Machine tool monitoring using data analytics and physics-based models

使用数据分析和基于物理的模型进行机床监控

基本信息

批准号：
523509-2018
负责人：
Mechefske, Christopher
金额：
$ 2.33万
依托单位：
Queen's University
依托单位国家：
加拿大
项目类别：
Collaborative Research and Development Grants
财政年份：
2019
资助国家：
加拿大
起止时间：
2019-01-01 至 2020-12-31
项目状态：
已结题

来源：
https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=684483
关键词：
Machine tool monitoring using data

项目摘要

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.

为了保持商业竞争性的CheckFluid，不断搜索并实施最佳制造方法。该项目的重点是降低制造成本的方法，同时最大程度地提高产品质量并最大程度地减少环境影响。校验流希望通过越来越多的知识来利用其现有的专业知识，了解在线动态信号分析技术，用于检测，诊断和预测金属加工操作中的问题（机床磨损，低质量的零件生产，机器组件和/或系统降解），并将这些领域的领域与新的数据分析相结合（用于改进的缺陷型和诊断）和物理学的组件和系统和系统，并将这些领域结合在一起。拟议的项目有四个主要目标。第一个目标是最终确定和验证准确的数据驱动的建模工具，用于检测和诊断切割工具性能的降解。第二个目标是定义，测试和验证切割工具分析模型，该模型可用于在可变操作条件下以及代表过去的工具磨损率和工具故障的现有数据下用于切割工具磨损预测。第三个目标是定义将数据驱动模型和基于物理模型组合到混合方法的各种策略，并测试这些策略以确认其优于“标准”方法的优势性能。第四个目标是研究开发方法在不同机器（例如变速箱）上的应用来研究这些新技术的通用应用。在这项研究工作期间，总共将对至少6个HQP进行培训。