Active thermography for the investigation of corrosion in steel surfaces

The present work aims at developing an experimental methodology for the analysis of corrosion phenomena of steel surfaces by means of Active Thermography (AT), in reflexion configuration (RC). The peculiarity of this AT approach consists in exciting by means of a laser source the sound surface of the specimens and acquiring the thermal signal on the same surface, instead of the corroded one: the thermal signal is then composed by the reflection of the thermal wave reflected by the corroded surface. This procedure aims at investigating internal corroded surfaces like in vessels, piping, carters etc. Thermal tests were performed in Step Heating and Lock-In conditions, by varying excitation parameters (power, time, number of pulse, ….) to improve the experimental set up. Surface thermal profiles were acquired by an IR thermocamera and means of salt spray testing; at set time intervals the specimens were investigated by means of AT. Each duration corresponded to a surface damage entity and to a variation in the thermal response. Thermal responses of corroded specimens were related to the corresponding corrosion level, referring to a reference specimen without corrosion. The entity of corrosion was also verified by a metallographic optical microscope to measure the thickness variation of the specimens

High cycle fatigue life prediction of laser additive manufactured stainless steel:A machine learning approach

Variations in the high cycle fatigue response of laser powder bed fusion materials can be caused by the choice of processing and post-processing strategies. The numerous influencing factors arising from the process demand an effective and unified approach to fatigue property assessment. This work examines the use of a neuro-fuzzy-based machine learning method for predicting the high cycle fatigue life of laser powder bed fusion stainless steel 316L. A dataset, consisting of fatigue life data for samples subjected to varying processing conditions (laser power, scan speed and layer thickness), post-processing treatments (annealing and hot isostatic pressing) and cyclic stresses, was constructed for simulating a complex nonlinear input-output environment. The associated fracture mechanisms, including the modes of crack initiation and deformation, were characterised. Two models, by employing the processing/post-processing parameters and the static tensile properties respectively as the inputs, were developed from the training data. Despite the diverse fatigue and fracture properties, the models demonstrated good prediction accuracy when checked against the test data, and the computationally-derived fuzzy rules agree well with understanding of the fracture mechanisms. Direct application of the model to literature results, however, yielded a range of prediction accuracies because of the variability in the reported data. Retraining the model by incorporating the literature results into the dataset led to improved modelling performance.Accepted versio

Application of surrogate modeling methods in simulation-based reliability and performance assessment of civil structures

Structures and infrastructure systems are subjected to various deterioration processes due to environmental or mechanical stressors. Proper performance assessment approaches capable of detecting potential structural damage and quantifying the probability associated with structural failure are required to formulate optimal maintenance and retrofit plans that minimize the risk of failure and maximize the safety of structures. However, due to the presence of several sources of uncertainty that can affect the performance assessment and decision-making processes (e.g., uncertainties associated with loading conditions and performance prediction models), applying probabilistic methods, such as Monte Carlo simulation, is essential. In this context, a large number of simulations is generally required to quantify the low failure probability associated with civil structures. Executing the required number of simulations may be computationally expensive, especially if complex and/or nonlinear structural models (e.g., finite element models) are involved. The use of surrogate modeling tools such as artificial neural networks, polynomial chaos expansion, and kriging can help in reducing the computational costs associated with simulation-based probabilistic analysis. The research proposed herein aims to develop probabilistic approaches for performance assessment and damage detection of structures using advanced simulation-based techniques coupled with surrogate modeling. The proposed methodology is applied to quantify the risk of bridge failure due to flood events considering the impact of climate change. The approach was extended to establish the time-variant flood fragility surfaces for bridges under flood conditions. This approach (a) integrates deep learning neural networks into a simulation-based probabilistic approach to predict the future river streamflow necessary for assessing the flood hazard at the bridge location and (b) simulates the structural behavior of the bridge foundation under sour conditions. In addition, the proposed methodology is used to quantify the reliability of bolted and welded steel connections by integrating finite element analysis and surrogate models. Low-rank tensor approximation and polynomial chaos kriging surrogate models are adopted to perform Monte Carlo simulation and quantify the reliability of the investigated combination connection. Finally, artificial neural networks were used to develop a statistical damage detection and localization approach capable of evaluating the performance of prestressed concrete bridge girders using fiber optic sensors

Book of abstracts of the 14th International Symposium of Croatian Metallurgical Society - SHMD \u272020, Materials and metallurgy

Book of abstracts of the 14th International Symposium of Croatian Metallurgical Society - SHMD \u272020, Materials and metallurgy held in Šibenik, Croatia, June 21-26, 2020. Abstracts are organized in four sections: Materials - section A; Process metallurgy - Section B; Plastic processing - Section C and Metallurgy and related topics - Section D

Weld Residual Stress Profiles for Structural Integrity Assessment

Economic and safe management of operating nuclear power plants can be highly dependent on the structural integrity assessments for safety critical pressure vessels and piping components. In engineering fracture assessment procedures the full (3-D) residual stress field at a welded joint is usually simplified by considering a representative onedimensional profile through the wall-thickness of the stress tensor component acting normal to the crack face. The stress intensity factor, calculated from this estimated through-thickness stress profile, is used directly in the fracture assessment. Therefore, assessments of defects in welds can be highly sensitive to the through-thickness residual stress profiles assumed in the calculations. There is a need for reliable characterisation of residual stresses in welded structures such as in stainless steel girth welded pipes as there are a lot of discrepancies in the current methodologies used. For example, bounding residual profiles found in fitness for service assessment procedures have been based on examination of residual stress measurements, finite element weld simulation and expert judgment. This approach suffers from the drawback that the upper bound curve can increase as more measurements and data scatter are obtained. The consequence of this is that structural integrity assessments of defective plant can be over-conservative by a large margin, and may lead to unnecessary and costly repair or inspection. This thesis illustrates how a neural network model, can be developed and applied to predict through-thickness residual stress profiles in austenitic stainless steel pipe girth welds for simplified fracture assessments. The model is validated by comparing predictions with new experimental measurements made using neutron diffraction and contour method. The new measurements were undertaken by fabricating six pipe girth welds with a range of wall-thickness, weld heat input and weld groove geometries. The robustness of the developed artificial neural network (ANN) approach is demonstrated by sensitivity studies in input variables and training data. The performance and suitability of the ANN approach is discussed by comparison with stress profiles recommended in defect assessment procedures. This is followed by an evaluation of whether the use of neural network bounding profiles can lead to non-conservative estimates of stress intensity factor in fracture assessments. The neural network approach shows sufficient potential to be developed into an alternative prediction tool for use in fracture assessment of welded components

Software for evaluating probability-based integrity of reinforced concrete structures

In recent years, much research work has been carried out in order to obtain a more controlled durability and long-term performance of concrete structures in chloride containing environment. In particular, the development of new procedures for probability-based durability design has proved to give a more realistic basis for the analysis. Although there is still a lack of relevant data, this approach has been successfully applied to several new concrete structures, where requirements to a more controlled durability and service life have been specified. A probability-based durability analysis has also become an important and integral part of condition assessment of existing concrete structures in chloride containing environment. In order to facilitate the probability-based durability analysis, a software named DURACON has been developed, where the probabilistic approach is based on a Monte Carlo simulation. In the present paper, the software for the probability-based durability analysis is briefly described and used in order to demonstrate the importance of the various durability parameters affecting the durability of concrete structures in chloride containing environment

Engineering Principles

Over the last decade, there has been substantial development of welding technologies for joining advanced alloys and composites demanded by the evolving global manufacturing sector. The evolution of these welding technologies has been substantial and finds numerous applications in engineering industries. It is driven by our desire to reverse the impact of climate change and fuel consumption in several vital sectors. This book reviews the most recent developments in welding. It is organized into three sections: “Principles of Welding and Joining Technology,” “Microstructural Evolution and Residual Stress,” and “Applications of Welding and Joining.” Chapters address such topics as stresses in welding, tribology, thin-film metallurgical manufacturing processes, and mechanical manufacturing processes, as well as recent advances in welding and novel applications of these technologies for joining different materials such as titanium, aluminum, and magnesium alloys, ceramics, and plastics

Monitoring and characterization of abnormal process conditions in resistance spot welding

Resistance spot welding (RSW) is extensively used for sheet metal joining of body-in-white (BIW) structure in the automobile industry. Key parameters, such as welding current, electrode force and welding time, are involved in the RSW process. Appropriate welding parameters are vital for producing good welds; otherwise, undersized weld and expulsion are likely to be caused. For a specific type of sheet metal, an acceptable nugget is produced when an appropriate combination of welding parameters is used. However, undersized welds and expulsion are still commonly seen in the plant environment, where some abnormal process conditions could account for the production of the poor quality welds. Understanding the influence of abnormal process conditions on spot weld quality and other RSW related issues is crucial. A range of online signals, strongly related to the nugget development history, have attracted keen interest from the research community. Recent monitoring systems established the applied dynamic resistance (DR) signal, and good prediction of nugget diameter was made based on signal values. However, the DR curves with abnormal process conditions did not agree well with those under normal condition, making them less useful in detecting abnormal process conditions. More importantly, none of the existing monitoring systems have taken these abnormal process conditions into account. In addition, electrode degradation is one of the most important issues in the plant environment. Two major electrode degradation mechanisms, softening and intermetallic compound (IMC) formation, are strongly related to the characteristics of welding parameters and sheet metals. Electrode misalignment creates a very distinct temperature history of the electrode tip face, and is believed to affect the electrode degradation mechanism. Though previous studies have shown that electrode misalignment can shorten electrode life, the detailed mechanism is still not understood. In this study, an online-monitoring system based on DR curve was first established via a random forest (RF) model. The samples included individual welds on the tensile shear test sample and welds on the same sheet, considering the airgap and shunting effect. It was found that the RF model achieved a high classification accuracy between good and poor welds. However, the DR signals were affected by the shunting distance, and they displayed opposite trends against individual welds made without any shunting effect. Furthermore, a suitable online signal, electrode displacement (ED), was proposed for monitoring abnormal process conditions such as shunting, air gap and close edged welds. Related to the thermal expansion of sheet metal, ED showed good consistency of profile features and actual nugget diameters between abnormal and normal welds. Next, the influence of electrode misalignment on electrode degradation of galvannealed steel was qualitatively and quantitatively investigated. A much-reduced electrode life was found under the angular misalignment of 5°. Pitting and electrode softening were accelerated on the misaligned electrodes. δ Fe-Zn phase from the galvannealed layer that extends electrodes was found non-uniformly distributed on the worn electrode. Furthermore, electron backscatter diffraction (EBSD) analysis was implemented on the worn electrode, showing marked reduction in grain diameter and aspect ratio. The grain deformation capacity was estimated by the distribution of the Taylor factor, where the portion of pore grain was substantially weakened in the recrystallized region compared to the base metal region