Mechanical Engineering, Industrial and Manufacturing Engineering, General Engineering, Multidisciplinary
39
Scopus Publications
354
Scholar Citations
11
Scholar h-index
13
Scholar i10-index
Scopus Publications
Enhanced Tensile Strength Prediction for 3D-Printed Parts: Integrating Kookaburra Optimization with XGBoost Machine Learning A. Tamilarasan Journal of Advanced Manufacturing Systems, 2026 This study proposes a hybrid predictive framework that integrates the Kookaburra Optimization Algorithm with Extreme Gradient Boosting, referred to as KOA-XGB, for estimating the tensile strength of fused deposition modeled polylactic acid parts. A dataset consisting of 204 experimental samples, covering tensile strength values from 17.67 MPa to 57.65 MPa and five key printing parameters, was used for model development and validation. The proposed KOA-XGB model achieved a coefficient of determination of 0.92 on the test set, with a mean absolute error of 2.15 MPa, representing a 23% improvement over standard XGBoost and a 35% improvement compared to random forest models. Approximately 95% of predicted values fell within ±3 MPa of experimental measurements, indicating high prediction consistency. Feature importance and SHAP analysis revealed that infill density and layer thickness were the dominant parameters, with mean SHAP values of 0.45 and 0.38, respectively. Increasing infill density from 20% to 80% resulted in an average tensile strength improvement of nearly 67%, while reducing layer thickness from 0.3 mm to 0.1 mm enhanced tensile strength by approximately 14%. The optimization process converged within 150 iterations, reducing test mean squared error to 5.32 and achieving convergence about 40% faster than conventional grid search methods. The findings demonstrate that the proposed KOA-XGB framework supports resource efficiency, responsible production and sustainable manufacturing by reducing experimental effort by up to 45% and lowering material waste by approximately 20-25%, thereby contributing to data-driven decision making in additive manufacturing systems.
Abrasive Waterjet Machining of r-GO Infused Mg Fiber Metal Laminates: ANFIS Modelling and Optimization Through Antlion Optimizer Algorithm Devaraj Rajamani, Mahalingam Siva Kumar, Arulvalavan Tamilarasan Materials, 2025 This research proposes an intelligent modeling and optimization strategy for abrasive waterjet machining (AWJM) of magnesium-based fiber metal laminates (FMLs) reinforced with reduced graphene oxide (r-GO). Experiments were designed using the Box–Behnken method, considering waterjet pressure, stand-off distance, traverse speed, and r-GO content as inputs, while kerf taper (Kt), surface roughness (Ra), and material removal rate (MRR) were evaluated as outputs. Adaptive Neuro-Fuzzy Inference System (ANFIS) models were developed for each response, with their critical optimized hyperparameters such as cluster radius, quash factor, and training data split through the dragonfly optimization (DFO) algorithm. The optimized ANFIS networks yielded a high predictive accuracy, with low RMSE and MAPE values and close agreement between predicted and measured results. Four metaheuristic algorithms including particle swarm optimization (PSO), salp swarm optimization (SSO), whale optimization algorithm (WOA), and the antlion optimizer (ALO) were applied for simultaneous optimization, using a TOPSIS-based single-objective formulation. ALO outperformed the others, identifying 325 MPa waterjet pressure, 2.5 mm stand-off, 800 mm/min traverse speed, and 0.00602 wt% r-GO addition in FMLs as optimal conditions. These settings produced a kerf taper of 2.595°, surface roughness of 8.9897 µm, and material removal rate of 138.13 g/min. The proposed ANFIS-ALO framework demonstrates strong potential for achieving precision and productivity in AWJM of hybrid laminates.
Towards efficient image segmentation: A fuzzy entropy-based approach using the snake optimizer algorithm A. Tamilarasan, D. Rajamani Results in Engineering, 2025 • The study highlights the effectiveness of fuzzy entropy thresholding methods for image segmentation. • A novel Snake Optimizer (SO) is introduced to optimize multilevel threshold levels using fuzzy entropy. • The SO mimics snake mating behaviours to enhance optimization efficiency. • Experiments on ten test images demonstrate the method's superiority over existing techniques. • The Snake Optimizer achieves better fitness values, reduced CPU time, and higher quality metrics. Image segmentation is a critical aspect of image processing, particularly for applications requiring precise object identification. This study introduces a novel multilevel thresholding technique for grayscale image segmentation based on the Snake Optimizer (SO) algorithm, which is inspired by the mating behaviour of snakes. The proposed method aims to maximize fuzzy entropy to determine optimal threshold values, thereby enhancing segmentation accuracy. Extensive experiments were conducted on ten benchmark images, utilizing 2 to 5 thresholds, and comparing the performance of the SO algorithm against three state-of-the-art algorithms: Salp Swarm Algorithm (SSA), Grey Wolf Optimization (GWO), and Moth Flame Optimization (MFO).The results demonstrate that the SO algorithm consistently outperforms its counterparts across various metrics, achieving the highest average fitness values in 35 out of 40 attempts, with notable improvements in PSNR, SSIM, and FSIM. Specifically, the SO achieved an average PSNR of 26.617 for the 'Barbara' image at 5 thresholds, surpassing SSA (26.568), GWO (26.561), and MFO (26.515). Additionally, the SO recorded an SSIM of 0.9575 for the same image, significantly higher than SSA (0.9339), GWO (0.9114), and MFO (0.8934). The computational efficiency of the SO is also highlighted, with lower average CPU times, achieving 3.1217 seconds for the 'Ostrich' image at 2 thresholds, compared to SSA (3.1298), GWO (3.8539), and MFO (3.9343).Statistical analysis using Wilcoxon rank-sum tests further confirms the significant performance advantages of the SO algorithm, establishing it as a robust and effective solution for multilevel thresholding in image segmentation tasks.
Metaheuristic Prediction Models for Kerf Deviation in Nd-YAG Laser Cutting of AlZnMgCu1.5 Alloy Arulvalavan Tamilarasan, Devaraj Rajamani Modelling, 2025 In the present research, the AlZnMgCu1.5 alloy was machined via an industrial-type Nd-YAG laser cutting process. The Box–Behnken design of response surface methodology was used to plan the trials. The experiments were carried out by varying the nitrogen pressure (4–10 bar), pulse energy (2.5–5.5 J), cutting speed (10–18 mm/min), and pulse width (1.5–2 ms). ANOVA was conducted to assess the impact of process factors on response characteristics. The ANOVA results suggest that nitrogen pressure has the greatest influence on the input process parameters. A detailed investigation was conducted to examine the effects of various parameters on kerf deviation. The metaheuristic algorithms (i.e., Giant Trevally Optimizer—GTO; and Zebra Optimization Algorithm—ZOA) were implemented to determine the optimum process parameters for producing the best performance measures. A comparative analysis demonstrated that the parametric value provided by the GTO algorithm, which adheres to the ZOA method, yielded the lowest response. Optimization using GTO resulted in a 6.71% improvement in kerf deviation prediction accuracy compared to experimental values, while ZOA achieved a 2.37% improvement. Furthermore, GTO demonstrated superior computational efficiency, converging in 5.687 s, significantly faster than the 11.548 s required by ZOA. The optimal solution suggested by the GTO algorithm is further verified using a confirmation test on the random settings. In addition, the surface morphology of the laser-cut kerf surfaces was analyzed using SEM images. Through this, it is confirmed that the metaheuristic algorithm of GTO is more suitable for finding the optimum process parameters.
ENHANCED SURFACE QUALITY AND STRENGTH OF FDMed SPECIMENS USING BBD AND BIO-INSPIRED ALGORITHMS A. TAMILARASAN, A. RENUGAMBAL Surface Review and Letters, 2025 This research investigated and optimized the parameters of the FDM process by employing bio-inspired algorithms for determining the optimal parameter settings in terms of surface quality and mechanical performance. Four important process parameters including layer thickness (0.11–0.33[Formula: see text]mm), part orientation (0–90∘), raster width (0.2–0.56[Formula: see text]mm), and the raster angle (0–60∘) at three variation levels were selected for fabricating the specimens (ABS material P430) using the statistical Box–Behnken design. ANOVA analysis and multiple regression analysis were used to fit the experimental data to a second-order polynomial equation. Through, the RSM analysis, the layer thickness is the key important factor that accounts for all of the responses. The fracture behavior of specimens was examined using a scanning electron microscope (SEM). From the SEM analysis, a substantial amount of plastic deformation on the fracture surface indicative of craze cracking is visible from a 0∘ orientation, indicating a totally ductile fracture mechanism. Then, three swarm intelligence algorithms such as Tasmanian Devil Optimization (TDO), Remora Optimization Algorithm (ROA), Tuna Swarm Optimization (TSO) were implemented to optimize the input parameters that would lead to minimum surface roughness and maximum tensile strength. Experimental data and predicted values varied between 1.64% and 1.84%, as shown by verification experiments.
Automated Satellite Image Segmentation Using Kapur's Entropy Through Black-Winged Kite Algorithm N. Sri Krishna Chaitanya, A. Tamilarasan Proceedings of 2025 10th International Conference on Science Technology Engineering and Mathematics Iconstem 2025, 2025 This study presents an automated satellite image segmentation approach utilizing Kapur's Entropy-based multi-level thresholding optimized through the Black-Winged Kite Algorithm (BWKA). The methodology was evaluated against the Genetic Algorithm (GA) using the Berkeley image dataset comprising 40 satellite image samples with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$4024 \times 10$</tex> pixel regions, divided equally between both algorithms (N=20 per group). Statistical analysis was conducted using a significance level of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\alpha=0.05$</tex> and statistical power of 0.8. The BWKA demonstrated superior performance with a mean segmentation accuracy of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$90.52 \% (\text{SD}=0.63049, \quad \text{SEM}=0.19938), \quad$</tex> significantly outperforming GA which achieved 76.94% accuracy (SD=1.19203, SEM=0.37695). Independent t -test analysis revealed a statistically significant difference between the algorithms <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathrm{p}=0.001, \mathrm{p}<0.005)$</tex>, with a 95% confidence interval confirming the reliability of results. The BWKA exhibited lower variability and higher consistency in segmentation quality across diverse satellite imagery, demonstrating computational efficiency with reduced classification errors and improved boundary detection. These findings establish BWKA combined with Kapur's Entropy as an effective optimization framework for automated satellite image segmentation tasks, offering substantial improvements over conventional genetic algorithm approaches in both accuracy and stability.
Towards Practical Phishing Detection: Addressing Challenges with Hybrid Machine Learning Architectures C. Sivasankar, A. Tamilarasan, S. Christy, S. Parthiban International Conference on Computational Robotics Testing and Engineering Evaluation Iccrtee 2025, 2025 This study presents a new Hybrid Detection Model combining XGBoost with Deep Neural Networks (DNN) to take advantage of feature engineering power and pattern recognition strengths. The recommended solution is then thoroughly tested against three well-developed machine learning methods - Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR) - on six performance measures: Accuracy, Precision, Recall, F1-Score, ROC-AUC, and PR-AUC. Although traditional models (RF, SVM, LR) show evident perfect classification, overfitting issues are implied by these unattainable results. Conversely, the Hybrid Model is more accurate in its performance on noisy, real-world data (Accuracy: 0.866 ± 0.012, ROC-AUC: 0.934 ± 0.008). The highest discriminative features in URL length, domain age, and special character frequency are extracted by SHAP interpretability analysis. These results indicate that although simpler models might have artificially high metrics on clean datasets, the suggested hybrid architecture provides better robustness for practical deployment environments with concept drift and adversarial noise.
Multi-objective optimization of Nd: YAG laser cutting parameters based on BBD-SA hybrid approach Indian Journal of Engineering and Materials Sciences, 2017
Multi-response optimization of hard milling process: Rsm coupled with grey relational analysis International Journal of Engineering and Technology, 2013
RECENT SCHOLAR PUBLICATIONS
Enhanced Tensile Strength Prediction for 3D-Printed Parts: Integrating Kookaburra Optimization with XGBoost Machine Learning A Tamilarasan Journal of Advanced Manufacturing Systems , 2026 2026
Waste Classification for Smart Recycling Systems: Image-Based Deep Learning with ResNet-50 CNN A Gayathri, S Christy, C Sivasankar, A Tamilarasan Advanced Pathways in Electrical, Communication, and Automation, 160-167 , 2026 2026
Influence of different pin profiles on the evaluation of interfacial microstructure and mechanical properties of Ti/Al by FSW process A Tamilarasan International Journal of Computational Materials Science and Surface … , 2026 2026
Ultrasound supported synthesis of waste mangifera indica linn biodiesel: an optimization using whale algorithm S Arumugam, C Peddamangari Venkatesulu Reddy, T Arulvalavan, ... Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 47 … , 2025 2025 Citations: 7
Optimizing student depression prediction using WaOA-XGBoost: A bio-inspired approach A Tamilarasan International Journal of Information Technology, 1-20 , 2025 2025
Abrasive Waterjet Machining of r-GO Infused Mg Fiber Metal Laminates: ANFIS Modelling and Optimization Through Antlion Optimizer Algorithm D Rajamani, M Siva Kumar, A Tamilarasan Materials 18 (19), 4480 , 2025 2025 Citations: 1
Towards Practical Phishing Detection: Addressing Challenges with Hybrid Machine Learning Architectures SCSP C. Sivasankar, A. Tamilarasan IEEE Xplore 1 (1), 1-9 , 2025 2025
Towards Practical Phishing Detection: Addressing Challenges with Hybrid Machine Learning Architectures C Sivasankar, A Tamilarasan, S Christy, S Parthiban 2025 International Conference on Computational Robotics, Testing and … , 2025 2025 Citations: 2
Towards Efficient Image Segmentation: A Fuzzy Entropy-Based Approach Using the Snake Optimizer Algorithm A Tamilarasan, D Rajamani Results in Engineering 1 (1), 1-42 , 2025 2025 Citations: 5
Metaheuristic Prediction Models for Kerf Deviation in Nd-YAG Laser Cutting of AlZnMgCu1. 5 Alloy A Tamilarasan, D Rajamani Modelling 6 (1), 17 , 2025 2025 Citations: 1
D-optimal-based parametric study for improving the FFFed product quality of the ABS material AR A. Tamilarasan, K. Radhika, D. Rajamani Journal of Mechanical Science and Technology 1 (1), 1-11 , 2024 2024 Citations: 4
Experiment-based process modelling and analysis in Nd:YAG laser cutting of Hastelloy C-276: RSM-CCD technique MS Tamilarasan, A., Renugambal, A., Rajamani Opsearch 1 (1), 1-21 , 2024 2024 Citations: 2
Comparative Assessment on Unsteady Aerodynamics of Thin and Thick Airfoils Subjected to Pitching Motion A Shaik M, Lekkala, N, Pentakota, U, Kothali Bapu, AN, Tamilarasan Journal of Computational and Theoretical Transport 1 (1), 1-22 , 2024 2024 Citations: 1
Enhanced Surface Quality and Strength of FDMed Specimens Using BBD and Bio-Inspired Algorithms AR A.Tamilarasan Surface Review and Letters 1 (1), 1-25 , 2024 2024 Citations: 1
Hybrid WCMFO algorithm for microhardness improvement in roller burnishing of brass (C3604): hybrid WCMFO algorithm for microhardness improvement A Tamilarasan, A Renugambal Journal of Scientific & Industrial Research (JSIR) 83 (2), 139-145 , 2024 2024 Citations: 2
Multi-Objective Optimization on Grave Properties of Mixed Transformer Oil using RSM and NSGA-II Approach K S.S, A Tamilarasan, M Malleswaran IEEE Transactions on Dielectrics and Electrical Insulation 1 (1), 1-10 , 2024 2024 Citations: 3
Multi-performance optimization for AWJ drilling process in cutting of ceramic tile: BBD with EOBL-GOA algorithm A.Tamilarasan, A.Renugambal, K.Shunmugesh Multidiscipline Modeling in Materials and Structures 19 (6), 1199-1225 , 2023 2023 Citations: 4
Hybrid SCCSA: An efficient multilevel thresholding for enhanced image segmentation A Renugambal, KS Bhuvaneswari, A Tamilarasan Multimedia Tools and Applications 82 (21), 32711-32753 , 2023 2023 Citations: 6
An Integrated RSM-improved salp swarm algorithm for quality characteristics in AWJM of Ananas comosus-HIPS composites A Tamilarasan, A Renugambal International Journal of Lightweight Materials and Manufacture 6 (3), 297-310 , 2023 2023 Citations: 11
AWJC of NiTi interleaved r-GO embedded carbon/aramid fibre intermetallic laminates: Experimental investigations and optimization through BMOA D Rajamani, E Balasubramanian, A Murugan, A Tamilarasan Materials and Manufacturing Processes 38 (9), 1144-1158 , 2023 2023 Citations: 8
MOST CITED SCHOLAR PUBLICATIONS
Multi-response optimization of Nd: YAG laser cutting parameters of Ti-6Al-4V superalloy sheet A Tamilarasan, D Rajamani Journal of Mechanical Science and Technology 31 (2), 813-821 , 2017 2017 Citations: 59
Nd: YAG laser cutting of Hastelloy C276: ANFIS modeling and optimization through WOA D Rajamani, M Siva Kumar, E Balasubramanian, A Tamilarasan Materials and Manufacturing Processes 36 (15), 1746-1760 , 2021 2021 Citations: 33
RSM and crow search algorithm-based optimization of ultrasonicated transesterification process parameters on synthesis of polyol ester-based biolubricant S Arumugam, P Chengareddy, A Tamilarasan, V Santhanam Arabian Journal for Science and Engineering 44 (6), 5535-5548 , 2019 2019 Citations: 25
Multi-response optimisation of hard milling process parameters based on integrated Box-Behnken design with desirability function approach A Tamilarasan, K Marimuthu International Journal of Machining and Machinability of Materials 15 (3-4 … , 2014 2014 Citations: 24
Parametric estimation for AWJ cutting of Ti-6Al-4V alloy using Rat swarm optimization algorithm A Tamilarasan, A Renugambal, D Vijayan Materials and Manufacturing Processes 37 (16), 1871-1881 , 2022 2022 Citations: 20
Fuzzy and regression modeling for Nd: YAG laser cutting of Ti-6Al-4V superalloy sheet D Rajamani, A Tamilarasan Journal for Manufacturing Science and Production 16 (3), 153-162 , 2016 2016 Citations: 17
Application of crow search algorithm for the optimization of abrasive water jet cutting process parameters A Tamilarasan, A Renugambal, D Manikanta, GBC Sekhar Reddy, ... IOP conference series: materials science and engineering 390 (1), 012034 , 2018 2018 Citations: 16
Multi-response optimization of hard milling process: RSM coupled with grey relational analysis A Tamilarasan, K Marimuthu International Journal of Engineering and Technology 5 (6), 4901-4913 , 2014 2014 Citations: 16
An approach on fuzzy and regression modeling for hard milling process A Tamilarasan, D Rajamani, A Renugambal Applied Mechanics and Materials 813, 498-504 , 2015 2015 Citations: 14
Parametric optimisation in Nd-YAG laser cutting of thin Ti-6Al-4V super alloy sheet using evolutionary algorithms A Tamilarasan, D Rajamani, B Esakki International Journal of Materials and Product Technology 57 (1-3), 71-91 , 2018 2018 Citations: 12
An Integrated RSM-improved salp swarm algorithm for quality characteristics in AWJM of Ananas comosus-HIPS composites A Tamilarasan, A Renugambal International Journal of Lightweight Materials and Manufacture 6 (3), 297-310 , 2023 2023 Citations: 11
AWJ parameters optimisation via BBD-ISOA approach while machining NFRP composite A Tamilarasan, A Renugambal Materials and Manufacturing Processes 38 (9), 1130-1143 , 2023 2023 Citations: 10
Synthesis and characterization of sintered AZ91D magnesium matrix composites reinforced with red mud particles D Rajamani, A Tamilarasan, B Esakki, K Ananthakumar Material Science Research India 13 (2), 95-100 , 2016 2016 Citations: 10
Investigations and optimization for hard milling process parameters using hybrid method of RSM and NSGA-II A Tamilarasan, K Marimuthu, A Renugambal Rev. Téc. Ing. Univ. Zulia 39 (1), 41-54 , 2016 2016 Citations: 9
AWJC of NiTi interleaved r-GO embedded carbon/aramid fibre intermetallic laminates: Experimental investigations and optimization through BMOA D Rajamani, E Balasubramanian, A Murugan, A Tamilarasan Materials and Manufacturing Processes 38 (9), 1144-1158 , 2023 2023 Citations: 8
Ultrasound supported synthesis of waste mangifera indica linn biodiesel: an optimization using whale algorithm S Arumugam, C Peddamangari Venkatesulu Reddy, T Arulvalavan, ... Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 47 … , 2025 2025 Citations: 7
Hybrid SCCSA: An efficient multilevel thresholding for enhanced image segmentation A Renugambal, KS Bhuvaneswari, A Tamilarasan Multimedia Tools and Applications 82 (21), 32711-32753 , 2023 2023 Citations: 6
Multi-objective optimization of Nd: YAG laser cutting parameters based on BBD-SA hybrid approach A Tamilarasan, D Rajamani Indian J Eng Mater Sci 24, 295-300 , 2017 2017 Citations: 6
Towards Efficient Image Segmentation: A Fuzzy Entropy-Based Approach Using the Snake Optimizer Algorithm A Tamilarasan, D Rajamani Results in Engineering 1 (1), 1-42 , 2025 2025 Citations: 5
On process analysis and optimisation of Nd: YAG laser cutting characteristics of Ti-6Al-4V alloy using RSM and NSGA-II D Rajamani, A Tamilarasan International Journal of Manufacturing Technology and Management 35 (5), 389-406 , 2021 2021 Citations: 5
Publications
1. A. Tamilarasan, A. Renugambal 2024. Enhanced Surface Quality and Strength of FDMed Specimens Using BBD and Bio-Inspired Algorithms, Surface Review and Letters, (Accepted) (Impact Factor: 1.1).
2. A.Tamilarasan, A.Renugambal 2024. Hybrid WCMFO algorithm for Microhardness improvement in roller burnishing of Brass (C3604), Journal of Scientific and Industrial Research (JSIR), 83 (2), 139-145. (Impact Factor: 0.6)
3. Kumaresh S.S, A.Tamilarasan and M. Malleswaran 2024. Multi-Objective Optimization on Grave Properties of Mixed Transformer Oil using RSM and NSGA-II Approach, IEEE Transactions on Dielectrics and Electrical Insulation (Published Online) (Impact Factor: 3.1).
4. A.Tamilarasan, A.Renugambal and K. Shunmugesh 2023. Multi-performance optimization for AWJ drilling process in cutting of ceramic tile: BBD with EOBL- GOA algorithm, Multidiscipline Modeling in Materials and Structures, 19(6): 1199- 1225. (Impact Factor: 2).
5. A. Tamilarasan, A.Renugambal 2023. AWJ Parameters optimisation via BBD-ISOA approach while Machining NFRP Composite. Materials and Manufacturing Processes 38(9): 1130-1143 (Impact Factor: 4.8).
6. A.Tamilarasan and A.Renugambal 2023. An Integrated RSM - Improved Salp Swarm Algorithm for Quality Characteristics in AWJed Ananas Comosus-HIPS composites, International Journal of Lightweight Materials and Manufacture, Science Direct- Elsevier 6(3): 297-310 (Scopus).
7. A.Renugambal, K.Selva Bhuvaneswari and A.Tamilarasan 2023. Hybrid SCCSA: An Efficient
