The introduction (Chapter 1) and the literature review (Chapter 2) shaped the thesis subject. This chapter presents the overall structure with the article overview. The following three chapters corresponds to three (two journal and one conference) articles. In Chapter 7 additional results are presented. Those chapters show the main doctoral research. The thesis dissertation ends with a general discussion (Chapter 8) and conclusion (Chapter 9).
All tests and calculations presented in this thesis were conducted on a five-axis machine tool using the SAMBA calibration method, which has a multi-input, multi-output model with iterative solution and (scale enriched) uncalibrated artefact.
The article entitled “Uncertainty estimation of a five-axis machine tool calibration using the adaptive Monte Carlo method”, which was submitted in March 2017 to Precision Engineering Journal, is in Chapter 4. The research work, presented therein, is exploring the possibility of calculating the uncertainty of the calibration method that uses the uncalibrated artefact. Moreover, the machine performance and its variations due to the different environmental conditions are analysed. The following uncertainty sources are considered: the artefact variations occurring during the test, the probe repeatability and the machine itself. Thus, the measurand (machine geometry) is, at the same time, the source of the calibration uncertainty. In order to include all those factors, the repeated measurements (over 24 hours) are proposed as the estimation of the probing uncertainty (input quantity). That way, standard deviations, covariance and distributions are obtained and propagated through the (adaptive) MCM method on the calibration results (output quantity). The results with their expanded uncertainties are compared for different environmental conditions.
Chapter 5 is constituted of the article “Machine geometry time dependent variations and their effect on calibration results”, which was submitted in March 2017 to Measurement. In this paper, the machine performance changes are analysed. However, this time the focus is on short- and medium- term variations. In the previous paper, it was established that the machine changes occur during one day. But do the machine tool varies between the days? To answer this question, the SAMBA calibration was repeated on the machine four times a day over five days. That allows comparing the mean and variances obtained for each day. Moreover, the trend in the probing and calibration results is discussed.
Chapter 6 presents the paper “Application of GUF for a multi-output iterative measurement model estimation according to GUM S2 in indirect five-axis CNC machine tool calibration”, which was presented during the XI LAMDAMAP 2015 conference and published in the proceedings Laser Metrology and Machine Performance XI, in March 2015. MCM has many advantages. It does not require the analytical function of the measurement model or its linearization. Moreover, distributions of the estimated parameters are obtained. However, depending on the number of MC trials and based on a single trial running time, it can be time-consuming. GUF allows estimating the uncertainties faster but it requires the sensitivity Jacobian matrix of the output quantity to the input quantity. Since SAMBA has an iterative solution to the least square method the Jacobian cannot be established analytically. Thus, it is estimated with the numerical Jacobian. Then, the GUF method is used to calculate the uncertainties on the calibration results for different number of master balls present in the artefact.
Finally, in Chapter 7 the validation of time-efficient GUF with the MCM is presented. The estimated parameters, their uncertainties, covariance matrices and the coverage factors are compared for both methods. The MCM simulation results are calculated for different numbers of MC trials in order to show its impact on the obtained values.
T he si s or ga ni za ti on A rt ic le 1 (C ha pt er 4 ) A rt ic le 2 (C ha pt er 5 ) C hapt er 7 A rt ic le 3 (C ha pt er 6 )
Defining the SAMBA probing (calibration input data) uncertainty sources Probing uncertainty estimation with SAMBA repeated calibration
Machine performance analysis for different environmental conditions Input and output data trends and correlations analysis
Uncertainty propagation using adaptive MCM
Probing uncertainty estimation with SAMBA repeated calibration
Conducting machine performance tests including short- and medium- term variations Daily means, variances, trends and correlations analysis of probing data and
calibration results
Pooled (by days) uncertainty estimation Uncertainty propagation using adaptive MCM
Conducting MCM for different number of MC trials
Comparison of GUF and MCM results (machine geometric errors parameters, uncertainties, correlations etc.)
GUF validation with MCM
Iterative model sensitivity matrix estimation with a numerical Jacobian
Machine geometric errors parameters uncertainty estimation using time-efficient GUF
Uncertainty results comparison for SAMBA calibration with different numbers and sets of master balls in the artefact
Uncertainty estimation of a five-axis machine tool calibration using the adaptive Monte Carlo method
Machine geometry time dependent variations and their effect on calibration results
Application of GUF for a multi-output iterative me asurement model estimation according to GUM S2 in indirect five-axis CNC m achine tool calibration
Generalized uncertainty framework validation with the Monte Carlo method