Geometric error measurement and compensation of machines—An update

Schwenke, H.; Knapp, Wolfgang; Haitjema, Han; Weckenmann, Albert; Schmitt, Robert; Delbressine, Flm Frank

doi:10.1016/j.cirp.2008.09.008

Cited by 797 publications

(405 citation statements)

References 60 publications

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“…A Monte Carlo simulation is used to investigate how the deviation is affected by changes in the thermal conductance matrix. The deviation can be expressed as a percentage of the measured temperature as calculated using equation (10). The total deviation (   at each temperature measurement point can be calculated with equation (11) where N is the total number of measurement taken in time and   is the deviation at each time instant.…”

Section: Model Uncertainty and Monte Carlo Simulationmentioning

confidence: 99%

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Transient thermal modelling of an Axial Flux Permanent Magnet (AFPM) machine with model parameter optimisation using a Monte Carlo method

Hey

Howey

Martinez-Botas

et al. 2011

Vehicle Thermal Management Systems Conference and Exhibition (VTMS10)

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This paper presents the development of a transient thermal model of the EVO Electric AFM 140 Axial Flux Permanent Magnet (AFPM) machine based on a hybrid finite difference and lumped parameter method. A maximum deviation between simulated and measured temperature of 2.4°C is recorded after using a Monte Carlo simulation to optimise model parameters representing a 53% reduction in temperature deviation. The simulated temperature deviations are lower than the measurement error on average and the thermal model is computationally simple to solve. It is thus suitable for transient temperature prediction and can be integrated with the system control loop for feed forward temperature prediction to achieve active thermal management of the system.

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Section: Model Uncertainty and Monte Carlo Simulationmentioning

confidence: 99%

“…A systematic method has to be employed to deal with this uncertainty. The approach is based on the Monte Carlo simulation which has been widely used in predicting output from non deterministic systems [10]. These systems are usually characterized by uncertainty in the inputs or system parameters.…”

Section: Model Uncertainty and Monte Carlo Simulationmentioning

confidence: 99%

Transient thermal modelling of an Axial Flux Permanent Magnet (AFPM) machine with model parameter optimisation using a Monte Carlo method

Hey

Howey

Martinez-Botas

et al. 2011

Vehicle Thermal Management Systems Conference and Exhibition (VTMS10)

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“…CMM kinematic errors were the subject of a number of studies [4,5] which resulted in the development of models of machine errors, for example full rigid body or reduced rigid body models. The most commonly used description of CMM geometrical errors include 21 components:…”

Section: The Errors Related To Cmm Kinematicsmentioning

confidence: 99%

Determination of Temperature Impact on CMM Residual Kinematic Errors Distribution

Harmatys

Gąska

et al. 2015

Adv. Sci. Technol. Res. J.

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In case of coordinate machines that use CAA correction matrix, the issue of kinematic errors analysis may be based on the determination of residual error distribution. Temperature changes have an impact on CMM kinematic structure, which may cause the differences in the map of residual errors. As for today, the residual errors were analysed only for the reference temperature. No research was undertaken on the residual errors changes depending on the temperature variations. This paper presents the experiment aimed at residual errors analysis and resulting errors distributions for different temperatures.

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“…The biggest challenge for a traceable machine tool measurement is to deal with non-controlled shop floor environment. Time and space dependent thermal effects become the dominant uncertainty source for the measurement of large scale devices [4] [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26].…”

Section: Converting a Mt Into A Cmmmentioning

confidence: 99%

Traceability on Machine Tool Metrology: A Review

Mutilba¹,

Gomez-Acedo²,

Kortaberria³

et al. 2017

Preprint

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Errors during manufacture of high value components are not acceptable nowadays in driving industries such as energy and transportation. Sectors such as aerospace, automotive, shipbuilding, nuclear power, large science facilities or wind power manufacture complex and accurate components that demand close measurements and fast feedback into manufacturing processes. New measuring technologies are already available in machine tools, including integrated touch probes and fast interface capabilities. They shall provide the possibility to measure the workpiece during or after the manufacturing process, maintaining the original setup of the workpiece and avoiding the manufacturing process from being interrupted to transport the workpiece to a measuring position. However, the traceability of the measurement process on a machine tool is not ensured yet and measurement data is still not fully reliable for process control or product validation. Due to the similarity between a coordinate measuring machine and a machine tool, some of the methods applied for a correct assessment of uncertainty in coordinate measuring machines are adapted to the challenges of a machine tool. The scientific objective is to determine the uncertainty on a machine tool measurement and, in this way, convert it into a machine integrated traceable measuring process. This paper reviews the fundamentals of machine tool metrology.

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Geometric error measurement and compensation of machines—An update

Cited by 797 publications

References 60 publications

Transient thermal modelling of an Axial Flux Permanent Magnet (AFPM) machine with model parameter optimisation using a Monte Carlo method

Transient thermal modelling of an Axial Flux Permanent Magnet (AFPM) machine with model parameter optimisation using a Monte Carlo method

Determination of Temperature Impact on CMM Residual Kinematic Errors Distribution

Traceability on Machine Tool Metrology: A Review

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