Lógica ANFIS aplicada na estimação da rugosidade e do desgaste da ferramenta de corte no processo de retificação plana de cerâmicas avançadas
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Data
2010-07-29
Autores
Spadotto, Marcelo Montepulciano [UNESP]
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Editor
Universidade Estadual Paulista (Unesp)
Resumo
A necessidade de aplicação de novos equipamentos em ambientes cada vez mais agressivos demandou a busca por novos produtos capazes de suportar altas temperaturas, inertes às corroções químicas e com alta rigidez mecânica. O avanço tecnógico na produção de materiais cerâmicos tornou possível o emprego de processos de fabricação que antes eram somente empregados em metais. Dentre os processos de usinagem de cerâmicas avançadas, a retificação é o mais utilizado devido às maiores taxas de remoção diferentemente do brunimento e das limitações geométricas do processo de lapidação. A rugosidade é um do parâmetros de saída do processo de retificação que influi, dentre outros fatores, na qualidade do deslizamento entre estruturas, podendo gerar aquecimento. Além disso, o desgaste da ferramenta de corte gerado durante o processo está associado aos custos fixos e a problemas relacionados com o acabamento superficial bem como a danos estruturais. Essas duas variáveis, rugosidade e desgaste, são objetos de estudos de muitos pesquisadores. Entretanto, o controle automático tem sido uma difícil tarefa de ser realizada devido às variações de parâmetros ocorridas no processo. Dessa maneira, o presente trabalho tem por objetivo aplicar a lógica ANFIS (Adaptive Neuro-Fuzzy Inference System) na estimação da rogosidade e do desgaste da ferramenta de corte no processo de retificação plana de cerâmicas avançadas. A ferramenta de corte aplicada para retificar os corpos-de-prova de alumina (96%) foi um rebolo diamantado. A partir do processamento digital dos sinais de emissão acústica e potência média de corte foram calculadas as estatísticas: média, desvio padrão, potência máxima, DPO e DPKS. As estatísticas foram aplicadas com entradas de duas redes ANFIS, uma estimando valores de rugosidade e outra estimando valores de desgaste...
The need for implementation of new equipaments in an increasingly agressive environmentl demanded a search for new products capable of withstanding high temperatures, inert to chemical corrosion and high mechanical stiffeness. Technological advances in the production of ceramic materials have become possible with the employment of manufacturing processes that previously were only employed in metals. Among the advanced ceramics machining processes, the grinding process is the most used, because of higher removal rates in constrast with the honing process and geometric limitations of lapping process. The surface reoughness is one of the output parameters of grinding process that affects, among other factors, the quality of sliding between structures that may generate heat. Moreover, the wear of the cutting tool generated during the process is associated with fixed costs and problems related to suface finishing as well as structural damages. These two variables, surface roughness and wear, have been studied by many researchers; however, the automatic control has been a difficult task to be carry out due to parameters variations occurring in the process. Hence, this work aims to apply logic ANFIS (Adaptive Neuro-Fuzzy Inference System) in the estimation of surface roughness and wear of the cutting tool in the tangential griding process of advanced ceramics. The cutting tools used to grind workpieces of alumina (96%) was a diamond grinding wheel. From the digital processing of acoustic emission and average cutting power signals some statistics were calculated: mean, standard deviation, maximum power, DPO and DPKS. The statistics were applied as inputs of two ANFIS networks estimating surface roughess and wear values. The results had demonstrated that the statistics associated with the ANFIS network can be used in the estimation of surface roughness and wear. However, the wear ANFIS network... (Complete abstract click electronic access below)
The need for implementation of new equipaments in an increasingly agressive environmentl demanded a search for new products capable of withstanding high temperatures, inert to chemical corrosion and high mechanical stiffeness. Technological advances in the production of ceramic materials have become possible with the employment of manufacturing processes that previously were only employed in metals. Among the advanced ceramics machining processes, the grinding process is the most used, because of higher removal rates in constrast with the honing process and geometric limitations of lapping process. The surface reoughness is one of the output parameters of grinding process that affects, among other factors, the quality of sliding between structures that may generate heat. Moreover, the wear of the cutting tool generated during the process is associated with fixed costs and problems related to suface finishing as well as structural damages. These two variables, surface roughness and wear, have been studied by many researchers; however, the automatic control has been a difficult task to be carry out due to parameters variations occurring in the process. Hence, this work aims to apply logic ANFIS (Adaptive Neuro-Fuzzy Inference System) in the estimation of surface roughness and wear of the cutting tool in the tangential griding process of advanced ceramics. The cutting tools used to grind workpieces of alumina (96%) was a diamond grinding wheel. From the digital processing of acoustic emission and average cutting power signals some statistics were calculated: mean, standard deviation, maximum power, DPO and DPKS. The statistics were applied as inputs of two ANFIS networks estimating surface roughess and wear values. The results had demonstrated that the statistics associated with the ANFIS network can be used in the estimation of surface roughness and wear. However, the wear ANFIS network... (Complete abstract click electronic access below)
Descrição
Palavras-chave
Retificação, Emissão acústica, Processamento de sinais, Adaptive Neuro-Fuzzy Inference System (ANFIS), Grinding, Acoustic Emission, Digital Signal Processing
Como citar
SPADOTTO, Marcelo Montepulciano. Lógica ANFIS aplicada na estimação da rugosidade e do desgaste da ferramenta de corte no processo de retificação plana de cerâmicas avançadas. 2010. 88 f. Dissertação (mestrado) - Universidade Estadual Paulista, Faculdade de Engenharia, 2010.