Monitoramento do desgaste de ferramenta no processo de fresamento via emissão acústica

• Ano de defesa: 2007
• Autor(a) principal: Souto, Ulisses Borges
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Uberlândia; BR; Programa de Pós-graduação em Engenharia Mecânica; Engenharias; UFU
• Palavras-chave em Português: Fresamento; Desgaste; Monitoramento; Emissão acústica; Redes neurais; Fresagem (Trabalhos em metal); Milling; Tool wear; Tool wear monitoring; Acoustic emission; Neural networks; CNPQ::ENGENHARIAS::ENGENHARIA MECANICA
• Link de acesso: https://repositorio.ufu.br/handle/123456789/14788

The main goal of this work is the development of techniques for application of acoustic emission signal in tool wear monitoring in face milling operation. In this work a Sensis (DM 42) equipment for acquisition of the acoustic emission signal was used during the milling of a high strength low alloy steel (Din 38MnS6). A milling cutter with 125 mm diameter for eight inserts with specification R245 125Q40-12M was used. The ISO specification of the inserts were SEMN 12 04 AZ TiN coated. The tests were divided into two parts. In the first part, wear and some other machining phenomena were monitored through the construction of a luminous intensity map. For these tests it was used one, two, four or eight inserts simultaneously. In this stage the acoustic emission signal was evaluated using the RMS values. In the second part of this work the acoustic emission raw signal was used. Amongst the statistical parameters that correlate to tool wear extracted from the raw signal, the best fit ones were selected to train and validate a Probabilistic Neural Network. The results of the PNN indicate that the acoustic emission signal can be used to recognition of tool wear levels in the milling process.