[HTML][HTML] On the data quality and imbalance in machine learning-based design and manufacturing—A systematic review
Abstract Machine learning (ML) has recently enabled many modeling tasks in design,
manufacturing, and condition monitoring due to its unparalleled learning ability using …
manufacturing, and condition monitoring due to its unparalleled learning ability using …
A novel digital twin-assisted prediction approach for optimum rescheduling in high-efficient flexible production workshops
The optimum reschedules usually need to be considered in the flexible production workshop
according to the actual production requirements to ensure the higher efficiency of production …
according to the actual production requirements to ensure the higher efficiency of production …
End-to-end on-line rescheduling from Gantt chart images using deep reinforcement learning
JA Palombarini, EC Martínez - International Journal of Production …, 2022 - Taylor & Francis
With the advent of the socio-technical manufacturing paradigm, the way in which
rescheduling decisions are taken at the shop floor has radically changed in order to …
rescheduling decisions are taken at the shop floor has radically changed in order to …
Research on Process Quality Prediction and Control of Spindle Housings in Flexible Production Lines
B Huang, J Yan, X Liu, J Xie, J Wang, K Liu, Y Xu… - Applied Sciences, 2023 - mdpi.com
The characteristics of flexible production lines, ie,“multiple steps and few processes”,
increase the complexity of the process and the difficulty of process quality control, but are not …
increase the complexity of the process and the difficulty of process quality control, but are not …
A Deep Reinforcement Learning-based Rescheduling Method for Flexible Job Shops under Machine Breakdowns
L Lv, C Zhang, W Shen - 2024 27th International Conference on …, 2024 - ieeexplore.ieee.org
This paper considers a flexible job shop rescheduling problem under machine breakdowns
to minimize the sum of the deviations from the preschedule. A deep reinforcement learning …
to minimize the sum of the deviations from the preschedule. A deep reinforcement learning …