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Mechanistic approach for the evaluation of machine tools quasi-static capability
KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
2017 (English)In: NEWTECH 2017: Proceedings of 5th International Conference on Advanced Manufacturing Engineering and Technologies, Springer Berlin/Heidelberg, 2017, p. 229-243Chapter in book (Refereed)
Abstract [en]

One of the greatest challenges in the manufacturing industry is to increase the understanding of the error sources and their effect on machine tool capability. This challenge is raised by the complexity of machining systems and the high requirements on accuracy. In this paper, a mechanistic evaluation approach is developed to handle the complexity and to describe the underlying mechanisms of the machine tools capability under quasi-static condition. The capability in this case is affected by the geometric errors of the multi-axis system and the quasi-static deflections due to process loads. In the assessment of these sources a mechanistic model is introduced. The model is composed of two parts, combining direct and indirect measurements. The direct measurement modelling method was applied to predict the effects of individual axis geometric errors on the functional point of machine tools. First, the direct measurement is employed to allow measuring each single machine tool axis motion error individually. The computational in the direct measurement model calculates the deviations from a given toolpath in the work space. Then, indirect measurements are used to determine the static stiffness and its variation in the workspace of machine tools. A case study demonstrates the applicability of the proposed approach, where laser interferometry was implemented as direct and loaded double ball bar as indirect measurement. The methodology was investigated on a three and a five axis machine tool and the results demonstrate the potential of the approach.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2017. p. 229-243
Keywords [en]
Accuracy, Machine tool, Static stiffness
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
URN: urn:nbn:se:kth:diva-216365DOI: 10.1007/978-3-319-56430-2_16Scopus ID: 2-s2.0-85019572220OAI: oai:DiVA.org:kth-216365DiVA, id: diva2:1151288
Note

QC 20171023

Available from: 2017-10-23 Created: 2017-10-23 Last updated: 2018-10-15Bibliographically approved
In thesis
1. Modelling and Management of Uncertainty in Production Systems: from Measurement to Decision
Open this publication in new window or tab >>Modelling and Management of Uncertainty in Production Systems: from Measurement to Decision
2018 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

The advanced handling of uncertainties arising from a wide range of sources is fundamental in quality control and dependability to reach advantageous decisions in different organizational levels of industry. Es-pecially in the competitive edge of production, uncertainty shall not be solely object of estimation but the result of a systematic management process. In this process, the composition and utilization of proper in-formation acquisition systems, capability models and propagation tools play an inevitable role. This thesis presents solutions from production system to operational level, following principles of the introduced con-cept of uncertainty-based thinking in production. The overall aim is to support transparency, predictability and reliability of production sys-tems, by taking advantage of expressed technical uncertainties. On a higher system level, the management of uncertainty in the quality con-trol of industrial processes is discussed. The target is the selection of the optimal level of uncertainty in production processes integrated with measuring systems. On an operational level, a model-based solution is introduced using homogeneous transformation matrices in combination with Monte Carlo method to represent uncertainty related to machin-ing system capability. Measurement information on machining systems can significantly support decision-making to draw conclusions on man-ufactured parts accuracy, by developing understanding of root-causes of quality loss and providing optimization aspects for process planning and maintenance.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2018. p. 60
Series
TRITA-ITM-AVL ; 2018:37
Keywords
Precision engineering, Uncertainty modelling, Machin-ing system capability
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Production Engineering
Identifiers
urn:nbn:se:kth:diva-235825 (URN)978-91-7729-846-5 (ISBN)
Presentation
2018-11-09, M311, Kungliga Tekniska högskolan, Brinellvagen 68, Stockholm, 10:00 (English)
Opponent
Supervisors
Funder
XPRES - Initiative for excellence in production research
Note

QC 20181015

Available from: 2018-10-15 Created: 2018-10-06 Last updated: 2018-10-16Bibliographically approved

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