MATEC Web Conf.
Volume 343, 202110th International Conference on Manufacturing Science and Education – MSE 2021
|Number of page(s)||13|
|Section||Quality Engineering and Management|
|Published online||04 August 2021|
Prediction of defects using machine learning techniques in order to improve quality management system – A case study
Doctoral School, Faculty of Engineering, Lucian Blaga University of Sibiu, Romania
2 Faculty of Mathematics and Computer Science, University of Bucharest, Romania
3 Quality Department, S.C. Apulum S.A., Alba Iulia, Romania
4 Business Administration and Marketing Department, Faculty of Economic Science, 1 Decembrie 1918 University of Alba Iulia, Romania
* Corresponding author: email@example.com
According to ISO 9000, a quality management system is part of a set of related or interacting elements of an organization that sets policies and objectives, as well as the processes necessary to achieve the quality objectives. Quality is the extent to which a set of intrinsic characteristics of an object meets the requirements. Based on these definitions, the factory, considered in this paper, S.C. APULUM S.A.,decided to implement a quality management system since 1998. Subsequently, the organization’s attention is focus on the continuous improvement of the implemented quality management system. The purpose of this paper is to study the percent of specified defects specific to ceramic products in the future to improve the quality management system. In this regard, machine learning techniques were applied for defects forecasting for different types of products: mugs, pressed plates and jiggered plates. The experimental evaluation was performed on real data sets that contain percentages about different types of defects collected in 2018-2019. The experimental results show that for each type of product exists an algorithm that forecasts the future defects.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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