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Prediction of the machine tool errors under quasi-static load: Developing methodology through the synthesis of bottom-up and top-down modeling approach
KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
2015 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

One of the biggest challenges in the manufacturing industry is to increase the understanding of the sources of the errors and their effects on machining systems accuracy. In this thesis a new robust empirical evaluation method is developed to predict the machine tool errors under quasi-static load including the effect of the variation of stiffness in the workspace, the geometric and the kinematic errors. These errors are described through combined computational models for a more accurate assessment of the machine tool’s capability. The purpose of this thesis is to establish such methodology through the synthesis of the bottom-up and the top-down modeling approach, which consists the combination of the direct (single axis measurements by laser interferometer) and indirect (multi-axis measurements by loaded double ball-bar) measurement technics. The bottom-up modeling method with the direct measurement was applied to predict the effects of the geometric and kinematic errors in the workspace of a machine tool. The top-down modeling method with the indirect measurement was employed to evaluate the variation of the static stiffness in the workspace of a machine tool. The thesis presents a case study demonstrating the applicability of the proposed approach. The evaluation technic extended for machine tools with various kinematic structures. The methodology was implemented on a three and a five axis machine tool and the results expose the potential of the approach.

Place, publisher, year, edition, pages
2015. , 58 p.
, Degree Project in Production Engineering Management, Second Level, 665
National Category
Engineering and Technology
URN: urn:nbn:se:kth:diva-186143OAI: diva2:925671
Available from: 2016-05-03 Created: 2016-05-03 Last updated: 2016-05-03Bibliographically approved

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