Using Machine Learning for Predicting Efficiency in Manufacturing Industry

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

doi:10.1007/978-3-030-36671-1_68

Cited by 8 publications

(2 citation statements)

References 13 publications

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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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“…• 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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“…Many ML methods have been applied on the quality prediction in various fields. Scime and Beuth 8 used Decision Tree, Support Vector Machine for additive manufacturing quality identification; El Mazgualdi et al 9 applied Random Forest, XGBoost, and Deep Learning for prediction of efficiency in manufacturing industry; Li et al 10 and Zhang et al 11 applied and showed that data-driven algorithms are highly effective tool for automatic feature extraction and quality monitoring performance.…”

Section: Literature Reviewmentioning

confidence: 99%

A new automatic machine learning based hyperparameter optimization for workpiece quality prediction

Wen

Gao

2020

Measurement and Control

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Workpiece quality prediction is very important in modern manufacturing industry. However, traditional machine learning methods are very sensitive to their hyperparameters, making the tuning of the machine learning methods essential to improve the prediction performance. Hyperparameter optimization (HPO) approaches are applied attempting to tune hyperparameters, such as grid search and random search. However, the hyperparameters space for workpiece quality prediction model is high dimension and it consists with continuous, combinational and conditional types of hyperparameters, which is difficult to be tuned. In this article, a new automatic machine learning based HPO, named adaptive Tree Pazen Estimator (ATPE), is proposed for workpiece quality prediction in high dimension. In the proposed method, it can iteratively search the best combination of hyperparameters in the automatic way. During the warm-up process for ATPE, it can adaptively adjust the hyperparameter interval to guide the search. The proposed ATPE is tested on sparse stack autoencoder based MNIST and XGBoost based WorkpieceQuality dataset, and the results show that ATPE provides the state-of-the-art performances in high-dimensional space and can search the hyperparameters in reasonable range by comparing with Tree Pazen Estimator, annealing, and random search, showing its potential in the field of workpiece quality prediction.

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Smart Ergonomy: Development of an Automated METEO Assessment Based on Computer Vision

El Hassani,

Masrour,

Hajji

et al. 2023

Lecture Notes in Networks and Systems

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Using Machine Learning for Predicting Efficiency in Manufacturing Industry

Cited by 8 publications

References 13 publications

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

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

A new automatic machine learning based hyperparameter optimization for workpiece quality prediction

Smart Ergonomy: Development of an Automated METEO Assessment Based on Computer Vision

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