A simplified modelling approach for thermal behaviour analysis in hybrid plasma arc-laser additive manufacturing

Wang, Chong; Sun, Yongle; Chen, Guangyu; Chen, Xin; Ding, Jialuo; Suder, Wojciech; Diao, Chenglei; Williams, Stewart

doi:10.1016/j.ijheatmasstransfer.2022.123157

Cited by 11 publications

(3 citation statements)

References 50 publications

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“…A common prediction method currently used is to build a finite element model. Chong Wang et al [7] analysed the thermal behaviour of a composite additive process by developing a three-dimensional steady-state finite element model with two independent surface heat sources, and the predicted melt pool geometry and heat affected zone were in high agreement with the experimental data. However, the utilization of finite element simulation in the context of laser-arc hybrid additive manufacturing encounters inherent limitations.…”

Section: Introductionmentioning

confidence: 92%

Prediction of Deposition Layer Morphology Dimensions Based on PSO-SVR for Laser–arc Hybrid Additive Manufacturing

Wang

Liu

et al. 2023

Coatings

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Laser–arc composite additive manufacturing holds significant potential for a wide range of industrial applications, and the control of morphological dimensions in the deposited layer is a critical aspect of this technology. The width and height dimensions within the deposited layer of laser–arc hybrid additive manufacturing serve as essential indicators of its morphological characteristics, directly influencing the shape quality of the deposited layer. Accurate prediction of the shape dimensions becomes crucial in providing effective guidance for size control. To achieve precise prediction of shape dimensions in laser–arc composite additive manufacturing and ensure effective regulation of the deposited layer’s shape quality, this study introduces a novel approach that combines a particle swarm algorithm (PSO) with an optimized support vector regression (SVR) technique. By optimizing the SVR parameters through the PSO algorithm, the SVR model is enhanced and fine-tuned to accurately predict the shape dimensions of the deposited layers. In this study, a series of 25 laser–arc hybrid additive manufacturing experiments were conducted to compare different approaches. Specifically, the SVR model was built using selected radial basis function (rbf) kernel functions. Furthermore, the penalty factors and kernel parameters of the SVR model were optimized using the particle swarm optimization (PSO) algorithm, leading to the development of a PSO-SVR prediction model for the morphological dimensions of the deposited layers. The performance of the PSO-SVR model was compared with that of the SVR, BPNN, and LightGBM models. Model accuracy was evaluated using a test set, revealing average relative errors of 2.39%, 7.719%, 9.46%, and 5.356% for the PSO-SVR, SVR, BPNN, and LightGBM models, respectively. The PSO-SVR model exhibited excellent prediction accuracy with minimal fluctuations in prediction error. This performance demonstrates the model’s ability to effectively capture the intricate and non-linear relationship between process parameters and deposition layer dimensions. Consequently, the PSO-SVR model can provide a foundation for the control of morphological dimensions in the deposition layer, offering an effective guide for deposition layer morphology dimension control in laser–arc composite additive manufacturing.

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Section: Introductionmentioning

confidence: 92%

Prediction of Deposition Layer Morphology Dimensions Based on PSO-SVR for Laser–arc Hybrid Additive Manufacturing

Wang

Liu

et al. 2023

Coatings

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“…Two independent double-ellipsoidal heat sources were used to apply the heat input in dual-arc AM. A thorough description of the heat source equation and definition of the heat source parameters can be found in reference [12,14,25]. The initial temperature of the substrate and deposition material was assumed to be 300 K. During the AM process, the majority of heat energy is lost to the substrate by conduction.…”

Section: Theoretical Investigationmentioning

confidence: 99%

“…Multi-energy source (MES) is a concept that applies more than one energy source to fabricate components at the same time. In recent years, MES has become a promising development trend of additive manufacturing technology [13,14]. MES has the advantages of high deposition efficiency and low cost, which provides a new way to realise large-size, high-performance, and batch-manufacturing process for metal parts.…”

Section: Introductionmentioning

confidence: 99%

Comparison of thermomechanical responses of single-arc and dual-arc parallel additive manufacturing

Tang

Yang

et al. 2023

Science and Technology of Welding and Joining

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Printed metallic parts often suffer from thermomechanical defects such as delamination, buckling and warping, and this effect is exacerbated in multi-arc additive manufacturing (AM) due to the extensive heat input and large molten pool. These defects originate primarily because of high residual stresses accumulated during layer-by-layer deposition. Here we develop, validate and employ a three-dimensional finite element model with two independent heat sources to analyse the thermomechanical responses in dual-arc parallel AM of Ti6Al4V thin-walled parts. The results are compared with those of the conventional single-arc AM. Although the deformation in dual-arc AM is slightly larger than that in single-arc AM, the stresses at the substrate-deposit interface for dual-arc AM are reduced by 53% due to the lower cooling rate.

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Parametric study of melt pool geometry in hybrid plasma arc-laser melting process for additive manufacturing application

et al. 2023

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Wire-based hybrid arc-laser additive manufacturing is suitable for producing large metallic parts (metres in scale) with high deposition rates and near-net-shape. In this process, the surface quality and dimensional accuracy of the deposited parts are determined by the melt pool geometry. However, how to control the melt pool in the hybrid process is complex due to the multiple parameters that can be used. In this study, control of melt pool geometry by investigating different process parameters, including laser power, travel direction, arc-laser separation distance, laser beam size, and arc current in the hybrid plasma transferred arc (PTA)-laser process, was studied systematically. It was found that a larger melt pool was achieved with the PTA-leading configuration compared to that with the laser-leading configuration due to a higher laser absorption occurred with the former configuration. The melt pool was enlarged by either increasing the laser power or arc current due to the increased energy input. However, if the laser power density is high enough to determine the melt pool depth, the increasing arc current has very little effect on melt pool depth but only increases the melt pool width. In addition, the melt pool became shallower and wider when using a larger laser beam. The arc-laser separation distance had a minor effect on the melt pool geometry due to the fixed energy input used in the studied cases. The results of this study provide a reference for melt pool control in wire-based hybrid arc-laser additive manufacturing.

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A simplified modelling approach for thermal behaviour analysis in hybrid plasma arc-laser additive manufacturing

Cited by 11 publications

References 50 publications

Prediction of Deposition Layer Morphology Dimensions Based on PSO-SVR for Laser–arc Hybrid Additive Manufacturing

Prediction of Deposition Layer Morphology Dimensions Based on PSO-SVR for Laser–arc Hybrid Additive Manufacturing

Comparison of thermomechanical responses of single-arc and dual-arc parallel additive manufacturing

Parametric study of melt pool geometry in hybrid plasma arc-laser melting process for additive manufacturing application

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