pile up behavior was also examined in the simulation setup to determine the amount of plastic zone in the indent.

A void closure analytic model for the diffusion bonding of titanium TC4 alloy is developed in this study, in which an FEA-based deformation mechanism is coupled with a numerical analysis for diffusion. The focus was to evaluate the effect of pressure and the temperature on the bonded ratio. As the value of bonding pressure or the bonding temperature increased, the bonding time decreased. The dependence of bonded ratio and time was modeled as an exponential curve. The geometrical model for the mechanism was utilized so that it can incorporate the void division aspect in the process of diffusion bonding.

This study used friction stir welding to make comparable (AA2219-AA2219 and Cu-Cu) and dissimilar (AA2219-Cu) lap joints. The tool retained its rotating speed at 1500 rpm and tilt angle at 2°, while welding speed varied from 23.5 mm/min to forward. Changing work-piece material affects welding speed, joint strength and hardness, and intermetallic compound formation. Similar lap joints have a welding speed window of 23.5–475 mm/min for Cu-Cu lap joints and 23.5–275 for AA2219-AA2219. However, dissimilar welding had a significantly smaller window of favorable speed: 23.5–30 mm/min for AA2219-Cu joints and 23.5–37.5 for Cu-AA2219 joints. The tensile strength analysis showed a maximum lap shear strength of ∼33 MPa for similar joints at extreme speeds, whereas dissimilar joints achieved ∼23 and ∼12 MPa for AA2219-Cu and Cu-AA2219 configurations, respectively, at 23.5 mm/min, mainly due to the formation of IMCs. Hardness testing indicated a significant increase to ∼79 Hv in dissimilar joints at 30 mm/min when copper was layered over AA2219, a result of extrusion and stirring effects. The high hardness in the dissimilar Al-Cu joints can be attributed to the formation brittle IMCs AlCu (Al₂Cu) and Al₄Cu₉.