Tapan Kumar Mahanta

Bio: Tapan Kumar Mahanta is an academic researcher. The author has contributed to research in topics: Fiber & Machining. The author has an hindex of 2, co-authored 3 publications receiving 29 citations.

Prediction of constant amplitude fatigue crack growth life of 2024 T3 Al alloy with R-ratio effect by GP

Abstract: A genetic programming approach (GP) for predicting fatigue crack growth rate (da/dN) of Al-alloy has been described.The crack growth rate has been calculated by using an exponential model from experimental crack length (a) and number of cycles (N) data which has been subsequently used as training data base for GP model formulation along with load ratio (R), maximum stress intensity factor (Kmax) and stress intensity factor rage (ΔK).The validity of the proposed GP model has been confirmed by comparing the model prediction by experimental data and also with previously proposed ANN model. The objective of this study is to develop a genetic programming (GP) based model to predict constant amplitude fatigue crack propagation life of 2024 T3 aluminum alloys under load ratio effect based on experimental data and to compare the results with earlier proposed ANN model. It is proved that genetic programming can effectively interpret fatigue crack growth rate data and can efficiently model fatigue life of the material system under investigation in comparison to ANN model.

Solid Particle Erosion of Date Palm Leaf Fiber Reinforced Polyvinyl Alcohol Composites

Abstract: Solid particle erosion behavior of short date palm leaf (DPL) fiber reinforced polyvinyl alcohol (PVA) composite has been studied using silica sand particles (200 ± 50 μm) as an erodent at different impingement angles (15–90°) and impact velocities (48–109 m/s). The influence of fiber content (wt% of DPL fiber) on erosion rate of PVA/DPL composite has also been investigated. The neat PVA shows maximum erosion rate at 30° impingement angle whereas PVA/DPL composites exhibit maximum erosion rate at 45° impingement angle irrespective of fiber loading showing semiductile behavior. The erosion efficiency of PVA and its composites varies from 0.735 to 16.289% for different impact velocities studied. The eroded surfaces were observed under scanning electron microscope (SEM) to understand the erosion mechanism.

Effect of Polarity on MRR and TWR in Electric Discharge Machining

Abstract: Electrical energy is used to machine metals in electric discharge machining (EDM) process. EDM is a thermoelectric process, in which machining occurs without cutting force. Hence, electrical energy is converted to thermal energy to remove material. Usually, straight polarity is used in EDM, i.e., workpiece is connected to positive terminal (anode) and tool is connected to negative terminal (cathode). But, reverse can be possibly have rarely studied. An in-house designed table-top EDM was built to perform the experiments in straight and reverse polarity. Copper material was used as tool and workpiece to avoid the effect of different material. The voltage and polarity have significant effect on removal of material from workpiece.

Novel Fuzzy Measurement Alternatives and Ranking according to the Compromise Solution-Based Green Machining Optimization

Abstract: Due to the increase in the impact of different manufacturing processes on the environment, green manufacturing processes are the prime focus of many current pieces of research. In the current article, a green machining process for stainless steel and SS304 and AISI1045 steel has been optimized using newly developed Fuzzy Measurement Alternatives and Ranking according to the COmpromise Solution (F-MARCOS) method in the form of two case studies. In the first case study, nose radius, cutting speed, depth of cut, and feed rate are selected as the process parameters whereas surface roughness, consumption of electrical energy, and power factor are the outputs. In the second case study width of cut, depth of cut, feed rate, and cutting speed were the process parameters and material removal rate (MRR), active energy consumption (ACE), and surface roughness (Ra) are the response variables. The MARCOS method ranks the alternatives based on the ideal and anti-ideal solutions for the different criteria. The inclusion of fuzzy logic adds worth to the model by using a linguistic scale to make the method more practical and flexible. Based on the detailed analysis, it ranked the best alternative in case study one which results in a power factor of 0.862, 26.68 kJ of electrical energy consumption, and surface roughness of 0.36 . In the second case study, the best alternative selected by this method gave an MRR of 2400 and Ra of 2.29 and utilizes 53.988 kJ ACE.

A Novel MOGNDO Algorithm for Security-Constrained Optimal Power Flow Problems

Abstract: The current research investigates a new and unique Multi-Objective Generalized Normal Distribution Optimization (MOGNDO) algorithm for solving large-scale Optimal Power Flow (OPF) problems of complex power systems, including renewable energy sources and Flexible AC Transmission Systems (FACTS). A recently reported single-objective generalized normal distribution optimization algorithm is transformed into the MOGNDO algorithm using the nondominated sorting and crowding distancing mechanisms. The OPF problem gets even more challenging when sources of renewable energy are integrated into the grid system, which are unreliable and fluctuating. FACTS devices are also being used more frequently in contemporary power networks to assist in reducing network demand and congestion. In this study, a stochastic wind power source was used with different FACTS devices, including a static VAR compensator, a thyristor- driven series compensator, and a thyristor—driven phase shifter, together with an IEEE-30 bus system. Positions and ratings of the FACTS devices can be intended to reduce the system’s overall fuel cost. Weibull probability density curves were used to highlight the stochastic character of the wind energy source. The best compromise solutions were obtained using a fuzzy decision-making approach. The results obtained on a modified IEEE-30 bus system were compared with other well-known optimization algorithms, and the obtained results proved that MOGNDO has improved convergence, diversity, and spread behavior across PFs.

A Review of Natural Fiber Reinforced Poly(Vinyl Alcohol) Based Composites: Application and Opportunity

Abstract: Natural fibers are fine examples of renewable resources that play an important role in the composites industry, which produces superior strength comparable to synthetic fibers. Poly(vinyl alcohol) (PVA) composites in particular have attracted enormous interest in view of their satisfactory performance, properties and biodegradability. Their performance in many applications such as consumer, biomedical, and agriculture is well defined and promising. This paper reviews the utilization of natural fibers from macro to nanoscale as reinforcement in PVA composites. An overview on the properties, processing methods, biodegradability, and applications of these composites is presented. The advantages arising from chemical and physical modifications of fibers or composites are discussed in terms of improved properties and performance. In addition, proper arrangement of nanocellulose in composites helps to prevent agglomeration and results in a better dispersion. The limitations and challenges of the composites and future works of these bio-composites are also discussed. This review concludes that PVA composites have potential for use in numerous applications. However, issues on technological feasibility, environmental effectiveness, and economic affordability should be considered.

A Comparison Study of Machine Learning Based Algorithms for Fatigue Crack Growth Calculation

Abstract: The relationships between the fatigue crack growth rate ( d a / d N ) and stress intensity factor range ( Δ K ) are not always linear even in the Paris region. The stress ratio effects on fatigue crack growth rate are diverse in different materials. However, most existing fatigue crack growth models cannot handle these nonlinearities appropriately. The machine learning method provides a flexible approach to the modeling of fatigue crack growth because of its excellent nonlinear approximation and multivariable learning ability. In this paper, a fatigue crack growth calculation method is proposed based on three different machine learning algorithms (MLAs): extreme learning machine (ELM), radial basis function network (RBFN) and genetic algorithms optimized back propagation network (GABP). The MLA based method is validated using testing data of different materials. The three MLAs are compared with each other as well as the classical two-parameter model ( K * approach). The results show that the predictions of MLAs are superior to those of K * approach in accuracy and effectiveness, and the ELM based algorithms show overall the best agreement with the experimental data out of the three MLAs, for its global optimization and extrapolation ability.

An Artificial Neural Network-Based Algorithm for Evaluation of Fatigue Crack Propagation Considering Nonlinear Damage Accumulation.

Abstract: In the aerospace and aviation sectors, the damage tolerance concept has been applied widely so that the modeling analysis of fatigue crack growth has become more and more significant. Since the process of crack propagation is highly nonlinear and determined by many factors, such as applied stress, plastic zone in the crack tip, length of the crack, etc., it is difficult to build up a general and flexible explicit function to accurately quantify this complicated relationship. Fortunately, the artificial neural network (ANN) is considered a powerful tool for establishing the nonlinear multivariate projection which shows potential in handling the fatigue crack problem. In this paper, a novel fatigue crack calculation algorithm based on a radial basis function (RBF)-ANN is proposed to study this relationship from the experimental data. In addition, a parameter called the equivalent stress intensity factor is also employed as training data to account for loading interaction effects. The testing data is then placed under constant amplitude loading with different stress ratios or overloads used for model validation. Moreover, the Forman and Wheeler equations are also adopted to compare with our proposed algorithm. The current investigation shows that the ANN-based approach can deliver a better agreement with the experimental data than the other two models, which supports that the RBF-ANN has nontrivial advantages in handling the fatigue crack growth problem. Furthermore, it implies that the proposed algorithm is possibly a sophisticated and promising method to compute fatigue crack growth in terms of loading interaction effects.

Mechanical and tribological performance of Luffa cylindrica fibre-reinforced epoxy composite.

Abstract: This work focuses on the mechanical properties and solid particle impact behaviour of Luffa cylindrica fibre (LCF)-reinforced epoxy composites. Single (SL)-, double (DL)-, and triple (TL)-layered composites were prepared using the general hand lay-up technique. The erosive wear test was carried out using an air jet erosion tester according to the ASTM G76 standard. The erodent used was silica sand particles (200 ± 50 µm). The experimental parameters studied for the erosion rate of the LCF epoxy composites were impingement angle (30° to 90°) and particle velocity (48 m/s to 82 m/s). Analysis of the results revealed that at the peak erosion rate, semi ductile behaviour of the composite was apparent. Possible erosion mechanisms were discussed and were investigated using scanning electron microscopy (SEM).