The technological revolution, known as industry 4.0, aims to improve efficiency/productivity and reduce production costs. In the Industry 4.0 based smart manufacturing environment, machine learning techniques are deployed to identify patterns in live data by creating models using historical data. These models will then predict previously undetectable incidents. This paper initially performs a descriptive statistics and visualization, subsequently issues like classification of data with imbalanced class distribution are addressed. Then several binary classification-based machine learning models are built and trained for predicting production line disruptions, although only logistic regression and artificial neural networks are discussed in detail. Finally, it evaluates the effectiveness of the machine learning models as well as the overall utilization of the manufacturing operation in terms of availability, performance and quality.
|Titel||Data Management Technologies and Applications - 8th International Conference, DATA 2019, Revised Selected Papers : Communications in Computer and Information Science (CCIS) book series|
|Redaktører||Slimane Hammoudi, Christoph Quix, Jorge Bernardino|
|Status||Udgivet - 2020|