Using Machine Learning Algorithms for the Prediction of Industrial Process Parameters Based on Product Design

Khdoudi, Abdelmoula; Masrour, Tawfik; Mazgualdi, Choumicha El

doi:10.1007/978-3-030-36671-1_67

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…It was concluded that the convolutional neural network (CNN) was the most accurate for energy prediction, the support vector regression (SVR) model for amplitude prediction, and the regression model for pressure prediction. The best prediction method was a combination of machine learning techniques [29,30].…”

Section: Methodsmentioning

confidence: 99%

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

Dobra

Jósvai

2023

BDCC

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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.

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Section: Methodsmentioning

confidence: 99%

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

Dobra

Jósvai

2023

BDCC

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“…Multiple Linear Regression Model (MLR) is a model that summarizes the relationship between a set of predictor variables and a response variable, also called a criterion. It involves the estimation of multiple regression equation by using parameters entered linearly and estimated by the least squares method (Khdoudi, 2019). The most general form of the regression equation can be expressed as follows:…”

Section: Multiple Linear Regression (Mlr)mentioning

confidence: 99%

Online prediction of automotive tempered glass quality using machine learning

Khdoudi¹,

Barka

Masrour³

et al. 2023

Int J Adv Manuf Technol

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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 eld was motivated by the lack of a robust engineering technique to assess the production quality in real time, this arise the need of using advanced smart manufacturing solution as AI in order to save the extremely high cost of destructive tests. In concrete, this paper investigates the performance of machine learning techniques including Ridge regression, Linear Regression, Light Gradient Boosting Machine, Lasso Regression and more, for predicting the at glass tempering quality within the building glass industry. In the rst part, we applied the selected machine learning models to a dataset collected manually and made up by the more relevant process parameters of the heating and the quenching process. Evaluating the results of the applied models, based on several performance indicators such as Mean Absolute Error, Mean Squared Error, R Squared, declared that Ridge Regression was the most accurate model. The second part consist of developing a digitalized device connected with the manufacturing process in order to provide predictions in real time. This device operates as an errorproo ng system that send a reverse signal to the machine in case the prediction shows a non-compliant quality of the current processed product. This study can be expanded to predict the optimal process parameters to use when the predicted values does not meet the desired quality, and can advantageously replace the trial and error approach that is generally adopted for de ning those parameters. The contribution of our work relies on the introduction of a clear methodology (from idea to industrialization) for the design and deployment of an industrial-grad predictive solution within a new eld which is the glass manufacturing.

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“…• 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.

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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.

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Using Machine Learning Algorithms for the Prediction of Industrial Process Parameters Based on Product Design

Cited by 5 publications

References 9 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

Online prediction of automotive tempered glass quality using machine learning

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

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