An experimental and finite element study of the low-cycle fatigue failure of a galvanised steel lighting column

This paper presents the results of a low-cycle fatigue test on a lighting column. The wind induced vibration phenomena responsible for low cycle fatigue in such structures is discussed and the failure mechanism is examined. It was initially thought that poor quality weld detail was the major influence on the fatigue life of such columns. However, the significant role of the galvanised coating in the failure process is also highlighted. The experimental results are compared with those from a detailed 3D finite element model. Various methods of calculating hot-spot stresses at welded joints are examined and use of a simple peak stress removal approach is shown to produce significantly different values compared with the other methods examined

A study of the low-cycle fatigue failure of a galvanised steel lighting column

This paper presents the results of a low-cycle fatigue test on a galvanised steel lighting column. The aim of the test was to simulate the behaviour of the column undergoing large amplitude resonant vibration caused by wind. A metallurgical study of the failure revealed the significant role of the galvanised coating in the failure process. Results from a detailed 3D finite element model are also used to explain the failure mechanism. The swage joint in the column was confirmed as a failure location by both experiment and finite element analysis. This in itself is not surprising and the position of the fatigue failure is consistent with those observed in the field. Of more importance is the fact that the experiment shows that galvanizing can lead to premature failure of such columns. This is a highly significant conclusion as it implies that improving the weld detail in an effort to improve fatigue life may be ineffective for lighting columns coated in this manner. Given the detrimental effect of galvanizing on fatigue performance and the fact that the most severe corrosion will be on the outside of columns, then the fatigue life of such structures may benefit if the inner surface was not galvanised in high stress regions. An alternative improvement would be the use of a galvanizing coating with higher toughness and less susceptibility to cracking and damage. Attention is drawn to the need for a better understanding of the fatigue performance of galvanised steel columns resulting from large amplitude wind induced resonant vibration. The approach adopted so far for lighting column resonant vibration, has been to try and avoid it. While this is a laudable objective, clearly this has not always been possible, as designs push the limits permitted by Codes of Practice

Fault diagnosis and comparing risk for the steel coil manufacturing process using statistical models for binary data

[EN] Advanced statistical models can help industry to design more economical and rational investment plans. Fault detection and diagnosis is an important problem in continuous hot dip galvanizing. Increasingly stringent quality requirements in the automotive industry also require ongoing efforts in process control to make processes more robust. Robust methods for estimating the quality of galvanized steel coils are an important tool for the comprehensive monitoring of the performance of the manufacturing process. This study applies different statistical regression models: generalized linear models, generalized additive models and classification trees to estimate the quality of galvanized steel coils on the basis of short time histories. The data, consisting of 48 galvanized steel coils, was divided into sets of conforming and nonconforming coils. Five variables were selected for monitoring the process: steel strip velocity and four bath temperatures. The present paper reports a comparative evaluation of statistical models for binary data using Receiver Operating Characteristic (ROC) curves. A ROC curve is a graph or a technique for visualizing, organizing and selecting classifiers based on their performance. The purpose of this paper is to examine their use in research to obtain the best model to predict defective steel coil probability. In relation to the work of other authors who only propose goodness of fit statistics, we should highlight one distinctive feature of the methodology presented here, which is the possibility of comparing the different models with ROC graphs which are based on model classification performance. Finally, the results are validated by bootstrap procedures.The authors are indebted to the anonymous referees whose suggestions improved the original manuscript. This work was supported by a grant from PAID-06-08 (Programa de Apoyo a la Investigacion y Desarrollo) of the Universitat Politecnica de Valencia.Debón Aucejo, AM.; García-Díaz, JC. (2012). Fault diagnosis and comparing risk for the steel coil manufacturing process using statistical models for binary data. Reliability Engineering and System Safety. 100:102-114. https://doi.org/10.1016/j.ress.2011.12.022S10211410

Advanced predictive quality control strategy involving different facilities

There are many industries that use highly technological solutions to improve quality in all of their products. The steel industry is one example. Several automatic surface-inspection systems are used in the steel industry to identify various types of defects and to help operators decide whether to accept, reroute, or downgrade the material, subject to the assessment process. This paper focuses on promoting a strategy that considers all defects in an integrated fashion. It does this by managing the uncertainty about the exact position of a defect due to different process conditions by means of Gaussian additive influence functions. The relevance of the approach is in making possible consistency and reliability between surface inspection systems. The results obtained are an increase in confidence in the automatic inspection system and an ability to introduce improved prediction and advanced routing models. The prediction is provided to technical operators to help them in their decision-making process. It shows the increase in improvement gained by reducing the 40 % of coils that are downgraded at the hot strip mill because of specific defects. In addition, this technology facilitates an increase of 50 % in the accuracy of the estimate of defect survival after the cleaning facility in comparison to the former approach. The proposed technology is implemented by means of software-based, multi-agent solutions. It makes possible the independent treatment of information, presentation, quality analysis, and other relevant functions

Forecasting Irregular Seasonal Power Consumption. An Application to a Hot-Dip Galvanizing Process

[EN] The method described in this document makes it possible to use the techniques usually applied to load prediction efficiently in those situations in which the series clearly presents seasonality but does not maintain a regular pattern. Distribution companies use time series to predict electricity consumption. Forecasting techniques based on statistical models or artificial intelligence are used. Reliable forecasts are required for efficient grid management in terms of both supply and capacity. One common underlying feature of most demand-related time series is a strong seasonality component. However, in some cases, the electricity demanded by a process presents an irregular seasonal component, which prevents any type of forecast. In this article, we evaluated forecasting methods based on the use of multiple seasonal models: ARIMA, Holt-Winters models with discrete interval moving seasonality, and neural networks. The models are explained and applied to a real situation, for a node that feeds a galvanizing factory. The zinc hot-dip galvanizing process is widely used in the automotive sector for the protection of steel against corrosion. It requires enormous energy consumption, and this has a direct impact on companies' income statements. In addition, it significantly affects energy distribution companies, as these companies must provide for instant consumption in their supply lines to ensure sufficient energy is distributed both for the process and for all the other consumers. The results show a substantial increase in the accuracy of predictions, which contributes to a better management of the electrical distribution.Trull, O.; García-Díaz, JC.; Peiró Signes, A. (2021). Forecasting Irregular Seasonal Power Consumption. An Application to a Hot-Dip Galvanizing Process. Applied Sciences. 11(1):1-24. https://doi.org/10.3390/app11010075S12411

Pengaruh Waktu Celup Proses Hot Dip Galvalum (A155%-Zn-Si) Terhadap Sifat Adhesive; Ketebalan Lapisan Dan Ketahanan Korosi Pada Baja API Grade B

Syarat untuk setiap coating dikatakan baik mencakup beberapa variabel yaitu ketebalan coating, kelekatannya, serta mampu memproteksi substrat. Pada penelitian kali ini diteliti pengaruh waktu celup terhadap ketebalan coating, kelekatan, serta ketahanan korosi yang ada. Penelitian dilakukan dengan menggunakan bahan substrat baja API 5L Grade B yang dilapisi dengan paduan Al55%-Zn-1,5%Si (Galvalum). Proses coating dilakukan dengan metode hot-dip menggunakan rentang waktu 1, 5, 9, 13 menit.Hasil dari penelitian ini lalu diuji dengan uji OES, SEM, ketebalan (DFT), kelekatan, korosi dan XRD. Dari penelitian didapatkan bahwa ketebalan dan kelekatan coating berbanding lurus dengan lama waktu celup hot dip yang memiliki nilai tertinggi masing-masing pada waktu celup 13 menit. Sementara untuk laju korosi masih terjadi penurunann signifikan pada waktu celup 5 menit dan setelahnya cenderung stagnan sebesar 0,0928 mpy. ================================================================================================ There are some requirements for a good coating which includes it’s thickness, adhesivity, and it’s ability to protect the substrate. In this research, the influence of immerse time to the coating thickness, adhesivity, and the corrosion resistance were investigated. This research used API 5L Grade B Steel as substrate which was coated by Al55%-Zn-Si alloy (Galvalume). Hot-dip method was used to deposit galvalume with immerse time 1, 5, 9, 13 minutes. After that, the coating results were tested with OES, SEM, DFT, adhesive test, corrosion test (salt spray method), and XRD test. Based on the results, the coating thickness and adhesivity increase as the increasing of immerse time and the highest values are at 13 minutes immerse time for every variable. For the corrosion test results, the corrosion rate decrease significantly as the increasing immerse time until 5 minutes. More than 5 minutes, the corrosion rate still decrease but tend to be stagnant with the corrosion rate value 0.0928 mpy

Advanced Methods of Power Load Forecasting

This reprint introduces advanced prediction models focused on power load forecasting. Models based on artificial intelligence and more traditional approaches are shown, demonstrating the real possibilities of use to improve prediction in this field. Models of LSTM neural networks, LSTM networks with a SESDA architecture, in even LSTM-CNN are used. On the other hand, multiple seasonal Holt-Winters models with discrete seasonality and the application of the Prophet method to demand forecasting are presented. These models are applied in different circumstances and show highly positive results. This reprint is intended for both researchers related to energy management and those related to forecasting, especially power load

Crack Control and Bond Performance of Alternative Coated Reinforcements in Concrete

Concrete cracking in structures is a ubiquitous problem which can lead to the deterioration of the structure. Other than affecting the strength aspect of a structure, cracking impacts the serviceability criteria as well. Although cracking phenomenon in any structure is highly inevitable, it has to be minimized in order to maintain a structure’s life effectively. Cracking in reinforced concrete structures is related to the bond strength developed between the bar and the concrete. It also depends on an ability of the bar to resist the stresses due to shrinkage to minimize the crack. Another important aspect is the resistance offered by the reinforcement to minimize the residual crack width after withdrawal of high loads beyond or near the yielding capacity. All these parameters were considered and have been studied as a part of this dissertation through experimental testing. The variables used in the tests are the alternative coated reinforcements like textured epoxy, hot dipped galvanized, and continuously galvanized reinforcements. Variables also included uncoated (black) and conventional epoxy (smooth epoxy) reinforcements which have been used in structure for many decades. Considering all the tests conducted, an overview analysis was done to determine the best performing bar coating for crack control and rebar-concrete bond. The results show that textured epoxy bars were the best performer in 47% of tests. On the other hand, smooth epoxy bars were the worst performer in 47% of tests. Uncoated, hot dipped galvanized, and continuously galvanized bars were typically in-between textured and smooth epoxy bars in their performance. This dissertation also analytically evaluated the bond mechanics associated with the variable bar coatings considered in the experimental program. Two different models of bar force variation at and around a crack location were considered to calculate the length over which forces transfer between the bar and concrete. The calculated lengths were compared to data from an associated peer study. It is inferred from the results that a small portion of a bar is de-bonded adjacent to the cracks and the forces transfer gradually at locations beyond the debonding. This inference applies to all the bar coatings in the data except the continuously galvanized reinforcement. Conclusions for continuously galvanized reinforcement could not be made because of limited and randomness in the data