Detection of sub-surface stresses in ferromagnetic materials using a new Barkhausen noise method

In this work, a new, non-destructive method for obtaining stress-depth gradients in ferromagnetic structures was developed, using the information contained within magnetic Barkhausen emissions. A depth- and stress-dependent model for the frequency spectrum of Barkhausen emissions was derived and fitted to measured data obtained from steel samples with controlled stress-depth gradients. To achieve this, a library of signal processing and optimization algorithms was developed, which allowed the analysis of large datasets. To validate experimental procedures, a number of solid mechanics finite element simulations were carried out. Proof of concept is demonstrated by assuming linear stress-depth gradients and successfully calculating the slopes of those, using a fitting algorithm

Barkhausen spectroscopy: Non-destructive characterization of magnetic materials as a function of depth

In this study, we conceptually divided a ferromagnetic specimen into layers along its depth. For each layer, we derived a non-linear integral equation that describes the attenuation with frequency and distance of magnetic Barkhausen emissions coming from that layer. We postulate that the Barkhausen spectrum measured at the surface by an induction coil can be expressed as the sum of the individual layer spectra. We show how a non-linear least squares algorithm can be used to recover the properties in individual layers. These are related to stress using an extension to the theory of ferromagnetic hysteresis. We found that the quality of the fit is influenced by the sensitivity of the ferromagnetic material to strain, as well as by the sensor-specimen coupling. The proposed method can be used for the non-destructive characterization of stress as a function of depth in magnetic materials

Detection of sub-surface stresses in ferromagnetic materials using a new Barkhausen noise method

In this work, a new, non-destructive method for obtaining stress-depth gradients in ferromagnetic structures was developed, using the information contained within magnetic Barkhausen emissions. A depth- and stress-dependent model for the frequency spectrum of Barkhausen emissions was derived and fitted to measured data obtained from steel samples with controlled stress-depth gradients. To achieve this, a library of signal processing and optimization algorithms was developed, which allowed the analysis of large datasets. To validate experimental procedures, a number of solid mechanics finite element simulations were carried out. Proof of concept is demonstrated by assuming linear stress-depth gradients and successfully calculating the slopes of those, using a fitting algorithm.</p

Barkhausen spectroscopy: Non-destructive characterization of magnetic materials as a function of depth

In this study, we conceptually divided a ferromagnetic specimen into layers along its depth. For each layer, we derived a non-linear integral equation that describes the attenuation with frequency and distance of magnetic Barkhausen emissions coming from that layer. We postulate that the Barkhausen spectrum measured at the surface by an induction coil can be expressed as the sum of the individual layer spectra. We show how a non-linear least squares algorithm can be used to recover the properties in individual layers. These are related to stress using an extension to the theory of ferromagnetic hysteresis. We found that the quality of the fit is influenced by the sensitivity of the ferromagnetic material to strain, as well as by the sensor-specimen coupling. The proposed method can be used for the non-destructive characterization of stress as a function of depth in magnetic materials.The following article appeared in Journal of Applied Physics 115 (2014): 17E305 and may be found at http://dx.doi.org/10.1063/1.4862095.</p

A model for the Barkhausen frequency spectrum as a function of applied stress

We derive a two parameter multi-exponential model to describe the frequency spectrum ofBarkhausen noise in bulk steel under high excitation rates and applied tensile stress. We show how the amplitude and shape of the frequency spectrum depend on two directly measurable quantities, Barkhausen voltage and effective magnetic permeability, respectively, and how these change with stress. By incorporating frequency and depth dependence components into our model, we provide a framework for identifying stress variations along depth, which can be used for the purposes of non-destructive characterization.The following article appeared in Journal of Applied Physics 115 (2014): 083906 and may be found at http://dx.doi.org/10.1063/1.4866195.</p

Optimization of sensor design for Barkhausen noise measurement using finite element analysis

The effects of design parameters for optimizing the performance of sensors for magneticBarkhausen emission measurement are presented. This study was performed using finite element analysis. The design parameters investigated include core material, core-tip curvature, core length, and pole spacing. Considering a combination of permeability and saturation magnetization, iron was selected as the core material among other materials investigated. Although a flat core-tip would result in higher magnetic flux concentration in the test specimen, a curved core-tip is preferred. The sensor-to-specimen coupling is thereby improved especially for materials with different surface geometries. Smaller pole spacing resulted in higher flux concentration.The following article appeared in Journal of Applied Physics 115 (2014): 17E512 and may be found at http://dx.doi.org/10.1063/1.4864438.</p

CoFly: An automated, AI-based open-source platform for UAV precision agriculture applications

This paper presents a modular and holistic Precision Agriculture platform, named CoFly, incorporating custom-developed AI and ICT technologies with pioneering functionalities in a UAV-agnostic system. Cognitional operations of micro Flying vehicles are utilized for data acquisition incorporating advanced coverage path planning and obstacle avoidance functionalities. Photogrammetric outcomes are extracted by processing UAV data into 2D fields and crop health maps, enabling the extraction of high-level semantic information about seed yields and quality. Based on vegetation health, CoFly incorporates a pixel-wise processing pipeline to detect and classify crop health deterioration sources. On top of that, a novel UAV mission planning scheme is employed to enable site-specific treatment by providing an automated solution for a targeted, on-the-spot, inspection. Upon the acquired inspection footage, a weed detection module is deployed, utilizing deep-learning methods, enabling weed classification. All of these capabilities are integrated inside a cost-effective and user-friendly end-to-end platform functioning on mobile devices. CoFly was tested and validated with extensive experimentation in agricultural fields with lucerne and wheat crops in Chalkidiki, Greece showcasing its performance