Abhijit Bhowmik
@dreaminstituteonline.com
Assistant Professor, Mechanical Engineering Department
Dream Institute of Technology Kolkata
RESEARCH, TEACHING, or OTHER INTERESTS
Mechanical Engineering, Materials Science
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Abhijit Bhowmik, Binayak Sen, N. Beemkumar, Jasgurpreet Singh Chohan, Pardeep Singh Bains, Gurpartap Singh, Ambati Vijay Kumar, and Johnson Santhosh A
Elsevier BV
Binayak Sen, Abhijit Bhowmik, Chander Prakash, and Muhammad Imam Ammarullah
AIP Publishing
This study emphasizes the criticality of measuring specific cutting energy in machining Hastelloy C276 for biomedical industry applications, offering valuable insights into machinability and facilitating the optimization of tool selection, cutting parameters, and process efficiency. The research employs artificial intelligence-assisted meta-models for cost-effective and accurate predictions of specific cutting energy consumption. Comparative analyses conducted on Hastelloy C276, utilizing a TiAlN-coated solid carbide insert across various media (dry, MQL, LN2, and MQL+LN2), reveal the superiority of hybrid LN2+MQL in reducing specific cutting energy consumption. Subsequently, the analysis of variance underscores the cutting speed as the most influential parameter as compared to other inputs. Finally, a statistical evaluation compares the Gene Expression Programming (GEP) model against the Artificial Neural Network (ANN), and Response Surface Methodology model, demonstrating the superior predictive performance of the GEP meta-model. The GEP model demonstrates validation results with an error range of 0.25%–1.52%, outperforming the ANN and RSM models, which exhibit an error range of 0.49%–8.33% and 2.68%–10.18%, respectively. This study suggests the potential integration of contemporary intelligent methodologies for sustainable superalloy machining in biomedical industry applications, providing a foundation for enhanced productivity and reduced environmental impact of surgical instrument and biomedical device machining.
Lu Yang, Raman Kumar, Rupinder Kaur, Atul Babbar, Gurfateh Singh Makhanshahi, Arshpreet Singh, Raman Kumar, Abhijit Bhowmik, and Ahmed Hussien Alawadi
Springer Science and Business Media LLC
Abhijit Bhowmik, Raman Kumar, Atul Babbar, Valentin Romanovski, Sujit Roy, Lokeswar Patnaik, J. Pradeep Kumar, and Ahmed Hussien Alawadi
Springer Science and Business Media LLC
V. S. S. Venkatesh, Prabhakara Rao Ganji, R. Narasimha Rao, and Abhijit Bhowmik
Springer Science and Business Media LLC
Abhijit Bhowmik, Biplab Bhattacharjee, V. S. S. Venkatesh, Guttikonda Manohar, T. Satish Kumar, Valentin Romanovski, Asad Syed, and Ling Shing Wong
Springer Science and Business Media LLC
T. Satish Kumar, Titus Thankachan, Abhijit Bhowmik, Emad Makki, Jayant Giri, and Shirsendu Das
AIP Publishing
Magnesium AZ91/AlN-based surface composites have been synthesized using the friction stir processing (FSP) technique. The influence of particle addition during FSP on microstructural and mechanical properties has been investigated. Composite plates of 5, 10, and 15 vol. % AlN were synthesized using two-pass FSP with an axial force of 10 KN, a tool travel speed of 40 mm/min, and a tool rotation speed of 1200 rpm. The AZ91/AlN surface composites were analyzed microscopically with the use of an optical microscope, an x-ray diffractometer, and a scanning electron microscope. The prepared composites were tested for hardness and tensile strength. Micrographs taken in the stir zone revealed a consistent distribution of AlN particles throughout the AZ91 matrix. The AZ91/AlN surface composites were tested for hardness and tensile strength, and the findings showed that the AlN particles improved the mechanical properties without significantly decreasing ductility. The fracture surfaces of the composites were studied, and the mechanisms of fracture were identified.
Ibrak Hossain, Abhijit Bhowmik, Ashutosh Pattanaik, Rahul Kumar, Abhishek Kumar Singh, and Shatrudhan Pandey
IOP Publishing
Abstract Thermal stress is a very common phenomenon that occurs at the welded joint. Determination of the same at the joint is however difficult due to inhomogeneity of the weld joint metals and spreading of heat to the surroundings from the Heat Affected Zone (HAZ). Thermal stress induced at the welded joint changes the microstructure of grains which affects the mechanical properties of the welded material. Due to this, cracks may appear in the joint leading to failure of the weld. In the present study, three-dimensional model of two types of welded joint, i.e., Tee Joint and lap joint of two plates having dimensions 100 mm × 75 mm × 5 mm are prepared using ANSYS Workbench 2020 R2. Hex dominant meshing is chosen in order to have clear picture of the spread of temperature over the entire region. The change of Residual stress with variation of welding current and keeping welding voltage constant is also observed for weld joint made of Aluminium Alloy. In this study, conduct steady-state thermal analysis and structural analysis on an aluminium alloy 6063 to assess von Mises stress, von Mises strain, and deformation distribution induced by heating. Evaluate various welding joints to identify the most effective technique.
Binayak Sen and Abhijit Bhowmik
Elsevier BV
Abhijit Bhowmik, Biplab Bhattacharjee, Arunkumar T, Abayomi Adewale Akinwande, Asad Syed, and Ling Shing Wong
IOP Publishing
Abstract Now-a-days, metal matrix composites based on pure aluminium are widely used as a replacement for a wide range of applications thanks to their high strength-to-weight ratio, ductility, and thermal conductivity. Micron-sized (10−3 mm) borosilicate (mixer of Silica (SiO2) and Boron Oxide (B2O3)) glass particles were used to create a dispersion in an aluminium matrix composite (Al7075- borosilicate glass MMC) utilizing a stir casting technique while in a liquid condition. The present study examines the effect of borosilicate glass particle content (0, 3, 6, and 9 wt%) and changes in sliding velocity (0.5, 1, 1.5, and 2 m s−1) on the wear behaviour of a composite rubbing against an EN31 steel disc at a constant load of 20N and a sliding distance of 1000 m. Analysis of wear showed that the presence of borosilicate glass increased the wear rate and countered the trend shown in the coefficient of friction. In a similar vein, the sliding velocity of the counter plate rotated faster resulted in a higher coefficient of friction and a higher wear rate. A reliable mathematical model is developed to find the best combination of machining parameters for Al7075-borosilicate glass metal matrix composite (MMC). This model will consider important performance measures like surface smoothness and material removal rate. The authors will also use a sophisticated statistical method called the Taguchi L9 orthogonal array design of experiments (DOE) to understand how different machining settings affect how easily the composite can be machined. By analyzing the results from the DOE, it can optimize the machining parameters for efficient and cost-effective processing. This work can lead to manufacturing innovation in the future.
Lokesh Yadhav Bittanakurike Ramaiah, Kiran Menasiganahalli Doddaputtegowda, Govindaraju Hiregangoor Krishnamurthy Setty, Srinivas Prabhu Murur, Abdulrajak Buradi, Sagr Alamri, Alaauldeen A. Duhduh, Ali A. Rajhi, Mohd Asif Shah, and Abhijit Bhowmik
American Chemical Society (ACS)
Recent studies show that nanofillers greatly contribute to the increase in the mechanical and abrasive behaviors of the polymer composite. In the current study, epoxy composites were made by hand lay-up with the reinforcement of carbon fabric and titanium dioxide (TiO2) nanoparticles as secondary reinforcement in weight percentages of 0.5, 1.0, and 2.0. Hardness, tensile, and abrasive wear tests have been carried out for the fabricated composites. The obtained results confirm that as the percentage of filler addition increases, hardness of the carbon epoxy (CE) composite increases, and significant enhancement of 10.25% hardness is confirmed in 2 wt % nano TiO2-added CE composite. The CE composite filled with 2 wt % of TiO2 nanofiller shows 15.77 and 9.15% improvement of tensile strength and modulus, respectively, compared to unfilled CE composites. The abrasive wear volume exhibits a nearly linear increasing trend as the abrading distance increases. In addition, it is discovered that the abrasive wear volume is greater for higher applied loads. The inclusion of nano TiO2 reduced the wear loss in the CE composite for all abrading distances, regardless of the load, low or high. The scanning electron microscopy analysis of worn surfaces was carried out to analyze the contribution of the filler to improve the wear resistance.
Julius Fusic S, Sugumari T, Jayant Giri, Emad Makki, R. Sitharthan, Shunmathi Murugesan, and Abhijit Bhowmik
AIP Publishing
Detecting diseases is a vital and crucial step in maintaining healthy, high-yielding plants. The challenge of manually identifying infections is arduous as well. The proposed work is to diagnose plant leaf diseases and discuss their origins and remedies. Image processing is used to discover the infected leaf and provide remedial measures through a mobile robot application. The use of machine learning techniques allows for the detection of leaf diseases using the support vector machine model, the K nearest neighbor model, and the Naïve Bayes classification to categorize the sample leaves. In this paper, the Momordica charantia leaf and the common four diseases dataset are developed, and a classification model is developed to identify and categorize leaf curl, downy mildew, powdery mildew, and angular leaf spot. Based on the disease classification, appropriate chemical pesticides are sprayed by controlling the servo actuated valve in the proposed agriculture robot, which is controlled and validated. The result reveals that the proposed approach has an average accuracy of 82% in identifying the disease type that remains more prevalent in Momordica charantia leaves than other compared classification algorithms.
Shirsendu Das, Rajdeep Paul, Abhijit Bhowmik, Jayant Giri, Ibrahim Albaijan, and Chander Prakash
AIP Publishing
The present study is an integrated approach of experimental and simulation processes to investigate the influences of the flushing on the wear, textural feature, heat dissipation, tool-tip temperature, and elemental contents of the electrical discharge machining-tool. The general heat transfer equation in cylindrical coordinates is used to explain the thermal phenomenon through the tool, where the boundary conditions are influenced by convective interactions and dimensionless numbers. The inter-electrode flushing is considered a micro-channel flow, and the used model is conceptualized from “Hazen–Poiseuille observation.” It is observed that the adopted model has a prediction error of 8.503% and can accurately explain the thermal consequences of the process. The study reveals that tool wear is influenced by flushing velocity and flushing pressure. The tool-tip temperature reduces with the Reynolds number, and effectual expelling of the debris can be ensued with a flushing of a higher Reynolds number (Re). However, the increment of Re beyond 4500 provides rapid heat dissipation, which produces extensive residual stress and creates cracks on the surface.
Abhijit Bhowmik, Indradeep Kumar, VSS Venkatesh, Sarbjeet Kaushal, Rahman S. Zabibah, and Manish Gupta
EDP Sciences
Composites are replacing more conventional materials due to their advantageous properties, such as high strength, hardness, low weight, and wear resistance. In this study, the stir casting method is used to create an Al7075/SiC aluminium matrix composite, and its dry sliding wear behaviour is examined. The EDX and SEM results both show that the silicon carbide is evenly distributed throughout the matrix. The dry sliding wear behaviour of the composites is investigated using the Taguchi L16 orthogonal array to reduce the number of experimental runs. Four key process parameters—reinforcement quantity (0%, 3%, 6%, and 9%), load (15N, 30N, 45N, and 60N), sliding velocity (0.75m/s), sliding distance (1.5m/s), and sliding distance (3m/s)—are evaluated across four levels to determine the best parameter combination for reducing wear rate. S/N ratios are best when the following conditions are met: 3 wt.% SiC reinforcement, 15 N load, 3 m/s sliding velocity, and 800 m sliding distance (as shown in the main effect graphic). Wear rate, frictional force, and coefficient of friction are all affected by the four process parameters, and their effects are often studied using analysis of variance (ANOVA). The Analysis of Variance (ANOVA) outcome indicated that the probability value associated with the applied load was below 0.05, signifying statistical significance.
Abayomi Adewale Akinwande, Henry Kayode Talabi, Olanrewaju Seun Adesina, Olugbenga Ogunbiyi, Abhijit Bhowmik, and Valentin Romanovski
Elsevier BV
Rudradeep Das, Abhijit Bhowmik, Souvik Makhal, Sagnik Mukherjee, Rahul Satra, and Plaban Deb
AIP Publishing
Sujay Kumar Dolai, Arindam Mondal, Abhijit Bhowmik, and Plaban Deb
AIP Publishing
Abhijit Bhowmik, Guttikonda Manohar, Prasanta Majumder, and Plaban Deb
AIP Publishing
Binayak Sen, Shantanu Debnath, and Abhijit Bhowmik
Springer Science and Business Media LLC
Vignesh Packkirisamy, Arunkumar Thirugnanasambandam, Abhijit Bhowmik, and Ashokkumar Mohankumar
SAGE Publications
In this work, the wear behavior of a novel AZ31 magnesium alloy reinforced with 5% yttria-stabilized zirconia (YSZ) composite was evaluated using a hybrid Response Surface Methodology (RSM) and Genetic Algorithm (GA) approach. The composite was fabricated using ultrasonic-assisted stir squeeze casting technique, ensuring homogeneous distribution of spherical YSZ particles, as validated by the scanning electron microscope integrated with energy dispersive spectroscope. Wear tests were carried out according to the ASTM standards using a pin-on-disc (POD) tribometer, with applied load (AL), sliding speed (SS), and sliding distance (SD) as the main parameters. An empirical wear rate regression model was developed using RSM/Box-behnken design, and Genetic Algorithm was deployed for parametric optimization, achieving a minimal wear rate of 0.0144 g/m under a load of 30 N, sliding speed of 260 rpm, and sliding distance of 400 m. Confirmation tests were performed to validate the GA predictions. The wear mechanisms were observed, showing reduced wear in GA-optimized samples due to optimized load distribution resulting minimized ploughing, grooving and delamination. This work highlights the efficacy of the hybridized RSM / GA for the wear performance in advanced magnesium alloy matrix composites.
Binayak Sen, Abhijit Bhowmik, Nikunj Rachchh, Nagaraj Patil, Ali Khatibi, and Raman Kumar
Springer Science and Business Media LLC
Binayak Sen, Archisman Dasgupta, and Abhijit Bhowmik
Springer Science and Business Media LLC
Md Farid Hossain, Abhijit Bhowmik, Samim Alam, Sameer Sheshrao Gajghate, Golam Kibria, Chander Prakash, and Himadri Majumder
Hindawi Limited
It is quite evident that some of the experimental research works have been carried out by researchers in the area of spiral polishing using abrasive for surface finishing improvement but most of the experiment has been employed on Steel or Alloy Steel as a material. Very few number of research works have been performed by researchers across the globe on the Spiral Polishing Method of Titanium holes made by Electrical Discharge Machining (EDM). Therefore, experimental investigations were carried out in the area of Spiral Polishing and Finishing of EDM-drilled holes of various materials to meet the desired goal of demands on the surface quality. This experimentation aims to develop a novel method with spiral polishing using abrasive flow finishing. To explore the search investigation and find out the better surface finishing for through holes made on EDM, the process parameters have been designed using the Taguchi L16 orthogonal array with input parameters such as Current (I), Pulse on time (Ton), and Pulse off time (Toff). The addition of Boron Carbide Powder with Handwash, Glycerine, Shampoo, and Liquid Soap is used to decrease the Surface Roughness (Ra) with a ratio of 5 : 1. The Taguchi technique is used to assess the P/M process parameter setting for a given signal to noise (S/N) ratio.
Pulkit Kumar, Harpreet Kaur Channi, Raman Kumar, Chander Prakash, Abhijit Bhowmik, Shatrudhan Pandey, Abhishek Kumar Singh, Muhammad Mahmood Ali, and Manzoore Elahi M. Soudagar
Elsevier BV
Abhijit Bhowmik, Biplab Bhattacharjee, Abayomi Adewale Akinwande, Prasanta Majumder, Jayant Giri, P Satish Kumar, and Jitendra Kumar Katiyar
SAGE Publications
The utilization of TiB2 particle reinforcement in aluminium matrix composites, particularly with Al6063, has been explored in this study for its resilience to mechanical erosion, low oxidation rate, and excellent heat conductivity. The composite was produced using stir casting with 9 wt% TiB2. The investigation focuses on wear behaviour, examining three key process parameters such as load, sliding speed, and covering sliding distance across four settings to identify the optimal combination for achieving a favourable wear rate. Statistical analysis of variance reveals significant differences among the tested parameters. Conclusively, the study highlights the superiority of the grey-fuzzy approach over a simple grey relational grade in validating decision-making for wear performance attributes. The research identifies the ost significant grey relational grade and grey fuzzy grade values as 0.913 and 0.902, respectively. These values correspond to optimal operating conditions, specifically a 15 N load, a sliding speed of 15 m/s, and a sliding distance of 1200 m. The findings underscore the efficacy of the grey-fuzzy technique in authenticating decision-making processes related to wear performance characteristics, emphasizing its superiority over relying solely on a plain grey relational grade.