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DE-102023104595-B4 - Digital-physical testing system and digital-physical testing procedure

DE102023104595B4DE 102023104595 B4DE102023104595 B4DE 102023104595B4DE-102023104595-B4

Abstract

Comprehensive digital-physical testing system: a simulation device (1) comprising a numerical calculation model for digital modeling the static crack tip stress of a cracked component and a test device (2) for the physical testing of the dynamic damage and crack propagation behavior of a cracked test specimen (10); wherein the test device (2) comprises a testing machine (20) with a clamping device (21) for fixing and dynamically loading a cracked test specimen (10) by means of the testing machine (20) and a detection device (3) for detecting and tracking at least one cracked area of the test specimen (10), the testing system is trained to automatically execute the following process steps: a) Initial calculation of parameters of the crack tip stress of the cracked component via the simulation device (1) under specification of an initial crack geometry a initial of the component, acting external loads F on the component and the time course of the acting loads ΔF; b) Applying a number of load cycle increments N to the cracked test specimen (10) by specifying the calculated parameters of the crack tip stress from step a) and a load cycle number N using the testing machine (20) and measuring the changed crack geometry a N , comprising the crack path geometry as well as the crack tip coordinates a x and a y , on the test specimen (10) using the detection device (3) as a crack progression increment due to the applied load cycle increment; c) Introducing the modified crack geometry a N from step b) into the numerical calculation model of the overall component and subsequently calculating parameters of the crack tip stress of the cracked overall component with the modified crack geometry a N via the simulation device (1) given the external loads F acting on the system and the time course t of the loads acting on it ΔF; and d) Optional repetition of process steps b) and c) by any integer number of repetitions, wherein in step b) the calculated parameters of the crack tip stress from the previous step are adopted and wherein in step c) the modified crack geometry a N from the previous step is adopted and introduced to determine a progressive fatigue crack.

Inventors

  • Eric Breitbarth
  • Tobias Strohmann

Assignees

  • Deutsches Zentrum für Luft- und Raumfahrt e.V.

Dates

Publication Date
20260513
Application Date
20230224

Claims (12)

  1. Digital-physical testing system comprising: a simulation device (1) comprising a numerical calculation model for the digital modeling of the static crack tip stress of a cracked component and a test device (2) for the physical testing of the dynamic damage and crack propagation behavior of a cracked test specimen (10); wherein the test device (2) comprises a testing machine (20) with a clamping device (21) for fixing and dynamically loading a cracked test specimen (10) by means of the testing machine (20) and a detection device (3) for detecting and tracking at least one cracked area of the test specimen (10), wherein the testing system is configured to automatically perform the following process steps: a) Initial calculation of parameters of the crack tip stress of the cracked overall component via the simulation device (1) specifying an initial crack geometry a initial of the overall component, acting external loads F on the overall component and the time course of the acting loads ΔF; b) Applying a number of load cycle increments N to the cracked test specimen (10) by specifying the calculated parameters of the crack tip stress from step a) and a number of load cycles N using the testing machine (20) and measuring the modified crack geometry a N , comprising the crack path geometry as well as the crack tip coordinates a x and a y , on the test specimen (10) using the detection device (3) as a crack propagation increment due to the applied load cycle increment; c) Introducing the modified crack geometry a N from step b) into the numerical calculation model of the overall component and subsequently calculating parameters of the crack tip stress of the cracked overall component with the modified crack geometry a N using the simulation device (1) under specification of acting external loads F and the time course t of the acting loads ΔF; and d) Optional repetition of process steps b) and c) by any integer number of repetitions, wherein in step b) the calculated parameters of the crack tip stress from the previous step are adopted and wherein in step c) the modified crack geometry a N from the previous step is adopted and introduced to determine a progressive fatigue crack.
  2. Test system according to Claim 1 , wherein the simulation device (1) is designed to calculate parameters of the crack tip stress for a stationary crack depending on external loads F acting on the overall component, the crack geometry a and the time course of the acting external loads ΔF.
  3. Test system according to Claim 1 or 2 , wherein the parameters to be calculated for the crack tip stress are selected from at least one element from the group of: - the stress intensity factors K I ,K II ,K III , - the T-stress, and/or - higher order terms of the Williams series, - the stresses σ x ,σ y ,τ xy .
  4. Testing system according to one of the preceding claims, wherein the testing machine (20) is configured to impose a mixed-mode load on the test specimen (10) or to introduce defined stresses parallel and perpendicular to an introduced crack in the test specimen (10) via a biaxial load application.
  5. Testing system according to one of the preceding claims, wherein a standardized test specimen is subjected to the crack tip load in the testing machine (20).
  6. Test system according to one of the preceding claims, wherein the test specimen (10) represents the cracked section of the overall component.
  7. Testing system according to one of the preceding claims, wherein the test device (2) is configured to perform the following further sub-process steps in process step b): b1) Generating at least one first macroscopic reference image (30) using a first stationary image acquisition device (31) and a plurality of microscopic reference images (50) using a second positionable image acquisition device (5) of the unloaded cracked test specimen (10); b2) Applying a load or load regime to the cracked test specimen (10); b3) Generating at least one macroscopic image (30) of the cracked test specimen (10) under applied load according to process step b2) and/or after unloading via the first stationary image acquisition device (31); b4) Calculating the current localization of at least one region of a crack tip in the test specimen, preferably using a trained neural network for crack detection; b5) preferably aligning the second positionable image acquisition device (5) based on the at least one region of a crack tip in the test specimen (10) according to process step b4) and generating at least one microscopic image (50) of the at least one region of a crack tip in the test specimen (10) using the second image acquisition device (5) under applied load according to process step b2) and/or after unloading; and b6) optionally repeating process steps b2) to b5) by any integer number of repetitions, wherein in process step b2) a load or load regime different from the previously applied load is applied.
  8. Test system according to Claim 7 , wherein the trained neural network for crack detection comprises a convolutional neural network (CNN) with a U-network architecture, including multiple encoding and decoding blocks as the segmentation branch; and a fully connected neural network (FCNN) as the regression branch; wherein the decoding blocks are connected to the encoding blocks via a base block, the encoding and decoding blocks of the same level are directly connected by a jump link, the regression branch is connected to the base block of the segmentation branch, and wherein the computer-implemented training procedure for maintaining the trained neural network for damage detection comprises the steps of: 1) initializing the neural network with random weights; 2) calculating the mean square error between the prediction of the crack tip position and the actual crack tip position ŷ = ( ŷ₁ , ŷ₂ ) ∈ [-1, 1] ² using the formula: MSE ( y , y ^ ) = ( y 1 − y ^ 1 ) 2 + ( y 2 − y ^ 2 ) 2 as an error of the regression branch of the neural network; 3) Calculating the crack tip segmentation error (Dice error) for the segmentation task using the formula: Dice ( z , z ^ ) = 1 − 2 ∑ i j z i j z ^ i j + ε ∑ i j ( z i j + z ^ i j ) + ε where z = (z ij ) with z ij ∈ [0,1] denotes the segmentation output after sigmoid activation and ẑ = (ẑ ij ) represents the basic truth. With ε > 0 as a small constant introduced to cover the case z = ẑ ≡ 0, where ε = 10⁻⁶ is preferably chosen as the error of the segmentation branch of the neural network; 4) Calculating a weighted total loss function Loss ω (z,y,ẑ,ŷ) using the formula: Loss ω ( z , y , z ^ , y ^ ) = Dice ( z , z ^ ) + ω MSE ( y , y ^ ) where ω ≥ 0 is a weighting factor that adjusts the training influence of the FCNN/segmentation branch of the neural network; 5) Optimizing the model parameters of the neural network using a backpropagation algorithm utilizing the weighted total loss function Loss ω (z,y,ẑ,ŷ) to maintain the trained neural network for damage detection.
  9. Test system according to Claim 7 or 8 , wherein the sub-procedure prior to procedural step b2) further comprises the step: b1-1) Calibrating the positioning of the second image acquisition device (5) by aligning the current spatial position of the second image acquisition device (5) relative to the first stationary image acquisition device (31).
  10. Test system according to one of the Claims 7 until 9 , wherein in process step b1) macroscopic reference images (30) of the entire unloaded cracked test specimen (10) are taken using a first stationary image acquisition device (31) and a plurality of microscopic reference images Images (50) of the entire unloaded test specimen (10) are generated using the second positionable image acquisition device (5).
  11. Test system according to one of the Claims 7 until 10 , wherein the procedure in step b5) additionally includes the step: b51) optically focusing the second image acquisition device (5) on the area of the damage or the crack tip.
  12. Digital-physical testing method comprising the following process steps: a) Initial calculation of parameters of the crack tip stress of the cracked component via a simulation device (1) comprising a numerical calculation model for the digital modeling of the static crack tip stress of a cracked component, specifying an initial crack geometry a initial of the component, acting external loads F on the component and the time course of the acting loads ΔF; b) Applying a number of load cycle increments N to a cracked test specimen (10) using a testing machine (20) by specifying the calculated parameters of the crack tip stress from step a) and a number of load cycles N and measuring the altered crack geometry a N , comprising the crack path geometry as well as the crack tip coordinates a x and a y , using a detection device (3) for detecting and tracking at least one cracked area on the test specimen (10) as a crack propagation increment due to the applied load cycle increment; c) Introducing the altered crack geometry a N from step b) into the numerical calculation model of the overall component and subsequently calculating parameters of the crack tip stress of the cracked overall component with the altered crack geometry a N via the simulation device (1) by specifying acting external loads F and the time offset t of the acting loads F ; and d) Optional repetition of process steps b) and c) by any integer number of repetitions, wherein in step b) the calculated parameters of the crack tip stress from the previous step are adopted and wherein in step c) the modified crack geometry a N from the previous step is adopted and introduced to determine a progressive dynamic crack behavior.

Description

The present invention relates to a digital-physical testing system and a digital-physical testing method. When inspecting and monitoring structural components and parts subjected to non-constant loads during operation, it is essential to ensure sufficiently accurate detection and tracking of defects, damage, and cracks. Fatigue cracking, or rather the propagation of fatigue cracks, is one of the most relevant types of damage in all technical fields where components and parts are subjected to the aforementioned non-constant loads, and especially dynamic and alternating loads. Particularly in lightweight structures, such as those found in aircraft, the occurrence of fatigue cracks is a critical safety aspect that must be considered during the design phase of components. In material mechanics tests for characterizing cracks, particularly for investigating thresholds, crack propagation, fracture toughness, and crack resistance curves, the cracks must be recorded and measured very precisely in terms of their length and orientation. Such tests are conducted at the coupon level with specimen sizes ranging from 1 to 500 mm. It is also known from the prior art to test entire aircraft structures. The aforementioned "full-scale" tests are extremely time-consuming and expensive in practice. Therefore, they should be reduced to the absolute minimum or, where possible, replaced entirely. Within the framework of faster and increasingly virtual approval processes, it is a set goal to reduce the number of structural level trials (“Large-Scale” or “Full-Scale” trials) to the necessary minimum. Due to the time-consuming and costly nature of large-scale and full-scale tests, the development and approval processes for components and component structures, such as aircraft structures, are very lengthy and require extended development times. Furthermore, replacing these large-scale and full-scale tests, particularly regarding fatigue crack propagation, necessitates comprehensive material models that also incorporate the interaction of local crack tip mechanics and the microstructure. Pure simulation, e.g., using the finite element method according to the state of the art, is often insufficient because the complex structure-property relationships at the microscopic scale around a crack tip can only be partially represented by numerical models, thus not always guaranteeing or achieving the necessary level of safety. Out of STROHMANN, Tobias [et al.]: Automatic detection of fatigue crack paths using digital image correlation and convolutional neural networks. In: Fatigue & Fracture of Engineering Materials & Structures, Vol. 44, 2021, No. 5, pp. 1336-1348 A deep convolutional neural network has been developed to segment crack paths and their respective crack tips from displacement fields obtained via digital image correlation during fatigue crack propagation experiments. The neural network was trained to predict the coordinates of the crack paths and crack tips based on the displacement fields around the fatigue crack. The digital image correlation data used for training were augmented with data from finite element calculations to increase the dataset. Melching, David [et al.]: Explainable machine learning for precise fatigue crack tip detection. In: Scientific Reports, Vol. 12, 2022, Art.No. 9513, pp.1-4 describes a novel architecture of a neural network that combines segmentation and regression of crack tip coordinates. Based on the aforementioned prior art, the present invention aims to simplify the methods or testing systems for characterizing fatigue and/or cracking processes in components or component structures and to significantly reduce the required time expenditure, while simultaneously ensuring the necessary accuracy and reliability of the methods and testing systems. According to the invention, the problem according to a first aspect of the invention is solved by a digital-physical testing system, in particular for material mechanics testing. The digital-physical testing system according to the invention comprises a simulation device including a numerical calculation model for digitally modeling the static crack tip stress of a cracked component and a test device for physically testing the dynamic damage and crack propagation behavior of a cracked specimen. The test device includes a testing machine with a clamping device for fixing and dynamically loading the cracked specimen by means of the testing machine and a detection system for detecting and Tracking of at least one cracked area of the test specimen. The testing system is designed to automate the following process steps: a) Initial calculation of parameters of the crack tip stress of the cracked component using the simulation device, specifying an initial crack geometry a initial of the component, acting external loads F on the component and the time course of the acting loads ΔF; b) Applying an initial number of load cycles to the cracked test specimen by specifyi