Prediction of Impact Energy of TIG Mild Steel Welds Using ANN

The present trend in the fabrication industries is the use of automated welding processes to obtain high production rates and high quality output.TIG welding, happens to be the best welding method employed in  the manufacturing industry. one of the problem facing the fabrication industry is the control of the process input parameters to obtain a good welded joint . however it is essential to establish the relationship between process parameters and weld quality output to predict and control weld bead quality .The aim of this study is to predict the impact energy of TIG mild steel welds using ANN.In this study, twenty experimental runs were carried out, each experimental run comprising the current, voltage and gas flow rate, the TIG welding process was used to join two pieces of mild steel plates measuring 60 x40 x10 mm , the impact energy was measured respectively. Thereafter the data collected from the experimental results was analysed with the ANN. The experimental results for the impact energy was analyzed with the Artificial Neural Networks. The overall R-value is shown to be 98.7%.  The best validation performance is 0.48429 and occurred at epoch five (5). The coefficient of correlation for training shows of 99.9%closeness ,99.4% for validation and 89.8% for  testing respectively

The Effect of Metal Inert Gas Welding Parameters on the Weldability of Galvanised Steel

The Taguchi technique is employed to establish the optimal parameter for each tensile property of the weldments. The tensile properties determined are the ultimate tensile strength, the yield strength, and the percentage elongation, whereas the process parameter used is the welding current (A), welding voltage (B), and the gas flow rate (C). By applying the Taguchi method, the optimal process parameters for obtaining the weldment with better yield strength are A3B1C3, whereas A3B3C3 can produce the weldment for better elongation. These optimum process parameters have shown considerably improved signal-to-noise ratios over the current process parameters adapted by the welders

Artificial neural networks in freight rate forecasting

Reliable freight rate forecasts are essential to stimulate ocean transportation and ensure stakeholder benefits in a highly volatile shipping market. However, compared to traditional time-series approaches, there are few studies using artificial intelligence techniques (e.g. artificial neural networks, ANNs) to forecast shipping freight rates, and fewer still incorporating forward freight agreement (FFA) information for freight rate forecasts. The aim of this paper is to examine the ability of FFAs to improve forecasting accuracy. We use two different dynamic ANN models, NARNET and NARXNET, and we compare their performance for 1, 2, 3 and 6 months ahead. The accuracy of the forecasting models is evaluated with the use of mean squared error (MSE), based on actual secondary data including historical Baltic Panamax Index (BPI) data (available online), and primary data on Baltic forward assessment (BFA) collected from the Baltic Exchange. The experimental results show that, in general, NARXNET outperforms NARNET in all forecast horizons, revealing the importance of the information contained in FFAs in improving forecasting accuracy. Our findings provide better forecasts and insights into the future movements of freight markets and help rationalise chartering decisions. © 2019, Springer Nature Limited

Kaynak dikiş formunun yapay sinir ağı ve vokselleme yöntemleriyle modellenmesi

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Bu çalışmada, kaynakçı adaylarının eğitimi amacıyla geliştirilen düşük maliyetli sanal kaynak simülatörü için gerçek zamanlı ve üç boyutlu bir kaynak dikiş formu modellenmiştir. Adaylar bu simülatör vasıtasıyla kaynak tekniklerini herhangi bir iş kazasına neden olmadan güvenli bir ortamda öğrenebilir ve kısa sürede normalden daha fazla uygulama yaparak becerilerini geliştirebilirler. Geliştirilen simülatörde, Flock of Birds konum ve oryantasyon sensörü ile başa takılan ekran gibi özel sanal gerçeklik aygıtları kullanılmıştır. Simülasyon, torcun konumunu izleyen Flock of Birds sensör cihazından gelen verilere dayanarak, kaynak dolgu şeklini ve nufuziyet miktarını belirler. Kaynak dolgu şekli oluşturulurken, kaynak dikiş kesitinin parabol ile benzerliği nedeniyle bu şekil temel dolgu birimi olarak kullanılmıştır. Kaynak dikişimizi oluşturacak temel dolgu şeklinin yükseklik, genişlik ve nufuziyet parametrelerine ait değerler literatürdeki kaynak dikişi deneylerinden elde edilmiştir. Sanal kaynak işlemi esnasında, kaynak dolgu şekli parametre değerleri belirli zaman aralıklarında, ileri beslemeli geri yayılımlı yapay sinir ağı kullanılarak hesaplanır. Ağ kurgusu yapılırken eğitim fonksiyonu olarak TrainLM (Levenberg-Marquardt) referans alınmıştır. En uygun transfer fonksiyonu belirlenirken de en iyi sonucu LogSig() fonksiyonunun verdiği saptanmıştır. Ara katman sayısı ve her ara katmandaki proses elemanı (nöron) sayısının kaç olacağına deneme/yanılma yöntemiyle karar verilmiştir. Aynı zaman aralığında voksel haritası ve buna karşılık gelen hash tabanlı sekizli ağaç veri yapısı gerçek zamanlı olarak oluşturulur. Voksellenen veriler kullanılarak, kaynak dolgusunun üçgenlerden oluşan eş yüzeyleri, yürüyen küpler algoritması ile yeniden oluşturulur. Bu sayede daha gerçekçi bir kaynak dikiş görüntüsü elde edilir. Bu görüntü ve sanal sahne devamlı olarak başa takılan ekrana yollanarak sanal ortam içindeki gerçeklik hissi devam ettirilir. Vokselleme ve eş yüzey oluşturma işlemleri için yüksek çözünürlüklü sanal sahnelerde işlem süresini kısaltmak için de çok iş parçacıklı programlama tekniği kullanılmıştır. Farklı iş parçacığı sayıları için eş yüzey oluşturma süreleri de gösterilmiştir.In this study, a real time and three dimensional weld seam form was modeled for a low cost virtual welding simulator developed for training welder candidates. Through this simulator, candidates can learn welding techniques in a safe environment without causing any work accidents and improve their skills by performing more applications than usual in a short time. In the developed simulator, special virtual reality devices such as Flock of Birds position and orientation sensor and head mounted display are used. The simulation determines the weld bead shape and amount of penetration based on data from the Flock of Birds sensor device monitoring the position of the torch. When forming the weld bead shape, parabola was used as the basic bead shape unit due to the similarity of the weld bead slice with the parabola. The values of the height, width and penetration parameters of the basic weld bead shape that will form our weld seam were obtained from the weld seam experiments in the literature. During the virtual welding process, the weld bead shape parameter values are calculated at specified time intervals using the feed-forward back-propagation artificial neural network. TrainLM (Levenberg-Marquardt) was used as the training function for network design. While determining the most appropriate transfer function, it was found that LogSig () function gave the best result. The number of hidden layers and the number of process elements (neurons) in each hidden layer were determined by trial and error method. In the same time interval, the voxel map and the corresponding hash-based octree data structure are generated in real time. By using voxelized data, the triangular isosurfaces of the weld bead are reconstructed using the marching cubes algorithm. This results a more realistic weld seam appearance. This image and virtual scene are continuously sent to the head mounted display to maintain the sense of reality in the virtual environment. Multi-threaded programming technique is also used to shorten the processing time in high resolution virtual scenes for voxelization and isosurface extraction processes. The isosurface extraction times for different number of threads are also shown

Desenvolvimento de modelo de fonte de calor para os processos de soldagem TIG e MAG

No presente trabalho, é proposta uma fonte de calor volumétrica móvel Duplo Elipsoide para simular o processo de soldagem TIG e uma fonte de calor Duplo Bi-Elipsoide para simular o processo de soldagem MAG, baseadas na distribuição de calor duplo elipsoide de Goldak. As geometrias dos cordões de solda resultantes dos procedimentos experimentais, foram estudadas mediante projetos de experimentos Box-Behnken para determinar a influência dos parâmetros de soldagem e suas interações sobre as dimensões da seção transversal dos cordões de solda e da poça de fusão. Com auxílio da análise da variância foram obtidas equações de regressão polinomial em função da corrente, distância eletrodo-peça e velocidade de soldagem no caso dos cordões de solda TIG, e em função da tensão de soldagem, velocidade de alimentação do arame e velocidade de soldagem para os cordões de solda MAG, as quais conseguiram descrever analiticamente cada parâmetro dimensional com relativa exatidão. A comparação entre as dimensões dos cordões de solda estimadas pelos modelos analíticos e os dados experimentais, apresentaram erros máximos de 24,08% no caso dos cordões de solda TIG. As porcentagens de erros máximas calculadas para os cordões de solda MAG com arame de 1,0 mm e 1,2 mm foram 24,15% e 14,51% respetivamente. A grande vantagem da implementação dos modelos de regressão polinomial na modelagem computacional é a obtenção das dimensões características que governam os modelos de fonte de calor a partir dos parâmetros de soldagem, prescindindo de complexos procedimentos experimentais. As equações de regressão polinomial adquiridas mediante a análise estatística foram utilizadas para resolver as equações do modelo de fonte de calor Duplo Elipsoide e Duplo Bi-Elipsoide em simulações computacionais de elementos finitos (FEM) utilizando o software COMSOL® Multiphysics. A comparação entre geometrias estimadas pelas simulações FEM e os dados experimentais permitiu calcular uma porcentagem de erro máximo de 21,38% no caso dos cordões de solda TIG. No caso das soldas MAG com arame de 1,0 mm e 1,2 mm foram calculados erros máximos de 21,73% e 27,61% respetivamente. A diferença entre as temperaturas máximas obtidas mediante as simulações FEM e medidas experimentalmente foi inferior a 10 °C para os cordões de solda TIG, e menor a 22 °C para os cordões de solda MAG.In the present work, a double ellipsoid mobile volumetric heat source is proposed to simulate the TIG welding process and a double Bi-ellipsoid heat source to simulate the MAG welding process, based on the Dual-heat distribution of Goldak's Ellipsoid. The geometries of weld beads resulting from the experimental procedures were studied using Box-Behnken designs to determine the influence of the welding parameters and their interactions on the dimensions of the cross-section of weld bead and melting pool. With the aid of analysis of variance, polynomial regression equations were obtained as a function of current, electrode-part distance and welding speed in the case of TIG weldments, and as a function of welding voltage, wire feeding speed and welding speed for MAG weldments, which were able to describe each dimensional parameter analytically with relative accuracy. The comparison between the dimensions of the weld beads estimated by the analytical models and the experimental data, presented maximum errors of 24,08% in the TIG weld beads. The maximum error percentages calculated for the MAG weld beads with 1,0 mm and 1,2 mm welding wire were 24,15% and 14,51% respectively. The main advantage of implementing the polynomial regression models in computational modeling is the obtaining of the characteristic dimensions that govern the models of the heat source from the welding parameters, without complex experimental procedures. The polynomial regression equations acquired through statistical analysis were used to solve the equations of the Double-Ellipsoid and Double Bi-Ellipsoid heat source model in finite element computational simulations (FEM) using the COMSOL® Multiphysics software. The comparison between geometries estimated by the FEM simulations and the experimental data allowed to calculate a maximum error percentage of 21,38% in the TIG weld beads. In the case of MAG welds beads with 1,0 mm and 1,2 mm welding wire, maximum errors of 21,73% and 27,61% were calculated respectively. The difference between the maximum temperatures obtained by the FEM simulations and measured experimentally was less than 10°C for the TIG weld beads, and less than 22 °C for the MAG weld beads

Decision Making Analysis for an Integrated Risk Management Framework of Maritime Container Port Infrastructure and Transportation Systems

This research proposes a risk management framework and develops generic risk-based decision-making, and risk-assessment models for dealing with potential Hazard Events (HEs) and risks associated with uncertainty for Operational Safety Performance (OSP) in container terminals and maritime ports. Three main sections are formulated in this study: Section 1: Risk Assessment, in the first phase, all HEs are identified through a literature review and human knowledge base and expertise. In the second phase, a Fuzzy Rule Base (FRB) is developed using the proportion method to assess the most significant HEs identified. The FRB leads to the development of a generic risk-based model incorporating the FRB and a Bayesian Network (BN) into a Fuzzy Rule Base Bayesian Network (FRBN) method using Hugin software to evaluate each HE individually and prioritise their specific risk estimations locally. The third phase demonstrated the FRBN method with a case study. The fourth phase concludes this section with a developed generic risk-based model incorporating FRBN and Evidential Reasoning to form an FRBER method using the Intelligence Decision System (IDS) software to evaluate all HEs aggregated collectively for their Risk Influence (RI) globally with a case study demonstration. In addition, a new sensitivity analysis method is developed to rank the HEs based on their True Risk Influence (TRI) considering their specific risk estimations locally and their RI globally. Section 2: Risk Models Simulations, the first phase explains the construction of the simulation model Bayesian Network Artificial Neural Networks (BNANNs), which is formed by applying Artificial Neural Networks (ANNs). In the second phase, the simulation model Evidential Reasoning Artificial Neural Networks (ERANNs) is constructed. The final phase in this section integrates the BNANNs and ERANNs that can predict the risk magnitude for HEs and provide a panoramic view on the risk inference in both perspectives, locally and globally. Section 3: Risk Control Options is the last link that finalises the risk management based methodology cycle in this study. The Analytical Hierarchal Process (AHP) method was used for determining the relative weights of all criteria identified in the first phase. The last phase develops a risk control options method by incorporating Fuzzy Logic (FL) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to form an FTOPSIS method. The novelty of this research provides an effective risk management framework for OSP in container terminals and maritime ports. In addition, it provides an efficient safety prediction tool that can ease all the processes in the methods and techniques used with the risk management framework by applying the ANN concept to simulate the risk models