Browsing by Author "Henning, Elisa"
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- Planos fatoriais e método de Taguchi : aplicação ao estudo de resinas compostas fotopolimerizadas, usando o RPublication . Henning, Elisa; Oliveira, Amílcar; Oliveira, TeresaEsta dissertação aborda técnicas statísticas de planejamentos de experimentos com ênfase em planos fatoriais fracionados com níveis mistos. Planos de Taguchi e planos racionados obtidos com funções dos pacotes AlgDesign, planorR e qualityTools do R são aplicados a um caso real. O objetivo é analisar os efeitos produzidos à microdureza de uma resina composta ativada por fotopolimerizador, testando-se dois tipos de resina, quatro fotopolimerizadores em quatro diferentes profundidades. Os resultados dos planos fracionados são comparados com os decorrentes de um planejamento fatorial completo e também da literatura da área. Eles indicam que estas técnicas de frações de plano experimental foram efetivas para a análise proposta e se apresentam como alternativas viáveis. Os três fatores (resina, fotopolimerizador e profundidade) influenciam na microdureza. A partir do plano de Taguchi foi possível estabelecer uma combinação ótima de fatores e níveis que maximiza a microdureza. Foram também investigados pacotes e funções para as aplicações propostas e desenvolvidas algumas rotinas em linguagem R para aplicação do Método de Taguchi.
- The Mixed CUSUM-EWMA (MCE) control chart as a new alternative in the monitoring of a manufacturing processPublication . Oliveira, Amilcar; Oliveira, Teresa A.; Paladini, Edson Pacheco; Walter, Olga Maria Formigoni Carvalho; Henning, Elisa; Konrath, Andréa Cristina; Alves, Custodio da CunhaGoal: The objective is to conclude, based on a comparative study, if there is a significant difference in sensitivity between the application of MCE and the individual application of the CUSUM or EWMA chart, i.e., greater sensitivity particularly for cases of lesser magnitude of change. Design/Methodology/Approach: These are an applied research and statistical techniques such as statistical control charts are used for monitoring variability. Results: The results show that the MCE chart signals a process out of statistical control, while individual EWMA and CUSUM charts does not detect any situation out of statistical control for the data analyzed. Limitations: This article is dedicated to measurable variables and individual analysis of quality characteristics, without investing in attribute variables. The MCE chart was applied to items that are essential to the productive process development being analysed. Practical Implications: The practical implications of this study can contribute to: the correct choice of more sensitive control charts to detect mainly small changes in the location (mean) of processes; provide clear and accurate information about the fundamental procedures for the implementation of statistical quality control; and encourage the use of this quality improvement tool. Originality/Value: The MCE control chart is a great differential for the improvement of the quality process of the studied company because it goes beyond what CUSUM and EWMA control charts can identify in terms of variability.
- The Mixed CUSUM-EWMA (MCE) control chart as a new alternative in the monitoring of a manufacturing processPublication . Oliveira, Amilcar; Oliveira, Teresa; Paladini, Edson Pacheco; Walter, Olga Maria Formigoni Carvalho; Henning, Elisa; Konrath, Andréa Cristina; Alves, Custodio da CunhaGoal: The objective is to conclude, based on a comparative study, if there is a significant difference in sensitivity between the application of MCE and the individual application of the CUSUM or EWMA chart, i.e., greater sensitivity particularly for cases of lesser magni-tude of change. Design/Methodology/Approach: These are an applied research and statistical tech-niques such as statistical control charts are used for monitoring variability. Results: The results show that the MCE chart signals a process out of statistical control, while individual EWMA and CUSUM charts does not detect any situation out of statistical control for the data analyzed. Limitations: This article is dedicated to measurable variables and individual analysis of quality characteristics, without investing in attribute variables. The MCE chart was applied to items that are essential to the productive process development being analysed. Practical Implications: The practical implications of this study can contribute to: the cor-rect choice of more sensitive control charts to detect mainly small changes in the location (mean) of processes; provide clear and accurate information about the fundamental pro-cedures for the implementation of statistical quality control; and encourage the use of this quality improvement tool. Originality/Value: The MCE control chart is a great differential for the improvement of the quality process of the studied company because it goes beyond what CUSUM and EWMA control charts can identify in terms of variability.