Self-aware and self-correcting machine tools for robust accuracy

具有自我意识和自我修正功能的机床可实现稳定的精度

• 批准号: RGPIN-2016-06418
• 负责人: Mayer, René
• 金额: $ 2.11万
• 依托单位: École Polytechnique de Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=710282
• 关键词: Self; aware; self; correcting; machine

Much wealth is created by machining complex parts for the aerospace, automotive, oil and medical industry to name a few. The ability to make ever more complex parts, with tighter tolerances, enables original equipment manufacturers (OEMs) to design products that stay ahead of international competitors in terms of performance and cost. It also enables part manufacturers to competitively supply Canadian and international OEMs. In both cases, such industries are users of machine tools often widely available. What differentiates users is their capacity to select and then get the most out of their installed manufacturing base. In recent years, a growing trend has been towards machines which combine processes such as turning and milling to produce complex parts in a single setup thus potentially increasing productivity and quality. But their high capital cost means that uptime is crucial and making an out-of-tolerance part is unacceptable even for the first part made. Accuracy and uptime of machine tools demand a fundamental understanding of the machine error sources, their tracking in the industrial environment (self-awareness), their compensation (self-correction) and the anticipation of malfunctions or excessive loss of accuracy. Beside the basic geometric construction deviations, thermal effects, elastic deformations and wear are also known to occur but are not understood in the context of multi-axis machine tools. In an industrial setting, the ability to measure such errors and distinguish them requires rich mathematical models. This is because directly measuring those errors individually on a frequent basis is too time consuming and not always possible since errors sources act simultaneously. Instead, indirect approaches are favoured whereby their combined effect as volumetric errors are measured using non-intrusive in-situ methods and then error separation techniques applied. So, at a fundamental level this research program proposes an original holistic approach integrating new geometric, thermal, elastic and wear models of multi-axis machine tools developed using painstaking laboratory techniques. Then, indirect approaches are explored for the estimation of the parameters of such models for a particular installed machine in industry. The sought industrially viable data gathering techniques will favour an original approach based on scanning probes and uncalibrated brought-in and indigenous artefacts, the latter consisting of features already available in the machining volume, to gather data about the machine instantaneous status. The ease with which data will be made available will enable the timely detection of trends in machine behaviour. The rich calibrated models form the basis for real time machine compensation and detection and anticipation of excessive deviations so that corrective actions can be planned by machine users.

许多财富是通过加工航空航天、汽车、石油和医疗行业的复杂零件创造的。能够以更严格的公差制造更复杂的部件，使原始设备制造商（OEM）能够设计出在性能和成本方面领先于国际竞争对手的产品。它还使零件制造商能够有竞争力地为加拿大和国际OEM供应产品。在这两种情况下，这些行业都是机床的用户，这些机床通常广泛使用。用户的不同之处在于他们选择的能力，然后从他们的安装制造基地中获得最大收益。 近年来，越来越多的机床趋向于将车削和铣削等联合收割机工艺结合起来，在一次装配中生产复杂零件，从而潜在地提高生产率和质量。但它们的高资本成本意味着，制造公差是至关重要的，即使对于第一个制造的零件，制造公差外的零件也是不可接受的。 机床的精度和可靠性要求对机床误差源、在工业环境中的跟踪（自我意识）、补偿（自我校正）以及对故障或精度过度损失的预期有基本的了解。除了基本的几何结构偏差之外，热效应、弹性变形和磨损也是已知的，但在多轴机床的背景下不被理解。在工业环境中，测量这些误差并区分它们的能力需要丰富的数学模型。这是因为频繁地单独直接测量这些误差太耗时，并且由于误差源同时作用，因此并不总是可行的。相反，间接的方法是有利的，即它们的综合效果，体积误差测量使用非侵入性的原位方法，然后应用误差分离技术。 因此，在基础层面上，该研究计划提出了一种独创的整体方法，将使用艰苦的实验室技术开发的多轴机床的新几何，热，弹性和磨损模型集成在一起。然后，间接的方法进行了探索，为一个特定的安装在工业机器的参数估计这样的模型。所寻求的工业上可行的数据收集技术将有利于基于扫描探头和未校准的带入和本地人工制品的原始方法，后者由加工体积中已经可用的特征组成，以收集关于机器瞬时状态的数据。提供数据的便利性将使人们能够及时发现机器行为的趋势。丰富的校准模型构成了真实的时间机器补偿以及检测和预测过度偏差的基础，因此机器用户可以计划纠正措施。