EP3622284A1 - Verfahren zur vorhersage von versagenslasten von strukturen aus faserverbundwerkstoffen auf basis von schallemissionsdaten - Google Patents
Verfahren zur vorhersage von versagenslasten von strukturen aus faserverbundwerkstoffen auf basis von schallemissionsdatenInfo
- Publication number
- EP3622284A1 EP3622284A1 EP18728046.6A EP18728046A EP3622284A1 EP 3622284 A1 EP3622284 A1 EP 3622284A1 EP 18728046 A EP18728046 A EP 18728046A EP 3622284 A1 EP3622284 A1 EP 3622284A1
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- EP
- European Patent Office
- Prior art keywords
- criteria
- ratio
- load
- calculated
- determined
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
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Classifications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
- G01N29/44—Processing the detected response signal, e.g. electronic circuits specially adapted therefor
- G01N29/449—Statistical methods not provided for in G01N29/4409, e.g. averaging, smoothing and interpolation
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
- G01N29/14—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object using acoustic emission techniques
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
- G01N29/44—Processing the detected response signal, e.g. electronic circuits specially adapted therefor
- G01N29/4472—Mathematical theories or simulation
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2291/00—Indexing codes associated with group G01N29/00
- G01N2291/02—Indexing codes associated with the analysed material
- G01N2291/023—Solids
- G01N2291/0231—Composite or layered materials
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2291/00—Indexing codes associated with group G01N29/00
- G01N2291/02—Indexing codes associated with the analysed material
- G01N2291/025—Change of phase or condition
- G01N2291/0258—Structural degradation, e.g. fatigue of composites, ageing of oils
Definitions
- the present invention relates to a method for predicting failure loads of structures of fiber composites based on sound emission data, its use and a use of a data carrier with a Schallemissi- onsprüfSystem in the method.
- Acoustic emissions are elastic waves released by jerky micro-deformations inside a solid. These arise, for example, through the formation of a crack.
- the elastic waves propagate as acoustic wave in the solid state and can be detected on the surface with ⁇ means of piezoelectric sensors. These convert the sound wave into an electrical voltage signal, which is used for further interpretation.
- the material class of the fiber composites is characterized by a particularly high sound emission activity. Numerous microscopic fracture processes are triggered well before the final failure of the material under load, which can be recorded as Schallemis ⁇ sion. Thus, a generation of damage may already Sanderszei ⁇ tig detected in the material advertising the.
- a structure or a specimen is subjected to a continuous load increase or continuously increasing load.
- the sound emission signals are recorded at a uniform (quasi-static) load of the material.
- the damage in the material is recorded as a function of the external load.
- Modifications of the methodology consist in not subjecting a structure or a specimen to a cyclical or incremental increase in load or load on several occasions. pull.
- the material is loaded with so-called load increase tests.
- load increase tests for example ASTM E 1067
- the calculation of the "felicity ratio" or the "felicity ratio” is used in particular as a measure of the degree of damage of the component.
- the Felicity ratios can be as follows de ⁇ finishing:
- the Felicity ratio thus drops during the cycle-or step-wise ⁇ load continues to drop until it comes at a critical value for failure, as among other things, Abraham, ARA, Johnson, KL, Nichols, CT., Saulsberry, RL , Waller, JM: "Use of Statistical Analysis of Acoustic Emission Data on Carbon-Epoxy COPV Materials-of-Construction for Enhanced Felicity Ratio Onset Determination," JSC-CN-26080, 2011, and Waller, JM, Nichols, CT. Wentzel, DJ, Saulsberry, RL, Thompson, DO, Chimenti, DE: "Use of Modal Acoustic Emission to Monitor Damage Progression in Carbon Fiber Epoxy Composites", AIP Conference Proceedings, pp.
- a sinking Felicity ratio clearly indicates that the first use of the sound emission relative to the previous load level takes place earlier and earlier. This is due to the increasing number of micro-damages, which are spreading earlier and leading to the final failure.
- the present invention is therefore based on the object of providing a method for predicting failure loads of structures made of fiber composite materials on the basis of acoustic emission data, with which the above disadvantages can be prevented, which consequently has a very simple stabilization and significant increase Predictive accuracy and predictive reliability allows to provide its use and use of a data carrier with a sonic emission test system in the method.
- L max maximum load for forward extrapolation and prediction of the failure load of the structure
- the inventive method allows in a very simple way a prediction of failure loads of structures or specimens or the like components or components of fiber composites based on sound emission data.
- the method according to the invention is characterized in a special way by a significant stabilization of the procedure itself.
- forecasting accuracy and Progno ⁇ sezuver interkeit by the inventive method can increase materiality ⁇ Lich.
- the method according to the invention differs from all the methods known hitherto in that the prediction is based not only on a single criterion or a single parameter, but simultaneously on at least two criteria or parameters or more. This makes it possible for the first time ever for complex fiber composite structures, derive a secure prediction from acoustic emission data and watch the necessary stability for a high forecast accuracy before ⁇ . Further advantageous details of the method according to the invention are described in claims 2 to 11.
- the at least two criteria are determined and calculated based on one or more of the Felicity Ratio and / or the Shelby Ratio and / or an Energy Ratio or Energy Based Ratio.
- the at least two criteria based on the felicity ratio according to claim 4 based on the first use of the significant acoustic emission in particular the absolute first use L a k s , the average power ⁇ reference value of the first N recorded signals L ⁇ N , of the results / s of a trend analysis of the accumulated quantity Ltrend or the history index L ⁇ j , are determined and calculated.
- the at least two criteria are advantageously based on the shelby ratio on the basis kri ⁇ tica first use, in particular a certain number of signals, a certain accumulated energy value, a certain accumulated amplitude, the result of a trend analysis of the accumulated size or History Index L ⁇ j , be ⁇ correct and calculated.
- the at least two criteria according to claim 6 based on the energy ratio or energy-based ratio, which are directly or indirectly related to the intensity of the acoustic emission signals, determined and calculated.
- the at least two criteria according to claim 7 are preferably determined and calculated on the basis of the signal amplitude, the signal energy, the average amplitudes, their derived signal quantities or the like.
- the at least two criteria of claim 8 may also be determined and calculated based on the number of threshold transgressions, spectral intensity, or the like.
- the at least two criteria according to claim 9 are selected on the basis of a list of criteria given in DIN standards DIN EN 15857 and / or EN1330 and then calculated. This ensures ⁇ that all indirect signal parameters, such as number of threshold crossings, spectral intensity, etc., as the at least two criteria are used for performing the method according to the invention in an advantageous manner and can be used. Specifically, in the To ⁇ connexion to the DIN standard EN1330 on "Non-destructive testing - Part 9 - Terminology: Terms used in acoustic emission testing," Triangular version EN 1330-9: 2009 refers).
- the multivariant data analysis with the at least two determined and calculated criteria according to step b) by means offorensicsap ⁇ proximation per cycle according to claim 10 in the form of, in particular single or multilayer, artificial, neural Networks, Hidden Markov Models, Gaussian Mixture Models, Deep Learning Models or Markov Chain Monte Carlo Methods becomes .
- the criteria per cycle can be determined without knowledge of the maximum load values that can be reached when attempting and normalized regardless of the structure size of the / the examined structure or specimen or the like component defines ,
- the inventive method according to claim 12 for the prediction of failure loads of structures made of fiber composites on the basis of sound emission data, in particular of structural components in the automotive industry, structural components of aerospace engineering and drives and machine parts with high speeds, preferably in vehicles, aircraft or aircraft aerospace, aircraft and spacecraft, vessels such as submarines or hovercrafts, land vehicles, passenger cars and lorries, use.
- the method according to the invention is also suitable for the prediction of versa ⁇ genslasten of particular safety-relevant structures made of fiber composites.
- FIGS. 1A and 1B show illustrations of load profiles for
- Fig. 2 is a representation of a load profile for Defini ⁇ tion of Felicity ratio
- Fig. 3 is an illustration of a load profile for the determina tion of the critical ⁇ Felicity ratio
- Fig. 4 is an illustration of a load profile for defini ⁇ tion of the Lease dikriterien Felicity ratio
- Fig. 6 illustrates a load profile for defini ⁇ tion of Energy Ratio or Ratio energy based
- Fig. 7 is a diagram for forecasting failure load according to the inventive method
- FIG. 8 is an illustration for predicting the failure load by forward extrapolation according to a known method
- FIG. 9A and 9B are schematic representations of specimen geometries including AE sensor positions for tensile tests and Lochleibungs Listee
- Fig. 10A and 10B examples of a loading scheme in the train ⁇ trial and for the Lochleibungstest including about ⁇ superimposed accumulated acoustic emission signals
- FIGS. 12A to 12C calculate the Felicity criteria for the
- FIG. 13A and 13B extrapolation of the calculated load level to predict the strength of tensile test specimen 2 and hole ⁇ leibungsprobe 3,
- FIG. 14 is a forward prediction of the train samples with different approaches to the composition of the training data of a neural network; and FIG. 15 Forward prediction of the bearing experiences with different approaches to the composition of the training data of a neural network.
- the method according to the invention is preferably used to predict failure loads of structures of fiber composite materials of any type and / or in particular safety-relevant structures made of fiber composite materials based on acoustic emission data, in particular of structural components in the automotive industry, structural components of aerospace engineering and drives and machine parts with high rotational speeds, preferably in vehicles, aircraft or aircraft of aerospace, airplanes and spacecraft, watercraft, such as Submarines or hovercraft, land vehicles, passenger cars and lorries.
- the sound emission signals are recorded at a uniform (quasi-static) load of the material.
- FIG. 1B schematically shows a structure or a test body which is not subjected to a cycle or stepwise load increase or load, not once but several times.
- the felicity ratio continues to decrease until it fails at a critical Felicity ratio, as shown in FIG.
- the method according to the invention provides a substantial supplement to the manner of predicting or predicting failure loads of structures or test specimens made of fiber composite materials on the basis of sound emission data.
- An essential to the invention feature consists in determining Wenig ⁇ th two criteria K, which define an event occurring in the cycle or stepwise loading degree of damage to the structure from the detected acoustic emission signals and to calculate, which then cooperate in their combination, and in this way lead to a significant increase in forecasting accuracy.
- the criteria K used have in common that they have a characteristic trend as a function of the external load. In order to be suitable for a forward prognosis according to the invention, it is necessary that: a) The criteria K per cycle can be determined, ie without knowledge of the maximum load values achieved in the experiment. b) The criteria K are defined in a normalized manner so that they are independent of the structure size of the examined structure or test piece or the like component.
- a criterion K is, for example, the Felicity ratio or the Felicity ratio, as defined in equation (1) and illustrated in FIG. 2.
- determining and calculating the Felicity ratio it is irrelevant how the first use is determined.
- technical documents for example ASTM E 1067
- ASTM E 1067 it is pointed out that the first use of "significant" acoustic emission is to be determined.
- FIG. 4 shows further known variants of the Felicity ratio.
- / may be the absolute only ⁇ use L a k s, the average force reference value of the first N up ⁇ recorded signals L ⁇ N, the / the results / it has a trend analysis of the accumulated size Ltrend (inflection point of the curve in the initial ⁇ areas) and the determination by the history index L j ⁇ j ent ⁇ speaking definition (see. eg DIN EN 15857) to be.
- the Shelby ratio or the Shelby ratio can be used as a further criterion K in the sense of the invention.
- the Shelby ratio considers the sound emission when relieving the structure or specimen or the like. Will star ⁇ tend in the plateau of the stress cycle a certain number of signals passed ⁇ crit, this is defi ⁇ ned as a critical first use.
- the relative load drop AL at this time can then be used in the sense of equation (1).
- L onSi 2 AL can be set.
- K can be used as yet another criterion K and can be used as further criteria K example, the Energy Ratio / s or the / the Energy ratio / se or energy- ⁇ -based / n or energy / n ratio / se used in the context of the invention.
- accumulated signal parameters are considered which have a direct or indirect relation to the signal intensity.
- the primary or direct signal parameter of this kind include the signal amplitude (the maximum voltage at the sensor per wave), the signal energy (integrated squared Sig ⁇ nalhard per wave), the mean amplitudes (root mean squared signal voltage "Root Mean Square” per wave train) and / or their derived signal quantities (in dB).
- no criteria within the meaning of the invention are: a) All criteria K which use absolute reference quantities in equation (1) (for example, number of signals at a given load value). b) All criteria K, which for their calculation require the maximum load value for failure of the component or structure in the experiment. c) All criteria K, which for their calculation require the maximum value of a sound emission parameter in the experiment. d) All criteria K, which for their calculation require the maximum accumulated value of a sound emission parameter in the experiment.
- Another essential feature of the present invention be ⁇ is on the use or application of an approach of the multi- tivisionn data analysis and performing the at least two predetermined and calculated criteria K.
- Using a neural network could thus contribute to ⁇ a sufficient amount of data for training the nodes aid of a prediction accuracy of less than 5% with an uncertainty of only 8% can be achieved.
- This approach could also be transferred from the material level (ie laboratory test specimen) to the structural level (ie component with a length of 4 m).
- the cross prediction (50% training data, 50% validation data) showed very high accuracy. Moreover, ⁇ same neural network for acoustic emission data of a major component (structural level) could be applied. ⁇ worth the forecast gave only 5% or more of the generated failure load. The approach can be applied to comparable questions at any time. The inventive method will be explained in more detail with reference to ver ⁇ different embodiments.
- thermoplastic composites of Torayca T700S 12k carbon fibers and PPS matrix material were used.
- two different types of mechanical tests were performed in load / unload cycles.
- data from tensile tests and bearing experiments were used.
- test geometries for Lochleibungs were used.
- Fig. 9A one of the test specimen geometries is shown.
- test volumes and statistics are given in Tab. 1, including the average accumulated acoustic emission energy of each test condition.
- the number of acoustic emission sensors was chosen based on sample attenuation measurements to ensure equal sensitivity in each configuration. The following briefly describes the test conditions of each configuration. All samples were tested under standard climatic conditions at 23 ° C temperature and 50% relative humidity.
- test laminates were prepared in unidirectional array using in-situ laser consolidation. All samples were cut to the dimensions of 250 mm ⁇ 15 mm x 1 mm (length ⁇ width ⁇ thickness) with the fiber axis direction parallel to the longitudinal direction of the sample (see Fig. 6-b).
- the room temperature curable Stycast 2850 FT Adhesive System was used to add specimens and doublers with ( ⁇ 45 ° layers) stick together.
- the specimens were tested by means of travel control with a test speed of 2 mm / min according to DIN EN 2561 with a universal testing machine with 250 kN load cell and hydraulic clamping tools with complete loading and unloading cycles. Stress increments were chosen to be 200 MPa with a relief up to 50 MPa tensile stress at a test speed of 10 mm / min.
- bearing samples with dimensions 108.0 mm ⁇ 54.0 mm x 5.7 mm (length ⁇ width ⁇ thickness) with a pin diameter of 9.0 mm and a scaled version with 216.0 ⁇ 108.0 ⁇ 11.4 mm ( Length x width ⁇ thickness) with pin diameter 18.0 mm (both geometries see Fig. 9B) used.
- the load was initiated with a device in the sense of DIN EN 6037 (type 2 configuration). However, both bolts have the same diameter, so that failure can occur on both sides of the laminate.
- the layer sequence uses the quasi-isotropic configuration (0, +45, 45, 0, 90) 4 S ym for the normal size and (0, +45, -45, 0, 90) 8 Sy m for the scaled size.
- the test was carried out in the controlled-path mode at a test speed of 2 mm / min using a universal testing machine with a 250 kN load cell.
- the cycle is a gradual increase by 40 MPa until failure with discharge to 20 MPa voltage as the lower limit.
- acoustic emission sensors were mounted on the sample with suitable clamp systems to ensure a reproducible contact pressure between the sensor and the sample.
- the acoustic coupling agent used was viscous co-rasilone silicone grease. All signals were amplified by 20 dB with a 2/4/6 preamplifier and recorded using the AEwin software with 10 MSP / s sampling rate and 35 dBAE threshold. For all configurations, a band pass filter of 20 kHz to 1 MHz used. In all cases, the trigger settings were chosen to be 10 ⁇ is for the peak definition time and 80 ⁇ is for the hit definition time.
- the hit-lockout time was empirically adjusted for each test case to avoid recording the same event multiple times.
- the ver ⁇ values used be 1500 ⁇ is the tensile specimens and 10,000 ⁇ is for Lochleibungsproben.
- two acoustic emission sensors were used in a linear array (see Fig. 9A).
- an event definition time filter was used. The settings for this filter have been customized for each sample to avoid detecting sources outside the tapered area.
- only signals localized in the tapered region by a classical At-based algorithm were considered.
- five acoustic emission sensors having the geometrical arrangement shown in Fig.
- Felicity- Criteria are defined as the relative proportions of 5%, 10%, 15% and 20% of the total number of Tref ⁇ fer during the rising portion of the cycle. This will be referred to below as FR5, FR10, FR15 and FR20.
- the average Felicity ratio is defined as the arithmetical mean of these values ULTRASONIC ⁇ :
- ⁇ FR> 1/4 (FR5 + FR10 + FR15 + FR20) (3) Therefore, a total of five different Felicity criteria are evaluated for each load cycle.
- the felicity ratio data are shown in Figure HA, where the values of the mean felicity ratio ⁇ FR> are linked by straight lines for better visibility.
- the FR5 starts first in each cycle, followed by FR10, FR15 and FR20, which can be seen in the graph as a systematic shift of the numerical values from low to high.
- the total number of sound emission hits is so low that all FR values are evaluated identically because individual cascades of acoustic emission hits occurred at practically the same load level.
- ⁇ FR> acts as the arithmetic mean of the other FR values.
- the representation of the Shelby criterion follows the same gedan ⁇ ken, and is shown in FIG. HB.
- the Shelby criteria are defined as relative proportions for 95%, 90 "6 85" 6, and 80% of the total number of signals during the falling part of the cycle. This will be referred to below as SR95, SR90, SR85 and SR80.
- Figs. 12A to 12C An example of the calculated criteria for a bedding sample is shown in Figs. 12A to 12C.
- Fig. 12A the last cycle shows the sensitivity of the Felicity criterion when based on a small number of acoustic emission signals as a first use.
- the first sound emission ⁇ signals appear already at 20% of the previous load, whereas the FR10, FR15 and FR20 show that the primary use of the scarf ⁇ lemission is still above 50%.
- the example of the bearing experience is the result of sample 3, which has less scattering.
- the deviation between the predicted load and the measured load is only 2.3%.
- the measured resistance is shown as a black rhombus, and the dashed black line indicates the actual linear relationship between the attached ⁇ stored voltage and LR values.
- Both cases are predictive Examples for the approaches described in the following section 3.
- NEN approaches using an artificial neural network are summarized in Figures 14 and 15 for the test configurations.
- FIGS. 14 and 15 show the measured strength value as a solid gray horizontal line as a reference.
- This approach is a simple test case because only identical geometries and test settings are considered. For this strength prediction, only training data from the same species is considered and all six samples are used as a database. The purpose of this approach is to use only one artificial neural network with 2 hidden layers and 5 neurons so that the predictive quality can be directly compared within a range of species. However, this is not seen as "real" prediction, as the item to predict sample hold ent ⁇ already in the training data set, which is not a good practice. The resulting pre ⁇ hersagehong are as black squares in FIGS. 14 and 15, wherein the ExtrapolationsunPart is added as error bars.
- an upper limit for the Extrapolationsbasis of LR is selected ⁇ 0.85, which means that the minimum extrapolation is 15% of the maximum tensile ⁇ ACTION the samples.
- the exact limit is specific for each sample but ranges from 0.80 ⁇ LR ⁇ 0.85.
- the predicted LR values could be tailored to their real LR values with a higher number of artificial neurons and a higher number of hidden layers. However, this would be considered overfitting, which is generally to be avoided when using artificial neural networks. It was therefore the same neural network structural ⁇ structure as in the following section 2. approach described used to obtain a representative result for this part of the study. Accordingly, the agreementccige ⁇ said values agree with the real strength values pretty good. Within the error margin, all strength values of the samples are predicted. However, the exact prediction value as well as the size of the error bars appear unique for each sample since this is mainly due to the variability of the available data points. This proves that the prediction concept is basically feasible.
- the training data of all samples and all load cases are mixed and only the data points of the sample to be predicted are removed. Therefore, another 12 artificial neural networks are trained with 2 hidden layers and 10 neurons each and their results are shown in Figs. 14 and 15 as black triangles with prediction uncertainty as error bars and LR ⁇ 0.85 as extrapolation boundary.
- the training database is 11 samples.
- the mixed training data set is able to predict the behavior well in both configurations.
- this value is interpreted as the load value of the initial failure of the bolts (sinking in), ie the first significant damage formation in the composite material at the contact surface between bolt and laminate. This can easily be understood as a failure.
- the corresponding mean prediction uncertainty is 8.0% for the tensile tests and 15.8% for the bearing experiments.
- the invention is not limited to the illustrated embodiments. Without being shown in detail, which he ⁇ -making proper procedures for any type of Faserver ⁇ composite structures can be used.
- the inventive method can thereby readily and / or more preferably chen in Berei-, are used in which safety-relevant fiber composite structures (for example, structural components in automotive ⁇ construction, all of the components in the aircraft industry, machine parts at high speeds, etc.) are applied. These are usually subjected to extensive (non-destructive) tests for acceptance.
- An alternative is to apply them specifically with ih ⁇ ren operating loads while monitoring methods such as acoustic emission analysis applied.
- the inventive method is thus suitable for product-specific development ⁇ cycles, for example, to optimize components without burdening them during the test in the destructive area. In both cases, the inventive method leads to considerable Kos tenersparnissen ⁇ or a significant increase in product safety in an approximately comparable to the prior art screen.
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Abstract
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DE102017110228.0A DE102017110228A1 (de) | 2017-05-11 | 2017-05-11 | Verfahren zur Vorhersage von Versagenslasten von Strukturen aus Faserverbundwerkstoffen auf Basis von Schallemissionsdaten |
PCT/EP2018/062219 WO2018206770A1 (de) | 2017-05-11 | 2018-05-11 | Verfahren zur vorhersage von versagenslasten von strukturen aus faserverbundwerkstoffen auf basis von schallemissionsdaten |
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CN110824018B (zh) * | 2019-11-12 | 2022-05-27 | 宁波市劳动安全技术服务公司 | 常压储罐风险评估及安全检测评价方法 |
CN112394702B (zh) * | 2020-12-10 | 2024-05-17 | 安徽理工大学 | 基于lstm的光缆制造设备故障远程预测系统 |
CN113566953B (zh) * | 2021-09-23 | 2021-11-30 | 中国空气动力研究与发展中心设备设计与测试技术研究所 | 一种柔壁喷管的在线监测方法 |
CN114778270B (zh) * | 2022-04-20 | 2024-06-25 | 西北核技术研究所 | 一种预测热环境下材料力学失效的实验方法及实验系统 |
DE102022211558A1 (de) | 2022-11-01 | 2024-05-02 | Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung eingetragener Verein | Hybrides Prüfverfahren, insbesondere für einen Antriebsstrang von Windenergieanlagen |
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DE102015101942A1 (de) * | 2015-02-11 | 2016-08-11 | Dr. Ing. H.C. F. Porsche Aktiengesellschaft | Verfahren zur Prüfung eines aus einem Faserverbundwerkstoff hergestellten Bauteils |
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