B. P. C. Rao

Bio: B. P. C. Rao is an academic researcher from Indira Gandhi Centre for Atomic Research. The author has contributed to research in topics: Eddy current & Eddy-current testing. The author has an hindex of 17, co-authored 69 publications receiving 842 citations.

Characterization of aging behaviour in M250 grade maraging steel using eddy current non-destructive methodology

Abstract: The aim of the study is to investigate the effect of aging of solution annealed M250 grade maraging steel on the microstructure, room temperature hardness and eddy current (EC) parameters. Specimens of M250 maraging steel were subjected to solution annealing at 1093 K for 1 h followed by aging at 755 K for various durations in the range of 0.25–100 h. The hardness was found to increase continuously with aging due to precipitation of intermetallics reaching a maximum and decreased there after due to reversion of martensite to austenite. The aging treatment led to three major microstructural changes, i.e. decrease in dislocation density and quenched-in point defects during initial aging (up to 0.25 h), increase in volume fraction of intermetallic precipitates at intermediate duration and a systematic increase in reverted austenite at longer duration (beyond 40 h). These microstructural changes alter the electrical resistivity/magnetic permeability which, in turn, influence the induced voltage in eddy current coil. Two EC parameters, viz. magnitude and phase angle of the induced voltage were studied for heat treated specimens. The study revealed that a good correlation exists between EC parameters and microstructural changes in M250 maraging steel specimens.

Detection of leakage magnetic flux from near-side and far-side defects in carbon steel plates using a giant magneto-resistive sensor

Abstract: Giant magneto-resistive (GMR) sensors are attractive for magnetic flux leakage measurements, especially for the detection of shallow near-side cracks and deeply located defects. An optimized measurement system with magnetic yoke, GMR sensor and selective amplifier has been devised to detect the tangential component of leakage flux from various near-side notches and far-side notches (widths 0.5 mm and 1.0 mm, respectively) in 12 mm thick carbon steel plates. Far-side notches located at nearly 11 mm below the measurement surface have been detected with a good signal-to-noise ratio. The performance of the GMR sensor with lift off has also been studied for possible non-contact examination of hot surfaces and a lift off of 2 mm is expected to ensure the saturation-free detection of near-side as well as far-side notches.

X-ray diffraction-based characterization of magnetite nanoparticles in presence of goethite and correlation with magnetic properties

Abstract: Nanoparticles synthesized using co-precipitation technique from iron salt solutions at different pH and temperatures are found to consist of magnetite, magnetite-goethite mixtures and goethite particles depending on process conditions. The synthesized particles are characterized using X-ray diffraction (XRD) technique and their magnetic properties are evaluated using a vibrating sample magnetometer (VSM). As properties of these nanoparticles are essentially dependent on various phases present, a detailed phase analysis and quantification of the magnetite phase in magnetite-goethite mixed system has been carried out using XRD technique. For quantification, a calibration graph has been made using samples of magnetite and goethite mixed in different weight percentages. Using this calibration graph, magnetite weight percentages have been estimated in mixed particles produced by co-precipitation from iron salt solutions at various pH values. A good correlation between saturation magnetization values and magnetite weight percentage has been observed in these mixed particles. When the synthesized particles are pure magnetite, the saturation magnetization value is found to depend on the diameter of the particles, estimated using XRD and VSM data.

An artificial neural network for eddy current testing of austenitic stainless steel welds

Abstract: An artificial neural network is presented for on-line eddy current testing of austenitic stainless steel welds. Time-domain parameters that are functions of digitized in-phase and quadrature components of probe impedance are used as input to the neural network and the network output, in depth units, is evaluated and displayed continuously. The neural network is trained to recognize disturbing variables such as variations in weld microstructure, lift-off and edge-effect as well as notches of different depth. The neural network is able to detect and characterize longitudinal and transverse surface-breaking notches, despite the presence of disturbing variables.

Recent developments in stainless steels

Abstract: This article presents an overview of the developments in stainless steels made since the 1990s. Some of the new applications that involve the use of stainless steel are also introduced. A brief introduction to the various classes of stainless steels, their precipitate phases and the status quo of their production around the globe is given first. The advances in a variety of subject areas that have been made recently will then be presented. These recent advances include (1) new findings on the various precipitate phases (the new J phase, new orientation relationships, new phase diagram for the Fe–Cr system, etc.); (2) new suggestions for the prevention/mitigation of the different problems and new methods for their detection/measurement and (3) new techniques for surface/bulk property enhancement (such as laser shot peening, grain boundary engineering and grain refinement). Recent developments in topics like phase prediction, stacking fault energy, superplasticity, metadynamic recrystallisation and the calculation of mechanical properties are introduced, too. In the end of this article, several new applications that involve the use of stainless steels are presented. Some of these are the use of austenitic stainless steels for signature authentication (magnetic recording), the utilisation of the cryogenic magnetic transition of the sigma phase for hot spot detection (the Sigmaplugs), the new Pt-enhanced radiopaque stainless steel (PERSS) coronary stents and stainless steel stents that may be used for magnetic drug targeting. Besides recent developments in conventional stainless steels, those in the high-nitrogen, low-Ni (or Ni-free) varieties are also introduced. These recent developments include new methods for attaining very high nitrogen contents, new guidelines for alloy design, the merits/demerits associated with high nitrogen contents, etc.

Non-Destructive Techniques Based on Eddy Current Testing

Abstract: Non-destructive techniques are used widely in the metal industry in order to control the quality of materials. Eddy current testing is one of the most extensively used non-destructive techniques for inspecting electrically conductive materials at very high speeds that does not require any contact between the test piece and the sensor. This paper includes an overview of the fundamentals and main variables of eddy current testing. It also describes the state-of-the-art sensors and modern techniques such as multi-frequency and pulsed systems. Recent advances in complex models towards solving crack-sensor interaction, developments in instrumentation due to advances in electronic devices, and the evolution of data processing suggest that eddy current testing systems will be increasingly used in the future.

Review: A review of data mining applications for quality improvement in manufacturing industry

Abstract: Many quality improvement (QI) programs including six sigma, design for six sigma, and kaizen require collection and analysis of data to solve quality problems. Due to advances in data collection systems and analysis tools, data mining (DM) has widely been applied for QI in manufacturing. Although a few review papers have recently been published to discuss DM applications in manufacturing, these only cover a small portion of the applications for specific QI problems (quality tasks). In this study, an extensive review covering the literature from 1997 to 2007 and several analyses on selected quality tasks are provided on DM applications in the manufacturing industry. The quality tasks considered are; product/process quality description, predicting quality, classification of quality, and parameter optimisation. The review provides a comprehensive analysis of the literature from various points of view: data handling practices, DM applications for each quality task and for each manufacturing industry, patterns in the use of DM methods, application results, and software used in the applications are analysed. Several summary tables and figures are also provided along with the discussion of the analyses and results. Finally, conclusions and future research directions are presented.