A component prepared by shaped metal deposition (SMD) using 300M steel wire is studied to reveal structure and material properties. The component exhibits a bainitic microstructure changing from distinct sheaves to homogenous from top to bottom. This change in microstructure is reflected by a step-like increase of the volume fraction of austenite towards the bottom of the component, and a step-like decrease of the microhardness. A certain deviation of the general change in the morphology of bainite from top to bottom appears at the very bottom of the component, caused by the influence of the base plate. Tensile tests show a very high ultimate tensile strength of about 1500 MPa and an elastic modulus of 200 GPa, which is comparable to that of material in cast condition.

Shaped metal deposition of 300M steel

We examined the microstructure evolution in medium-carbon low-alloy steel upon laser engineering net shape (LENS) (LENS is a trademark of Sandia National Laboratories and the US Department of Energy, Albuquerque, NM). Involved was the deposition of 14 superimposed fine layers. Several characterization techniques such as scanning electron microscopy (SEM), transmission electron microscopy (TEM), nanoindentation and electron back scattered diffraction (EBSD) were used in conjunction with three-dimensional finite element thermal modeling to rationalize the transformation mechanisms. Delta ferrite was the primary phase to solidify from the melt. Solid-state austenitisation led to allotriomorphic hexagonal prisms that grew following a unique direction depending upon the parent delta-grain crystallographic orientation. A supersaturated lower bainitic plate was the main phase to have transformed from austenite, except for the first two deposited layers where martensite predominated. The supersaturated plate underwent a sudden-tempering reaction at the 10th layer but was confined at the plate boundaries. This tempering reaction became more transgranular and increasingly affected retained austenite and microphases for the underlying layers. Coalescence of carbon-depleted ferrite plates gave rise to the steady-stage microstructure at the fourth layer. This complicated microstructural evolution corroborated the microhardness fluctuations through all deposited layers.

/pdf/phase-transformations-in-low-alloy-steel-laser-deposits-3bhfprorw1.pdf

Phase transformations in low-alloy steel laser deposits

Bearing steel 100CrMnSi6-4 and tool steel C105U were used to carry out this research with the steels being austempered to obtain a martensitic-bainitic structure. During the process quite a large number of acoustic emissions (AE) were observed. These signals were then analysed using neural networks resulting in the identification of three groups of events of: high, medium and low energy and in addition their spectral characteristics were plotted. The results were presented in the form of diagrams of AE incidence as a function of time. It was demonstrated that complex transformations of austenite into martensite and bainite occurred when austempering bearing steel at 160 °C and tool steel at 130 °C respectively. The selected temperatures of isothermal quenching of the tested steels were within the area near to MS temperature, which affected the complex course of phase transition. The high activity of AE is a typical occurrence for martensitic transformation and this is the transformation mechanism that induces the generation of AE signals of higher energy in the first stage of transition. In the second stage of transformation, the initially nucleated martensite accelerates the occurrence of the next bainitic transformation.

Analysis of acoustic emission signals at austempering of steels using neural networks

Non-destructive testing of concrete for defects detection, using acoustic techniques, is currently performed mainly by human inspection of recorded images. The images consist of the inside of the examined elements obtained from testing devices such as the ultrasonic tomograph. However, such an automatic inspection is time-consuming, expensive, and prone to errors. To address some of these problems, this paper aims to evaluate a convolutional neural network (CNN) toward an automated detection of flaws in concrete elements using ultrasonic tomography. There are two main stages in the proposed methodology. In the first stage, an image of the inside of the examined structure is obtained and recorded by performing ultrasonic tomography-based testing. In the second stage, a convolutional neural network model is used for automatic detection of defects and flaws in the recorded image. In this work, a large and pre-trained CNN is used. It was fine-tuned on a small set of images collected during laboratory tests. Lastly, the prepared model was applied for detecting flaws. The obtained model has proven to be able to accurately detect defects in examined concrete elements. The presented approach for automatic detection of flaws is being developed with the potential to not only detect defects of one type but also to classify various types of defects in concrete elements.

Detection of Flaws in Concrete Using Ultrasonic Tomography and Convolutional Neural Networks.

This paper presents the results of research aimed at identifying the degree of degradation of fibre-cement boards exposed to fire. The fibre-cement board samples were initially exposed to fire at various durations in the range of 1–15 min. The samples were then subjected to three-point bending and were investigated using the acoustic emission method. Artificial neural networks (ANNs) were employed to analyse the results yielded by the acoustic emission method. Fire was found to have a degrading effect on the fibres contained in the boards. As the length of exposure to fire increased, the fibres underwent gradual degradation, which was reflected in a decrease in the number of acoustic emission (AE) events recognised by the artificial neural networks as accompanying the breaking of the fibres during the three-point bending of the sample. It was shown that it is not sufficient to determine the degree of degradation of fibre-cement boards solely on the basis of bending strength (MOR).

Identification of the Degree of Degradation of Fibre-Cement Boards Exposed to Fire by Means of the Acoustic Emission Method and Artificial Neural Networks.

A model of bainite transformation for two simultaneous reactions in the range of swing back is presented. The bainitic microstructure includes two different sheaves. The sheaves of first type had a midrib in the direction of the plates axes and form a butterfly shape. The second type of sheaves were of the grain boundary nucleated bainite. A mathematical model permitted dividing the kinetic curves and establishing separate time–temperature-transformation (TTT) diagrams for the individual structural components. The experimental data were obtained from dilatometric experiments. The assumptions of the model consist of the exponential relationship of the volume of the individual sub-plates and the autocatalytic coefficients related to the changes of nucleation rates. The results of the computation showed that the lengths of the individually formed sub-plates are dependent on the type of the structural component.

Modeling kinetics of the austenite simultaneous decomposition into two bainitic products in the range of swing back

The first attempt of an acoustic emission (AE) application for the control of isothermal transformation, at the temperature near the martensite start (M
 S
 ) in 100CrMnSi6-4 bearing steel, is made herein. A unique method for recording the signals of the AE was developed. The Short Time Fourier Transform algorithm was used for displaying the signal values on the spectrogram graphs. Acoustic emission showed the nucleation and growth of the isothermal martensite in the residual austenite. The maximum power spectral density of signals was determined using the relative intensity of the RMS values. The observed energy of separated signals during the second stage of residual austenite decomposition is comparable to the energy of the initial stage at lower temperatures. It was proposed as a new process concept under paraequilibrium that involves the midrib formation and swing back in the kinetics of lower bainite.

Acoustic emission in bearing steel during isothermal formation of midrib

The paper concerns acoustic emission (AE) measurements during isothermal martensitic and bainitic heat treatments in 100CrMnSi6-4 bearing steel. A distribution of the total RMS voltage in arbitrary unit (AU) during austempering together with the MS temperature results was determined.MS represents the temperature at which martensite starts being formed. This temperature was determined with the use of the dilatometric method. The Short Time Fourier Transform algorithm was used for displaying the signal values on spectrogram graphs. AE signals were attributed to a midrib. Midrib is a thin-plate isothermal martensite formed before lower bainite nucleation. The evolution of midribs in remaining austenite was investigated with AE measurements at the temperature near MS. Macroscopic effects of elongation changes and their division into various intensity stages related to a martensite transformation were analyzed. A comparison of the AE curves and dilatometric results suggests that the AE method determines a relatively initial stage of the martensite transformation, during which additional processes of austempering and carbon partitioning occur. Lower bainite containing the midrib was found to evolve in two stages. K e y w o r d s: bearing steels, phase transformation kinetics, acoustic methods, isothermal heat treatments, midrib, bainite

/pdf/application-of-acoustic-emission-to-monitor-bainitic-and-1ljs12ghw0.pdf

Application of acoustic emission to monitor bainitic and martensitic transformation

Acoustic emission and dilatometry were applied to investigate the characteristics of phase transformations in bearing steel 100CrMnSi6-4 during austempering below the martensite start temperature (MS 175 °C) at 150 °C. The aim of this study is to characterize the product of transformation occurring below the MS temperature using various research methods. Analysis of the dilatometric curves shows that, after the formation of athermal martensite below the MS temperature, the austenite continues to undergo isothermal transformation, indicating the formation of bainite. Additionally, tests were carried out with the use of acoustic emission during isothermal hardening of the adopted steel. The obtained acoustic emission signals were analyzed using an artificial neural network. The results, in the form of a graph of the frequency of acoustic emission (AE) event occurrence as a function of time, make it possible to infer about the bainite isothermal transformation. The results of this research may be used in the future to design optimal heat treatment methods and, consequently, may enable desired microstructure shaping.

https://www.mdpi.com/1996-1944/14/3/551/pdf

Tadeusz Z. Wozniak

Papers

Analysis of acoustic emission signals at austempering of steels using neural networks

Modeling kinetics of the austenite simultaneous decomposition into two bainitic products in the range of swing back

Acoustic emission in bearing steel during isothermal formation of midrib

Application of acoustic emission to monitor bainitic and martensitic transformation

The Use of Acoustic Emission and Neural Network in the Study of Phase Transformation below MS.