Machine tool monitoring using data analytics and physics-based models

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In order to remain commercially competitive Checkfluid continually searches for and implements optimum manufacturing methods. The focus of this project is on ways to reduce manufacturing costs while maximizing the product quality and minimizing environmental impact. Checkfluid wishes to leverage their existing expertise with the growing knowledge of on-line dynamic signal analysis techniques for detecting, diagnosing, and predicting problems in metal machining operations (machine tool wear, low quality part production, machine component and/or system degradation) and combine these areas of expertise with new data analytics (for improved fault detection and diagnosis) and physics-based component and system modeling (for improved fault and degradation prediction).The proposed project has four main objectives. The first objective is to finalize and verify accurate data-driven modeling tools for use in detecting and diagnosing degradation of cutting tool performance. The second objective is to define, test and verify a cutting tool analytical model that can be used for cutting tool wear prediction under variable operating conditions and where existing data representing past tool wear rates and tool failures is not available. The third objective is to define various strategies for combining the data-driven models and the physics-based models into hybrid methodologies and test these to confirm their advantageous performance over 'standard' methods. The fourth objective is to investigate the application of the developed methods on different machines (such as gearboxes) to investigate the generic application of these new techniques. A total of at least 6 HQP will be trained during this research work.