A review on enabling technologies for resilient and traceable on-machine measurements
 
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Laboratory for Machine Tools and Production Engineering (WZL) of RWTH Aachen University, Chair of Production Metrology and Quality Management, Aachen, Germany
Submission date: 2020-03-01
Acceptance date: 2020-05-02
Online publication date: 2020-06-24
Publication date: 2020-06-24
 
Journal of Machine Engineering 2020;20(2):5–17
 
KEYWORDS
ABSTRACT
On Machine Measurements are a key factor for shorter closed quality control loops in industrial manufacturing. Especially for the production of large components, they promote the first-time-right approach, which is highly desirable, due to small quantities and steep value chains. In contrast to measurement rooms for CMMs, the production environment conditions are unregulated and impact multiple factors along the on-machine measurement metrological chain. As presented as a keynote speech at the XXXI CIRP Sponsored Conference on Supervising and Diagnostics of Machining Systems "MANUFACTURING ACTIVE IMPROVEMENT" by Professor Dr. Robert H. Schmitt, this article reviews current research and ideas regarding on-machine measurements. The authors collect necessary process data with the help of new technologies in the course of digitalization and thus propose a holistic model for systematic error compensation and measurement uncertainty prediction. For assessing the machine’s volumetric accuracy under thermal loads, the authors develop a novel modelling approach, which determines transient geometric errors by abstracting structural parts as spline curve with typical deformation modes. To address the workpiece’s influence on the measurement process, a data-driven framework, fusing real-time sensor-data with the virtual component, is used to model and predict transient thermo-mechanical workpiece states. For dissemination, the authors continue working on ISO standardization and, as subjects of future research, explore new paths in terms of data-driven modelling approaches, using physical abstractions coupled with machine learning and live process data.
 
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