Processing of a Martensitic Tool Steel by Wire-Arc Additive Manufacturing

Ziesing, Ulf; Lentz, Jonathan; Röttger, Arne; Theisen, W.; Weber, Sebastian

doi:10.3390/ma15217408

Cited by 3 publications

(2 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Researchers [14] examined the feasibility of processing "hot-work tool steel by DED-Arc" without first preheating the material. It was treated without any cracks, despite the fact that cold-cracking of C-martensitic tool steels is an established danger during GMAW.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Optimizing Melt Pool Temperature Prediction Using Convolutional Bilstm with Insights from Dragonfly Behavior in Wire-Arc Additive Manufacturing

Sharma,

Nagappan,

Shahid

et al. 2023

Preprint

View full text Add to dashboard Cite

Wire-Arc Additive Manufacturing (WAAM) has received a lot of attention in recent years because of its ability to create large-scale metallic components layer by layer. Monitoring and controlling the melt pool temperature in real-time, which is a significant factor in deciding the quality of the manufactured part, is a significant problem in WAAM. In this research, we introduce a novel approach for predicting melt pool temperature in wire arc additive manufacturing by employing a Dragonfly optimized convolutional Bi-directional Long Short-Term Memory (DragOCBiLSTM), inspired by insights derived from the behavior of dragonflies. The Convolutional layers efficiently extract spatial characteristics from multi-sensor data, while the Bi-directional LSTM (BiLSTM) layers capture temporal correlations within the data. The utilization of these two elements, refined using the algorithm inspired by dragonfly behavior, presents a significant advantage in comparison to existing predictive models. The data are normalized using the Z-score normalization approach. Principal Component Analysis (PCA) is then used to extract the characteristics from the cleaned data. After that, Dragonfly Optimization (DO) is used to find the best feature subsets.The proposed method’s performance is assessed in terms of Mean Absolute Error (MAE) (10.984), Mean Absolute Percentage Error (MAPE) (3.404), and Mean Squared Error (MSE) (11.25)metrics and compared with existing methods. We provide a promising approach for optimizing the prediction of melt pool temperatures in WAAM, with possible implications for other manufacturing processes, by utilizing the distinctive behavioral insights of dragonflies and merging these with innovative deep learning architectures.

show abstract

Section: Related Workmentioning

confidence: 99%

“…Factors for updating the velocity and location of dragonflies are calculated based on Equations ( 12) to (14),…”

Section: Features Selection Using Dragonfly Optimization(do)mentioning

confidence: 99%