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

航空航天、汽车、石油和医疗等行业的复杂零件加工创造了大量财富。制造更复杂零件的能力和更严格的公差，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。它还使零件制造商能够有竞争力地供应加拿大和国际oem。在这两种情况下，这些行业都是机床的用户，通常可以广泛使用。用户的不同之处在于他们有能力选择并最大限度地利用他们的生产基地。近年来，将车削和铣削等工序结合起来，在一次设置中生产复杂零件的机器日益成为一种趋势，从而有可能提高生产率和质量。但是他们的高资本成本意味着正常运行时间是至关重要的，即使是第一个零件，制造一个超出公差的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器误差源，工业环境中的跟踪（自我意识），补偿（自我纠正）以及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了解。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。这是因为频繁地直接单独测量这些错误太耗时，而且由于错误源同时起作用，因此并不总是可能的。相反，间接方法更受青睐，即使用非侵入式原位方法测量体积误差的综合效应，然后应用误差分离技术。******因此，在基础层面上，本研究计划提出了一种原始的整体方法，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，针对工业上已安装的特定机器，探讨了间接估计模型参数的方法。寻求工业上可行的数据收集技术将有利于基于扫描探针和未经校准的引入和本地人工制品的原始方法，后者由加工批量中已有的特征组成，以收集有关机器瞬时状态的数据。提供数据的便利性将使及时发现机器行为的趋势成为可能。丰富的校准模型构成了实时机器补偿、检测和预测过度偏差的基础，以便机器用户可以计划纠正行动。

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.