Wire Arc Additive Manufacturing: Systems, Microstructure, Defects, Quality Control, and Modelling

Chen, Zhe; Soh, Gim Song

doi:10.1007/978-3-031-04721-3_6

Cited by 4 publications

(2 citation statements)

References 96 publications

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“…A suitable material choice for high-temperature requirements is the INCONEL ® alloy, with a typical volumetric mass density ( ρ ) of 8908 kg/m 3 [ 1 ]. Being one of the most representative-strengthening Ni-superalloys, INCONEL ® 718 has a face-centred cubic (FCC) γ matrix, having as main precipitations the gamma prime phase (γ’, primary strengthening phase in Ni-based superalloy with an ordered L 12 FCC crystal structure [ 2 ]) Ni 3 (Al, Ti, Nb) and the double gamma phase (γ”, the main strengthening phase in INCONEL ® 718 with ordered D022 Body-Centred Tetragonal, BCT, structure [ 2 ]) Ni 3 Nb [ 3 , 4 ]. On the other hand, INCONEL ® 625 has a different solid solution hardening with Nb and Mo within the Ni-Cr matrix [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

An In-Depth Exploration of Unconventional Machining Techniques for INCONEL® Alloys

Pedroso,

Sebbe,

Silva

et al. 2024

Materials

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Build-up-edge (BUE), high-temperature machining and tool wear (TW) are some of the problems associated with difficult-to-machine materials for high-temperature applications, contributing significantly to high-cost manufacturing and poor tool life (TL) management. A detailed review of non-traditional machining processes that ease the machinability of INCONEL®, decrease manufacturing costs and suppress assembly complications is thus of paramount significance. Progress taken within the field of INCONEL® non-conventional processes from 2016 to 2023, the most recent solutions found in the industry, and the prospects from researchers have been analysed and presented. In ensuing research, it was quickly noticeable that some techniques are yet to be intensely exploited. Non-conventional INCONEL® machining processes have characteristics that can effectively increase the mechanical properties of the produced components without tool-workpiece contact, posing significant advantages over traditional manufacturing.

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Section: Introductionmentioning

confidence: 99%

An In-Depth Exploration of Unconventional Machining Techniques for INCONEL® Alloys

Pedroso,

Sebbe,

Silva

et al. 2024

Materials

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“…Each approach offers advantages as well as drawbacks. Even though they are quick and practical, empirical approaches might not be able to fully capture the complex physics at work [7]. Comparatively, while comprehensive, computational models can require significant computational resources and in-depth input data.…”

Section: Introductionmentioning

confidence: 99%

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

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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.

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A Concept for Mass Production

Kumar

2023

Synthesis Lectures on Engineering, Science, and Technology

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Wire Arc Additive Manufacturing: Systems, Microstructure, Defects, Quality Control, and Modelling

Cited by 4 publications

References 96 publications

An In-Depth Exploration of Unconventional Machining Techniques for INCONEL® Alloys

An In-Depth Exploration of Unconventional Machining Techniques for INCONEL® Alloys

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

A Concept for Mass Production

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