Reducing Computational Time in Pixel-Based Path Planning for GMA-DED by Using Multi-Armed Bandit Reinforcement Learning Algorithm
This work presents an artificial intelligence technique to minimise path planning computer processing time for successful GMA-DED 3D printings. An advanced version of the Pixel space-filling-based strategy family is proposed and developed, using, originally for GMA-DED, an artificially intelligent R...
Saved in:
| Main Authors: | Rafael P. Ferreira, Emil Schubert, Américo Scotti |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
|
| Series: | Journal of Manufacturing and Materials Processing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-4494/9/4/107 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Multi-Dimensional Arms for Combinatorial Multi-Armed Bandit
by: Qi Li, et al.
Published: (2025-01-01) -
Adaptive Noise Exploration for Neural Contextual Multi-Armed Bandits
by: Chi Wang, et al.
Published: (2025-01-01) -
Gaussian Process with Vine Copula-Based Context Modeling for Contextual Multi-Armed Bandits
by: Jong-Min Kim
Published: (2025-06-01) -
Modified Index Policies for Multi-Armed Bandits with Network-like Markovian Dependencies
by: Abdalaziz Sawwan, et al.
Published: (2025-01-01) -
Thompson Sampling for Non-Stationary Bandit Problems
by: Han Qi, et al.
Published: (2025-01-01)