TY - GEN
T1 - Anomalies Detection in Smart Manufacturing Using Machine Learning and Deep Learning Algorithms
AU - Gamal, Mohamed
AU - Donkol, Ahmed
AU - Shaban, Ahmed
AU - Costantino, Francesco
AU - Di Gravio, Giulio
AU - Patriarca, Riccardo
N1 - Publisher Copyright:
© IEOM Society International.
PY - 2021
Y1 - 2021
N2 - Nowadays, the rapidly changing of manufacturing environment has pushed companies to achieve more customer satisfaction by enhancing product quality, reducing production cost, and realizing sustainability. Anomaly detection has a strong influence on the quality of products and it is usually conducted through visual quality inspection. The visual quality inspection of a product can be performed either manually or automatically. The manual inspection suffers from being a monotonous task, leading to overlooked errors and subjective assessments. Accordingly, the manufacturing industry has high ambitions to rely upon automated quality inspection systems to cope with the requirements of smart manufacturing and the emergence of industry 4.0. Efficient utilization of big data can enable the development of intelligent quality inspection systems. Machine learning as one of the prevailing data analytics methods is widely used to support and improve the performance of the automated quality inspection systems. This research compares the performance of Recurrent Neural Networks (RNN) like Multilayer Perceptron (MLP) with the traditional machine learning algorithms (TMLA) for anomalies detection in manufacturing such as Decision Trees, Random Forest (RF), k-Nearest-Neighbor (KNN), Support Vector Machine (SVM), Naïve Bayes, and Logistic Regression (LR). A data set for faults is adapted from the literature to fairly compare the performance of these algorithms considering different accuracy measures such as accuracy, precision, sensitivity, and F1-score.
AB - Nowadays, the rapidly changing of manufacturing environment has pushed companies to achieve more customer satisfaction by enhancing product quality, reducing production cost, and realizing sustainability. Anomaly detection has a strong influence on the quality of products and it is usually conducted through visual quality inspection. The visual quality inspection of a product can be performed either manually or automatically. The manual inspection suffers from being a monotonous task, leading to overlooked errors and subjective assessments. Accordingly, the manufacturing industry has high ambitions to rely upon automated quality inspection systems to cope with the requirements of smart manufacturing and the emergence of industry 4.0. Efficient utilization of big data can enable the development of intelligent quality inspection systems. Machine learning as one of the prevailing data analytics methods is widely used to support and improve the performance of the automated quality inspection systems. This research compares the performance of Recurrent Neural Networks (RNN) like Multilayer Perceptron (MLP) with the traditional machine learning algorithms (TMLA) for anomalies detection in manufacturing such as Decision Trees, Random Forest (RF), k-Nearest-Neighbor (KNN), Support Vector Machine (SVM), Naïve Bayes, and Logistic Regression (LR). A data set for faults is adapted from the literature to fairly compare the performance of these algorithms considering different accuracy measures such as accuracy, precision, sensitivity, and F1-score.
KW - Anomalies detection
KW - Deep learning
KW - Industry 4.0
KW - Machine learning
KW - Smart manufacturing
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M3 - Conference contribution
AN - SCOPUS:85126253779
SN - 9781792361272
T3 - Proceedings of the International Conference on Industrial Engineering and Operations Management
SP - 1611
EP - 1622
BT - Proceedings - 4th European Rome Conference 2021
A2 - Fargnoli, Mario
A2 - Lombardi, Mara
A2 - Tronci, Massimo
A2 - Dallasega, Patrick
A2 - Savino, Matteo Mario
A2 - Costantino, Francesco
A2 - Di Gravio, Giulio
A2 - Patriarca, Riccardo
PB - IEOM Society
T2 - 4th European International Conference on Industrial Engineering and Operations Management, IEOM 2021
Y2 - 2 August 2021 through 5 August 2021
ER -