Dr A Tamilarasan
@dscet.ac.in
Dhanalakshmi Srinivasan College of Engineering and Technology
EDUCATION
Ph.D - Anna University Chennai
RESEARCH, TEACHING, or OTHER INTERESTS
Mechanical Engineering, Industrial and Manufacturing Engineering, General Engineering, Multidisciplinary
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
A. TAMILARASAN and A. RENUGAMBAL
World Scientific Pub Co Pte Ltd
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.
S S Kumaresh, A Tamilarasan, and M Malleswaran
Institute of Electrical and Electronics Engineers (IEEE)
A. Tamilarasan, A. Renugambal, and K. Shunmugesh
Emerald
PurposeThe goal of this study is to determine the values of the process parameters that should be used during the machining of ceramic tile using the abrasive water jet (AWJ) process in order to achieve the lowest possible values for surface roughness and kerf taper angle.Design/methodology/approachIn the present work, ceramic tile is processed by the AWJ process and experimental data were recorded using the RSM approach based Box–Behnken design matrix. The input process factors were water jet pressure, jet traverse speed, abrasive flow rate and standoff distance, to determine the surface roughness and kerf taper angle. ANOVA was used to check the adequacy of model and significance of process parameters. Further, the elite opposition-based learning grasshopper optimization (EOBL-GOA) algorithm was implemented to identify the simultaneous optimization of multiple responses of surface roughness and kerf taper angle in AWJ.FindingsThe suggested EOBL-GOA algorithm is suitable for AWJ of ceramic tile, as evidenced by the error rate of ±2 percent between experimental and predicted solutions. The surfaces were evaluated with an SEM to assess the quality of the surface generated with the optimal settings. As compared with initial setting of the SEM image, it was noticed that the bottom cut surface was nearly smooth, with less cracks, striations and pits in the improved optimal results of the SEM image. The results of the analysis can be used to control machining parameters and increase the accuracy of AWJed components.Originality/valueThe findings of this study present an innovative method for assessing the characteristics of the nontraditional machining processes that are most suited for use in industrial and commercial applications.
A. Tamilarasan and A. Renugambal
Elsevier BV
A. Renugambal, K. Selva Bhuvaneswari, and A. Tamilarasan
Springer Science and Business Media LLC
A. Tamilarasan and A. Renugambal
Informa UK Limited
ABSTRACT In this work, an experimental attempt has been made to model theresponse variables, i.e kerf taper angle and MRR in AWJ process whilemachining of NFRP composite. The experimental plan is based on BBDdesign. The feed rate of sand, stand-off distance, traverse speed andwater jet pressure have been varied to investigate their effect onresponses. The ANOVA was used to determine the significance ofdeveloped models. The model is further coupled with the Improved SOAalgorithm to optimise AWJ machining parameters. The result provedthat the standard SOA optimisation algorithm is inferior to theimproved optimisation algorithm, which can achieve greater processingquality. Subsequently, the outcomes of the optimisation results werereviewed and experimentally confirmed. The error between experimentaland predicted values was proven to be less than 2% at the inputvariable combinations with the best performance, demonstrating theviability and efficacy of the chosen strategy.
D. Rajamani, E. Balasubramanian, A. Murugan, and A. Tamilarasan
Informa UK Limited
ABSTRACT Laminated metal-composite structures or fiber metal laminates (FMLs) are advanced engineering materials currently being utilized in several distinct applications, especially in aircraft and automobile manufacturing industries where an improved impact and fatigue resistance are required. Machining of FMLs is an important task in obtaining near-net shapes for joining and assembly of the components. However, the delamination occurs during conventional machining making FMLs as difficult-to-machine materials. Therefore, the present study will look into the abrasive water jet cutting (AWJC) of novel fiber intermetallic laminates (FILs) made of with alternatively stacked carbon/aramid fiber adhesively bonded with r-GO filled epoxy resin matrix and Nitinol shape memory alloy sheet embedded laminates. The AWJC experiments were performed on fabricated FILs to investigate the cut quality features including kerf taper (K t ), surface roughness (Ra) and kerf deviation (KD) by varying addition of r-GO from 0-2 wt% in the laminates, traverse speed (400-600 mm/min), waterjet pressure (200-300MPa) and nozzle height (2-4 mm), respectively. Statistical results obtained through ANOVA reveals that the traverse speed and nozzle height are the utmost significant variables which influencing the cut quality characteristics followed by waterjet pressure. Surface morphology analysis shows the wear and erosion mechanism of FILs at varying AWJC conditions. For an improved cut quality, the optimal AWJC parameters are achieved through a metaheuristic-based Barnacles Mating Optimization Algorithm (BMOA).
A. Tamilarasan, A. Renugambal, and D. Vijayan
Informa UK Limited
ABSTRACT The kerf taper angle is one of the most significant parameters for AWJ cutting. Choosing the best cutting parameters is critical to reducing the kerf taper. Therefore, the aim of this work is to demonstrate on to implement RSM in conjunction with the Rat swarm optimizer algorithm to optimize the kerf taper angle of a Titanium alloy. Thirty-two number of experiments were conducted according to CCD considering five process parameters namely jet traverse rate, water jet pressure, abrasive flow rate, stand-off distance and diameter of focusing nozzle to achieve kerf taper angle. A second-order full quadratic model is used for mathematical modeling. The result demonstrates that the model is sufficiently adequate and acceptable. A recent Rat Swarm Optimization (RSO) algorithm is considered for finding out the global optimal solution. It is observed that after optimizing the process that the jet traverse rate of 1.952 mm/sec, water jet pressure of 250.548 MPa, abrasive flow rate of 0.554 kg/min, stand-off distance of 3.75 mm and diameter of focusing nozzle of 2.0 mm gives minimized with 0.754 degree of kerf taper angle. A series of verification tests are carried out to ensure that the optimization technique is accurate in determining the best levels of process.
A. Tamilarasan, D. Rajamani, P. Pranay, P. Manohar, A. Venkata Akhil, and B. Thirupathi Reddy
Springer Singapore
A. Tamilarasan, G. Sriram, S. Arumugam, D. Vijayan, D. Rajamani, and A. Venkata Akhil
Springer Singapore
D. Vijayan, A. Tamilarasan, and B. Vignesh Aravind
Springer Singapore
A. Tamilarasan, T. Rajmohan, S. Arumugam, A. Arunpremnath, K. Mohan, and P. Manohar
Springer Singapore
A. Tamilarasan, S. Arumugam, D. Rajamani, S. Vijayabhaskar, R. Balakumar, and B. Thirupathi Reddy
Springer Singapore
D. Rajamani and A. Tamilarasan
Inderscience Publishers
D. Rajamani, M. Siva Kumar, E. Balasubramanian, and A. Tamilarasan
Informa UK Limited
ABSTRACT A hybrid approach through combining genetic algorithm (GA) and adaptive neuro-fuzzy inference system (ANFIS) for modeling the correlation of laser beam cutting (LBC) parameters and enhancing the quality performance characteristics of machined Hastelloy C276 is emphasized. The LBC experiments are performed by considering gas pressure (GP), cutting speed (CS), pulse energy (PE) and stand-off distance (SOD) as input parameters. The output responses are material removal rate (MRR), kerf taper (KT) and surface roughness (Ra ) for the present investigation. The optimal ANFIS training variables are obtained through GA. The training, testing errors, and statistical validation parameter results exposed that the ANFIS learned by GA is outperformed in forecasting LBC responses. In addition, to obtain the optimal combinations of LBC parameters, the multi-response optimization based on maximizing MRR and minimizing KT and Ra was performed using a trained ANFIS network coupled with a whale optimization algorithm (WOA). The responses such as MRR of 236.98 mg/min, KT of 1.135° and Ra of 1.109 µm are forecasted for the optimum cutting conditions: GP of 3 bar, CS of 319.8 mm/min, PE of 5.93 J and SOD of 2.97 mm, respectively. Furthermore, the WOA predicted results are validated by conducting confirmatory experiments.
A. Tamilarasan, S. Arumugam, D. Rajamani, P. Changareddy, E. Balasubramanian, and P. Pranay
Springer Singapore
A Tamilarasan, T Rajmohan, KG Ashwinkumar, B Dinesh, M Praveenkumar, R. Dinesh Reddy, K V V Surya Kiran, R Elangumaran, and S Krishnamoorthi
IOP Publishing
Abstract Selecting optimal and proper cutting parameters for achieving desired results in the AWJ process is a demanding task. Therefore, the main contribution of this work is to apply a new reliable hybrid WCMFO algorithm for process optimization in the AWJ process. Based on the box-behnken design technique, 17 experimental runs were conducted and quadratic model for surface roughness was developed to fit with experimental data. Then, the WCMFO algorithm is implemented in the consideration of surface roughness as a fitness function. The key advantage of this algorithm is that it does not accumulate to any local optima, and the existence of a hybrid algorithm allows it to store the best solutions available so far. The predicted optimal settings were verified through confirmatory experiments, and the results validated.
S. Arumugam, P. Chengareddy, A. Tamilarasan, and V. Santhanam
Springer Science and Business Media LLC
A Tamilarasan, A Renugambal, D Manikanta, G B C Sekhar Reddy, K Sravankumar, B Sreekar, and G V Prasadreddy
IOP Publishing
The present work deals with the prediction of optimal parametric data-set to yield the minimum surface roughness in Abrasive Water Jet (AWJ) cutting of ceramic Tile. By means of a Box–Behnken experiment design technique, an experiment matrix with three factors and three levels was designed. Quadratic model for surface roughness was developed to fit with experimental data. Then, the improved optimal combination of the process parameters is evaluated by proposed methodology of most efficient crow search algorithm. Further, experimentally validation test has been conducted for the optimal cutting conditions suggested by crow search algorithm.
A Tamilarasan, A Renugambal, T Mohan, Akshay R. Iyer, V Pramoth Kumar Krish, A Rajkumar, and Himanshu N Sakriya
IOP Publishing
The aim of this work is to optimize the roller burnishing process parameters for minimizing the surface roughness using response surface methodology(RSM) and lion optimization algorithm. A RSM based box-behnken design is utilized for experimentations. The empirical model of surface roughness is developed for showing the relation between inputs and output of the burnishing process. The analysis of variance (ANOVA) method was used to check the accuracy of the developed mathematical model. With the developed mathematical model, the roller burnishing process parameters were then optimized to minimize the surface roughness by a new type of lion optimization algorithm. Further, validation test has been conducted for the optimal conditions suggested by lion optimization algorithm. The experimental value agreed with those predicted value with 3.225 of relative error percentage.
A. Tamilarasan, D. Rajamani, and Balasubramanian Esakki
Inderscience Publishers
A. Tamilarasan and D. Rajamani
Springer Science and Business Media LLC
A. Tamilarasan and K. Marimuthu
Maxwell Scientific Publication Corp.
The characteristic features of hard milling are variable chip thickness and intermittent cutting. Such tendency rapidly increases the tool wear and reduces the metal removal rate against the cutting temperature results poor surface finish. Therefore, the objective of this present study was to present the mathematical models for modeling and analysis on the effects of process parameters, including the feed per tooth, radial depth of cut, axial depth of cut and cutting speed on cutting temperature, tool wear and metal removal rate in hard milling of 100MnCrW4 (Type O1) tool steel using (TiN+TiAlN) coated carbide inserts. A central composite rotatable design with four factors and five levels was chosen to minimize the number of experimental conditions. Further, the reduced developed models were used for multiple-response optimization by desirability function approach in order to determine the optimum cutting parameters. These optimized machining parameters are validated experimentally and the experimental and predicted values were in a good agreement with small consistent error.
RECENT SCHOLAR PUBLICATIONS
MOST CITED SCHOLAR PUBLICATIONS
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