Swadesh Kumar Singh

Immunological adjuvant effect of Boswellia serrata (BOS 2000) on specific antibody and cellular response to ovalbumin in mice.

In this study, the biopolymeric fraction BOS 2000 from Boswellia serrata was evaluated for its potential ability as adjuvants on the immune responses to ovalbumin (OVA) in mice. Balb/c mice were immunized subcutaneously with OVA 100 μg alone or with OVA 100 μg dissolved in saline containing alum (200 μg) or BOS 2000 (10, 20, 40 and 80 μg) on Days 1 and 15. Two weeks later, OVA specific antibodies in serum; concanavalin A (Con A), OVA stimulated splenocyte proliferation, CD4/CD8/CD80/CD86 analysis in spleen cells and its estimation of cytokines (IL-2 and IFN gamma) from cell culture supernatant were measured. OVA specific IgG, IgG1 and IgG2a antibody levels in serum were significantly enhanced by BOS 2000 (80 μg) compared with OVA control group. Moreover, the adjuvant effect of BOS 2000 (80 μg) on the OVA-specific IgG, IgG1, and IgG2a antibody responses to OVA in mice were more significant than those of alum. BOS 2000 significantly enhanced the Con A and OVA induced splenocyte proliferation in the OVA immunized mice especially at a dose of 80 μg (p<0.001). However, no significant differences were observed among the OVA group and OVA/alum group. At a dose of 80 μg (p<0.001), there was a significant increase in the CD4/CD8 and CD80/CD86 analysis in spleen cells and cytokine (IL-2 and IFN-gamma) profile in the spleen cell culture supernatant was observed. In conclusion, BOS 2000 seems to be a promising balanced Th1 and Th2 directing immunological adjuvants which can enhance the immunogenicity of vaccine.

Influence of material models on theoretical forming limit diagram prediction for Ti–6Al–4V alloy under warm condition

Abstract Forming limit diagram (FLD) is an important performance index to describe the maximum limit of principal strains that can be sustained by sheet metals till to the onset of localized necking. It offers a convenient and useful tool to predict the forming limit in the sheet metal forming processes. In the present study, FLD has been determined experimentally for Ti–6Al–4V alloy at 400 °C by conducting a Nakazima test with specimens of different widths. Additionally, for theoretical FLD prediction, various anisotropic yield criteria (Barlat 1989, Barlat 1996, Hill 1993) and different hardening models viz., Hollomon power law (HPL), Johnson–Cook (JC), modified Zerilli–Armstrong (m-ZA), modified Arrhenius (m-Arr) models have been developed. Theoretical FLDs have been determined using Marciniak and Kuczynski (M–K) theory incorporating the developed yield criteria and constitutive models. It has been observed that the effect of yield model is more pronounced than the effect of constitutive model for theoretical FLDs prediction. However, the value of thickness imperfection factor ( f 0 ) is solely dependent on hardening model. Hill (1993) yield criterion is best suited for FLD prediction in the right hand side region. Moreover, Barlat (1989) yield criterion is best suited for FLD prediction in left hand side region. Therefore, the proposed hybrid FLD in combination with Barlat (1989) and Hill (1993) yield models with m-Arr hardening model is in the best agreement with experimental FLD.

Metallurgical Studies of Austenitic Stainless Steel 304 under Warm Deep Drawing

Austenitic stainless steel 304 was deep drawn with different blank diameters under warm conditions using 20 t hydraulic press. A number of deep drawing experiments both at room temperature and at 150 °C were conducted to study the metallography. Also, tensile test experiments were conducted on a universal testing machine up to 700 °C and the broken specimens were used to study the fractography of the material using scanning electron microscopy in various regions. The microstructure changes were observed at limiting draw ratio (LDR) when the cup is drawn at different temperatures. In austenitic stainless steel, martensite formation takes place that is not only affected by temperature, but also influenced by the rate at which the material is deformed. In austenitic stainless steel 304, dynamic strain regime appears above 300 °C and it decreases the formability of material due to brittle fracture as studied in its fractography. From the metallographic studies, the maximum LDR of the material is observed at 150 °C before dynamic strain regime. It is also observed that at 150 °C, grains are coarse in the drawn cups at LDR.

Numerical prediction of limiting draw ratio and thickness variation in hydromechanical deep drawing

Hydromechanical deep drawing is a process for producing cup shaped parts with the assistance of a pressurised fluid. In the present work, a complete experimental set up has been designed and fabricated to conduct hydromechanical deep drawing on a 315 tonne hydraulic press. Limiting draw ratio has been determined by using blanks of various diameters. The process was simulated using explicit finite element code LSDYNA. A comparison was made between simulated and experimental results of conventional and hydromechanical deep drawing using low carbon steel sheets of extra-deep drawing grade of 0.96mm thickness. The simulated results showed reasonable agreement with the experimental data. It was found that by hydromechanical deep drawing higher drawability and more uniform thickness distribution could be obtained when compared to conventional deep drawing.

A comparison of different neural network training algorithms for hydromechanical deep drawing

In the hydromechanical deep drawing process, a pressure chamber is attached to the drawing die and the cup is drawn into the chamber against the fluid pressure. This process offers several advantages over conventional deep drawing, such as higher drawability, more uniform thickness distribution, better surface finish etc. Optimisation of this process is more difficult because of the large number of variables which interact in a complex way. Artificial Neural Networks (ANN) are being applied to an increasing number of real-world problems of considerable complexity. They offer reliable solutions to a variety of problems (viz. prediction and modelling), where the physical processes are not understood or are highly complex. This paper compares several training algorithms in an attempt to find an ideal artificial neural network-training algorithm to model hydromechanical deep drawing. A comparison was made between ANN trained and experimental results of hydromechanical deep drawing using low carbon extra deep drawing (EDD) grade steel sheets of 0.96mm thickness.

Numerical Analysis of Warm Deep Drawing for Ti–6Al–4V Alloy

The objective of this study was to focus on the current state of finite element methods with respect to reliability in modeling of sheet metal forming processes. The trustworthiness of the FE simulations largely depends on the input material models used and correctness of the input material data. Formability parameters required for finite element analysis have been determined for Ti–6Al–4V alloy at temperature ranging from room temperature to 400 °C at intervals of 50 °C. Based on these formability parameters, various anisotropic yield criteria, namely, von-Mises, Hill 1948, Barlat 1989, Barlat 1996 and Cazacu–Barlat, have been implemented for Ti–6Al–4V alloy. In addition to that, circular deep drawing experiments have been performed in order to study the formability of Ti–6Al–4V alloy sheet at warm condition. It has been observed that formability of the material is very poor at room temperature. At temperatures above 150 °C till 400 °C, the limiting drawing ratio is found to be 1.8, which is substantially lesser than other structural alloys. Additionally, in the properly drawn cups, thickness distribution and earing tendency have been studied over the range of temperature. In order to validate the experimental results, finite element analysis has been done using commercially available software DYNAFORM version 5.6.1 with LSDYNA solver version 971. Also, failure regions during the experimentation have been identified using FE analysis. Comparison of yield criteria based on thickness distribution, earing profile, complexity in material parameter identification, and computational time has shown Cazacu–Barlat to be well suited for deep drawing of Ti–6Al–4V alloy.

Formability study of Ti-6Al-4V alloy under warm conditions

Abstract In this work, formability of Ti-6Al-4V alloy has been investigated using deep-drawing and stretching process under warm conditions. Accuracy of finite element simulations in sheet metal forming is significantly dependent on correctness of input properties and appropriate selection of material models. Primarily, the selection of an appropriate yield criterion is vital since it provides precise prediction of the observed initial and subsequent yield behaviours of a material. Barlat 2000 anisotropic yield criterion has been implemented for Ti-6Al-4V alloy at 400 °C. The material constants required for the yield criterion have been determined using uniaxial tensile test at 400 °C. The calculated material constants have been used for finite element (FE) simulation of deep-drawing and stretching process using commercially available DYNAFORM software. FE results have been compared based on thickness distribution, earing profile, dome height and forming limit diagram. Based on the comparison, the Barlat 2000 yield criterion is very well suited for formability prediction of Ti-6Al-4V alloy under warm conditions.

Flow Stress Prediction of Ti-6Al-4V Alloy at Elevated Temperature Using Artificial Neural Network

Flow stress during hot deformation depends mainly on the strain, strain rate and temperature, and shows a complex nonlinear relationship with them. In this work, experimental flow stress have been predicted for Ti-6Al-4V alloy using isothermal uniaxial tensile tests ranging from 323K to 673K at an interval of 50K and strain rates 10-5, 10-4, 10-3 and 10-2 s-1. Based on the input variables strain, strain rate and temperature, a back propagation neural network model has been developed to predict the flow stress as output. The whole experimental data is randomly divided in two parts: 90% data as training data and 10% data as testing data. The artificial neural network enhanced with differential evolution algorithm is successfully trained based on the training data and employed to predict the flow stress values for the testing data, which were compared with the experimental values. Correlation coefficient for training and testing data is found to be 0.9997 and 0.9985 respectively. Based on the correlation coefficient, it indicates that predicted flow stress by using artificial neural network is in good agreement with experimental results.

Prediction of springback in V-bending and design of dies using finite element simulation

The main application of sheet bending is in automobile industries. The phenomenon of springback depends on the elastic recovery and the extent of deformation that is provided on the material. In this research, Springback is being calculated in 90° V-bended sheets using finite element simulation. In bending of thin sheets, Springback causes the angles finally produced to be different from what was intended. Finite Element Method (FEM) is adopted to determine the amount of springback occurring in V-bending of sheet metal of a particular material, and from the results of simulation, dies are designed to produce 90° bend in the sheet.

Orientation-Dependent Tensile Flow Behavior of Zircaloy-4 at Room Temperature

Present work describes a correlation between texture and sample orientation-based flow parameters of three different zircaloy-4 sheet materials. The three different sheet materials [slab route sheet (SRS), tube route sheet (TRS) and low-oxygen sheet (LOS)] corresponded to three different routes of fabrication and hence represented as variation in starting condition. All the three materials exhibited the presence of moderate texture. The intensity is more in TRS samples in comparison with that of the SRS and LOS. This in turn resulted in moderate values of anisotropy parameters. The strength parameters and elongation values have increased and decreased with increase in strain rate, respectively. The flow behavior of the alloys followed typical Holloman equation. The instantaneous work-hardening rate curves of the present alloys exhibited all the three typical regimes (i.e., regime I, regime II and regime III).