Predicting the Product Classification of Hot Rolled Steel Sheets Using Machine Learning Algorithms

The mechanical properties of the SAPH440 hot rolled steel sheet are mainly controlled to satisfy product specifications. Three mechanical properties including the yield strength, ultimate tensile strength, and elongation are measured and utilized in product classification. Based on these properties, the steel is classified into 3 grades: Class 1 (meets specification), Class 2 (moderate quality), and Class 3 (low). However, various factors can affect the mechanical properties, leading to a long setup time for initial production runs. Therefore, this paper aims to improve the accuracy of these predictions by using machine learning algorithms. The results of experiments showed that the random forest algorithm had the best performance, with an accuracy of 70.0% and a macro average F-1 score of 70.0%. This more accurate prediction can reduce the initial setup time and save 37,000 USD per grade in trial run costs

Application of Artificial Neural Networks in Cold Rolling Process

Rolling is one of the most complicated processes in metal forming. Knowing the exact amount of basic parameters, especially inter-stand tensions can be effective in controlling other parameters in this process. Inter-stand tensions affect rolling pressure, rolling force, forward and backward slips and neutral angle. Calculating this effect is an important step in continuous rolling design and control. Since inter-stand tensions cannot be calculated analytically, attempt is made to describes an approach based on artificial neural network (ANN) in order to identify the applied parameters in a cold tandem rolling mill. Due to the limited experimental data, in this subject a five stand tandem cold rolling mill is simulated through finite element method. The outputs of the FE simulation are applied in training the network and then, the network is employed for prediction of tensions in a tandem cold rolling mill. Here, after changing and checking the different designs of the network, the 11-42-4 structure by one hidden layer is selected as the best network. The verification factor of ANN results according to experimental data are over R=0.9586 for training and testing the data sets. The experimental results obtained from the five stands tandem cold rolling mill. This paper proposed new ANN for prediction of inter-stand tensions. Also, this ANN method shows a fuzzy control algorithm for investigating the effect of front and back tensions on reducing the thickness deviations of hot rolled steel strips. The average of the training and testing data sets is mentioned 0.9586. It means they have variable values which are discussed in details in section 4. According to Table 7, this proposed ANN model has the correlation coefficients of 0.9586, 0.9798, 0.9762 and 0.9742, respectively for training data sets and 0.9905, 0.9798, 0.9762 and 0.9803, respectively for the testing data sets. These obtained numbers indicate the acceptable accuracy of the ANN method in predicting the inter-stand tensions of the rolling tandem mill. This method provides a highly accurate solution with reduced computational time and is suitable for on-line control or optimization in tandem cold rolling mills. Due to the limited experimental data, for data extraction for the ANN simulation, a 2D tandem cold rolling process is simulated using ABAQUS 6.9 software. For designing a network for this rolling problem, various structures of neural networks are studied in MATLAB 7.8 software

Modeling and Optimizing Tensile Strength and Yield Point on Steel Bar by Artificial Neural Network with Evolutionary Algorithm

[[incitationindex]]SCI[[conferencetype]]國際[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Gothenburg,Swede

Artificial Neural Networks Model for Springback Prediction in the Bending Operations

The aim of this paper is to develop an Artificial Neural Network (ANN) model for springback prediction in the free cylindrical bending of metallic sheets. The proposed ANN model was developed and tested using the Matlab software. The input parameters of the proposed ANN model were the sheet thickness, punch radius, and coefficient of friction. The resulting data is represented by the springback coefficient. Preparation, assessing and confirmation of the model were achieved using 126 data series obtained by Finite element analysis (FEA). ANN was trained by Levenberg - Marquardt back - propagation algorithm. The performance of the ANN model was evaluated using statistic measurements. The predictions of the ANN model, regarding FEA, had quite low root mean squared error (RMSE) values and the model performed well with the coefficient of determination values. This shows that the developed ANN model leads to the idea of being used as an instrument for springback prediction

Property prediction of continuous annealed steels

To compete in the current economic climate steel companies are striving to reduce costs and tighten process windows. It was with this in mind that a property prediction model for continuous annealed steels produced at Tata Steel’s plants in South Wales was developed. As continuous annealing is one of the final processes that strip steel undergoes before being dispatched to the customer the final properties of the strip are dependent on many factors. These include the annealing conditions, previous thermo-­‐mechanical processing and the steel chemistry. Currently these properties, proof stress, ultimate tensile strength, elongation, strain ratio and strain hardening exponent, are found using a tensile test at the tail end of the coil. This thesis describes the development of a model to predict the final properties of continuous annealed steel. Actual process data along with mechanical properties derived using tensile testing were used to create the model. A generalised regression network was used as the main predictive mechanism. The non-­‐linear generalised regression approach was shown to exceed the predictive accuracy of multiple regression techniques. The use of a genetic algorithm to reduce the number of inputs was shown to increase the accuracy of the model when compared to those trained with all available inputs and those trained using correlation derived inputs. Further work is shown where the fully trained models were used to predict the relationships that exist between the processing conditions and mechanical properties. This was extended to predict the interaction between two process conditions varying at the same time. Using this approach produced predictions that mirrored known relationships within continuous annealed steels and gives predictions specific to the plant that could be used to optimise the process.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

A Data-Driven Approach to Estimate the Power Loss and Thermal Behaviour of Cylindrical Gearboxes under Transient Operating Conditions

This paper proposes an innovative methodology to estimate the thermal behaviour of the cylindrical gearbox system, considering, as a thermal source, the power loss calculated under transient operating conditions. The power loss of the system in transient conditions is computed through several approaches: a partial elasto-hydrodynamic lubrication model (EHL) is adopted to estimate the friction coefficients of the gears, while analytical and semiempirical models are used to compute other power loss sources. Furthermore, considering a limited set of operating condition points as a training set, a reduced-order model for the evaluation of the power loss based on a neural network is developed. Using this method, it is possible to simulate thermal behaviour with high accuracy through a thermal network approach in all steady-state and transient operating conditions, reducing computational time. The results obtained by means of the proposed method have been compared and validated with the experimental results available in the literature. This methodology has been tested with the FZG rig test gearbox but can be extended to any transmission layout to predict the overall efficiency and component temperatures with a low computational burden

TEchMA2020: 3rd International Conference on Technologies for the Wellbeing and Sustainable Manufacturing Solutions: book of abstracts

Resumo não disponível.publishe

Property prediction of continuous annealed steels

To compete in the current economic climate steel companies are striving to reduce costs and tighten process windows. It was with this in mind that a property prediction model for continuous annealed steels produced at Tata Steel’s plants in South Wales was developed. As continuous annealing is one of the final processes that strip steel undergoes before being dispatched to the customer the final properties of the strip are dependent on many factors. These include the annealing conditions, previous thermo-­‐mechanical processing and the steel chemistry. Currently these properties, proof stress, ultimate tensile strength, elongation, strain ratio and strain hardening exponent, are found using a tensile test at the tail end of the coil. This thesis describes the development of a model to predict the final properties of continuous annealed steel. Actual process data along with mechanical properties derived using tensile testing were used to create the model. A generalised regression network was used as the main predictive mechanism. The non-­‐linear generalised regression approach was shown to exceed the predictive accuracy of multiple regression techniques. The use of a genetic algorithm to reduce the number of inputs was shown to increase the accuracy of the model when compared to those trained with all available inputs and those trained using correlation derived inputs. Further work is shown where the fully trained models were used to predict the relationships that exist between the processing conditions and mechanical properties. This was extended to predict the interaction between two process conditions varying at the same time. Using this approach produced predictions that mirrored known relationships within continuous annealed steels and gives predictions specific to the plant that could be used to optimise the process

Métodos machine learning para la predicción de inclusiones no metálicas en alambres de acero para refuerzo de neumáticos

ABSTRACT: Non-metallic inclusions are unavoidably produced during steel casting resulting in lower mechanical strength and other detrimental effects. This study was aimed at developing a reliable Machine Learning algorithm to classify castings of steel for tire reinforcement depending on the number and properties of inclusions, experimentally determined. 855 observations were available for training, validation and testing the algorithms, obtained from the quality control of the steel. 140 parameters are monitored during fabrication, which are the features of the analysis; the output is 1 or 0 depending on whether the casting is rejected or not. The following algorithms have been employed: Logistic Regression, K-Nearest Neighbors, Support Vector Classifier (linear and RBF kernels), Random Forests, AdaBoost, Gradient Boosting and Artificial Neural Networks. The reduced value of the rejection rate implies that classification must be carried out on an imbalanced dataset. Resampling methods and specific scores for imbalanced datasets (Recall, Precision and AUC rather than Accuracy) were used. Random Forest was the most successful method providing an AUC in the test set of 0.85. No significant improvements were detected after resampling. The improvement derived from implementing this algorithm in the sampling procedure for quality control during steelmaking has been quantified. In this sense, it has been proved that this tool allows the samples with a higher probability of being rejected to be selected, thus improving the effectiveness of the quality control. In addition, the optimized Random Forest has enabled to identify the most important features, which have been satisfactorily interpreted on a metallurgical basis.RESUMEN: Las inclusiones no metálicas se producen inevitablemente durante la fabricación del acero, lo que resulta en una menor resistencia mecánica y otros efectos perjudiciales. El objetivo de este estudio fue desarrollar un algoritmo fiable para clasificar las coladas de acero de refuerzo de neumáticos en función del número y el tipo de las inclusiones, determinadas experimentalmente. Se dispuso de 855 observaciones para el entrenamiento, validación y test de los algoritmos, obtenidos a partir del control de calidad del acero. Durante la fabricación se controlan 140 parámetros, que son las características del análisis; el resultado es 1 ó 0 dependiendo de si la colada es rechazada o no. Se han empleado los siguientes algoritmos: Regresión Logística, Vecinos K-Cercanos, Clasificador de Vectores Soporte (kernels lineales y RBF), Bosques Aleatorios, AdaBoost, Gradient Boosting y Redes Neurales Artificiales. El bajo índice de rechazo implica que la clasificación debe llevarse a cabo en un set de datos desequilibrado. Se utilizaron métodos de remuestreo y métricas específicas para conjuntos de datos desequilibrados (Recall, Precision y AUC en lugar de Accuracy). Random Forest fue el algoritmo más exitoso que proporcionó un AUC en los datos de test de 0.83. No se detectaron mejoras significativas después del remuestreo. Se ha cuantificado la mejora derivada de la implementación de este algoritmo en el procedimiento de muestreo para el control de calidad durante la fabricación de acero. En este sentido, se ha comprobado que esta herramienta permite seleccionar las muestras con mayor probabilidad de ser rechazadas, mejorando así la eficacia del control de calidad. Además, el Random Forest optimizado ha permitido identificar las variables más importantes, que han sido interpretadas satisfactoriamente sobre una base metalúrgica.Máster en Ciencia de Dato

Modelling as Research Methodology

Modelling as Research Methodology is written for the scientist and student researching the (expected) functioning of systems under specified conditions. As such, it represents an introduction to the use of modelling in natural, human and economical sciences. The book is divided into two sections. The first section illustrates the universal nature of modelling as aid to the researcher. In the second section, several typical examples of modelling are described