Model-Based, Experimental Thermoelastic Analysis of a Large Scale Turbine Housing
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Chair of Production Metrology and Quality Management, WZL Laboratory for Machine Tools and Production Engineering of RWTH Aachen University, Germany
Department of Production Metrology and Quality Management, Fraunhofer Institute for Production Technology IPT, Germany
Dominik Emonts   

Chair of Production Metrology and Quality Management, WZL Laboratory for Machine Tools and Production Engineering of RWTH Aachen University, Germany
Submission date: 2022-02-01
Acceptance date: 2022-02-05
Online publication date: 2022-02-17
Publication date: 2022-03-30
Journal of Machine Engineering 2022;22(1):84–95
Temporally and spatially unstable thermal conditions lead to inhomogeneous thermoelastic changes in the workpiece geometry. Consequently, non-negligible geometric deviations are evident, especially when measuring large workpieces with narrow tolerances, which often take place in non-climatized production environments and thus make thermal monitoring indispensable. Accurate determination of the thermoelastic behaviour for complex and large geometries is a challenging task with computationally effortful or less accurate existing solutions. Thus, the development of innovative measurement and modelling approaches is subject of current research, whereat physical validation is a prerequisite. Therefore, the authors developed a method, enabling the emulation of typical process heat cycles on a turbine housing in combination with a geometric measurement system. The idea is to provide reproducible and reversible thermal conditions on a representative large workpiece and to investigate the resulting geometric deformation in an economically viable way. Throughout this study, an analogy test rig is presented, integrating different temperature sensors, two geometric measurement systems and thermal deformation models into one demonstrator. The demonstrator's first applications show insightful results, revealing accordance, but also unexpected deviations between the predicted and measured quantities. Moreover, it provides great potential for validation of more complex modelling approaches and innovative thermal condition monitoring systems for large precision workpieces.
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