Numerical prediction of microstructure and hardness for low carbon steel wire Arc additive manufacturing components

Yu, Ling; Ni, Junyan; Antonissen, J.; Hamouda, H. Ben Hadj; Voorde, John Vande; Wahab, Magd Abdel

doi:10.1016/j.simpat.2022.102664

Cited by 34 publications

(10 citation statements)

References 27 publications

(37 reference statements)

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“…Dharmawan et al [21] used reinforcement learning to correct the prediction of the layer heights for each layer in the WAAM process and obtained a WAAM bronze block with a flatter upper surface. Ling et al [22] combined physical modeling and ANN to achieve the predictions of the microstructure and hardness of WAAM low-carbon steel components. In the LPBF process, Tapia et al [23] used Gaussian regression to realize the prediction of the porosity under any combination of laser power and scanning speed in the LPBF process.…”

Section: Introductionmentioning

confidence: 99%

Study on the Process Window in Wire Arc Additive Manufacturing of a High Relative Density Aluminum Alloy

Wu,

Li,

Wang

et al. 2024

Metals

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In recent years, there has been a heightened focus on multiplex porosity due to its significant adverse impact on the mechanical properties of aluminum alloy components produced through wire arc additive manufacturing (WAAM). This study investigates the impacts of the process parameters and dimension parameters on the relative densities of WAAM 2219 aluminum alloy components by conducting experiments and investigates the changes in high relative density process windows with different dimension parameters. The findings reveal a hierarchy in the influence of various parameters on the relative density of the 2219 aluminum alloy: travel speed (TS), wire feed speed (WFS), the number of printed layers (L), interlayer cooling time (ICT), and theoretical length of weld (TLW). A series of data for analysis was produced through a designed experiment procedure, and on the basis of this, by integrating the data augmentation method with the eXtreme Gradient Boosting (XGBoost) algorithm, the relationship among the process parameters, dimension parameters, and relative density was modeled. Furthermore, through leveraging the established model, we analyzed the changes in the optimized process window corresponding to a high relative density with the L. The optimal windows of WFS and TS change when the L reaches a certain value. In contrast, the optimal window of ICT remains consistent despite an increase in the L. Finally, the relative density and mechanical properties of the formed 20-layer specimens within the model-derived window were verified. The relative density of the specimens within the window reached 98.77%, the ultimate tensile strength (UTS) reached 279.96 MPa, and the yield strength (YS) reached 132.77 MPa. This work offers valuable insights for exploring the process window and selecting process parameters through a more economical and faster approach in WAAM aluminum components.

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Section: Introductionmentioning

confidence: 99%

Study on the Process Window in Wire Arc Additive Manufacturing of a High Relative Density Aluminum Alloy

Wu,

Li,

Wang

et al. 2024

Metals

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“…Yong Ling et al [8] conducted a series of thermo-mechanical analyses for the WAAM-built lowcarbon steel component using the ABAQUS CEA software and artificial neural network (ANN) methodology. The numerical investigation found good capability to predict the micro-hardness and tensile properties of the printed structure through the developed empirical model.…”

Section: Introductionmentioning

confidence: 99%

Simulation of Metallic Wire-Arc Additive Manufacturing (Waam) Process Using Simufact Welding Software

Kumar,

Singh,

Bishwakarma

et al. 2023

JME

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Wire arc additive manufacturing (WAAM) is one of the emerging low-cost metal additive manufacturing techniques used to fabricate medium-large complex structures. The process provides design flexibility, supports green manufacturing, power efficiency, good structural integrity, high performance, and cost benefits, particularly for large-scale components. However, the heating and cooling cycle prevails during the deposition of material layer upon layer resulting in heat accumulation within the deposited layers. It causes geometric inaccuracy, surface roughness, high residual stresses, and mechanical anisotropy in the built structures. Therefore, SIMUFACT-Welding software has been used to model and simulate the WAAM process to fabricate a straight steel wall structure. The simulated results were able to visualize the existing thermal cycle during layer deposition and the effect of heat input on the fabricated wall structure and the substrate.

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“…Welding metals is a process that includes heating and cooling cycles, which strongly influences chemical-metallurgical reactions in liquid metal, phase transformations, grain growth, and therefore final material mechanical properties [12]. To better understand the mechanisms taking place during WAAM and to minimize the costs of experimental investigations at the same time, a simulation approach is eagerly applied, where not only finite element models [11,[13][14][15][16][17], but also neural networks [16,18,19] or mathematical [20], recursive models [21] are increasingly used. However, to accurately simulate the final properties of the part obtained by the WAAM process, a realistic heat source shape and distribution is necessary [22].…”

Section: Introductionmentioning

confidence: 99%

Determination of welding heat source parameters for fem simulation based on temperature history and real bead shape

Szyndler

2023

Materials Research Proceedings

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Abstract. To improve process understanding and increase the numerical prediction quality of a wire arc additive manufacturing (WAAM) process, this paper focuses on determining the parameters of a numerical model that reproduces an experimental setup of a welding process, with particular attention given to the actual shape of the weld bead. The dimensions of the heat source (HS) model in a welding process are determined based on experimentally measured weld pool sizes as well as temperature history at selected points below and adjacent to a weld seam. The whole experimental setup is accurately reproduced within the Simufact.Welding software and an optimization procedure is applied to obtain the best possible agreement between experimental and numerical results. A validated numerical model with reliable parameters for two heat sources is later used to predict and observe material behavior during the WAAM process of more complex parts.

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Numerical prediction of microstructure and hardness for low carbon steel wire Arc additive manufacturing components

Cited by 34 publications

References 27 publications

Study on the Process Window in Wire Arc Additive Manufacturing of a High Relative Density Aluminum Alloy

Study on the Process Window in Wire Arc Additive Manufacturing of a High Relative Density Aluminum Alloy

Simulation of Metallic Wire-Arc Additive Manufacturing (Waam) Process Using Simufact Welding Software

Determination of welding heat source parameters for fem simulation based on temperature history and real bead shape

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