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    A review on aluminum alloys produced by wire arc additive manufacturing (WAAM): Applications, benefits, challenges and future trends
    (Elsevier, 2024) Sarikaya, Murat; Onler, Dilara Basil; Dagli, Salih; Hartomacioglu, Selim; Gunay, Mustafa; Krolczyk, Grzegorz M.
    Metal additive manufacturing is advancing with increasing momentum and attracting great attention. The Wire Arc Additive Manufacturing (WAAM) process, one of the metal additive manufacturing methods, involves melting a filler wire with an electric arc and depositing metal droplets layer by layer along the planned path. Aluminum alloys produced by the WAAM process have been in high demand in the industry, especially in the last decade. The WAAM process stands out as a suitable method for many industries due to its low investment cost, high deposition rates and the advantages of creating relatively complex parts. Key application areas of aluminum alloys produced using WAAM include aerospace, automotive, marine, and energy sectors, where lightweight structures, corrosion resistance, and high strength are critical. Much research has been done and innovative applications, including hybrid systems, have been developed to prevent defects such as residual stresses, cracks, porosity and delamination. This review article provides a comprehensive overview of the use of the WAAM process in aluminum alloys over the past decade. In the article, firstly, aluminum alloys, the WAAM technique and its types are introduced. In the following section, the methods used to improve mechanical properties and optimize the microstructure are examined in detail. In the next section, the difficulties encountered when using aluminum alloys in WAAM applications are discussed in detail. In the discussion section, current developments are evaluated, and in the last section, suggestions for future studies and inferences obtained from this study are presented. As a result, WAAM-CMT and hybrid systems were found to be effective in reducing defects such as porosity, distortion and residual stress. In addition, post-processing heat treatments and surface treatment methods are also crucial for improving mechanical properties. Finally, more research is needed in the areas of 7xxx series alloys, repair applications and environmental sustainability.
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    Analysis of machinability and sustainability aspects while machining Hastelloy C4 under sustainable cutting conditions
    (Elsevier, 2023) Yildirim, cagri Vakkas; Sirin, Senol; Dagli, Salih; Salvi, Harsh; Khanna, Navneet
    In recent years, developments in the defense, aerospace, and medical industries have significantly increased the expectations regarding material performances. In particular, the demand for materials that can withstand very high and/or very low temperatures and harsh mechanical/chemical conditions has increased. The superior qualities of superalloys can adequately meet this demand. However, the difficulties encountered in the machining of these alloys cause some burdens both ecologically and economically due to the use of cutting fluid. Therefore, the use of cost-friendly and sustainable cutting fluids in the production industry has a vital role, both in terms of machining performance and the environment. From this perspective, this paper focuses on the effects of various cutting environments, i.e., Dry, MQL, LN2, N-2, CO2, Vortex, LN2 + MQL, N-2 + MQL, CO2 + MQL, and Vortex +MQL on the machining performance of Ni-based C4 alloy. Additionally, it was aimed to reveal the effect of cooling/lubrication methods on sustainability by performing a sustainability analysis. Firstly, surface roughness, power consumption, tool wear and mechanisms, and cutting temperature were considered as performance characteristics. When examined in terms of machinability, Vortex + MQL gave the best result in terms of surface roughness and power consumption, while LN2 gave the best result in terms of cutting temperature. Then, a comprehensive sustainability analysis was carried out. As a result, the CESMO follows the order of Dry > MQL > LN2 > LN(2 +)MQL > CO2 > CO2 + MQL > N-2 > N-2 + MQL > Vortex > Vortex + MQL. While employing Vortex + MQL cutting condition, the CESMO decreased by about 11.37% as compared to Dry cutting condition. While using a combination of different sustainable lubrications or coolants, the overall carbon emissions decreased in the range of about 15-25% approximately as compared to the employment of the individual cutting conditions (i.e., coolant/lubricants).
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    Characterization, generative design, and fabrication of a carbon fiber-reinforced industrial robot gripper via additive manufacturing
    (Elsevier, 2024) Hartomacioglu, Selim; Kaya, Ersin; Eker, Beril; Dagli, Salih; Sarikaya, Murat
    Robot grippers are crucial components across various industrial applications, requiring special design and production for obtaining the optimal performance. Conventional plastic injection moulding techniques fall short in achieving the specificity needed for these grippers. To address this challenge, current paper focuses on developing a robot gripper using carbon fiber-reinforced polyamide with a next-generation composite filament and employing the innovative Generative Design technique. In the work, we began by characterizing and optimizing the composite material specifications. Then, the tensile strength and fracture mechanics of standard samples based on printing parameters, applying Taguchi experimental design for optimization were evaluated. Analysis of Variance (ANOVA) was used for factor analysis to fine-tune the process. Using the Generative Design technique, we determined optimal geometries, which were then fabricated through Fused Deposition Modeling (FDM). As a result, the optimization efforts led to significant improvements i.e., tensile strength increased from 103.2 to 116 MPa, and the elasticity modulus from 8386 to 8990 MPa. In practical industrial applications, we achieved a reduction in material weight from 14 to 4 g, lowered production costs from $5.16 to $1.50, and cut production time from 58 to 28 min. This study presents a validated method for developing industrial products with reduced material usage and costs, promoting sustainable production practices.
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    Data-Driven Prediction of Wave Energy Potential in the Black Sea Using Ensemble Learning and Real-Time Buoy Observations
    (Springer Heidelberg, 2026) Irim, Duygu Saydam; Sarikaya, Murat; Dagli, Salih
    This study proposes a machine learning-based framework for short- to medium-term (1-72 h) wave energy forecasting using buoy-measured wave parameters collected from Samsun and Ordu buoys in the Black Sea. Multiple machine learning (ML) techniques, including Long Short-Term Memory (LSTM), Extreme Learning Machine (ELM), Support Vector Machine (SVM), Bayesian Regression, Residual Neural Networks (ResNet), and Recurrent Neural Networks (RNN), were implemented to estimate wave power using forecasted wave parameters. Each model produced independent predictions, which were then integrated through a stacking ensemble approach based on Extreme Gradient Boosting (XGB) to obtain the final wave energy estimates. When compared individually, the ensemble framework provided noticeably lower error levels across all performance indicators, including RMSE, MAE, and MSE, and achieved higher coefficients of determination. Among the tested approaches, the XGB-based ensemble showed mostly reliable performance, particularly for short-term forecasts. For one-hour-ahead predictions, RMSE values were generally between 0.01 and 0.02 m, while R2 values were consistently above 0.99. Although prediction accuracy gradually decreased for longer horizons, the model remained stable for lead times of up to 72 h. Differences were also observed between buoy locations. Predictions derived from the Samsun buoy were more stable than those from Ordu, which is likely related to local variability in wave conditions. Our findings suggested that site-specific dynamics play an important role in wave energy forecasting. Overall, this work demonstrates the potential of combining real-time buoy measurements with ensemble machine learning to estimate wave energy in the Black Sea, providing a practical alternative to computationally intensive numerical wave models and supporting future offshore energy planning.
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    Evaluation of shear strength and interlayer damage of short carbon fiber reinforced PA6 in FDM printing along critical ZX orientation
    (Elsevier, 2025) Hartomacioglu, Selim; Yayla, Pasa; Yazman, Sakir; Dagli, Salih; Sarikaya, Murat
    This study experimentally and numerically investigates the shear strength and interlayer damage mechanisms of short carbon fiber-reinforced Polyamide 6 (PA6-CF15) composites fabricated via Fused Deposition Modeling (FDM) along the critical ZX build orientation. Using a Taguchi experimental design, the effects of key processing parameters-nozzle temperature, layer thickness, number of outer walls, and post-heat treatment duration-on shear strength were systematically analyzed. Experimental results indicated that PA6-CF15 specimens exhibited higher maximum shear loads compared to neat PA6, although they demonstrated more brittle behavior and lower ductility, primarily due to insufficient fiber bridging in the Z-direction. The optimal parameters for maximizing shear strength were identified as a nozzle temperature of 260 degrees C, a layer thickness of 0.15 mm, two outer walls, and 80 min of post-heat treatment. Finite Element Analysis (FEA) corroborated experimental findings by revealing stress concentrations near shear notch regions and confirming that optimized parameters enhance interlayer cohesion. Furthermore, comprehensive surface roughness measurements and Scanning Electron Microscopy (SEM) analyses provided detailed insights into damage progression. Higher nozzle temperatures and thinner layers resulted in smoother surfaces and denser structures, which correlated with improved shear strength. SEM images revealed complex failure mechanisms including fiber-matrix debonding, fiber pullout, matrix cracking, and fiber breakage, with evidence of matrix smearing and ductile drawing. The finite element simulations predicted a maximum shear stress of 151.6 MPa and an average shear stress of 23.4 MPa under optimized conditions, aligning well with experimental observations. Among the examined parameters, layer thickness had the most pronounced influence on shear strength, followed by wall line count and heat treatment duration, while nozzle temperature exhibited a comparatively moderate effect. The findings underscore the critical role of process parameter optimization in enhancing the shear strength and overall mechanical performance of FDM-printed short carbon fiber-reinforced PA6 composites, offering valuable guidance for both academic research and industrial applications.
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    Investigation of bending behavior of two and three-dimensional honeycomb and auxetic sandwich beams
    (Sage Publications Ltd, 2024) Yazici, Mustafa Enes; Kanber, Bahattin; Dagli, Salih
    The bending behavior of two (2D) and three-dimensional (3D) sandwich beams that have a negative Poisson's ratio (auxetic) and conventional honeycomb was investigated for different geometries, sheet thicknesses, and cell thicknesses. The re-entrant and solid specimens were produced with a stereolithographic (SLA) 3D printer and subjected to a three-point bending test under the conditions specified in the ASTM C393 standard. The data obtained from the tests were used to verify finite element analysis (FEA) results. The radius of curvatures was calculated for each specimen depending on the load step. In addition, Poisson's ratio was calculated for each auxetic sample. As a result, the 3D arrowhead beam, with a cell thickness of 1 mm and a sheet thickness of 2 mm, exhibits peak force values that are 497.340% and 461.500% higher than re-entrant and missing rib beams, respectively. Besides, the maximum strain energy values of same 3D arrowhead specimens (596.120 mJ) are higher than re-entrant (101.032 mJ) and missing rib (108.201 mJ) specimens. It was determined that the arrowhead is the most durable structure compared to other auxetic structure geometries. Therefore, when arrowhead and honeycomb 3D beams are compared, it was observed that the maximum strain energy of arrowhead specimens was higher in both horizontal (84.310%) and perpendicular (131.910%) positioned specimens. Comparing the arrowhead and honeycomb 2D beams with the highest maximum strain energy, it can be concluded that the arrowhead beam absorbs 20.000% more energy.
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    Mechanical and Thermal Performance of SLA-Printed Ceramic-Reinforced Lattice Structures: A Topology-Material Synergy Approach
    (Wiley, 2025) Sarikaya, Murat; Onler, Bugra; Dagli, Salih
    Additive manufacturing (AM), particularly stereolithography (SLA), has emerged as a transformative technology capable of producing lightweight, complex structures for high-performance applications. However, limited knowledge exists regarding the combined effects of material composition and lattice topology on the mechanical and thermal behavior of SLA-printed components. This study addresses this gap by investigating the influence of ceramic nanoparticle reinforcement-using Al2O3, hBN, and SiC-alongside three distinct lattice geometries (simple cubic, diamond, and octahedral) on the performance of SLA-fabricated structures. A comprehensive experimental approach was adopted, incorporating tensile, compression, and Charpy impact testing, as well as thermogravimetric (TGA), derivative thermogravimetric (DTG), and differential thermal analyses (DTA). The results indicated that hBN-reinforced samples exhibited a 17.5% increase in tensile strength and a 10.66% reduction in thermal degradation rate, while Al2O3-enhanced samples demonstrated a 124.5% improvement in impact resistance. In contrast, SiC additives slightly reduced tensile strength and thermal stability. Among the lattice geometries, simple cubic structures achieved the highest compressive strength (up to 0.76 MPa with hBN), whereas diamond lattices provided a balance between strength and ductility. The study concludes that the synergistic selection of ceramic fillers and lattice topology can be strategically employed to design multifunctional components with enhanced mechanical and thermal properties for advanced applications such as aerospace, automotive, and energy-absorbing systems.
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    Mechanical Characterization and Interface Evaluation of Multi-Material Composites Manufactured by Hybrid Fused Deposition Modeling (HFDM)
    (Mdpi, 2025) Dagli, Salih
    In this study, the mechanical behavior and interfacial bonding characteristics of multi-material composites produced using the Hybrid Fused Deposition Modeling (HFDM) technique were systematically investigated. Polylactic Acid (PLA), Polyethylene Terephthalate Glycol (PETG), and Acrylonitrile Butadiene Styrene (ABS) filaments were utilized within a single structure to explore the effects of material combinations on mechanical performance. Specimens were fabricated using two distinct levels of infill density (50-100%) and raster angle (45-90 degrees) to evaluate the influence of these parameters on tensile strength, flexural resistance, and impact toughness. Experimental tests were conducted following ASTM standards, and microstructural examinations were performed using Scanning Electron Microscopy (SEM) to assess interfacial adhesion between different polymers. The results revealed that PETG demonstrated the highest tensile strength among single-material samples, while the PLA-PETG-ABS configuration exhibited notable mechanical stability among hybrid structures. Increasing infill density and raster angle significantly enhanced mechanical performance across all configurations. SEM analyses confirmed that interfacial bonding quality critically affected structural integrity, with better adhesion observed in PLA-PETG interfaces compared to PLA-ABS transitions. The potential of HFDM in developing tailored multi-material components with optimized mechanical properties offers valuable insights for the advancement of functional additive manufacturing applications in engineering fields.