Reliability assessment of measurement accuracy for FBG sensors used in structural tests of the wind turbine blades based on strain transfer laws
ارزیابی قابلیت اطمینان از دقت اندازه گیری سنسورهای FBG مورد استفاده در تست های ساختاری تیغه های توربین بادی بر اساس قوانین انتقال فشار-2020
FBG sensors are often packaged within composites before they are pasted on the blade surface, and many studies have shown that the materials, fatigue properties, geometric parameters, etc. of intermediate layer have influences on the measuring accuracy of the FBG sensors. Thus, this paper established an reliability calculation model based on strain transfer efficiency for the measuring accuracy of FBG sensors packaged by composites, analyzed the influences of material properties and geometric parameters of the adhesive layer on the performance of FBG sensors based on finite element analysis (FEA) method, and then compared the differences of strain transfer efficiency and reliability of the FBG sensors under different load conditions. The results show that the bond length and the bond thickness of the adhesive layer have greater influences on the performance of the FBG sensors compared with other parameters, both the strain transfer efficiency and the reliability of the FBG sensors will reduce over time under suddenly applied load and increase with increasing frequency of the alternating load.
Keywords: FBG sensors | Reliability assessment | Strain transfer law | Static load | Suddenly applied load | Alternating load
Fatigue life prediction of metallic materials considering mean stress effects by means of an artificial neural network
پیش بینی طول عمر خستگی مواد فلزی با توجه به میانگین اثرات استرس با استفاده از شبکه عصبی مصنوعی-2020
The mean stress effect plays an important role in the fatigue life predictions, its influence significantly changes high-cycle fatigue behaviour, directly decreasing the fatigue limit with the increase of the mean stress. Fatigue design of structural details and mechanical components must account for mean stress effects in order to guarantee the performance and safety criteria during their foreseen operational life. The purpose of this research work is to develop a new methodology to generate a constant life diagram (CLD) for metallic materials, based on assumptions of Haigh diagram and artificial neural networks, using the probabilistic Stüssi fatigue S-N fields. This proposed methodology can estimate the safety region for high-cycle fatigue regimes as a function of the mean stress and stress amplitude in regions where tensile loading is predominance, using fatigue S-N curves only for two stress R-ratios. In this approach, the experimental fatigue data of the P355NL1 pressure vessel steel available for three stress R-ratios (−1, −0.5, 0), are used. A multilayer perceptron network has been trained with the back-propagation algorithm; its architecture consists of two input neurons (σm, N) and one output neuron (σa). The suggested CLD based on trained artificial neural network algorithm and probabilistic Stüssi fatigue fields applied to dog-bone shaped specimens made of P355NL1 steel showed a good agreement with the high-cycle fatigue experimental data, only using the stress R-ratios equal to 0 and −0.5. Furthermore, a procedure for estimating the fatigue resistance reduction factor, Kf , for the fatigue life prediction of structural details (stress R-ratios equal to 0, 0.15 and 0.3) in extrapolation regions is suggested and used to generate the Kf results for stress R-ratios from −1 to 0.3, based on machine learning artificial neural network algorithm.
Keywords: Fatigue | Artificial neural network | Back-propagation algorithm | Stüssi model | Constant life diagram
Solder joint reliability risk estimation by AI modeling
برآورد خطر قابلیت اطمینان اتصال لحیم کاری با مدل سازی هوش مصنوعی -2020
This paper studies AI modeling for the solder joint fatigue risk estimation under the thermal cycle loading of redistributed wafer level packaging. The artificial neural network (ANN), recurrent neural network (RNN) and vectorized-gate network long short-term memory (VNLSTM) architectures have been trained by the same dataset to investigate their performance for this task. The learning accuracy criterion, the implementation of all neural network architecture, the learning results and result analysis would be covered. Because the involvement of the time/temperaturedependent nonlinearity material characteristics, it is recommended that more than three hidden layers and a proper neural network architecture, which is capable of the sequential data processing, should be considered in order to guarantee the required accuracy and the satisfied convergence speed.
Keywords: Solder joint fatigue risk estimation | Time/temperature-dependent nonlinearity | ANN | RNN | LSTM | machine learning
Rethinking scaling laws in the high-cycle fatigue response of nanostructured and coarse-grained metals
بازنگری قانون مقیاس در پاسخ به خستگی چرخه بالا فلزات نانوساختار و دانه درشت-2020
The high-cycle fatigue life of nanocrystalline and ultrafine-grained Ni-Fe was examined for five distinct grain sizes ranging from approximately 50–600 nm. The fatigue properties were strongly dependent on grain size, with the endurance limit changing by a factor of 4 over this narrow range of grain size. The dataset suggests a breakdown in fatigue improvement for the smallest grain sizes < 100 nm, likely associated with a transition to grain coarsening as a dominant rate-limiting mechanism. The dataset also is used to explore fatigue prediction from monotonic tensile properties, suggesting that a characteristic flow strength is more meaningful than the widely-utilized ultimate tensile strength.
Keywords: Fatigue strength | Tensile loading | Polycrystalline | Nanocrystalline | Grain size
The release from refractoriness hypothesis of N1 of event-related potentials needs reassessment
رهایی از فرضیه مقاومتی N1 پتانسیلهای مربوط به رویداد نیاز به ارزیابی مجدد دارد-2020
N1 of event-related potentials (ERPs) is augmented in amplitude in ~50e150 ms by occasional changes (deviants) in the physical features of a sound repeated at intervals of from ~400 ms to seconds (standard). The release-from-refractoriness hypothesis links the N1 augmentation to a deviant-feature-specific neural population that is fresh to fully respond as opposed to a standard-feature-specific neural population that is unresponsive due to its post-response refractoriness. The present work explored this hypothesis in the context of ERP studies, behavioral habituation studies and studies on stimulus-specific adaptation (SSA). The idea of hundreds of milliseconds neural population-level refractoriness was observed to be founded upon negative N1 evidence (no observable effect of dishabituating stimuli on N1 to standards e the null hypothesis retained) and merely supported by positive N1 evidence (null hypotheses rejected). This idea was also found to be directly challenged by positive N1 evidence. No conclusive network- or single-neuron-level evidence was found for the refractoriness. Therefore, the validity of the release-from-refractoriness hypothesis of N1 to guide psychophysiological research needs reassessment.
Keywords: Habituation | Dishabituation | Event-related potential | N1 | Mismatch negativity (MMN) | Adaptation | Neural fatigue | Stimulus-specific adaptation (SSA)
From S-N to the Paris law with a new mixed-mode cohesive fatigue model for delamination in composites
از S-N گرفته تا قانون پاریس با یک مدل جدید خستگی منسجم مختلط برای لایه لایه شدن در کامپوزیت ها-2020
The relationship between fatigue life and fatigue crack propagation rate in composites is explored with a new cohesive damage model. The parameters of the model are obtained from idealizations of S-N diagrams used in engineering design. The model assumes that the quasi-static cohesive law that describes tearing is the envelope of the fatigue damage. Fatigue damage within the cohesive envelope accumulates at a rate that satisfies the S-N diagram and Miner’s cumulative fatigue damage rule. The fatigue model was implemented as a UMAT subroutine for Abaqus cohesive elements by adding fatigue damage accumulation within a cohesive model based on the Turon mixed-mode model. The analyses were conducted using a simplified cyclic loading procedure in which the maximum load is applied quasistatically and load cycling is represented within the constitutive model. The predicted propagation rates of delamination in mode I and mixed mode were compared to experimental results for IM7/8552 graphite/epoxy tape. Several aspects of the results were investigated, including the effects of R-curves, the stress ratio R, and the difference between displacement and force control.
Simulations of isothermal and thermomechanical fatigue of a polycrystal made out of austenitic stainless steels and relation to the Coffin-Manson law
شبیه سازی خستگی ایزوترمال و گرما مکانیکی یک پلی کریستال ساخته شده از فولادهای ضد زنگ آستنیتی و ارتباط آن با قانون تابوت-مانسون-2020
Based on an experimental result of the literature showing that the crack remains in the grain of its initiation up to about 20% of the lifetime at low cycle fatigue of austenitic stainless steels, the lifetime of a polycrystal made out of these steels undergoing uniaxial isothermal or thermomechanical fatigue from 30 to 340 °C at constant total strain amplitude is calculated. The stresses and strains in the grains and polycrystal are determined in term of mean field with the Hill-Hutchinson model. Dipolar slip markings in the grains are predicted and assumed sites of initiation and propagation of the cracks calculated in terms of critical stress and of shear plastic strain, depth and grain boundary, respectively. There is agreement of the macroscopic stress and plastic strain, but at the residual stress, making the modelling not suitable for thermomechanical fatigue. For isothermal fatigue, the lifetime is in agreement. The lifetime - plastic strain power relationships are addressed to the Coffin- Manson law assumed to derive from the kinetic equation of the crack in a dimensional approach. The calculated constants of the power relationship of five austenitic steels of the literature are in qualitative agreement with those measured of the law. The relationships between the constants of the power relationship, law and kinetic equation are determined. The results and modelling are discussed.
Keywords: Fatigue stage I | austenitic stainless steels | crack initiation | crack propagation | lifetime prediction | Coffin-Manson law
New damage evolution law for modeling fatigue life of asphalt concrete surfacing of long-span steel bridge
قانون جدید تکامل خسارت برای مدل سازی عمر خستگی سطح بتن آسفالت پل فولادی با طول بلند-2020
As the traffic volume and wheel load on the orthotropic deck of a long-span steel bridge increase, longitudinal cracks often develop in the asphalt concrete surfacing leading to deterioration. The purpose of this study was accordingly to derive the fatigue failure evolution law of this surfacing. The fatigue damage evolution mechanism was studied from a micro perspective to derive a general fatigue damage evolution law. Fatigue tests and theoretical predictions of fatigue life were then performed for different asphalt concrete surfacing types commonly used in the decks of long-span steel bridges to verify the derived law.
Keywords: Long-span steel bridges | Asphalt concrete surfacing | Fatigue life | Damage evolution law | Fatigue experiment
An Expert System Gap Analysis and Empirical Triangulation of Individual Differences, Interventions, and Information Technology Applications in Alertness of Railroad Workers
تجزیه و تحلیل شکاف سیستم خبره و مثلث تجربی تفاوت های فردی ، مداخلات و کاربردهای فناوری اطلاعات در هوشیاری کارگران راهآهن-2019
In this abstract we would like to provide some exciting concrete information including the article’s main impact and significance on expert and intelligent systems. The main impact is that the PTC expert intelligent system fills in the gaps between the human and software decision making processes. This gap analysis is analyzed via empirical triangulation of rail worker data collected from its groups, individuals and the rail industry itself. We utilize an expert intelligent system PTC information technology application to both measure and to improve the alertness of the groups and workers in order to improve the overall safety of the railways through reduced human errors and failures to prevent accidents. Many individual differences in alertness among military, railroad, and other industry workers stem from a lack of sufficient sleep. This continues to be a concern in the railroad industry, even with the implementation of positive train control (PTC) expert system technology. Information technology aids such as PTC cannot prevent all accidents, and errors and failures with PTC may occur. Furthermore, drug interventions are a short-term solution for improving alertness. This study investigated the effect of sleep deprivation on the alertness of railroad signalmen at work, individual differences in alertness, and the information technology available to improve alertness. We investigated various information and communication technology control systems that can be used to maintain operational safety in the railroad industry in the face of incompatible circadian rhythms due to irregular hours, weekend work, and night operations. To fully explain individual differences after the adoption of technology, our approach posits the necessary parameters that one must consider for reason-oriented action, sequential updating, feedback, and technology acceptance in a unified model. This triangulation can help manage workers by efficiently increasing their productivity and improving their health. In our analysis we used R statistical software and Tableau. To test our theory, we issued an Apple watch to a locomotive engineer. The perceived usefulness, perceived ease of use, and actual use he reported led to an analysis of his sleep patterns that eventually ended in his adoption of a sleep apnea device and an improvement in his alertness and effectiveness. His adoption of the technology also resulted in a decrease in his use of chemical interventions to increase his alertness. Our model shows that the alertness of signalmen can be predicted. Therefore, we recommend that the alertness of all railroad workers be predicted given the safety limitations of PTC.
Keywords : Sleep Deprivation | Fatigue | Stress | Expert System | Alertness | Empirical Analysis
Automating the clinical stools exam using image processing integrated in an expert system
خودکارسازی امتحان مدفوع بالینی با استفاده از پردازش تصویر یکپارچه در یک سیستم خبره-2019
Background and objective: The diagnosis of intestinal parasitosis disease relies on physiological symptoms and stool examination. Often, few specialists are available, and manual stool exam is slow, prone to error, and can cause eye fatigue. Our aim was to design and implement a medical expert system that would be automated and helpful for diagnosis of human intestinal parasitosis. Methods: The system was developed based on a decision algorithm. A knowledge base was constructed through information gleaned from books and physicians with information pertaining to the disease. The user interacts with the system by answering questions. The symptoms information collected led to a microscopic examination of stools, which was run on the system to detect parasites. The paradigm for automated microscopic examination of stools consisted of a combined distance regularized level set evolution, automatically initialized by a circular Hough transform, and a trained neuro-fuzzy classifier. The neuro-fuzzy classifier was trained for analysis of twenty human intestinal parasites. Results: We combined the reasoning scheme of diagnosis and the automated clinical exam of stools in the same system. The parasites found in microscopic imagery confirmed the suspicious disease. The final recommendation of diagnosis was then completed, with appropriate proposed therapy. The system was evaluated with sixty cases of infection, and compared to the diagnosis of two expert doctors; we obtained fifty eight correct diagnoses, corresponding to a 96.6% accuracy. Conclusions: The proposed system is automated, since the parameters of segmentation, feature extraction and classification are set to be computationally guided by the type of suspicious parasite. The system is potentially an important contribution for medical healthcare assistance.
Keywords: Parasitic diseases diagnosis | Distance regularized level set evolution | Neuro-fuzzy classifier | Expert system