Computational intelligence for predictive condition monitoring and approaches for online analysis
Torabi Jahromi, Amin
Date of Issue2013
School of Electrical and Electronic Engineering
Singapore Institute of Manufacturing Technology
The process of high speed milling (HSM) is regarded as one of the most sophisticated and complicated manufacturing operations. In the past four decades, many investigations have been conducted on this process aiming to better understand its nature and improve the surface quality of the products as well as extending tool life. To achieve these goals, it is necessary to form a general descriptive reference model of the milling process using experimental data, thermo-mechanical analysis, statistical or artificial-intelligent (AI) models. Besides, increasing demands for more efficient milling processes, qualified surface finishing, and modeling techniques have propelled the development of more effective modeling methods and approaches. In the first part this dissertation, an extensive literature survey of the state-of-the-art modeling techniques of milling processes is carried out, more specifically of recent advances and applications of AI-based modeling techniques. The comparative study of the available methods as well as the suitability of each method for corresponding types of experiments is also presented. In addition, the weaknesses of each method as well as open research challenges are presented. Therefore, a comprehensive comparison of recent developments in the field will be a guideline for choosing the most suitable modeling technique for this process regarding its goals, conditions, and specifications. After comprehensive study of the available methods for modeling HSM processes, to build up a proper condition monitoring system, sensor signals are to be utilized to form a reference model which non-intrusively reflects the performance of the system. Therefore, a desired reference model has to apply more efficient feature extraction and artificial intelligence (AI) techniques to be more repeatable and generalizable. Since milling signals are complex, a time-frequency analysis method, namely wavelet, is applied for feature extraction. Considering the high dimension of the wavelet features, clustering methods are used for dimension reduction and also as an interpretation layer between the signal feature extraction subsystem and artificial intelligence blocks. This research illustrates the performance of artificial intelligence based techniques for modeling of high speed end milling experimental data. Studied and developed methods are applied on wavelet features of force and vibration signals to illustrate the repeatability and accuracy of their results. It is shown that the proposed structure as well as the developed artificial intelligent method can present the status of the process and can be applied for fault diagnosis and TCM purposes. It is also discussed that how application of available data mining methods with a proper structure may improve the performance of existing reference models towards more efficient utilization of available experimental data and easily generalizable reference models.
DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering