Péter Dobra

57235397900

Publications - 13

Prediction of Overall Equipment Effectiveness in Assembly Processes Using Machine Learning

János Jósvai Péter Dobra

Publication Name: Strojnicky Casopis

Publication Date: 2024-11-01

Volume: 74

Issue: 2

Page Range: 57-64

Description:

Nowadays, a lot of data is generated in production and also in the domain of assembly, from which different patterns can be extracted using machine learning methods with the support of data mining. With the support of various modern technical and Information Technology (IT) tools, the recording, storage and processing of large amounts of data is now a routine activity. Based on machine learning, efficiency metrics including Overall Equipment Effectiveness (OEE), can be partially predicted, but industrial companies need more accurate and reliable methods. The analyzed algorithms can be used in general for all production units or machines where production data is recorded by Manufacturing Execution System (MES) or other Enterprise Resource Planning (ERP) systems are available. This paper presents and determinates which most used machine learning methods should be combined with each other in order to achieve a better prediction result.

Open Access: Yes

DOI: 10.2478/scjme-2024-0026

Cumulative and Rolling Horizon Prediction of Overall Equipment Effectiveness (OEE) with Machine Learning

János Jósvai Péter Dobra

Publication Name: Big Data and Cognitive Computing

Publication Date: 2023-09-01

Volume: 7

Issue: 3

Page Range: Unknown

Description:

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.

Open Access: Yes

DOI: 10.3390/bdcc7030138

Overall Equipment Effectiveness-Related Assembly Pattern Catalogue based on Machine Learning

János Jósvai Péter Dobra

Publication Name: Manufacturing Technology

Publication Date: 2023-06-01

Volume: 23

Issue: 3

Page Range: 276-283

Description:

Nowadays, a lot of data is generated in production and also in the domain of assembly, from which different patterns can be extracted using machine learning methods with the support of data mining. With the help of the revealed patterns, the assembly operations and processes can be further optimized, thus the profit achieved can be increased. This article attempts to explore the patterns related to the most used Key Performance Indicator (KPI) in manufacturing, the Overall Equipment Effectiveness (OEE) metric. The patterns and relationships discovered will be sorted into Assembly Pattern Catalogue (APC). Firstly, a literature review demonstrates scientific relevance. Secondly, it examines the circumstances and methods of samples in the Manufacturing Execution System (MES) data source and Enterprise Resource Planning (ERP) systems. In the third section, the detailed pattern catalogue is defined in the area of assembly. The novelty of the article is that beyond the generalization of patterns, it characterizes the pattern catalogue with mentioning practical industrial examples.

Open Access: Yes

DOI: 10.21062/mft.2023.036

Overall Equipment Effectiveness Prediction with Multiple Linear Regression for Semi-automatic Automotive Assembly Lines

János Jósvai Péter Dobra

Publication Name: Periodica Polytechnica Mechanical Engineering

Publication Date: 2023-01-01

Volume: 67

Issue: 4

Page Range: 270-275

Description:

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.

Open Access: Yes

DOI: 10.3311/PPme.22302

Predicting the Impact of Product Type Changes on Overall Equipment Effectiveness through Machine Learning

János Jósvai Péter Dobra

Publication Name: Periodica Polytechnica Mechanical Engineering

Publication Date: 2023-01-01

Volume: 67

Issue: 1

Page Range: 81-86

Description:

Nowadays, Industry 4.0 and the Smart Manufacturing environment are increasingly taking advantage of Artificial Intelligence. There are more and more sensors, cameras, vision systems and barcodes in the production area, as a result of which the volume of data recorded during manufacturing and assembly operations is growing extremely fast. The interpretation and processing of such production-type data by humans is no longer possible effectively. In the Big Data domain, machine learning is playing an increasingly important role within data mining. This paper focuses on the product change processes of semi-automatic assembly line batch production and examines the impact of product type changes on the Overall Equipment Effectiveness (OEE) and attempts to determine future values through supervised machine learning. Using decision tree technology, the effect on the OEE value can be predicted with an accuracy of up to 1%. The presented data and conclusions come from a real industrial environment, so the obtained results are proven in practice.

Open Access: Yes

DOI: 10.3311/PPme.21320

Overall Equipment Effectiveness (OEE) Complexity for Semi-Automatic Automotive Assembly Lines

János Jósvai Péter Dobra

Publication Name: Acta Polytechnica Hungarica

Publication Date: 2023-01-01

Volume: 20

Issue: 2

Page Range: 63-82

Description:

In industrial practice, measuring and monitoring production performance is an essential task. The production plan performance is monitored by middle and top management of companies daily, weekly and monthly and make short and long-term operational and strategic decisions when necessary. One of the most common ways of measuring the performance of production and, within this, of assembly lines, is to use the Overall Equipment Effectiveness (OEE) indicator. Although companies sometimes interpret and use this Key Performance Indicator (KPI) in their own way, it is the indicator that best reflects the development of the production efficiency for a given company. A high OEE percentage means high performance, which directly increases the company's profitability. This article explores the complexity of the OEE indicator, supported by the use of a cause and effect diagram. Firstly, a literature review demonstrates scientific relevance. Secondly, the factors affecting OEE are grouped and analyzed according to the following six aspects: man, environment, method, material, machine, and measurement. Each factor is further subdivided into five groups, and then these subgroups also cover five key factors of importance for the approachability of 100% OEE. The 150 aspects listed herein, provide a complete guideline for a semi-automatic assembly line, to consistently increase efficiency in industrial practice.

Open Access: Yes

DOI: 10.12700/APH.20.2.2023.2.4

Assembly Line Overall Equipment Effectiveness (OEE) Prediction from Human Estimation to Supervised Machine Learning

János Jósvai Péter Dobra

Publication Name: Journal of Manufacturing and Materials Processing

Publication Date: 2022-06-01

Volume: 6

Issue: 3

Page Range: Unknown

Description:

Nowadays, in the domain of production logistics, one of the most complex planning processes is the accurate forecasting of production and assembly efficiency. In industrial companies, Overall Equipment Effectiveness (OEE) is one of the most common used efficiency measures at semi-automatic assembly lines. Proper estimation supports the right use of resources and more accurate and cost-effective delivery to the customers. This paper presents the prediction of OEE by comparing human prediction with one of the techniques of supervised machine learning through a real-life example. In addition to descriptive statistics, takt time-based decision trees are applied and the target-oriented OEE prediction model is presented. This concept takes into account recent data and assembly line targets with different weights. Using the model, the value of OEE can be predicted with an accuracy of within 1% on a weekly basis, four weeks in advance.

Open Access: Yes

DOI: 10.3390/jmmp6030059

Data-Based Assembly Patterns for Overall Equipment Effectiveness at Semi-Automatic Assembly Lines

János Jósvai Péter Dobra

Publication Name: Periodica Polytechnica Mechanical Engineering

Publication Date: 2022-01-01

Volume: 66

Issue: 3

Page Range: 231-236

Description:

In industrial practice, production planning is a key factor for manufacturers and suppliers. The entire planning process spans from the appearance of the customer demand to the fulfillment of the demand. Operational execution is based on pre-planned production processes and operations using properly allocated resources. The accurate planning of assembly operations within production is an extremely complex process in terms of efficiency. Predicting stochastically variable efficiencies is difficult due to the ever-changing manufacturing conditions. This paper defines typical assembly process situations for a semi-automatic assembly line and examines their consequence for the Overall Equipment Effectiveness (OEE). Firstly, a literature review demonstrates the scientific relevance. Secondly, the classification of patterns based on assembly process description parameters is described taking into account the positive and negative effects on the OEE. In addition, the assembly patterns and their characteristics are illustrated through a real automotive example.

Open Access: Yes

DOI: 10.3311/PPme.19910

Overall Equipment Effectiveness (OEE) Life Cycle at the Automotive Semi-Automatic Assembly Lines

János Jósvai Péter Dobra

Publication Name: Acta Polytechnica Hungarica

Publication Date: 2022-01-01

Volume: 19

Issue: 9

Page Range: 141-155

Description:

In the automotive industry, manufacturing companies are constantly improving and monitoring their processes with different Key Performance Indicators (KPIs) in order to achieve higher profits. One of the KPIs is the Overall Equipment Effectiveness (OEE), which represents the efficiency of the different machines and assembly lines. High OEE percentage means good performance and quality. Using Manufacturing Execution System (MES) data the OEE contributors such as availability, performance and quality are calculated and followed at the manufacturing area day by day. This paper concentrates on the entire OEE life cycle at the automotive semi-automatic assembly lines. Firstly, a literature review demonstrates scientific relevance. Secondly, the phases of OEE life cycle are revealed and presented regarding a passenger car seat structure production life cycle. Third section points at the connection between OEE percentage and maintenance, labour and quality costs at the assembly lines. In addition to the theoretical approach, real, practical data are also demonstrated based on experiences from the last fifteen years.

Open Access: Yes

DOI: 10.12700/aph.19.9.2022.9.8

Predicting the impact of type changes on Overall Equipment Effectiveness (OEE) through machine learning

János Jósvai Péter Dobra

Publication Name: 2022 IEEE 1st International Conference on Internet of Digital Reality Iod 2022

Publication Date: 2022-01-01

Volume: Unknown

Issue: Unknown

Page Range: 11-16

Description:

Nowadays, Industry 4.0 and the Smart Manufacturing environment are increasingly taking advantage of Artificial Intelligence. There are more and more sensors, cameras, vision systems and barcodes in the production area, as a result of which the number of data recorded during manufacturing and assembly operations is growing extremely fast. The interpretation and processing of such production-type data by humans is believed less effective. In the Big Data domain, machine learning is playing an increasingly important role within data mining. This paper focuses on the product change processes of semi-automatic assembly line batch production and examines the impact of changes on Overall Equipment Effectiveness (OEE) and attempts to determine future values through supervised machine learning. Using decision tree, the effect on the OEE value can be predicted with an accuracy of up to 1%. The presented data and conclusions come from a real industrial environment, so the obtained results are proven in practice.

Open Access: Yes

DOI: 10.1109/IoD55468.2022.9986645

Towards 100% Overall Equipment Effectiveness (OEE) at Semi-automatic Assembly Lines – Case Study

Péter Dobra

Publication Name: Springer Proceedings in Mathematics and Statistics

Publication Date: 2021-01-01

Volume: 364

Issue: Unknown

Page Range: 281-289

Description:

In almost all cases, automotive companies and automotive suppliers monitor the efficiency of their production and measure it with different metrics. Based on predefined Key Performance Indicators (KPI’s), the production results of manufacturers show generally definite trends. Higher production efficiency induces higher financial results. Companies typically use Overall Equipment Effectiveness (OEE) to measure and evaluate their production as a gold standard and best practice. In production 100% score of OEE means only good parts (which are accepted by the customer), as fast as possible (based on production plan), without stoppage time. This article is looking for answer to the question how to reach maximum effectiveness and under what circumstances this can be overcome at a hybrid assembly line. Firstly, a literature review demonstrates its scientific relevance. Secondly, an example from the automotive industry illustrates how to perform close to 100% on a semi-automatic assembly line and in which cases it can be exceeded. OEE components, as availability, performance and quality are examined in detail to get excellent percentage. This paper highlights that if the operator performs at the gearbox semi-automatic line above the expected cycle time and with stable entire logistics process, the OEE value may be higher than 100%.

Open Access: Yes

DOI: 10.1007/978-3-030-92604-5_25

OEE measurement at the automotive semi-automatic assembly lines

János Jósvai Péter Dobra

Publication Name: Acta Technica Jaurinensis

Publication Date: 2021-02-24

Volume: 14

Issue: 1

Page Range: 24-35

Description:

Manufacturing companies continuously evaluate their achieved performance based on different Key Performance Indicators (KPI). This article gives an overview about the OEE values. The study aims to provide practical OEE data of semi-automatic assembly lines used in the automotive industry. Its novelty is the revealed relationship between seat assembly lines and seat subassembly lines. Firstly, a literature review shows the scientific relevance and several cases are collected to increase OEE percentage. Secondly, the connection between chassis, tracks, recliner and mechanism assembly lines is described. Each part of OEE (availability, performance, quality) are analysed in terms of their impact.

Open Access: Yes

DOI: 10.14513/actatechjaur.00576

Enhance of OEE by hybrid analysis at the automotive semi-automatic assembly lines

János Jósvai Péter Dobra

Publication Name: Procedia Manufacturing

Publication Date: 2020-01-01

Volume: 54

Issue: Unknown

Page Range: 184-190

Description:

Nowadays, industrial companies are constantly improving their processes and increasing their efficiency in order to achieve higher profits. One of the most common measures of efficiency at the semi-automatic assembly lines is the Overall Equipment Effectiveness (OEE) as a Key Performance Indicator (KPI). The goal of measurement of OEE is to improve the effectiveness of machines or production lines with minimal investments. The article presents a way to increase this indicator using an own hybrid analysis. Firstly, a literature review demonstrates scientific relevance. Secondly, a hybrid analysis is introduced for improving the efficiency. The effectiveness of the analysis is demonstrated by a practical example using data mining and line balancing, during which there was a 60% improvement in yield. Patterns recognitions by machine combined with human analysis provides this outstanding result. Hybrid analysis can be used not only on assembly lines, but also on individual machines where a lot of data is generated and the cycle time or takt time needs to be reduced for higher yields.

Open Access: Yes

DOI: 10.1016/j.promfg.2021.07.028