Отправляя данные, я подтверждаю, что ознакомилась/ознакомился с Политикой в отношении обработки персональных данных, принимаю её условия и предоставляю ООО «РИА «Стандарты и качество» Согласие на обработку персональных данных.
Отправляя данные, я подтверждаю, что ознакомилась/ознакомился с Политикой в отношении обработки персональных данных, принимаю её условия и предоставляю ООО «РИА «Стандарты и качество» Согласие на обработку персональных данных.
I.Z. ARONOV, А.М. RYBAKOVA, А.N. ZAKHAROVA QUALITY INFRASTRUCTURE AND EXPORT POTENTIAL OF COUNTRIES INTEGRATED IN ASSOCIATIONS WITH THE RF Abstract. The Global Quality Infrastructure Index (GQII) is a comprehensive indicator that characterizes the ability of the country’s quality infrastructure (QI) institutions to ensure the required quality and safety levels of goods traded on the market. The article presents the results of a study determining correlation between the GQIIs of BRICS, SCO, CIS, EAEU member countries and their exports. Keywords: quality infrastructure, quality infrastructure index, export, correlation analysis. |
V.V. LAVRIK EXPERTISE OF OPERATIONAL DOCUMENTATION DURING THE INITIAL TEST EQUIPMENT CERTIFICATION Abstract. The article examines the problems of conducting an expertise of test equipment operational documentation during the initial certification. The maximum possible and mandatory set of operational documents are reviewed. An examination procedure and criteria for assessing the compliance of such documentation with the set of standards are proposed. Suggestions and recommendations are formulated for organizations owning equipment in case of its absence or non-compliance with established requirements. Keywords: expertise, operational documentation, unified system of design documentation, test equipment, test equipment certification. |
A.S. DANIELYAN, E.I. KHUNUZIDI QFD METHOD: FROM INDUSTRY 3.0 TO INDUSTRY 4.0. Part 1 Abstract. The first part of the article considers the Quality Function Deployment (QFD) method evolution from its inception in the era of Industry 3.0, as well as the process of its standardization. The main problems are discussed that arise with the traditional approach to using QFD, such as limited predictive capabilities and low adaptability, and the need for its modernization is substantiated. Keywords: quality function deployment, QFD method, house of quality, Industry 4.0, product development, voice of customer. |
D.T. KURYAEVA, V.YU. SMELOV, V.L. SHPER QFD METHOD: HISTORY, CURRENT STATUS AND APPLICATION PROSPECTS Abstract. The article provides an overview of the origins, development, current state, and some future prospects of the Quality Function Deployment (QFD) method. The authors examine QFD key aspects, its adaptation to modern development methodologies, and potential limitations. Special attention is given to QFD application in IT product development, its integration with product-driven approaches, and agile management frameworks. Keywords: quality function deployment, voice of the customer, product quality, customer satisfaction. |
A.YA. DMITRIEV, T.A. MITROSHKINA, E.V. PYATKOVA IMPROVING THE EFFICIENCY AND ROBUSTNESS OF TESTING AND CONTROL PROCESSES IN HIGH-TECH INDUSTRIES Abstract. The article presents the results of structural and functional analysis of the possibility of using modern quality management tools for measuring and control processes in the machine tool and bearing industries. A model for improving the quality of measuring and control processes has been developed. Difficulties in analyzing the acceptability of automated testing of rolling elements have been identified. Keywords: measurement, quality control, testing, MSA, PFMEA, QFD, bearing industry, machine tool industry. |
V.N. KLYACHKIN MACHINE LEARNING METHODS IN QUALITY MANAGEMENT Abstract. The use of machine learning has several advantages over applying standard statistical methods. The article provides a brief overview of machine learning methods, as well as examples in which it is used to classify the conditions of a burner device and assess the quality of drinking water. Keywords: machine learning, regression, classification, random forest, boosting, support vector machine, hyperparameters. |