Model-Based, Experimental Thermoelastic Analysis of a Large Scale Turbine Housing
 
More details
Hide details
1
Chair of Production Metrology and Quality Management, WZL Laboratory for Machine Tools and Production Engineering of RWTH Aachen University, Germany
 
2
Department of Production Metrology and Quality Management, Fraunhofer Institute for Production Technology IPT, Germany
 
 
Submission date: 2022-02-01
 
 
Acceptance date: 2022-02-05
 
 
Online publication date: 2022-02-17
 
 
Publication date: 2022-03-30
 
 
Corresponding author
Dominik Emonts   

Chair of Production Metrology and Quality Management, WZL Laboratory for Machine Tools and Production Engineering of RWTH Aachen University, Germany
 
 
Journal of Machine Engineering 2022;22(1):84-95
 
KEYWORDS
TOPICS
ABSTRACT
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.
 
REFERENCES (24)
1.
RAUCH M., HASCOET J.-Y., ROUSSEAU C., RUCKERT G., 2021, Thermal Monitoring for Metallic Additive Manufacturing Multi-Beads Multi-Layers Parts, Journal of Machine Engineering, 21/3, 92–100.
 
2.
GRZESIK W., 2020. Modelling of heat generation and transfer in metal cutting: a short review, Journal of Machine Engineering, 20/1, 24–33.
 
3.
BEHRENS B.-A., BROSIUS A., HINTZE W., IHLENFELDT S., WULFSBERG J.P., 2021, Production at the Leading Edge of Technology, Springer Berlin Heidelberg, Berlin, Heidelberg.
 
4.
DEUTSCH J., ALBRECHT T., RIEDEL M., PENTER L., WIEMER H., MÜLLER J., IHLENFELDT S., 2020, Thermo-Elastic Structural Analysis of a Machine Tool Using a Multi-Channel Absolute Laser Interferometer, Journal of Machine Engineering, 20/3, 63–75.
 
5.
DAHLEM P., EMONTS D., SANDERS M.P., SCHMITT R.H., 2020, A Review on Enabling Technologies for Resilient and Traceable on-Machine Measurements, Journal of Machine Engineering, 20/2, 5–17.
 
6.
YANG B., ROSS-PINNOCK D., MUELANER J., MULLINEUX G., 2017. Thermal Compensation for Large Volume Metrology and Structures, Int. J. Metrol. Qual. Eng. 8, 21, https://doi.org/10.1051/ijmqe/....
 
7.
OHLENFORST M., 2019. Model-Based Thermoelastic State Evaluation of Large Workpieces for Geometric Inspection, RWTH Aachen University.
 
8.
ISO, 2013, Geometrical product specifications (GPS) – Coordinate measuring machines (CMM): Technique for determining the uncertainty of measurement – Part1: Overview and metrological characteristics (ISO 15530-1:2013), 1st ed.
 
9.
ISO, 2009, Geometrical product specifications (GPS) – Acceptance and reverification tests for coordinate measuring machines (CMM) – Part 2: CMMs used for measuring linear dimensions (ISO 10360-2:2009).
 
10.
ISO, 2003, Geometrical product specifications (GPS) – Systematic errors and contributions to measurement uncertainty of length measurement due to thermal influences (ISO/TR 16015:2003).
 
11.
PAVLIČEK F., et al., 2015, Acclimatisation Time of Precise Workpieces for Quality Inspection, EUSPEN’S 15th International Conference & Exhibition, Leuven, Belgium, June.
 
12.
ISO, 2011, Geometrical product specifications (GPS) – Coordinate measuring machines (CMM): Technique for determining the uncertainty of measurement – Part 3: Use of calibrated workpieces or measurement standards (ISO 15530-3:2011), 1st ed.
 
13.
ZELENY J., JANDA M., 2008, On-Machine Measurement Systems for High-Precision Workpieces, Produced on Five-Axis Milling Machines, Journal of Machine Engineering, 8/1, 11–18.
 
14.
PETEREK M., 2017, Messunsicherheitsbestimmung für Geometriemessungen mit Werkzeugmaschinen; 1. Auflage – Measurement uncertainty determination for geometry measurements using machine tools, Dissertation, Apprimus Verlag, Aachen, XV.
 
15.
ROSS-PINNOCK D., MULLINEUX G., 2018, Thermal Compensation Using the Hybrid Metrology Approach Compared to Traditional Scaling. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 232/13, 2364–2374.
 
16.
AKBAR F., MATIVENGA P.T., SHEIKH M.A., 2010, An Experimental and Coupled Thermo-Mechanical Finite Element Study of Heat Partition Effects in Machining, Int. J. Adv. Manuf. Technol., 46/5–8, 491–507.
 
17.
RAI J.K., XIROUCHAKIS P., 2009, FEM-Based Prediction of Workpiece Transient Temperature Distribution and Deformations During Milling, Int. J. Adv. Manuf. Technol., 42/5–6, 429–449.
 
18.
SCHINDLER S., ZIMMERMANN M., AURICH J.C., STEINMANN P., 2014, Finite Element Model to Calculate the Thermal Expansions of the Tool and the Workpiece in Dry Turning, Procedia CIRP, 14, 535–540.
 
19.
GLÄNZEL J., HERZOG R., IHLENFELDT S., MEYER A., UNGER R., 2016. Simulation-based Correction Approach for Thermo-elastic Workpiece Deformations During Milling Processes, Procedia CIRP, 46, 103–106.
 
20.
DAW A., KARPATNE A., WATKINS W., READ J., KUMAR V., 2017, Physics-Guided Neural Networks (PGNN): An Application in Lake Temperature Modeling, http://arxiv.org/pdf/1710.1143....
 
21.
VDI Heat Atlas, 2010, Springer Berlin Heidelberg, Berlin, Heidelberg.
 
22.
ISO, 2021, Statistical methods in process management – Capability and performance – Part 7: Capability of measurement processes (ISO 22514-7:2021).
 
23.
EMONTS D., YANG J., SCHMITT R.H., 2021, Solving Transient Inverse Heat Transfer Problems in Complex Geometries Using Physics-Guided Neural Networks (PGNN), MM SJ 2021, 3, 4540–4547.
 
24.
EMONTS D., SANDERS M.P., DAHLEM P., BODENBENNER M., MONTAVON B., SCHMITT H.R., 2021, Virtuelle Klimatisierung/Virtual Climatization, WT 111, 11–12, 887–892.
 
eISSN:2391-8071
ISSN:1895-7595
Journals System - logo
Scroll to top