Qualimetric Method of Product Quality Assessment

Authors

  • H. М. Trishch National Aerospace University “Kharkiv Aviation Institute”
  • О. О. Katrych National Aerospace University “Kharkiv Aviation Institute”;
  • H. І. Khimicheva Kyiv National University of Technology and Design
  • К. М. Cherniak National Aerospace University “Kharkiv Aviation Institute”
  • D. А. Huzov National Aerospace University “Kharkiv Aviation Institute”
  • D. І. Bosenko National Aerospace University “Kharkiv Aviation Institute”

DOI:

https://doi.org/10.31649/1997-9266-2024-175-4-15-21

Keywords:

qualimetric method, product quality, extreme statistics, distribution function, random variable, principle of symmetry, tolerance field

Abstract

The article discusses the qualimetric method of assessing the quality of any product. One of the promising areas of development of quality assessment is associated with the use of a generalized quality parameter. This approach achieves a number of advantages, including increasing the methodological reliability of quality assessment, reducing the list of controlled parameters, the possibility of unifying diagnostics, etc. However, this raises certain difficulties associated with the introduction of a generalized quality parameter. Building a generalized product quality assessment is associated with the creation of a single assessment that quantifies quality through its parameters. The difficulties encountered can be resolved by introducing an artificial metric that is common to all parameters. The set of values of each parameter should be matched with some standard analog, with a single scale of quality assessment from zero to one. This scale should be the same for all parameter values. The construction of this scale is related to the distribution of parameter quality.

To find the quality assessment of a single indicator and the entire product, it is necessary to consider a set of estimates of the values of factors with different distributions. The solution to the problem is found by introducing an artificial metric that is uniform for all factors. This means that the set of values of each factor must be matched to a certain standard, for example, a rating scale from zero to one. This scale should be the same for all factor values.

One important issue is the choice of the type of product quality assessment. Assessments can be point and interval. A quantitative assessment determined by a single number, the so-called point estimate, can be erroneous, especially when the value is determined by expert methods. Therefore, it is proposed that, in addition to the point estimate of quality, its interval estimate should also be found. This will allow for more reasonable quality management.

Author Biographies

H. М. Trishch, National Aerospace University “Kharkiv Aviation Institute”

Cand. Sc. (Eng.), Associate Professor of the Chair of Mechatronics and Electrical Engineering

О. О. Katrych, National Aerospace University “Kharkiv Aviation Institute”;

Cand. Sc. (Eng.), Senior Lecturer of the Chair of Mechatronics and Electrical Engineering

H. І. Khimicheva, Kyiv National University of Technology and Design

Dr. Sc. (Eng.), Professor, Professor of the Chair of Computer-Integrated Technologies and Measuring Engineering

К. М. Cherniak, National Aerospace University “Kharkiv Aviation Institute”

Post-Graduate Student of the Chair of Mechatronics and Electrical Engineering

D. А. Huzov, National Aerospace University “Kharkiv Aviation Institute”

Post-Graduate Student of the Chair of Mechatronics and Electrical Engineering

D. І. Bosenko, National Aerospace University “Kharkiv Aviation Institute”

 Post-Graduate Student of the Chair of Mechatronics and Electrical Engineering

References

Е. М. Векслер та ін., Менеджмент якості. Київ, Україна: «ВД «Професіонал», 2008, 320 с.

R. A. Fisher, “On the mathematical foundations of theoretical statistics,” Philosophical Transactions of the Royal Society of London. Series A, Containing Papers of a Mathematical or Physical Character, no. 222,1922 , pp. 309-368.

T. W. Anderson, and L. S. Stanley, The Statistical Analysis of Data, Palo Alto, CA: Scientific Press, 1986, 628 p.

U. Jakobsson, and A. Westergren, “Statistical methods for assessing agreement for ordinal data,” Scandinavian Journal of Caring Sciences, no. 19, pp. 427-431, 2005. https://doi.org/10.1111/j.1471-6712.2005.00368.x .

R. J. Moffat Using, “Uncertainty Analysis in the Planning of an Experiment,” ASME, no. 107(2), pp. 173-178, June 1985.

https://doi.org/10.1115/1.3242452 .

H. E. Robbins, “An Empirical Bayes Approach to Statistics,” Breakthroughs in Statistics. Springer Series in Statistics. Springer: New York, 1992. https://doi.org/10.1007/978-1-4612-0919-5_26 .

S. H. Steiner, and R. J. MacKay, Statistical engineering, Quality Press, 2005, 319 p.

A. Wald, Sequential analysis, Courier Corporation, 2004, 212 p.

R. M. Trishch, i Ye. A. Slityuk, «Obobshchonnaya tochechnaya i interval'naya otsenki kachestva izgotovleniya detali DVS,» Vostochno-Yevropeyskiy zhurnal peredovykh tekhnologiy, № 1/2 (19), c. 63-67, 2006.

E. C. Jr. Harrington, “The desirability Function,” Industr. Quality Control, pp. 2-9, 1965.

Downloads

Abstract views: 23

Published

2024-08-30

How to Cite

[1]
Trishch H. М., Katrych О. О., Khimicheva H. І., Cherniak . К. М., Huzov D. А., and Bosenko D. І., “Qualimetric Method of Product Quality Assessment”, Вісник ВПІ, no. 4, pp. 15–21, Aug. 2024.

Issue

Section

Automation and information-measuring equipment

Metrics

Downloads

Download data is not yet available.