Effect of V and N on the microstructure evolution during continuous casting of steel

Low Carbon (LC) steel is not expected to be sensitive to hot tearing and/or cracking while microalloyed steels are known for their high cracking sensitivity during continuous casting. Experience of the Direct Sheet Plant caster at Tata Steel in Ijmuiden (the Netherlands), seems to contradict this statement. It is observed that a LC steel grade has a high risk of cracking alias hot tearing, while a High Strength Low Alloyed (HSLA) steel has a very low cracking occurrence. Another HSLA steel grade, with a similar composition but less N and V is however very sensitive to hot tearing. An extreme crack results in a breakout. A previous statistical analysis of the breakout occurrence reveals a one and a half times higher possibility of a breakout for the HSLA grade compared to the LC grade. HSLA with extra N, V shows a four times smaller possibility of breakout than LC. This study assigns the unexpected effect of the chemical composition on the hot tearing sensitivity to the role of some alloying elements such as V and N as structure refiners.This research was carried out under project number M41.5.08320 within the framework of the Research Program of the Materials innovation institute M2i (www.m2i.nl)

Modeling of primary dendrite arm spacing variations in thin-slab casting of low carbon and low alloy steels

Solidification structure of a High Strength Low Alloy (HSLA) steel, in terms of dendrite arm spacing distribution across the shell thickness, is studied in a breakout shell from a thin-slab caster at Tata Steel in IJmuiden. Columnar dendrites were found to be the predominant morphology throughout the shell with size variations across the shell thickness. Primary Dendrite Arm Spacing (PDAS) increases by increasing the distance from meniscus or slab surface. Subsequently, a model is proposed to describe the variation of the PDAS with the shell thickness (the distance from slab surface) under solidifiction conditions experienced in the primary cooling zone of thin-slab casting. The proposed relationship related the PDAS to the shell thickness and, hence, can be used as a tool for predicting solidifcation structure and optimizing the thin-slab casting of low alloy steels

Bayesian clustering of multiple zero-inflated outcomes

Several applications involving counts present a large proportion of zeros (excess-of-zeros data). A popular model for such data is the hurdle model, which explicitly models the probability of a zero count, while assuming a sampling distribution on the positive integers. We consider data from multiple count processes. In this context, it is of interest to study the patterns of counts and cluster the subjects accordingly. We introduce a novel Bayesian approach to cluster multiple, possibly related, zero-inflated processes. We propose a joint model for zero-inflated counts, specifying a hurdle model for each process with a shifted Negative Binomial sampling distribution. Conditionally on the model parameters, the different processes are assumed independent, leading to a substantial reduction in the number of parameters as compared with traditional multivariate approaches. The subject-specific probabilities of zero-inflation and the parameters of the sampling distribution are flexibly modelled via an enriched finite mixture with random number of components. This induces a two-level clustering of the subjects based on the zero/non-zero patterns (outer clustering) and on the sampling distribution (inner clustering). Posterior inference is performed through tailored Markov chain Monte Carlo schemes. We demonstrate the proposed approach on an application involving the use of the messaging service WhatsApp. This article is part of the theme issue 'Bayesian inference: challenges, perspectives, and prospects'