Browsing by Author "Irigoien, Itziar"
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Item Predictive Maintenance on the Machining Process and Machine Tool(2020-01-01) Jimenez-Cortadi, Alberto; Irigoien, Itziar; Boto, Fernando; Sierra, Basilio; Rodriguez, German; Tecnalia Research & Innovation; FACTORY; FABRIC_INTELThis paper presents the process required to implement a data driven Predictive Maintenance (PdM) not only in the machine decision making, but also in data acquisition and processing. A short review of the different approaches and techniques in maintenance is given. The main contribution of this paper is a solution for the predictive maintenance problem in a real machining process. Several steps are needed to reach the solution, which are carefully explained. The obtained results show that the Preventive Maintenance (PM), which was carried out in a real machining process, could be changed into a PdM approach. A decision making application was developed to provide a visual analysis of the Remaining Useful Life (RUL) of the machining tool. This work is a proof of concept of the methodology presented in one process, but replicable for most of the process for serial productions of pieces.Item A statistical data-based approach to instability detection and wear prediction in radial turning processes(2018) Jimenez Cortadi, Alberto; Irigoien, Itziar; Boto, Fernando; Sierra, Basilio; Suarez, Alfredo; Galar, Diego; Tecnalia Research & Innovation; FACTORY; FABRIC_INTELRadial turning forces for tool-life improvements are studied, with the emphasis on predictive rather than preventive maintenance. A tool for wear prediction in various experimental settings of instability is proposed through the application of two statistical approaches to process data on tool-wear during turning processes: three sigma edit rule analysis and Principal Component Analysis (PCA). A Linear Mixed Model (LMM) is applied for wear prediction. These statistical approaches to instability detection generate results of acceptable accuracy for delivering expert opinion. They may be used for on-line monitoring to improve the processing of different materials. The LMM predicted significant differences for tool wear when turning different alloys and with different lubrication systems. It also predicted the degree to which the turning process could be extended while conserving stability. Finally, it should be mentioned that tool force in contact with the material was not considered to be an important input variable for the model.