ASAP - Advanced electromagnetic Sensors for Assessing Property scatter in high value steels

ASAP - 用于评估高价值钢材性能分散的先进电磁传感器

• 批准号: EP/K027700/1
• 负责人: Anthony Peyton
• 金额: $ 40.63万
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2014
• 资助国家: 英国
• 起止时间: 2014 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FK027700%2F1
• 关键词: ASAP; Advanced; electromagnetic; Sensors; Assessing

The development of new techniques to measure the microstructure of a material in a non-contact non-destructive fashion can lead to a dramatic improvement in the understanding of the material and its behaviour during processing and in-service. This, in turn, can lead to a greater ability to control the material properties and predict the evolution of these properties. At present, the majority of microstructural analysis techniques are destructive and / or require small samples. Consequently, existing techniques are limited in applicability especially if dynamic microstructural control during commercial processing is of interest. Several techniques have been proposed to directly measure microstructures in these situations but, as yet, no single technique offers a full solution. It is well known that the microstructure is directly related to the mechanical properties of steel, therefore if the microstructure can be measured on-line during processing, and the inherent variability in microstructure can be assessed (on-line or off-line), then the mechanical properties (including any scatter) can be inferred. This could provide enormous saving to the manufacturing industry as product quality can be improved through real-time feedback for processing control and / or reduction (or better still elimination) of the off-line destructive mechanical testing for release specifications. In this context, our proposal focuses directly on advanced sensors for measuring the key microstructural parameters that are directly linked to mechanical properties in high value steels.Our ambition is to exploit novel electromagnetic (EM) techniques to analyse microstructure directly, and therefore to infer mechanical properties, for a range of advanced steel types during and after processing. Typically these EM techniques have included multi-frequency interrogation combined with advanced signal and data processing and modelling. Both the UK and Indian teams have successfully considered the electromagnetic response, using different sensor types, to component phase fractions (such as ferrite), which in the UK has been supported by 3D modelling of both idealised and realistic microstructures. In parallel, the problem of inverting the complex inductance spectra acquired by the sensor systems to yield parameters of metallurgical significance has been addressed, and in India a link between sensor output and mechanical properties for relatively simple steels has been established. In the EU sensor configurations that can be deployed on-line for phase transformation monitoring by measuring the mutual inductance have been successfully tested, in collaboration with Tata Steel Europe, and are now in the process of being commercially exploited via a licensing agreement with an external company. The natural next step in the research is to combine EM sensor techniques to exploit the full electromagnetic characteristics of steel (for example using sensors that measure coercivity, saturation and incremental permeability) to characterise the more complex microstructures, and their spatial inhomogeneity, in advanced steels. This requires collaboration between the leading international groups on sensor design, implementation and microstructure-signal relationships. On-line deployment of these systems will allow greater feedback control during processing to enable these advanced steels to be produced on older as well as new mills. Mechanical property determination, through the microstructure-property relationships, provides the potential to reduce or eliminate the off-line property release tests that are currently performed with a significant saving to the industry.

以非接触性的非破坏性方式测量材料的微观结构的新技术的开发会导致对材料及其在加工和服务过程中其行为的理解的显着改善。反过来，这可能会导致更大的能力控制材料特性并预测这些特性的演变。目前，大多数微观结构分析技术都是破坏性和 /或需要少量样本的。因此，现有技术的适用性有限，尤其是在商业处理期间动态微观结构控制的情况下。在这些情况下，已经提出了几种直接测量微观结构的技术，但是到目前为止，尚无单一技术提供完整的解决方案。众所周知，微结构与钢的机械性能直接相关，因此，如果可以在处理过程中在线测量微观结构，并且可以评估微观结构的固有可变性（在线或离线），则可以推断机械性能（包括任何散点）。这可以为制造业提供巨大的节省，因为可以通过实时反馈来加工控制和 /或减少（或更好地消除）的释放规格来改善产品质量。在这种情况下，我们的提案直接关注高级传感器，用于测量与高价值钢中的机械性能直接相关的关键微观结构参数。我们的野心是利用新型电磁（EM）直接分析微观结构的新型电磁（EM）技术，从而推断出在加工和后处理后的高级钢范围。通常，这些EM技术包括多频询问以及高级信号以及数据处理和建模。英国和印度团队都成功地考虑了使用不同的传感器类型的电磁响应，用于组件相位分数（例如铁素体），在英国，这在英国得到了由理想化和现实的微观结构的3D建模所支持的。同时，已经解决了传感器系统获得的复杂电感光谱以产生冶金意义参数的问题，在印度，已经建立了相对简单的钢的传感器输出与机械性能之间的联系。在欧盟传感器配置中，可以在线部署通过测量相互电感的相互转换监测的配置，已成功测试与塔塔钢铁（Tata Steel Europe）合作，现在正在通过与外部公司的许可协议进行商业利用。该研究的自然下一步是将EM传感器技术组合起来，以利用钢的完整电磁特性（例如，使用测量胁迫性，饱和度和增量渗透性的传感器）来表征更复杂的微观结构及其空间不良性，在高级钢中。这需要领先的国际传感器设计，实施和微观结构信号关系之​​间的合作。这些系统的在线部署将允许在处理过程中进行更大的反馈控制，以使这些高级钢和新工厂能够生产这些高级钢。通过微观结构 - 统治关系，机械属性的确定提供了减少或消除当前在为该行业节省大量节省的离线属性释放测试的潜力。

项目成果

Magnetic characterisation of grain size and precipitate distribution by major and minor BH loop measurements

• DOI: 10.1016/j.jmmm.2019.02.088
• 发表时间: 2019-07-01
• 期刊: JOURNAL OF MAGNETISM AND MAGNETIC MATERIALS
• 影响因子: 2.7
• 作者: Liu，Jun;Wilson，John;Peyton，Anthony
• 通讯作者: Peyton，Anthony

Magnetic characterisation of microstructural feature distribution in P9 and T22 steels by major and minor BH loop measurements

• DOI: 10.1016/j.jmmm.2015.10.075
• 发表时间: 2016-03-01
• 期刊: JOURNAL OF MAGNETISM AND MAGNETIC MATERIALS
• 影响因子: 2.7
• 作者: Liu, Jun;Wilson, John;Peyton, Anthony
• 通讯作者: Peyton, Anthony

Reducing the Lift-Off Effect on Permeability Measurement for Magnetic Plates From Multifrequency Induction Data

• DOI: 10.1109/tim.2017.2728338
• 发表时间: 2018-01-01
• 期刊: IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
• 影响因子: 5.6
• 作者: Lu, Mingyang;Zhu, Wenqian;Qu, Zhigang
• 通讯作者: Qu, Zhigang

Non-destructive characterisation of N/Al level in P91 steels using electromagnetic sensors

使用电磁传感器无损表征 P91 钢中的 N/Al 水平

• DOI: 10.1179/1743284714y.0000000642
• 发表时间: 2014
• 期刊: Materials Science and Technology
• 影响因子: 1.8
• 作者: Liu J

Monitoring of power station steels using electromagnetic sensors

使用电磁传感器监测电站钢材

• 发表时间: 2014
• 作者: Karimian Noushin