Monitoramento da condição da ferramenta de dressagem usando sinais de vibração e modelos neurais

• Ano de defesa: 2014
• Autor(a) principal: Rocha, Camila Alves da [UNESP]
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Estadual Paulista (Unesp)
• Palavras-chave em Português: Redes neurais (Computação); Retificadores de corrente eletrica; Detectores; Vibração; Neural networks (Computer science)
• Link de acesso: http://hdl.handle.net/11449/122125

Real time monitoring of the dressing process is becoming more and more necessary because it plays a very important role in the finish of the part manufactured by the grinding process. On the other hand, dresser wear is very expensive and not much effective to be monitored visually, but it is usually so developed in industry. The vibration sensor can be a useful tool in the process automation; however, it is rarely used as can be verified in research works. This work presents a classification method for three wear conditions (new, semi-new, and worn) of single-point dresser by using vibration signal and neural networks. Experimental runs were carried out in a surface grinding machine equipped with aluminium oxide grinding wheel, where the vibration signal was acquired by a fixed sensor attached to the dresser bolder. The signal spectra analysis was performed with regarding to the aforementioned wear conditions, and seven frequency bands were selected. Several neural network models were tested, which had two input statistics from the digital processing of the raw signal filtered for a given frequency band selected. Following hundreds of input combinations, number of hidder layers and neurons, two best models were chosen and analyzed, which showed results with up to 98.3% success rate