M. Valsan

Low cycle fatigue and creep-fatigue interaction behavior of 316L(N) stainless steel and life prediction by artificial neural network approach

Low cycle fatigue (LCF) behavior of solutionized 316L(N) stainless steel (SS) has been studied at various temperatures, strain amplitudes, strain rates, hold times and in 20% prior cold worked condition. The alloy in general showed a reduction in fatigue life with, increase in temperature, increase in strain amplitude, decrease in strain rate, an increase in duration of hold time in tension and with prior cold work. The LCF and creep-fatigue interaction (CFI) behavior of the alloy was explained on the basis of several operative mechanisms such as dynamic strain ageing, creep, oxidation and substructural recovery. The capability of artificial neural network (ANN) approach to life prediction under LCF and CFI conditions has been assessed by using the data generated in the present investigation. It is demonstrated that the prediction is within a factor of 2.

Temperature dependence of low cycle fatigue of 316(N) weld metals and 316L(N)/316(N) weld joints

Abstract The low cycle fatigue behaviour of 316(N) weld metals and 316L(N)/316(N) weld joints have been investigated in the temperature range of 300–873 K, at a strain amplitude of ±0·6% and a strain rate 3 6 10–3 s–1, to study the influence of dynamic strain aging (DSA). The 316(N) weld metal exhibited better fatigue life than the weld joint, though the weld metal has shown higher cyclic stress response and higher plastic strain accumulation than the weld joint. Significant features observed in the temperature regime of 300–873 K include the maximum in fatigue life at 573 K and DSA in the range of 673–873 K. Occurrence of DSA has been manifested through drastic reduction in fatigue life in the range of 673–873 K, associated with anomalous stress response. Dominant DSA effects have been observed at about 773 K in the weld joint and at 823 K in the weld metal. However, the effect of DSA is found to be nominal beyond 823 K where the reduction in fatigue life is attributed to the combined effects of oxidation and DSA. Secondary crack density measurements (in the range of 300–873 K) in the weld joint specimens revealed the severity of the heat affected zone (HAZ) in inducing fatigue damage. Parameters have been identified to determine the temperature corresponding to dominant DSA effects.

Development of SQUID-Based System for Nondestructive Evaluation

This article describes the development of a superconducting quantum interference device (SQUID)-based system for nondestructive evaluation. The setup incorporates an in-house developed thin-film-based Nb SQUID with readout flux locked loop electronics and consists of a liquid helium cryostat with adjustable stand-off distance, a precision XY- thetas scanner for studying both flat and cylindrical samples, and a data acquisition system. The system has been used for the detection of artificially engineered subsurface defects in aluminum plates and to track magnetic-to-nonmagnetic phase transformation in stainless steel [grade 316L(N)] weldment specimens subjected to low cycle fatigue deformation.

Design of a dimensional database for materials data

The Indian Material Database (IMDB) is a national project aiming to develop a database through compilation of materials property data available in different laboratories in India. Selecting the appropriate data modeling technique is crucial for the successful deployment of such a system. Dimensional modeling is a logical design technique that seeks to present the data in a standard, intuitive framework that allows for high-performance access. Dimensional modeling of data results in a ‘star schema’, where the data constitutes a central fact table surrounded by dimension tables. This paper discusses the model and architecture of the material database using a ‘snowflake schema’ which is a variation of ‘star schema’, where some of the dimensions are normalized into multiple related tables. The database contains a central fact table linked to multiple dimension tables, each of which corresponding to one of the following dimensions 1) the materials 2) the material properties which are studied 3) specifications of the experiments conducted on materials and 4) the source from which data is obtained.