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Effect of Post Processing Heat Treatment on the Cyclic High Temperature Hot Corrosion Behavior of the Laser Powder Bed Fusion Processed Inconel 718
(2026-07-08)
Sahu, Saroj Kumar
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Dalai, Renu Prava
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Shaik, Nagoor Basha
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Yuangyai, Chumpol
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Behera, Ajit
In the present investigation, the cyclic hot corrosion behavior of laser powder bed fusion (LPBF)-processed Inconel-718 superalloy subjected to single-aging (HT-1) and double-aging (HT-2) heat treatments was systematically evaluated. Tests conducted at 850°C for 96 h in a Na<inf>2</inf>SO<inf>4</inf>-NaCl-NaVO<inf>3</inf> molten salt environment demonstrated that HT-2 exhibited significantly enhanced corrosion resistance. The cumulative weight gain for HT-2 was 4.8 mg/cm2, compared to 5.7 mg/cm2 for HT-1 (an approximate 15.8% reduction), alongside a notably lower calculated corrosion rate (584.0 vs. 693.5 mg/cm2/year). Cross-sectional analysis indicated that HT-2 formed a thinner, denser, and more adherent oxide scale—primarily Cr<inf>2</inf>O<inf>3</inf> and NiCr<inf>2</inf>O<inf>4</inf> spinel—with reduced chromium depletion beneath the oxide layer. This improved corrosion resistance of HT-2 is attributed to its refined precipitate distribution and enhanced chemical homogeneity, which promote uniform chromium diffusion and the formation of a stable protective oxide barrier. This stable oxide scale effectively limits oxygen ingress, molten salt penetration, and scale spallation during cyclic thermal exposure. Overall, optimized double-aging significantly enhances the cyclic hot corrosion resistance of LPBF-processed Inconel-718. These findings provide important insights into tailoring post-processing heat treatments to improve the high-temperature durability of additively manufactured superalloys.
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Thermo-Mechanical Stress Prediction in Steel IPE Profiles under Asymmetric Thermal Loading: A Finite Element and XGBoost-Based Approach
(2026-03-01)
Shaik, Nagoor Basha
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Derakhshan, Ali
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Nasim, Maryam
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Jongkittinarukorn, Kittiphong
Accurate prediction of thermally induced stresses in structural members remains a significant challenge in engineering, especially under complex real-world conditions. Traditional analytical and numerical methods, while robust, often struggle to capture the complicated relationship between uneven thermal loads and structural responses without significant computational effort. This study investigates the effect of asymmetric thermal loading on standard steel IPE profiles, which are widely employed in buildings and structures. These members, often exposed partially to outdoor conditions, experience uneven temperature distributions across their cross-sections, resulting in complex internal stress patterns. To simulate such scenarios, a range of thermal conditions is applied to beams and columns with varying geometries using the Finite Element Method (FEM) numerical analysis. The resulting stress components, including von Mises, axial, and shear stresses, are analyzed in detail. This study introduces a mixed approach that integrates FEM with eXtreme Gradient Boosting (XGBoost) to forecast thermal stresses in steel IPE profiles subjected to asymmetrical temperature gradients. The suggested technique, in contrast to traditional assessments that emphasize uniform heating, accounts for the interrelated impacts of irregular thermal exposures and geometric variations among IPE sections. The FEM database enabled the training of an improved XGBoost model that achieved exceptional accuracy (R² > 0.98) in predicting multiple stress components. The results highlight the critical role of cross-sectional geometry in stress development under thermal gradients and underscore the effectiveness of machine learning techniques in forecasting structural responses. This integration offers a quick, adaptable method for assessing thermal impacts in steel IPE structures, with considerable promise for design and real-time structural evaluation in industrial settings. This approach offers substantial benefits to the petroleum and broader oil and gas sectors, particularly in enhancing structural dependability under thermal and mechanical stresses.
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Influence of Graphene Oxide Nanoparticles on the Mechanical Behavior of Stereolithography Printed Polyether Ether Ketone Composites
(2026-01-01)
Ramkumar, N. P.
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Sharma, S. C.
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Adarsha, H.
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Shaik, Nagoor Basha
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Anurakparadorn, Kanat
The objective of this work is to analyze the impact of graphene oxide percentage on the mechanical behavior of polyether ether ketone (PEEK) nanocomposites produced by stereolithography. The dispersion of graphene oxide (GO) nanoparticles was analyzed using both the scanning electron microscopes and transmission electron microscopes. The mechanical properties of nanocomposites were analyzed by performing hardness and tensile tests in accordance with ASTM standards. The morphology of the final product shows a consistent distribution of GO nanoparticles and a robust interfacial bonding between the nanoparticle reinforcement and the PEEK matrix. It is found that the nanoparticles enhanced the dimensional stability of the nanocomposites, resulting in lower dimensional tolerance compared to the pure PEEK material. The microhardness test has been carried out on the samples, demonstrating the beneficial effect of nanoparticles; the PEEK nanocomposite containing 0.75% nanoparticles gives a higher hardness value of 71 VHN. The strength of nanocomposites was found to increase due to the robust interfacial cohesion between GO and PEEK, resulting in enhanced hardness. Here, the hardness exhibits a negative impact on elongation, which yields a declining trend from (1.7 ± 0.6)% to (1.4 ± 0.6)% with an increase in graphene oxide nanoparticles.
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Integrated Analysis of Mapping, Path Planning, and Advanced Motion Control for Autonomous Robotic Navigation
(2025-10-01)
Bingi, Kishore
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Singh, Abhaya Pal
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Ibrahim, Rosdiazli
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Rajamallaiah, Anugula
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Shaik, Nagoor Basha
Autonomous robotic navigation is essential in modern systems for revolutionising various industries that operate in both static and dynamic environments. To achieve this autonomous navigation, various conventional techniques that handle environment mapping, path planning, and motion control as individual modules often face challenges in addressing the complexities of autonomous navigation. Therefore, this paper presents an integrated technique that combines three essential components, such as environment mapping, path planning, and motion control, to enhance autonomous navigation performance. The first component, i.e., the mapping, utilises both binary and probabilistic occupancy maps to represent the environment. The second component is path planning, which incorporates various graph- and sampling-based algorithms such as PRM, A*, Hybrid A*, RRT, RRT*, and BiRRT, which are evaluated in terms of path length, computational time, and safety margin on various maps. The final component, i.e., motion control, utilises both conventional and advanced controller strategies such as PID, FOPID, SFC, and MPC, for better sinusoidal trajectory tracking. The four case studies for path planning and one case study on trajectory tracking on various occupancy maps demonstrated that the A* algorithm and MPC outperformed all the compared techniques in terms of optimal path length, computational time, safety margin, and trajectory tracking error. Thus, the proposed integrated approach reveals that the interplay between mapping fidelity, planning efficiency, and control robustness is vital for reliable autonomous navigation.

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