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: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=616298
• 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.

许多财富都是通过为航空航天、汽车、石油和医疗行业加工复杂零件而创造的。制造更复杂零件的能力，以及更严格的公差，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。它还使零部件制造商能够向加拿大和国际原始设备制造商提供具有竞争力的产品。在这两种情况下，这类行业都是机床的用户，往往随处可见。用户的不同之处在于他们的选择能力，然后最大限度地利用他们已安装的制造基础。近年来，一个日益增长的趋势是，机床将车削和铣削等工序结合在一起，在一个设备中生产复杂的零件，从而潜在地提高生产率和质量。但它们高昂的资本成本意味着正常运行时间至关重要，制造超出容差的零件即使是第一个零件也是不可接受的。 机床的精度和正常运行时间要求对机床误差源、它们在工业环境中的跟踪(自我意识)、它们的补偿(自我校正)以及对故障或过度精度损失的预期有一个基本的了解。除了基本的几何结构偏差外，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还不被理解。在工业环境中，测量此类误差并区分它们的能力需要丰富的数学模型。这是因为在频繁的基础上直接单独测量这些误差太耗时，而且由于误差源同时作用，并不总是可能的。取而代之的是，采用间接方法，通过使用非侵入性原位方法测量体积误差，然后应用误差分离技术来测量它们的综合影响。 因此，在根本层面上，该研究计划提出了一种原创性的整体方法，集成了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。在此基础上，探讨了工业上安装的某台机器的模型参数估计的间接方法。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引入和本地人工制品的原始方法，后者包括加工体积中已有的特征，以收集关于机器瞬时状态的数据。提供数据的便利性将使及时检测机器行为的趋势成为可能。丰富的校准模型构成了机器实时补偿以及检测和预测过度偏差的基础，以便机器用户可以计划纠正措施。