Damage Analysis in Dual-Phase Steel Using Deep Learning: Transfer from Uniaxial to Biaxial Straining Conditions by Image Data Augmentation

Medghalchi, Setareh; Kusche, Carl F.; Karimi, Ehsan; Kerzel, U.; Korte‐Kerzel, Sandra

doi:10.1007/s11837-020-04404-0

Cited by 15 publications

(5 citation statements)

References 22 publications

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“…In the calibration process, the modeling method did not need to identify the joint zero error separately, which simplified the calculation and improved the calibration efficiency. At the same time, the method also overcame the singularity problem in the traditional D-H model [5]. Yang et al found that mirror therapy had a significant effect on patients with nerve injury and poor motor function [6].…”

Section: Literature Reviewmentioning

confidence: 99%

A Training Method for a Sensor-Based Exercise Rehabilitation Robot

et al. 2022

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In order to solve the problem that the traditional mirror therapy did not take into account the real-time recovery of the affected limb and the training effect was limited, a training method of sports rehabilitation robot based on sensor was proposed. A mirror active rehabilitation training system was proposed, which was composed of four steps including trajectory acquisition of the limb inertial measurement unit (IMU), fuzzy adaptive proportion differentiation (PD) control in closed-loop variable domain, muscle force estimation of the surface electromyographic signal (sEMG) of the affected limb, and power compensation of the outer ring of the affected limb. The experimental results showed that the sagittal forward flexion angle of the healthy shoulder increased from 0° to 128° at a relatively uniform speed, and the sagittal forward flexion angle of the shoulder was basically consistent with that of the healthy limb after the adaptive power compensation of the affected limb. The calculated trajectory tracking error of the healthy limb controlled by the fuzzy adaptive PD controller in the variable domain was 0.21 ± 1.35 ° . The horizontal backward extension angle of the healthy shoulder joint increased from 0° to 43°, and the following trajectory of the affected limb was roughly consistent with the movement trajectory of the healthy limb. The calculated tracking error of the healthy limb trajectory was 0.39 ± 1.45 ° . It was concluded that the control system could provide the real-time power compensation according to the recovery of the affected limb, give full play to the training initiative of the affected limb, and make the affected limb achieve a better rehabilitation training effect.

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Section: Literature Reviewmentioning

confidence: 99%

A Training Method for a Sensor-Based Exercise Rehabilitation Robot

et al. 2022

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“…The spatial resolution of the obtained images was 32.5 nm/pixel. The high resolution electron microscopic images of the microstructure were segmented using deep learning based convolutional neural networks [4] developed with Tensorflow 2.0.0. For this purpose, the panoramic images were cropped to smaller window sizes of 512 * 512 pixels.…”

Section: Input Data: Segmented Microstructure Phase Mapsmentioning

confidence: 99%

A Machine Learning Enabled Image‐data‐driven End‐to‐end Mechanical Field Predictor For Dual‐Phase Steel

Lin

Medghalchi

Korte‐Kerzel

et al. 2023

Proc Appl Math and Mech

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This contribution presents convolutional neural nets (CNN) based surrogate models for prediction of von Mises stress and equivalent plastic strain fields of commonly used Dual‐Phase (DP) steels in automotive applications. The models predict field quantities in an end‐to‐end manner, driven by segmented phase images from real experimental scanning electron micrographs as inputs and FEM calculations as outputs. Hereby, we train CNN models with the U‐net neural network structure based on around 900 elastoplastic FEM simulations of various DP steel microstructure samples under tensile test. The trained CNN models are validated and tested on 250 and 50 samples, respectively. Thereby CNN models are employed sequentially for different tasks , from the real micrographs to segmented phase maps, then from segmented phase maps to stress, strain field predictions, in an end‐to‐end manner. The field predictor model results show good agreement with the test data and convincing performance on unseen microstructural dataset. This work demonstrates the large potential of a Machine Learning model to make accumulatively use of the physics‐based simulation data of large number of boundary value problems with varying microstructure. It recaptures not only the physics, implied in each simulation training data obtained from the partial different governing equations of mechanics, but also the overarching correlation between the microstructure and the stress and strain field responses.

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“…In this context, deformation induced damage sites can be detected and subsequently classified with respect to their appearance, as shown by Kusche et al and Medghalchi et al for damage sites in dual-phase steel. [34,35] In this study, we explore the prevalent mechanisms of codeformation and their dependence on strain and rate in a Mg-Ca(Mg,Al) 2 metallic-intermetallic composite microstructure. To this end, we use micromechanical testing and scanning electron microscopy, coupled with automated image analysis to identify and quantify the dominant damage mechanisms of brittle failure in the intermetallic, and interfacial decohesion at the internal interfaces.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Determination of the Rate Dependence of Damage Formation in Metallic‐Intermetallic Mg–Al–Ca Composites at Elevated Temperature using Panoramic Image Analysis

Medghalchi,

Zubair,

Karimi

et al. 2023

Adv Eng Mater

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We study a cast Mg‐4.65Al‐2.82Ca alloy with a microstructure containing an α‐Mg matrix, reinforced with a C36 Laves phase skeleton. Such ternary alloys are targeted for elevated temperature applications in automotive engines, since they possess excellent creep properties. However, in application, the alloy may be subjected to a wide range of strain rates, and thus accelerated testing is often essential. It is, therefore, crucial to understand the effect of such rate variations. Here, we focus on their impact on damage formation. Our analysis is based on high resolution panoramic imaging using scanning electron microscopy, combined with automated damage analysis using deep learning for object detection and classification (YOLOv5). We find, that with decreasing strain rate the dominant damage mechanism for a given strain level changes: at a strain rate of 5•10‐4/s, the evolution of microcracks in the C36 Laves phase dominates damage formation. However, when the strain rate is decreased to 5•10‐6/s, interface decohesion at the α‐Mg/Laves phase interfaces becomes equally important. We also observe a change in crack orientation, indicating an increasing influence of plastic co‐deformation of the α‐Mg matrix and Laves phase. We attribute this transition in the leading damage mechanism to thermally activated processes at the interface.This article is protected by copyright. All rights reserved.

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Damage Analysis in Dual-Phase Steel Using Deep Learning: Transfer from Uniaxial to Biaxial Straining Conditions by Image Data Augmentation

Cited by 15 publications

References 22 publications

A Training Method for a Sensor-Based Exercise Rehabilitation Robot

A Training Method for a Sensor-Based Exercise Rehabilitation Robot

A Machine Learning Enabled Image‐data‐driven End‐to‐end Mechanical Field Predictor For Dual‐Phase Steel

Determination of the Rate Dependence of Damage Formation in Metallic‐Intermetallic Mg–Al–Ca Composites at Elevated Temperature using Panoramic Image Analysis

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