Multi-response optimization of FSW parameters for Al-Mg-Zn alloys using Box-Behnken design and gray relational analysis and comparative study with ANFIS technique T. Venkateswara Rao, Adina Srinivasa Vara Prasad, M. Naga Swapna Sri, P. Anusha, Deepak Gupta, S. Vijayakumar, Hari Prasadarao Pydi, Nageswararao Cheepurupalli Aip Advances, 2025 In this study, cast Al–Mg–Zn and aging Al–Mg–Zn alloys were joined using the friction stir welding technique, with the welding parameters being the rotational speed of the tool, welding speed, tilt angle, and D/d ratio. The effects of these input parameters on output responses such as ultimate tensile strength, elongation percentage, hardness, and wear rate were investigated using Box–Behnken experimental design. Multi-response optimization was performed using the Box–Behnken Design (BBD) combined with Gray Relational Analysis (GRA). A rotational speed of 1200 rpm, welding speed of 100 mm/min, tilt angle of 1.5°, and D/d ratio of 2.25 were found to significantly enhance the mechanical properties and wear resistance. Furthermore, the Adaptive Neuro-Fuzzy Inference System (ANFIS) model was developed to predict outcomes based on the gray relational coefficient grades, leveraging its architecture to improve prediction accuracy. Finally, a comparative analysis was conducted between the BBD-GRA methods and the ANFIS technique. Results indicated that the ANFIS model was highly effective for predicting output parameters, achieving superior precision and accuracy with fewer iterative calculations. By selecting an appropriate ANFIS structure, the model’s performance is better than that of the BBD-GRA method.
Experimental Study and Optimization of Electrochemical Machining of Al-7Si-0.4Mg Alloy Using NaNO3 and NaOH Electrolytes Hari Prasadarao Pydi, Adina Srinivasa Vara Prasad, Movva Naga Swapna Sri, Chitrada Prasad, Anusha Peyyala, Boru Bedeya Jarso, Deepak Gupta, Vijayakumar Sivasundar Advances in Materials Science and Engineering, 2025 This study focused on the electrochemical machining (ECM) of Al‐7Si‐0.4Mg alloy using a mixture electrolyte of NaNO3 and NaOH. Experiments were designed based on the Taguchi method, utilizing an L16 orthogonal array, and input parameters such as voltage (V), electrolyte concentration (E), feed rate (F) and duty cycle (D) were varied to evaluate the effects on material removal rate (MRR) and surface roughness (SR). Residual analysis, ANOVA and signal‐to‐noise (SN) ratio analysis were conducted to determine the optimal settings for single‐response optimization. For multi‐response optimization, a hybrid statistical approach combining Taguchi and TOPSIS was employed. The Taguchi–TOPSIS combination analysis identified the optimal parameter combination as V4–E3–F2–D3, corresponding to a voltage of 14 V, an electrolyte concentration of 80 g/L, a feed rate of 0.5 mm/min and a duty cycle of 75%, which achieved the highest MRR and lowest SR. ANOVA results revealed that voltage is the most influential factor, contributing 91.3% to MRR and 60.6% for SR. Validation tests under these optimal conditions (V4–E3–F2–D3) yielded an MRR of 33.25 mm3/min and a SR of 0.164 μm. Compared to prior experimental results, the optimized parameters achieved through the Taguchi–TOPSIS approach led to a 7.32% increase in MRR and an 11.35% reduction in SR, confirming the reliability and effectiveness of this hybrid method in significantly improving machining performance. Finally, the integration of the hybrid statistical optimization strategy with the dual‐electrolyte formulation made a significant advancement in the ECM of Al alloys, providing a highly efficient and minimal surface damage.
A Comprehensive Review of the Effect of Additives on Microalgae Biodiesel Performance and Emissions Adina Srinivasa Vara Prasad, K V Murali Krishnam Raju, K Krishna Bhaskar, Tarun Kumar Kotteda Journal of Physics Conference Series, 2024 The production and utilization of biodiesel derived from microalgae have gained significant attention as a sustainable alternative to fossil fuels. However, the intrinsic properties of microalgae biodiesel can limit its performance and lead to undesirable emissions. This paper summarizes the impact of additives on microalgae biodiesel, focusing on their influence on performance and emissions. Additives play a crucial role in improving the overall quality and efficiency of microalgae biodiesel. They encompass a range of compounds, including antioxidants, stabilizers, and combustion enhancers. These additives have been studied extensively to elucidate their effects on fuel properties, engine performance, and exhaust emissions. By modifying the physicochemical properties of microalgae biodiesel, additives can enhance its stability, cetane number, and oxidative resistance, thereby promoting efficient combustion and reducing emissions of harmful pollutants. This review concludes that the usage of additives to the algae biodiesel leads to improved combustion performance and emission reduction.
Analysis of Wear Behaviour of AA5052 Alloy Composites by Addition Alumina with Zirconium Dioxide Using the Taguchi-Grey Relational Method Bhiksha Gugulothu, S. Lakshmi Sankar, S. Vijayakumar, Adina Srinivasa Vara Prasad, M. Thangaraj, M. Venkatachalapathy, T.V.Janardhana Rao Advances in Materials Science and Engineering, 2022 The present study deals with preparation of Al hybrid metal matrix composites by the stir casting technique. In this work, Al5052 alloy was selected as the main metal which reinforces with zirconium dioxide and aluminium oxide. The wear test has been conducted on the POD apparatus. 9 specimens are produced by varying reinforcements’ percentage for conduction of tests. The amount of ZrO2 reinforcement is fixed to 8wt%, while the concentration of Al2O3 varied from 5wt% to 10wt%. The parametric optimization of wear behaviour is done through the Taguchi method with L9 orthogonal array. Minitab statistical software helped to find SN ratio and ANOVA reading for each parameter: load (N), sliding speed (rpm), and wt% of reinforcements. From the ANOVA outcome, the load parameter is the highest influencing element that improves wear resistance of made samples around 46%, followed by weight% of reinforcement 29% and sliding speed 26% as second and third influenced parameters, respectively.
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MOST CITED SCHOLAR PUBLICATIONS
Friction and wear optimization of SiC/graphite reinforced AZ31 hybrid composite using Taguchi method I Veeranjaneyulu, V Haripriya, R Saminathan, BVV Naidu, JJM Hillary, ... International Journal on Interactive Design and Manufacturing (IJIDeM) 18 (3 … , 2024 2024.0 Citations: 58
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Experimental Study and Optimization of Electrochemical Machining of Al‐7Si‐0.4Mg Alloy Using NaNO 3 and NaOH Electrolytes HP Pydi, ASV Prasad, MNS Sri, C Prasad, A Peyyala, BB Jarso, D Gupta, ... Advances in Materials Science and Engineering 2025 (1), 3884745 , 2025 2025.0 Citations: 14
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Performance and emission analysis of VCR diesel engine through fuel ionization under the influence of magnetic field PSLD Srinivas, AR Krishna, NB Dunna, ASV Prasad International journal of research 7, 106-114 , 0 Citations: 2
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Activation of sugarcane bagasse and coconut shell biochar using H₃PO₄, KOH and ZnCl₂ for adsorption performance optimised through GRA technique ASV Prasad, VR Rajendranrao, S Ravichandran, MS Saraswathy, ... Interactions 247 (1), 22 , 2026 2026.0
INVESTIGATIONAL STUDY ON INFLUENCING PARAMETERS FOR FLEXURAL STRENGTH AND HARDNESS OF NITINOL-BASED ALUMINUM MATRIX COMPOSITES BSSPS Javvadi Eswara Manikanta , Naveen Kumar Gurajala , Chitrada Prasad , D ... European Chemical Bulletin 12 (3), 683 – 691 , 2023 2023.0
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