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

Deep Learning-Based Machinery Fault Diagnostics

This book offers a compilation for experts, scholars, and researchers to present the most recent advancements, from theoretical methods to the applications of sophisticated fault diagnosis techniques. The deep learning methods for analyzing and testing complex mechanical systems are of particular interest. Special attention is given to the representation and analysis of system information, operating condition monitoring, the establishment of technical standards, and scientific support of machinery fault diagnosis

Experimental investigation and modelling of the heating value and elemental composition of biomass through artificial intelligence

Abstract: Knowledge advancement in artificial intelligence and blockchain technologies provides new potential predictive reliability for biomass energy value chain. However, for the prediction approach against experimental methodology, the prediction accuracy is expected to be high in order to develop a high fidelity and robust software which can serve as a tool in the decision making process. The global standards related to classification methods and energetic properties of biomass are still evolving given different observation and results which have been reported in the literature. Apart from these, there is a need for a holistic understanding of the effect of particle sizes and geospatial factors on the physicochemical properties of biomass to increase the uptake of bioenergy. Therefore, this research carried out an experimental investigation of some selected bioresources and also develops high-fidelity models built on artificial intelligence capability to accurately classify the biomass feedstocks, predict the main elemental composition (Carbon, Hydrogen, and Oxygen) on dry basis and the Heating value in (MJ/kg) of biomass...Ph.D. (Mechanical Engineering Science

Pathways to Water Sector Decarbonization, Carbon Capture and Utilization

The water sector is in the middle of a paradigm shift from focusing on treatment and meeting discharge permit limits to integrated operation that also enables a circular water economy via water reuse, resource recovery, and system level planning and operation. While the sector has gone through different stages of such revolution, from improving energy efficiency to recovering renewable energy and resources, when it comes to the next step of achieving carbon neutrality or negative emission, it falls behind other infrastructure sectors such as energy and transportation. The water sector carries tremendous potential to decarbonize, from technological advancements, to operational optimization, to policy and behavioural changes. This book aims to fill an important gap for different stakeholders to gain knowledge and skills in this area and equip the water community to further decarbonize the industry and build a carbon-free society and economy. The book goes beyond technology overviews, rather it aims to provide a system level blueprint for decarbonization. It can be a reference book and textbook for graduate students, researchers, practitioners, consultants and policy makers, and it will provide practical guidance for stakeholders to analyse and implement decarbonization measures in their professions

Pathways to Water Sector Decarbonization, Carbon Capture and Utilization

The water sector is in the middle of a paradigm shift from focusing on treatment and meeting discharge permit limits to integrated operation that also enables a circular water economy via water reuse, resource recovery, and system level planning and operation. While the sector has gone through different stages of such revolution, from improving energy efficiency to recovering renewable energy and resources, when it comes to the next step of achieving carbon neutrality or negative emission, it falls behind other infrastructure sectors such as energy and transportation. The water sector carries tremendous potential to decarbonize, from technological advancements, to operational optimization, to policy and behavioural changes. This book aims to fill an important gap for different stakeholders to gain knowledge and skills in this area and equip the water community to further decarbonize the industry and build a carbon-free society and economy. The book goes beyond technology overviews, rather it aims to provide a system level blueprint for decarbonization. It can be a reference book and textbook for graduate students, researchers, practitioners, consultants and policy makers, and it will provide practical guidance for stakeholders to analyse and implement decarbonization measures in their professions

Pathways to Water Sector Decarbonization, Carbon Capture and Utilization

The water sector is in the middle of a paradigm shift from focusing on treatment and meeting discharge permit limits to integrated operation that also enables a circular water economy via water reuse, resource recovery, and system level planning and operation. While the sector has gone through different stages of such revolution, from improving energy efficiency to recovering renewable energy and resources, when it comes to the next step of achieving carbon neutrality or negative emission, it falls behind other infrastructure sectors such as energy and transportation. The water sector carries tremendous potential to decarbonize, from technological advancements, to operational optimization, to policy and behavioural changes. This book aims to fill an important gap for different stakeholders to gain knowledge and skills in this area and equip the water community to further decarbonize the industry and build a carbon-free society and economy. The book goes beyond technology overviews, rather it aims to provide a system level blueprint for decarbonization. It can be a reference book and textbook for graduate students, researchers, practitioners, consultants and policy makers, and it will provide practical guidance for stakeholders to analyse and implement decarbonization measures in their professions

Pathways to Water Sector Decarbonization, Carbon Capture and Utilization

The water sector is in the middle of a paradigm shift from focusing on treatment and meeting discharge permit limits to integrated operation that also enables a circular water economy via water reuse, resource recovery, and system level planning and operation. While the sector has gone through different stages of such revolution, from improving energy efficiency to recovering renewable energy and resources, when it comes to the next step of achieving carbon neutrality or negative emission, it falls behind other infrastructure sectors such as energy and transportation. The water sector carries tremendous potential to decarbonize, from technological advancements, to operational optimization, to policy and behavioural changes. This book aims to fill an important gap for different stakeholders to gain knowledge and skills in this area and equip the water community to further decarbonize the industry and build a carbon-free society and economy. The book goes beyond technology overviews, rather it aims to provide a system level blueprint for decarbonization. It can be a reference book and textbook for graduate students, researchers, practitioners, consultants and policy makers, and it will provide practical guidance for stakeholders to analyse and implement decarbonization measures in their professions