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Article summary:
1. This paper proposes an integrated learning-correction framework, adapted from Model-Based Reinforcement Learning, to iteratively learn the direct effect of process parameters on MLMB print while simultaneously correcting for any inter-layer geometric digression.
2. The proposed learning framework is implemented on an actual robotic WAAM system and experimentally evaluated.
3. The MLMB aspect of WAAM has been rarely addressed and studied due to the complexity of developing an accurate model and the amount of experimental overhead required to study MLMB behaviour.
Article analysis:
The article is generally reliable and trustworthy in its presentation of the proposed model-based reinforcement learning and correction framework for process control of robotic wire arc additive manufacturing (WAAM). The authors provide a detailed description of the proposed framework, as well as a thorough explanation of its implementation on an actual robotic WAAM system and experimental evaluation. Furthermore, they provide evidence for their claims by citing relevant literature in the field.
However, there are some potential biases that should be noted in this article. For instance, the authors do not explore any counterarguments or alternative approaches to their proposed framework, which could lead to a one-sided presentation of their work. Additionally, there is no discussion about possible risks associated with using this approach or any potential drawbacks that could arise from its implementation. Finally, it should also be noted that some claims made in the article are unsupported by evidence or missing points of consideration that could affect the accuracy or reliability of these claims.