Measuring mechanical loads on profile rail linear guides

Abstract: Linear guides are heavily loaded standard machine parts used to provide a bearing which provides free movement of the axis in one linear degree of freedom while being highly stiff in all other degrees of freedom. Being able to measure the load exerted on the linear guide would provide the foundation for many possible applications such as predicting the remaining lifetime or monitoring manufacturing processes. Due to the high stiffness requirements which are usually present, the sensor system would have to be integrated such that it does not influence the mechanical behavior. Another challenging requirement for the sensor system is that it should be able to detect and distinguish between the five possible load modes. This thesis is dedicated to designing and evaluating specific suitable sensor systems for this purpose and to examine the more general prerequisites which are needed for their engineering process, including a signal evaluation method. One of these prerequisites is the definition of a comprehensive load distribution model, which is able to predict the distribution of an external multi-modal mechanical load vector on the individual rolling elements inside the bearing. Therefore, a load distribution model was implemented, including a discussion of possible parts and parameters, which were verified later using a sensor system developed in this thesis. This verification provided unprecedented empirical insight, supporting and advancing modeling tasks in this area. As a further contribution to the modeling aspect, a previously undocumented hysteresis phenomenon, i.e., different behavior for increasing and decreasing loads was found and thoroughly studied. It could be shown that this phenomenon, which occurs after preload liftoff, is inherent to the linear guide itself and also manifests in the deflection, making it interesting beyond the scope of this work. The origin was hypothesized in a gliding movement of the raceways on the rolling elements subject to friction, which formed the basis for a simulation and compensation method. Two sensor systems, both of which use the piezoresistive properties of diamond-like-carbon (DLC) layers, were designed and evaluated in depth. The first system is called a sensor steel inlay, which measures the strain close to the raceways, i.e., directly determining the strain introduced by loading the rolling elements, making it especially suitable for verification of the load distribution model. The second sensor system measures the outer surface strain caused by deformation of the runner block body. It was based on a multitude of spatially distributed surface strain measurements. This was implemented with a novel contacting device, which can also be a useful tool for general engineering processes involving the surface strain of arbitrary structures. A general methodology for combining the information of the individual measurements was developed. It is based on the deduction of local rolling element loads from the sensor signals, independent of the external mechanical load mode. Those local loads are then joined by inversion of the load distribution model, providing an inherent ability for load mode detection and distinction. Using this method, the sensor steel inlay logged an equivalent Root-Mean-Square (RMS) error of 2.9 kN (1.4 kN in the preload range) while the outer surface strain measurement system achieved an error of 3.7 kN (3.3 kN in the preload range) in the main mechanical load mode applied during experiments, moment-type loads have been converted to equivalent force-type loads. The ability of load mode distinction can be measured by the error in the remaining load modes. Here, the sensor steel inlay logged a RMS error of 3.1 kN (0.7 kN in the preload range) while the outer surface strain measurement system had a remaining RMS error of 2.2 kN (1.6 kN in the preload range)

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch
• Notes: Universität Freiburg, Dissertation, 2023

Classification and Topics

• Classification: Ingenieurwissenschaften und Maschinenbau
• Keyword: Wälzlager ; Sensor ; Last ; Geradführung

Contributors, Places and Time

• Event: Veröffentlichung
• (where): Freiburg
• (who): Universität
• (when): 2023
• Creator: Krampert, David
• Contributor: Reindl, Leonhard M. ; Wilde, Jürgen ; Albert-Ludwigs-Universität Freiburg. Fakultät für Angewandte Wissenschaften

Further information

• DOI: 10.6094/UNIFR/233092
• URN: urn:nbn:de:bsz:25-freidok-2330921
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 15.08.2025, 7:21 AM CEST