Optimization of Laser Beam Welded Novel Dissimilar Material UNS S32304 and 304 L Steel Process Parameters Using Deep Learning Chodagam Lakshmi Poornima, Chalamalasetti Srinivasa Rao, Dantuluri Narendra Varma Journal of Advanced Manufacturing Systems, 2025 In recent years, laser beam welding of dissimilar materials has become crucial for diverse industrial applications. Our research targets the optimization of this process for joining UNS S32304 and 304 L steel, necessitating a delicate balance of input parameters like peak power, weld speed, pre-heat temperature, undercut, deformation and tensile strength. The challenge lies in the intricate relationship between these parameters and weld quality, demanding a robust prediction model. To tackle this, we propose a deep learning strategy incorporating feature scaling, SMOTE for class imbalance and Multi-Agent Salp Swarm Optimization (MASSO). Employing a Multi-Layer Perceptron (MLP) neural network ensures precision, bridging traditional welding with advanced deep learning for more efficient and reliable industrial applications.
IMPORTANCE OF DUPLEX STAINLESS STEELS IN THE AREA OF MANUFACTURING , Chodagam Lakshmi Poornima, Chalamalasetti Srinivasa Rao, and Russian Journal of Biomechanics, 2025 Stainless steels of the Duplex type (DSS) are becoming increasingly popular in the industrial world as they offer superior mechanical properties and corrosion resistance, when compared to those of austenitic stainless steels. Additionally, DSSs are found to be highly cost-effective compared to the alternatives. Due to the concentrated chemical composition of duplex stainless steel, both leaner and richer grades are available ac-cording to applications and requirements. There are a number of factors which contribute significantly to the robustness of DSS, including its high yield strength, chloride Stress Corrosion Cracking (SCC), and pitting corrosion. There are also challenges associated with DSS, such as the phase formation that occurs during the manufacturing process of the alloyed DSS that results in the steel cracking and formability issues. This paper sum-marizes the chemical composition and properties of the DSS. Furthermore, there is a brief review of the machining and welding processes involved in DSS fabrication. Additional-ly, the study gives an insight into the applications and the recent developments of DSS. Furthermore, it will also identify and present the research gaps associated with both the duplex stainless steel and its challenges.
Predicting Weld Quality in Duplex Stainless Steel Butt Joints During Laser Beam Welding: A Hybrid DNN–HEVA Approach Chodagam Lakshmi Poornima, Chalamalasetti Srinivasa Rao, Dantuluri Narendra Varma Journal of Advanced Manufacturing Systems, 2024 Duplex stainless steel (DSS) welding is critical for producing structures and components in a variety of industries. Because traditional optimization approaches cannot manage the complexity of welding, additional solutions must be investigated. This study addresses the need for a systematic exploration of the welding parameters affecting the quality and performance of DSS butt joints, specifically employing fiber laser welding. This study focuses on butt joints in UNS S32304 and 304L using a fiber laser. Welding process parameters were systematically varied, including laser power, scanning speed, beam diameter, and focal position. To efficiently examine the parameters in the design space, the response surface methodology (RSM) with Box–Behnken design (BBD) was used. The combination of the specific parameter values used in the 7th run (Laser Power: 1600 W, Scanning Speed: 2000 mm/min, Beam Diameter: 1.5 [Formula: see text]m, Focal Position: 30 mm) led to better yield point stress (YPS), maximum tensile stress (MTS), break stress (BS) and reduction in area (RA). ANOVA and lack of fit analysis confirmed the significance of the experiment. In particular, the selected welding parameters significantly influenced all responses. A hybrid machine learning method of deep neural network (DNN) and human eye vision algorithm (HEVA) was used for predicting weld quality during laser beam welding (LBW). The hybrid DNN-HEVA consistently outperformed other optimization methods (RSM-BBD, FNN, CNN, and RNN) across key welding quality parameters. High [Formula: see text]2 values (0.9922, 0.9962, 0.9983, and 0.9907) indicated a strong correlation between predicted and actual values for all calculated responses. Lower RMSE, MSE, and MAE values were also highlighted in the precision of predicted values to the experimental data.
Corrosion behavior of combination of laser beam welded UNS S32304 + SS304L in 3.5% NaCl solution Chodagam Lakshmi Poornima, Chalamalasetti Srinivasa Rao, Dantuluri Narendra Varma Journal of Engineering and Applied Science, 2024 This study investigates the corrosion behavior of laser beam-welded UNS S32304 and SS304L in 3.5% NaCl solutions, focusing on the effects of temperature. The primary objective is to enhance the understanding of corrosion resistance in welded materials and inspire advancements in corrosion mitigation strategies. The methodology involves assessing corrosion resistance under varying temperatures and comparing the performance of laser beam welding (LBW) with that of the base metals. Scanning electron microscopy analysis reveals effective passivation, while quantitative analysis indicates differences in chloride ion coverage between the weld metal and base metals. Tafel plots and electrochemical impedance spectroscopy demonstrate enhanced corrosion potential and improved barrier properties for the weld metal. Results indicate a marginal reduction in corrosion resistance at 50 °C for both base metals. LBW metals corrosion resistance demonstrates superior performance, with only 5% reduction in breakdown potential compared to 10% in base metals. Compared to the base metal, it exhibits a substantial reduction in corrosion rate, ranging from 60 to 75%. This supports enhanced corrosion resistance and material stability. Additionally, similar results are observed after the analysis with scanning electron microscopy images, reinforcing the efficacy of LBW in improving corrosion resistance of LBW UNS S32304 and SS304L. These findings underscore the potential of LBW for applications requiring robust corrosion performance. By contributing to the understanding of the corrosion behavior of laser beam-welded materials, this study addresses a critical research gap in material science and corrosion engineering. Future research may explore long-term durability and corrosion resistance under diverse environmental conditions to further elucidate the mechanisms driving the observed differences in corrosion behavior.
RECENT SCHOLAR PUBLICATIONS
Process Optimization and Correlation Analysis for Laser Beam Welded UNS S32304/SS304L CLPCS Rao Journal of Materials Engineering and Performance, 1-17 , 2026 2026
Importance of Duplex stainless Steel in the area of Manufacturing DCS Ch.Lakshmi poornima Russian journal of biomechanics 23 (no.03), 105-114 , 2026 2026
Multi-response characterization of ultra-thin strip rolling process-machine learning approach NV Dantuluri, M Grandhi, LP Chodagam, SR Chalamalasetti International Journal on Interactive Design and Manufacturing (IJIDeM) 19 (7 … , 2025 2025
Optimization of Laser Beam Welded Novel Dissimilar Material UNS S32304 and 304 L Steel Process Parameters Using Deep Learning CL Poornima, CS Rao, DN Varma Journal of Advanced Manufacturing Systems 24 (02), 261-282 , 2025 2025
Numerical simulation on the laser beam welded UNS S32304 duplex steel and 304L stainless steel joints using ANSYS and response surface methodology CL Poornima, CS Rao, N Varma International Journal on Interactive Design and Manufacturing (IJIDeM) 19 (4 … , 2025 2025 Citations: 5
IMPORTANCE OF DUPLEX STAINLESS STEELS IN THE AREA OF MANUFACTURING CL Poornima, CS Rao Российский журнал биомеханики 29 (3), 105-114 , 2025 2025
Corrosion behavior of combination of laser beam welded UNS S32304+ SS304L in 3.5% NaCl solution CL Poornima, CS Rao, DN Varma Journal of Engineering and Applied Science 71 (1), 151 , 2024 2024 Citations: 2
Predicting Weld Quality in Duplex Stainless Steel Butt Joints during Laser Beam Welding: A Hybrid DNN-HEVA Approach CL Poornima, CS Rao, DN Varma Journal of Advanced Manufacturing Systems , 2024 2024 Citations: 4
Optimization of Ultra-Thin Strip Rolling Process Parameters on Phosphor Bronze C5191 Using Grey Relational Analysis NV Dantuluri, SR Chalamalasetti, LP Chodagam Journal of The Institution of Engineers (India): Series D, 1-13 , 2024 2024 Citations: 2
OPTIMIZATION OF LASER BEAM WELDED NOVEL DISSIMILAR MATERIAL UNS S32304 AND 304 L STEEL PROCESS PARAMETERS USING DEEP LEARNING LP CHODAGAM, SR CHALAMALASETTI, NV DANTULURI JOURNAL OF ADVANCED MANUFACTURING SYSTEMS , 2024 2024
Fabrication and Corrosion Behaviour of Aluminium Hybrid and Non-hybrid MMCs Reinforced with B 4 C and Gr Additions by Powder Metallurgy Technique KS Ratna Kumar, C Ratnam, C Ramakrishna, C Lakshmi Poornima Recent Advances in Material Sciences: Select Proceedings of ICLIET 2018, 217-230 , 2019 2019 Citations: 1
Prediction of Elastic Properties of Glass Fiber Reinforced Epoxy Composites by Micromechanical Analysis BV Subrahmanyam, EV Rao, CHL Poornima, B Lakshmi 2016
MOST CITED SCHOLAR PUBLICATIONS
Numerical simulation on the laser beam welded UNS S32304 duplex steel and 304L stainless steel joints using ANSYS and response surface methodology CL Poornima, CS Rao, N Varma International Journal on Interactive Design and Manufacturing (IJIDeM) 19 (4 … , 2025 2025 Citations: 5
Predicting Weld Quality in Duplex Stainless Steel Butt Joints during Laser Beam Welding: A Hybrid DNN-HEVA Approach CL Poornima, CS Rao, DN Varma Journal of Advanced Manufacturing Systems , 2024 2024 Citations: 4
Corrosion behavior of combination of laser beam welded UNS S32304+ SS304L in 3.5% NaCl solution CL Poornima, CS Rao, DN Varma Journal of Engineering and Applied Science 71 (1), 151 , 2024 2024 Citations: 2
Optimization of Ultra-Thin Strip Rolling Process Parameters on Phosphor Bronze C5191 Using Grey Relational Analysis NV Dantuluri, SR Chalamalasetti, LP Chodagam Journal of The Institution of Engineers (India): Series D, 1-13 , 2024 2024 Citations: 2
Fabrication and Corrosion Behaviour of Aluminium Hybrid and Non-hybrid MMCs Reinforced with B 4 C and Gr Additions by Powder Metallurgy Technique KS Ratna Kumar, C Ratnam, C Ramakrishna, C Lakshmi Poornima Recent Advances in Material Sciences: Select Proceedings of ICLIET 2018, 217-230 , 2019 2019 Citations: 1
Process Optimization and Correlation Analysis for Laser Beam Welded UNS S32304/SS304L CLPCS Rao Journal of Materials Engineering and Performance, 1-17 , 2026 2026
Importance of Duplex stainless Steel in the area of Manufacturing DCS Ch.Lakshmi poornima Russian journal of biomechanics 23 (no.03), 105-114 , 2026 2026
Multi-response characterization of ultra-thin strip rolling process-machine learning approach NV Dantuluri, M Grandhi, LP Chodagam, SR Chalamalasetti International Journal on Interactive Design and Manufacturing (IJIDeM) 19 (7 … , 2025 2025
Optimization of Laser Beam Welded Novel Dissimilar Material UNS S32304 and 304 L Steel Process Parameters Using Deep Learning CL Poornima, CS Rao, DN Varma Journal of Advanced Manufacturing Systems 24 (02), 261-282 , 2025 2025
IMPORTANCE OF DUPLEX STAINLESS STEELS IN THE AREA OF MANUFACTURING CL Poornima, CS Rao Российский журнал биомеханики 29 (3), 105-114 , 2025 2025
OPTIMIZATION OF LASER BEAM WELDED NOVEL DISSIMILAR MATERIAL UNS S32304 AND 304 L STEEL PROCESS PARAMETERS USING DEEP LEARNING LP CHODAGAM, SR CHALAMALASETTI, NV DANTULURI JOURNAL OF ADVANCED MANUFACTURING SYSTEMS , 2024 2024
Prediction of Elastic Properties of Glass Fiber Reinforced Epoxy Composites by Micromechanical Analysis BV Subrahmanyam, EV Rao, CHL Poornima, B Lakshmi 2016
