A Genetic-Based SVM Approach for Quality Data Classification

Zouhri, Wahb; Rostami, Hamideh; Homri, Lazhar; Dantan, Jean‐Yves

doi:10.1007/978-3-030-51186-9_2

Cited by 4 publications

(2 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Zouhri et al used polynomial, sigmoid and (radial basic function (RBF) kernels as genetic-based SVM for chemical and rolling process quality data classification. The RBF kernel function was the recommended for classification with 87.15% (chemical data) and 99.08% (rolling process data) accuracy [33].…”

Section: Methodsmentioning

confidence: 99%

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

Dobra

Jósvai

2023

BDCC

View full text Add to dashboard Cite

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.

show abstract

Section: Methodsmentioning

confidence: 99%

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

Dobra

Jósvai

2023

BDCC

View full text Add to dashboard Cite

show abstract

“…• Support Vector Regression Generic Algorithm [23,26,27]; • Extreme Gradient Boost [15,23,26]; • Artificial Neural Network [19,24,28,29];…”

Section: Oee Prediction With Machine Learningmentioning

confidence: 99%

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

Dobra,

Jósvai

2023

Period. Polytech. Mech. Eng.

View full text Add to dashboard Cite

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.

show abstract

Manufacturing Genome: A Foundation for Symbiotic, Highly Iterative Product and Production Adaptations

Gartner

Akay

Löffler

et al. 2021

Lecture Notes in Mechanical Engineering

View full text Add to dashboard Cite

A Genetic-Based SVM Approach for Quality Data Classification

Cited by 4 publications

References 27 publications

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

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

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

Manufacturing Genome: A Foundation for Symbiotic, Highly Iterative Product and Production Adaptations

Contact Info

Product

Resources

About