Deep sequential adaptive reinforcement learning for manufacturing process optimization

This publication has 30 references indexed in Scilit:

• Optimizing process parameters of in-situ laser assisted cutting of glass–ceramic by applying hybrid machine learning models
  Advanced Engineering Informatics, 2024
• Autonomous target tracking of multi-UAV: A two-stage deep reinforcement learning approach with expert experience
  Applied Soft Computing, 2023
• Additive Manufacturing
  2023
• Multi-agent reinforcement learning based on graph convolutional network for flexible job shop scheduling
  Journal of Intelligent Manufacturing, 2022
• Correcting biased value estimation in mixing value-based multi-agent reinforcement learning by multiple choice learning
  Engineering Applications of Artificial Intelligence, 2022
• Digital twin-driven surface roughness prediction and process parameter adaptive optimization
  Advanced Engineering Informatics, 2022
• Robotic Curved Surface Tracking with a Neural Network for Angle Identification and Constant Force Control based on Reinforcement Learning
  International Journal of Precision Engineering and Manufacturing, 2020
• A reinforcement learning decision model for online process parameters optimization from offline data in injection molding
  Applied Soft Computing, 2019
• Laser Powder Bed Fusion of Ti-rich TiNi lattice structures: Process optimisation, geometrical integrity, and phase transformations
  International Journal of Machine Tools and Manufacture, 2019
• On the role of powder flow behavior in fluid thermodynamics and laser processability of Ni-based composites by selective laser melting
  International Journal of Machine Tools and Manufacture, 2019