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This study introduce the application of machine learning algorithms for supporting the manufacturing quality control of a complex process as an alternative for the destructive testing methodologies. The choice of this application field was motivated by the lack of a robust engineering technique to assess the production quality in real time, this ar...
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... • Bayesian Ridge Regression [20,21]; ...
In the field of industry, especially in the production areas, it is particularly important that the monitoring of assembly efficiency takes place in real-time mode, and that the related data-based estimation also works quickly and reliably. The Manufacturing Execution System (MES), Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM) systems used by companies provide excellent support in data recording, processes, and storing. For Overall Equipment Effectiveness (OEE) data showing the efficiency of assembly lines, there is a regular need to determine expected values. This paper focuses on OEE values prediction with Multiple Linear Regression (MLR) as supervised machine learning. Many factors affecting OEE (e.g., downtimes, cycle time) are examined and analyzed in order to make a more accurate estimation. Based on real industrial data, we used four different methods to perform prediction with various machine learning algorithms, these were the cumulative, fix rolling horizon, optimal rolling horizon and combined techniques. Each method is evaluated based on similar mathematical formulas.
Nowadays, one of the important and indispensable conditions for the effectiveness and competitiveness of industrial companies is the high efficiency of manufacturing and assembly. These enterprises based on different methods and tools systematically monitor their efficiency metrics with Key Performance Indicators (KPIs). One of these most frequently used metrics is Overall Equipment Effectiveness (OEE), the product of availability, performance and quality. In addition to monitoring, it is also necessary to predict efficiency, which can be implemented with the support of machine learning techniques. This paper presents and compares several supervised machine learning techniques amongst other polynomial regression, lasso regression, ridge regression and gradient boost regression. The aim of this article is to determine the best estimation method for semiautomatic assembly line and large batch size. The case study presented with a real industrial example gives the answer as to which of the cumulative or rolling horizon prediction methods is more accurate.
