Statistical Control of the Technological Process Stability to Manufacturing Cylindrical Parts into High Series

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Viorel-Mihai Nani (2016). Statistical Control of the Technological Process Stability to Manufacturing Cylindrical Parts into High Series. Mechanics, Materials Science & Engineering, Vol 7, pp. 97-110, doi:10.13140/RG.2.2.33528.65284

Authors: Viorel-Mihai Nani

ABSTRACT. This paper presents a calculation algorithm for verifying on-line of the manufacturing process stability in large and mass series of some cylindrical parts from axes type. Through experimental investigations, we conducted a statistical control on a sample parts batch to determine the machining accuracy of some checking turret lathes.

In the first phase, we performed a statistical analysis of the technological process preceding the manufacture of cylindrical parts in large and mass series. For checking the normality assumption of the deviations for parts machined, we established the main statistical parameters as being arithmetic mean and standard deviation. With these parameters, I could calculate the fraction of probable defective parts.

In the second phase, we determined the control limits for the arithmetic mean and standard deviation. With these parameters I could pursue in chronological order the actual achievement of the workpiece size. In this way, I could check the technological process stability on-line for well-defined period’s time, between two successive adjustments of the machine-tools.

Keywords: statistical control limits, arithmetic mean, standard deviation, fraction of probable defective parts, technological process stability

DOI 10.13140/RG.2.2.33528.65284


[1] Baran T., Statistical methods for analysis and quality control production, Didactic and Pedagogic Publishing House, Bucharest (1979)

[2] Draghici G., Concept machining processes, Polytechnic Publishing House, Timisoara (2005)

[3] Falsone G., Settineri D., Explicit solutions for the response probability density function of nonlinear transformations of static random inputs, Probabilistic Engineering Mechanics;33 (79):85 (2013), 10.1016/j.probengmech.2013.03.003

[4] Falsone G., Settineri D., On the application of the probability transformation method for the analysis of discretized structures with uncertain proprieties, Probabilistic Engineering Mechanics, 35, 44–51 (2014), doi: 10.1016/j.probengmech.2013.10.001

[5] Grigoriu M., R.V. Field Jr., A two-step method for analysis of linear systems with uncertain parameters driven by Gaussian noise, Probabilistic Engineering Mechanics, 34, 200–210 (2013), doi: 10.1016/j.probengmech.2013.10.003

[6] Nani V.M., Statistical control of processing prismatic pieces on grinding machines, Measurement, 47, 516 – 520 (2014), doi: 10.1016/j.measurement.2013.09.033

[7] Pau V., Bagiu L., David I., Technical Measurements, Printech Publishing Bucharest (1999)

[8] Ramamoorthy B., Radhakrishnan V., Weckenmann A., Knauer M., Geus D.A., Improvement of machining accuracy on a CNC lathe through error prediction and compensation, in: XV IMEKO World Congress, June 13–18, Osaka, Japan (1999)

[9] Renata T., Barros e Vasconcellos, Marcello L.R. de Campos, Error analysis in high-accuracy digital measurements, Measurement, 45, 819-832 (2012)

[10] Vizireanu D.N., Halunga S.V., Simple, fast and accurate eight points amplitude estimation method of sinusoidal signals for DSP based instrumentation, Journal of Instrumentation, 7 (04), P04001 (2012), doi: 10.1088/1748-0221/7/04/P04001

[11] Vizireanu D.N., Preda R.O., “Is “five-point” estimation better than “three-point” estimation?”, Measurement, 46, 840 – 842 (2013)

[12] Vratislav H., Analysis of basic probability distributions, their properties and use in determining type B evaluation of measurement uncertainties, Measurement, 46, 16-23 (2013), doi: 10.1016/j.measurement.2012.09.006

[13] Weckenmann A., Estler T., Peggs G., McMurtry D., Probing systems in dimensional metrology, CIRP Annals – Manufacturing Technology, 53 (2), 657–684 (2004)

[14] Xiang Y.B., Liu Y.M., Application of inverse first-order reliability method for probabilistic fatigue life prediction, Probabilistic Engineering Mechanics, 26, 148–156(2011)

[15] Yazhou Xu, Fatigue reliability evaluation using probability density evolution method, Probabilistic Engineering Mechanics, 42, 1–6 (2015), doi: 10.1016/j.probengmech.2015.09.005


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