Artificial neural network modeling of the tensile properties of indigeneously developed 15Cr-15Ni-2.2Mo-Ti modified austenitic stainless steel

The severe and hostile operating conditions of fast breeder reactors demand the development of new austenitic stainless steels that possess higher resistance to void swelling and irradiation embrittlement. This paper discusses the efforts made in the laboratory and industrial scale development of a 15Cr-15Ni-2.2Mo-Ti modified austenitic stainless steel and the evaluation of tensile properties. Melting and casting were carried out in a vacuum induction furnace and the data on recovery of various alloying elements was obtained for charge calculations. Based on the recovery data and decarburisation behavicur under different vacuum levels, a series of alloys with close chemistry variations were prepared. Heat treatment was optimised for these special steels to control the grain size at required level. The ingots were thermo-mechanically processed and tensile properties were evaluated. This experimental data has been used to train and test an artificial neural network. The input parameters of the neural network are chemical compositions and test temperature while the yield strength, ultimate tensile strength and uniform elongation were obtained as output. A multilayer perceptron (MLP) based feed-forward network with back-propagation learning algorithm has been employed. A very good performance of the developed network is obtained. The model can be used as a guideline for new alloy development

Critical Regionalism in the Post-Colonial architecture of the Indian subcontinent

Critical Regionalism as expounded by Kenneth Frampton has found its use in many parts of the world as a reaction to the international architecture practised in the Western world. India, which was deprived of exposure to the advanced developments in architecture in the US and Europe was at one stroke brought into world contact after gaining independence. This paper traces the exposure of the Indian architects to Western training and philosophy and how they developed their works to suit the regional context. Important aspects of the paper are mentioned below: ‒ International exposure of the Indian architects after independence. ‒ Their designs and their approaches to the creation of an Indian flavour on their return to homeland. ‒ Examined the works of a few prominent architects and inferred on their special regional contributions

Influence of second phase precipitates on mechanical and in-vitro corrosion behaviour of Mg-4Zn-0.5Ca-0.8Mn alloy in optimum homogenized conditions

The present study investigates the mechanical and in-vitro corrosion behavior of Mg-4Zn-0.5Ca-0.8Mn alloy in optimum homogenized conditions. The optimization of the homogenization parameters has been carried out employing thermodynamic calculations and kinetic modeling. The model utilizes the inter-diffusivity of the solute elements and predicts that ∼6–24 h of homogenization at 633 K effectively redistributes the elements in the Mg matrix. Based on the insights obtained from the simulations, the as-cast Mg-4Zn-0.5Ca-0.8Mn alloy was subjected to homogenization heat treatment process for 6–24h. The microstructural study through optical microscopy and scanning electron microscopy (SEM) revealed that the interconnected network of second phase precipitates substantially dissolve within 24 h, implying adequate homogenization. Moreover, fine Mg-Zn based precipitates with varied morphology and phase fractions also evolved during homogenization treatment, as confirmed through SEM and transmission electron microscopy. In the 12 h homogenized specimen, the highest fraction of uniformly dispersed fine precipitates resulted in the highest strength (∼225 MPa). On the other hand, a substantial disruption in coarse precipitate network and lower aspect ratio of fine Mg-Zn precipitates led to the highest ductility (∼8%) in this specimen. In the 24 h homogenized specimen, the ductility reduced marginally owing to higher aspect ratio of fine precipitates. The immersion and electrochemical tests (viz., potentiodynamic polarization and electrochemical impedance spectroscopy) carried out in Hank's solution revealed that the 24 h homogenized specimen exhibits the best corrosion properties. The least fraction of Ca2Mg6Zn3 phase with maximum disruption in interconnectivity, in combination with a small fraction of fine equilibrium MgZn2 precipitates, resulted in suppression of localized corrosion in this specimen. This promotes the formation of the most stable and compact product layer over the specimen, resulting in the highest corrosion resistance

An overview of neural network based modeling in alloy design and thermomechanical processing of austenitic stainless steels

This overview reports some of the research works carried out by us in recent past on the application of artificial neural network (ANN) based modeling in alloy design and thermomechanical processing of austenitic stainless steels. Different ANN models were created in order to simulate various correlations and phenomena in austenitic stainless steels. These include: prediction of mechanical properties of alloy D9 from its alloy content, modeling constitutive flow behavior of austenitic stainless steels, and prediction of torsional flow behavior of type 304L stainless steel. Attempt has been made to explain the simulated results by relevant fundamental metallurgical phenomena

Grain boundary microstructural control through thermomechanical processing in a titanium-modified austenitic stainless steel

The present study discusses the grain boundary microstructural control in a 15Cr-15Ni-2.2Mo-Ti modified austenitic stainless steel (commonly known as alloy D9) through a one-step thermomechanical treatment. The experimental methodology adopted in this investigation was based on the strain annealing approach in which a small amount of strain (5 to 15 pct) was imparted on the solution-annealed (SA) sample. The cold-deformed samples were subsequently annealed at various temperatures (1173 to 1273 K) for different time periods (0.5 to 2 hours). It was observed that annealing after 5 pct deformation induces anomalous grain growth with a moderate increase in number fraction of coincidence site lattice (CSL) boundaries. However, a prestrain of 10 to 15 pct followed by annealing at 1273 K for 0.5 to 2 hours was found to be a suitable thermomechanical processing schedule to increase the number fraction of CSL boundaries (particularly ∑3 and its variants) significantly. Further, the well-connected network of random grain boundaries present in the SA specimen was substantially disrupted in these processing conditions due to the incorporation of ∑3 and its variants. The preceding results were discussed with reference to strain-induced grain growth vis-à-vis strain-induced boundary migration (SIBM) following deformation and annealing

Kinetics, mechanism and modeling of microstructural evolution during dynamic recrystallization in a 15Cr-15Ni-2.2Mo-Ti modified austenitic stainless steel

Kinetics, mechanism and modeling of the microstructural evolution of a 15Cr-15Ni- 2.2Mo-0.3Ti modified austenitic stainless steel (alloy D9) during dynamic recrystallization (DRX) have been investigated. The kinetics of DRX has been investigated employing a modified Johnson- Mehl-Avrami-Kolmogorov (JMAK) model. The microstructural study shows that nucleation of new grains during DRX takes place on the parent grain boundary by a bulging mechanism. No significant texture component has been found to develop in the recrystallized matrix. A substantial amount of twins have been observed in the recrystallized matrix. It is proposed that twins play an important role during the nucleation and subsequent expansion of DRX in alloy D9, which in turn moderates the texture in the recrystallized matrix. An artificial neural network model has also been developed to predict the fraction of DRX and grain size, as a function of processing conditions. A good correlation between experimental findings and predicted results has been obtained

CHIKUNGUNYA, SCRUB TYPHUS MONO, AND CO-INFECTION AMONG PATIENTS WITH UNDIFFERENTIATED FEBRILE ILLNESS: A HOSPITAL-BASED STUDY

Objectives: Chikungunya virus is a common arthropod-related acute febrile disease and it is transmitted by Aedes aegypti or Aedes albopictus species. On the other hand, the bacterium Orientia tsutsugamushi causes scrub typhus, which is also an acute febrile illness with multiple organ involvement. Coinfection of chikungunya and scrub typhus may lead to severe manifestation including severe respiratory and central nervous system (CNS) complications. Coinfection of chikungunya and scrub typhus may lead to severe manifestation including severe respiratory and CNS complications. Therefore, the proper diagnosis can prevent the clinical complications. The aim and objective of our study is to find the seroprevalence of chikungunya and scrub typhus and coinfection of both through medical assessment and serological research of these patients presented with acute febrile infection at Diamond Harbour Government Medical College and Hospital. Methods: A prospective study was conducted from August 2022 to January 2023 at VRDL, Department of Microbiology, Diamond Harbour Government Medical College and Hospital. Serum was collected for IgM antibody enzyme-linked immunosorbent assay (ELISA) for scrub typhus (In bios kit) and Chikungunya (NIV Chikungunya IgM Capture ELISA Kit) test. Four hundred and eighty-seven samples were tested for IgM antibody by chikungunya and scrub typhus ELISA kit. Result: The present study demonstrated that, from the month of August 2022 to January 2023, 67% of chikungunya cases, 25% cases with only scrub typhus, and 8% cases with both chikungunya and scrub typhus presented positive. A present study shows that chikungunya is slightly more prevalent in males as compared to females, where scrub typhus is equally positive in both male and female patients. Conclusion: Laboratory testing of both of the diseases can prevent the complication of other suspected disease in coinfected patients

Artificial neural network modeling of composition-process-property correlations in austenitic stainless steels

This paper discusses the application of artificial neural network modeling in austenitic stainless steels research, including: (i) correlation between chemical composition, process variables and flow stress of austenitic stainless steels under hot compression; (ii) constitutive flow behavior of type AISI 304L stainless steel during hot torsion; (iii) microstructural evolution during dynamic recrystallization of alloy D9; (iv) correlation between chemical composition and tensile properties of alloy D9. Multilayer perceptron based feed-forward networks have been trained by comprehensive in-house datasets. Very good performances of the neural networks are achieved. Various effects are modeled, among which are: (i) influence of alloy composition and processing parameters on flow behavior of austenitic stainless steels; (ii) effect of strain rate on torsional flow behavior of 304L stainless steel; (iii) combined influence of temperature and strain on dynamic recrystallization behavior of alloy D9. The simulated results are found to be consistent with the metallurgical trends. Finally, the issue of neural network's "black box" approach to modeling is addressed