The focus of the present study is an analysis of remote technical education, with a particular emphasis on professional technical courses such as Industrial Automation, Electrotechnics, Mechatronics, and Mechanics during the Covid-19 pandemic crisis. We investigate the primary pedagogical strategies adopted by educators to overcome the challenges imposed by this scenario and the digital tools that were instrumental in combating the impacts caused by compulsory remote education. Within the scope of this study, the challenges encountered during this unprecedented period of remote education were analyzed, as well as the identification of the advantages and disadvantages of this modality, observed from the perspective of both teachers and students. The work provides a holistic view of these aspects, giving a voice to the main actors involved in this process. Moreover, the study explores Bloom's Taxonomy, providing an analysis from its theoretical foundations to its practical applications to better structure and categorize pedagogical content and the skills to be developed in students. We discuss the most effective ways of assessing student progress in this challenging context, providing useful insights for future implementations. In conclusion, the work also presents a series of digital tools employed during the pandemic to enhance the quality of remote technical education and promote the development of student competencies in this environment. The analysis of these tools offers valuable insight into the role and impact of technology on teaching and learning, which can be leveraged even during regular face-to-face education.

El presente trabajo se centra en el análisis de la educación técnica a distancia, con un énfasis particular en los cursos técnicos de Automatización Industrial, Electrotecnia, Mecatrónica y Mecánica durante la crisis de la pandemia de Covid-19. Investigaremos las principales estrategias pedagógicas adoptadas por los docentes para superar los desafíos impuestos por este escenario y las herramientas digitales que fueron esenciales para combatir los impactos causados por la educación a distancia. En el contexto de este estudio, se analizaron los desafíos enfrentados durante este período de enseñanza a distancia, así como la identificación de las ventajas y desventajas de esta modalidad, observadas desde la perspectiva tanto de los profesores como de los alumnos. El trabajo proporciona una visión holística de estos aspectos, dando voz a los principales actores en este proceso. Además, el estudio explora la taxonomía de Bloom, proporcionando un análisis desde sus fundamentos teóricos hasta sus aplicaciones prácticas para estructurar y categorizar mejor el contenido pedagógico y las habilidades a ser desarrolladas en los estudiantes. Discutimos las formas más efectivas de evaluar el progreso de los alumnos en este contexto desafiante, proporcionando ideas útiles para futuras implementaciones. El trabajo también presenta una serie de herramientas digitales empleadas durante la pandemia para potenciar la calidad de la educación técnica a distancia y promover el desarrollo de competencias de los alumnos. El análisis de estas herramientas ofrece una perspectiva valiosa sobre el impacto de la tecnología en la enseñanza, que pueden ser aprovechados incluso durante la enseñanza presencial regular.

O presente trabalho tem como foco a análise do ensino técnico à distância, com ênfase particular nos cursos técnicos profissionalizantes de Automação Industrial, Eletroeletrônica, Mecatrônica e Mecânica durante a crise da pandemia da Covid-19. Investigaremos as principais estratégias pedagógicas adotadas pelos docentes para superar os desafios impostos por este cenário e as ferramentas digitais que foram essenciais no combate aos impactos causados pelo ensino remoto. No âmbito deste estudo, foram analisados os desafios enfrentados durante este período inédito de ensino remoto, bem como a identificação das vantagens e desvantagens dessa modalidade, observadas sob a perspectiva tanto dos professores quanto dos alunos. O trabalho proporciona uma visão holística desses aspectos, dando voz aos principais atores envolvidos neste processo. Além disso, o estudo explora a taxonomia de Bloom, proporcionando uma análise desde seus fundamentos teóricos até suas aplicações práticas para melhor estruturar e categorizar o conteúdo pedagógico e as habilidades a serem desenvolvidas nos estudantes. Discutimos as formas mais eficazes de avaliar os progressos dos alunos nesse contexto desafiador, fornecendo insights úteis para futuras implementações. Concluindo, o trabalho também apresenta uma série de ferramentas digitais empregadas durante a pandemia para potencializar a qualidade do ensino técnico remoto e promover o desenvolvimento de competências dos alunos nesse ambiente. A análise dessas ferramentas oferece uma perspectiva valiosa sobre o papel e o impacto da tecnologia no ensino e aprendizagem e que podem ser aproveitados mesmo durante o ensino regular presencial.

Abstract Paper aims Frequently, researchers try to find a better way to allocate assets in order to have maximum return and low variability in a portfolio as diverse as possible. This paper aims to apply D-Optimal Design in the context of Mixture Design and portfolio optimization to efficiently select the runs of the proposed experimental design. Originality A new approach to find the optimal weights that maximize the returns and minimize the risk using D-Optimal Design was used. A multi-response optimization problem considering returns, variability and entropy as functions of the weights was proposed. However, as there is a significant correlation between the objective functions, a Factor Analysis combined with FMSE to dimensionality reduction was used. Research method All the steps for both stages of the methodology applied in this paper are presented below: select real time series; predict one step ahead; generate a D-Optimal mixture design; apply weights and generate mathematical models; solve the optimization problem. Main findings Using the desirability method, the optimal values were determined, obtaining approximately 79% for the compound desirability function. The proposed method presented a 16.80% higher return with a 4.98% higher risk exposure if compared against Naïve method. Implications for theory and practice The proposed methodology can be applied to any portfolio optimization study. Mixture Design studies have already been proposed for modeling portfolio optimization problems. However, the D-Optimal Design proved to be adequate, which represents less computational effort.

Abstract Paper aims This paper presents a nonlinear time series prediction methodology using Neural Networks and Tracking Signals method to detect bias and their responsiveness to non-random changes in the time series. Originality This study contributes with an innovative approach of nonlinear time series prediction methodology. Furthermore, the Design of Experiments was applied to simulate datasets and to analyze the results of Average Run Length, identifying in which conditions the methodology is efficient. Research method Datasets were generated to simulate different nonlinear time series by changing the error of the series. The methodology was applied to the datasets and the Design of Experiments was implemented to evaluate the results. Lastly, a case study based on total oil and grease was performed. Main findings The results showed that the proposed prediction methodology is an effective way to detect bias in the process when an error is introduced in the nonlinear time series because the mean and the standard deviation of the error have a significant impact on the Average Run Length. Implications for theory and practice This study contributes to a discussion about time series prediction methodology since this new technique could be widely used in several areas to improve forecast accuracy.

Abstract Paper aims This paper presents a comparative evaluation of different forecasting methods using two artificial neural networks (Multilayer Perceptron network and Radial Basis Functions Neural Network) and the Gaussian process regression. Originality Due to the current world scenario, solving economic problems has become extremely important. Artificial neural networks are one of the most promising tools to forecast economic trends and are being widely studied in economic analyses. Therefore, due to the concerns about the performance of different forecasting methods to solve economic problems, this study contributes with an example of the forecasting performance of artificial neural network models compared with Gaussian process regression using Nelson-Plosser and U.S. macroeconomic real-life data sets. Research method Two real-life data sets were used to evaluate the forecasting methods proposed in this paper. These data sets were normalised to values between zero and one. After that, the data training was performed and, once it was built, a model was used to generate forecasts. Thus, observations were made to verify how accurately the fitted model forecast the values. Main findings The results obtained from the study show that, for all forecasting horizons, multi-layer perceptron networks and Gaussian process regression models had the most satisfactory results. On the other hand, the radial basis functions neural network model was unsuitable for econometric data. Implications for theory and practice This study contributes to a discussion about artificial neural networks and Gaussian process regression models for econometric forecasting. Although artificial neural networks are mainly used in economic analyses, the results showed that not all models, such as radial basis functions neural networks, present good results. In addition, the regression of the Gaussian process showed promising results to forecast econometric data.

Abstract Paper aims Propose a continuous decision support system, a Digital Twin, integrating two widely used techniques, Discrete Event Simulation and forecasting methods. Originality With the evolution of the industry, there is a growing need for increasingly agile and assertive decision support systems. Also, familiar tools and techniques tend to change over time to suit such a scenario, supporting new researches on their use in the modern industry. Research method The proposed method allows the use of simulation, with the aid of forecasting methods, for continuous decision making, composing the so-called Digital Twin. The method was applied in a real process to validate it. Main findings The Moving Average, Single Exponential Smoothing, and Double Exponential Smoothing forecasting methods were used to supply the simulation model in order to test scenarios and guide decision making. The developed system enabled a virtual copy with a certain degree of intelligence and that provides answers to make the constant decision-making process more efficient. Implications for theory and practice The proposed method can be used for several operational problems like headcount, production planning and covers different levels of decision. Therefore, it can be used both on the shop floor and at managerial levels.

Resumo Neste estudo, buscou-se analisar a variabilidade de instrumentos utilizados na indústria para avaliar as características geométricas de um ponto de solda. Para tal, inicialmente, utilizou-se da técnica de planejamento de experimentos para gerar um arranjo fatorial fracionado para as configurações dos parâmetros de soldagem em corpos de prova de aços galvanizados por imersão a quente, a fim de representar a amplitude real do processo de soldagem por pontos. Para as análises, utilizou-se o estudo de repetitividade e reprodutibilidade (GR&R), pelo método de análise de variância, para identificar os componentes de variação do sistema de medição avaliando três instrumentos distintos, comparando as medições realizadas por um analisador de imagens, a partir do ensaio metalográfico, e métricas de instrumentos convencionais de metrologia como o relógio apalpador e o paquímetro manual. Foram avaliadas duas características da qualidade, sendo elas: a profundidade de indentação e o diâmetro do ponto. A partir dos resultados foi possível verificar que o analisador de imagem apresentou uma menor variabilidade nas medições, caracterizando-se como a melhor escolha para as medições das respostas de qualidade do processo de solda por pontos apresentando um GR&R classificado como aceitável.

Abstract In this study, we sought to analyze the variability of instruments used in industry to evaluate the geometric characteristics of a welding point. For this purpose, the design of experiments technique was used to generate a fractional factorial design for the welding parameter configurations in test specimens of hot dip galvanized steels in order to represent the real amplitude of the resistance spot welding process. For the analyzes, the repeatability and reproducibility (GR&R) study was used by the analysis of variance method to identify the variation components of the measurement system by evaluating three different instruments, comparing the measurements performed by an image analyzer, from the metallography and measurements of conventional metrology instruments such as the dial-gauge and the caliper. Two quality characteristics were evaluated: indentation depth and nugget width. From the results it was possible to verify that the image analyzer presented a lower variability in the measurements, being the best choice for the measurements of the quality responses of the spot welding process, presenting a GR&R classified as acceptable.

Resumo O propósito deste artigo é apresentar um método de detecção de mudança de nível na dinâmica de séries temporais não lineares que consiste no uso de um gráfico de Controle Multivariado T2 de Hotelling monitorando a variação de três descritores normalizados: os Descritores de Hjorth de atividade, mobilidade e complexidade. Esta abordagem foi aplicada em diferentes séries temporais não lineares criadas artificialmente e é ilustrada neste artigo por um exemplo detalhado. Também foi feito um estudo de caso com seis séries reais do consumo de energia elétrica no meio industrial, confirmando a eficácia do método.

Abstract The purpose of this paper is to present a method for detecting the dynamic changes in a nonlinear time series that uses the Hotelling T2 multivariate control chart to monitor the variation in three normalized descriptors: Hjorth's descriptors of activity, mobility and complexity. This approach was applied to different simulated nonlinear time series and is illustrated in this paper with a detailed example. A case study with six time series of short-term electricity load consumption was also used to confirm the method's effectiveness.

A soldagem de revestimento de aços carbono com aços inoxidáveis tem obtido destaque no meio industrial por permitir que superfícies anti-corrosivas sejam obtidas a partir de materiais de baixo custo. No entanto, visando garantir a qualidade final dos revestimentos, é importante que o procedimento de soldagem seja bem ajustado, para que os cordões sejam depositados com a geometria desejada, com produtividade e sem defeitos. O objetivo deste trabalho é a otimização de múltiplas características do processo de revestimento de chapas de aço carbono ABNT 1020 utilizando arame tubular de aço inoxidável ABNT 316L. As características otimizadas incluem a largura, penetração, reforço e diluição, que representam a geometria do cordão. A produtividade foi maximizada através da taxa de deposição e do rendimento do processo. Como respostas de qualidade, considerou-se a formação de escória e o aspecto superficial. A estratégia de modelagem e otimização foi baseada em uma combinação da Metodologia de Superfície de Resposta, Método do Critério Global e Algoritmo Genético. Os resultados indicam que os modelos de superfície de resposta desenvolvidos para as características do processo apresentaram altos ajustes. O Método do Critério Global e o Algoritmo Genético foram aplicados com sucesso, o que permitiu a identificação do ponto de ótimo.

The stainless steel cladding process has highlighted in the industrial environment for allowing anti-corrosive surfaces are made from low cost materials. However, in order to ensure the claddings final quality, it is important that the welding procedure is well adjusted, so that the welds are deposited with the desired geometry, with productivity and flawless. The aim of this work is the multiple characteristics optimization in cladding of ABNT 1020 carbon steel plates using ABNT 316L stainless steel cored wire. The optimized features include the bead width, penetration, reinforcement and dilution, representing the weld bead geometry. Productivity has been maximized through the deposition rate and process yield. As quality responses, we considered the slag formation and surface appearance. The modeling and optimization strategy was based on a combination of Response Surface Methodology, Global Criterion Method and Genetic Algorithm. The results indicate that the response surface models developed for the process characteristics had high adjustments. The Global Criterion Method and Genetic Algorithm were implemented successfully, allowing the results optimization.

Neste trabalho buscou-se avaliar o comportamento de amplificadores de radiofrequência (RF) de potência em aplicações de telecomunicações. Uma estratégia experimental foi empregada resultando na otimização de um conjunto de fatores responsáveis pelo aumento da linearidade e eficiência geral do amplificador durante o processo produtivo, reduzindo com isto as intermodulações geradas e a interferência em serviços adjacentes. Estes resultados permitiram a manufatura de amplificadores banda larga de alta eficiência, garantindo assim maior produtividade e confiabilidade.

This paper evaluates radio frequency (RF) power amplifiers behavior for telecommunications applications. An experimental strategy was employed resulting in the optimization of a number of factors responsible for increasing the overall efficiency and linearity of amplifiers during production process, reducing total intermodulation and interference in adjacent channels. These results enabled broadband amplifiers manufacturing with high efficiency, thus ensuring increased productivity and reliability.

The use of artificial neural networks for prediction in hard turning has received considerable attention in literature. An often quoted drawback of ANNs is the lack of a systematic way for the design of high performance networks. This study presents a DOE based approach for the design of ANNs of Radial Basis Function (RBF) architecture applied to surface roughness prediction in turning of AISI 52100 hardened steel. Experimental factors are the number of radial units on the hidden layer, the algorithm employed to calculate the spread factor of radial units and the algorithm employed to calculate radial function centers. DOE is employed to select levels of factors that benefit network prediction skills. Experiments with data sets of distinct sizes were conducted and network configurations leading to high performance were identified. ANN models obtained proved capable to predict roughness in accurate, precise and affordable way. Results pointed significant factors for network design and revealed that interaction effects between design parameters have significant influence on network performance for the task proposed. The work concludes that the DOE methodology constitutes a better approach to the design of RBF networks for roughness prediction than the most common trial and error approach.

Encontrar um conjunto ótimo de parâmetros para um processo de soldagem é uma tarefa pouco trivial, face às múltiplas características exigíveis ou desejáveis que devem ser analisadas. Além disso, a negligência da estrutura de variância-covariância destas características na otimização pode conduzir a ótimos inadequados. Com o intuito de auxiliar na busca desses parâmetros, um método para otimização multiobjetiva, desenvolvido para o estudo do processo de soldagem FCAW (do inglês Flux Cored Arc Welding), utilizando-se arames tubulares, baseado no conceito de Erro Quadrático Médio Multivariado, será apresentado. Trata-se de uma abordagem combinada da Metodologia de Superfície de Resposta, Projeto de Experimentos e Análise de Componentes Principais, na tentativa de localizar valores próximos a alvos especificados, para cada uma das características estudadas (Penetração, Taxa de deposição, Rendimento, Índice de convexidade e Diluição), considerando-se as variáveis de processo expressas em função da tensão (V), velocidade de alimentação do arame (Va) e da distância do bico de contato-peça (d). Os resultados obtidos apontam para uma boa adequação desta proposta.

The optimization of welding processes is not a trivial task, mainly due to the great number of exigible and desirable characteristics that must be analyzed. Moreover, the optimization of a welding process with multiple characteristics without to consider the variance-covariance structure, may lead to inadequate optimum. To help in this task, a method of multiobjective optimization based in the Multivariate Mean Square Error applied in the study of multiple correlated characteristics of a FCAW (Flux Cored Arc Welding) welding process will be presented. This method characterized by a combined approach based in the Response Surface Methodology, Design of Experiments and Principal Components Analysis consisted in an attempt to achieve the nearest values to specific targets, for each studied characteristic (penetration, deposition rate, deposition efficiency, convexity index of the weld bead and dilution) considering the welding variables expressed in function of welding voltage (V), wire feed speed (Va) and the contact tip to workpiece distance (d). The results point out a good adequacy of the proposed method.