Machine tool monitoring using data analytics and physics-based models

为了保持商业竞争力，切克流体不断寻求并实施最佳的制造方法。这个项目的重点是如何在降低制造成本的同时最大限度地提高产品质量和最大限度地减少对环境的影响。随着在线动态信号分析技术在金属加工操作(机床磨损、低质量零件生产、机器部件和/或系统退化)中检测、诊断和预测问题的知识的增长，CheckFluid希望利用他们现有的专业知识，并将这些专业领域与新的数据分析(用于改进故障检测和诊断)和基于物理的部件和系统建模(用于改进故障和退化预测)相结合。*拟议项目有四个主要目标。第一个目标是最终确定和验证用于检测和诊断刀具性能退化的准确的数据驱动建模工具。第二个目标是定义、测试和验证刀具分析模型，该模型可用于在不同运行条件下的刀具磨损预测，并且在现有数据表示过去的刀具磨损率和刀具失效的情况下不可用。第三个目标是定义各种策略，用于将数据驱动模型和基于物理的模型组合成混合方法，并对其进行测试，以确认其相对于标准方法的优势性能。第四个目标是调查开发的方法在不同机器(如变速箱)上的应用，以调查这些新技术的一般应用。在本次研究工作中，总共将培训至少6名HQP。**

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.**