Control of thermal damage in grinding by digital signal processing of raw acoustic emission

• Autor(a) principal: De Aguiar, Paulo R. [UNESP]
• Data de Publicação: 2002
• Outros Autores: Bianchi, Eduardo Carlos [UNESP], Serni, Paulo J.A. [UNESP], Lançoni, Patrik N. [UNESP]
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1109/ICARCV.2002.1234976; http://hdl.handle.net/11449/67051
• topic: Automation; Data Acquisition; Data Processing; Manufacturing Process; Monitored Control System; Signal Processing; Cutting tools; Data acquisition; Data processing; Digital signal processing; Grinding (comminution); Monitoring; Optimization; Process control; Production; Residual stresses; Statistical methods; Acoustic emissions; Error analysis; Grinding (machining); Parameter estimation; Sampling; Sensors; Cutting speeds; Machining operation; Root mean square (RMS); Thermal damage; Manufacturing process; Monitored control systems; Signal processing

Grinding process is usually the last finishing process of a precision component in the manufacturing industries. This process is utilized for manufacturing parts of different materials, so it demands results such as low roughness, dimensional and shape error control, optimum tool-life, with minimum cost and time. Damages on the parts are very expensive since the previous processes and the grinding itself are useless when the part is damaged in this stage. This work aims to investigate the efficiency of digital signal processing tools of acoustic emission signals in order to detect thermal damages in grinding process. To accomplish such a goal, an experimental work was carried out for 15 runs in a surface grinding machine operating with an aluminum oxide grinding wheel and ABNT 1045 e VC131 steels. The acoustic emission signals were acquired from a fixed sensor placed on the workpiece holder. A high sampling rate acquisition system at 2.5 MHz was used to collect the raw acoustic emission instead of root mean square value usually employed. In each test AE data was analyzed off-line, with results compared to inspection of each workpiece for burn and other metallurgical anomaly. A number of statistical signal processing tools have been evaluated.