Intelligent process model for bead geometry prediction in WAAM

Karmuhilan, M.; Sood, Anil K.

doi:10.1016/j.matpr.2018.10.193

Cited by 37 publications

(16 citation statements)

References 8 publications

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“…The raw data of weld bead profile was processed to obtain the geometrical features OD and BH. A data-processing algorithm was proposed with the following steps: (i) denoising filter of the signal, (ii) extraction of the bead profile, (iii) curve fitting of the profile, and (iv) OD and BH calculation Karmuhilan et al [21] Bead height and width Coordinate measuring machine (CMM)…”

Section: Authors Measured Entity Measurement Methodology Geometric Determination Proceduresmentioning

confidence: 99%

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Effect of the Metal Transfer Mode on the Symmetry of Bead Geometry in WAAM Aluminum

Veiga

Suárez²,

Aldalur³

et al. 2021

Symmetry

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The symmetrical nature in the case of wall fabrication by wire arc additive manufacturing (WAAM) has been observed in the literature, but it has not been studied as a source of knowledge. This paper focuses on the comparative study of three drop transfer methods employing Gas Metal Arc Welding (GMAW) technology, one of the most reported for the manufacture of aluminum alloys. The transfer modes studied are the well-known pulsed GMAW, cold arc, and the newer pulsed AC. The novelty of the last transfer mode is the reversal of the polarity during the preparation phase of the substance for droplet deposition. This study compares the symmetry of zero beads to determine the best parameters and transfer modes for wire arc additive manufacturing of 5 series aluminum. The pulsed transfer modes show values of 0.6 for symmetry ratio, which makes them more interesting strategies than cold arc with a symmetry ratio of 0.5. Furthermore, the methodology proposed in this study can be extrapolated to other materials manufactured with this technology.

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Section: Authors Measured Entity Measurement Methodology Geometric Determination Proceduresmentioning

confidence: 99%

“…In this paper, it is focused on the study of WAAM because the material is deposited on the substrate directly, without a joint preparation. The analysis of the first bead for the determination of the correct parameters of wall or part fabrication by WAAM is widely spread [20][21][22][23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Effect of the Metal Transfer Mode on the Symmetry of Bead Geometry in WAAM Aluminum

Veiga

Suárez²,

Aldalur³

et al. 2021

Symmetry

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show abstract

“…Recently, researchers have worked on the bead geometry prediction models using machine learning approaches to address issues relating to deposition accuracy [7,[31][32][33][34][35]. Xiong et al [31] used second-order regression with a neural network for predicting bead ge-ometry in wire and arc additive manufacturing (WAAM) to study the relationship between the process variables and the final bead geometry.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, Milhomme et al [33] studied first-order parameters to identify correlations between input parameters and geometrical outputs of Ti-6Al-4V beads manufactured with the laser metal powder deposition process. Finally, Karmuhilan and Kumarsood [34] discussed the use of an artificial neural network (ANN) to predict the bead parameters based on given process parameters for the WAAM system.…”

Section: Introductionmentioning

confidence: 99%

Bead Geometry Prediction in Laser-Wire Additive Manufacturing Process Using Machine Learning: Case of Study

et al. 2021

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In Laser Wire Additive Manufacturing (LWAM), the final geometry is produced using the layer-by-layer deposition (beads principle). To achieve good geometrical accuracy in the final product, proper implementation of the bead geometry is essential. For this reason, the paper focuses on this process and proposes a layer geometry (width and height) prediction model to improve deposition accuracy. More specifically, a machine learning regression algorithm is applied on several experimental data to predict the bead geometry across layers. Furthermore, a neural network-based approach was used to study the influence of different deposition parameters, namely laser power, wire-feed rate and travel speed on bead geometry. To validate the effectiveness of the proposed approach, a test split validation strategy was applied to train and validate the machine learning models. The results show a particular evolutionary trend and confirm that the process parameters have a direct influence on the bead geometry, and so, too, on the final part. Several deposition parameters have been found to obtain an accurate prediction model with low errors and good layer deposition. Finally, this study indicates that the machine learning approach can efficiently be used to predict the bead geometry and could help later in designing a proper controller in the LWAM process.

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“…There are also other approaches; for example, the capillary theory was used to calculate the profile of the deposited bead in the study [9], artificial neural network technologies were used in the work [22] and the authors of Reference [13] used regression analysis for the same purpose.…”

Section: Introductionmentioning

confidence: 99%

Numerical Estimation of the Geometry of the Deposited Layers during Direct Laser Deposition of Multi-Pass Walls

Udin

Valdaytseva

Kislov

2021

Metals

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Direct laser deposition (DLD) is a promising additive technology that allows for the rapid and cheap production of metal parts of complex geometry in various sectors of mechanical engineering. Thick-walled metal structures occupy a significant part in mechanical engineering. The purpose of this study was to develop and test an algorithm for predicting the geometry of deposited multi-pass walls. To achieve this goal, the main interrelated processes involved in the formation of a multi-pass wall were described—the process of laser radiation propagation, the process of heat transfer and the process of bead formation. To construct the calculation algorithm, five characteristic types of beads are identified. For these five types, the features of the bead formation and the features of the laser radiation intensity distribution are described. The calculated data were verified. A good match of the calculated data with the geometry of the deposited walls from AISI321 steel, Inconel718 and Ti-6Al-4V alloys was obtained.

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Intelligent process model for bead geometry prediction in WAAM

Cited by 37 publications

References 8 publications

Effect of the Metal Transfer Mode on the Symmetry of Bead Geometry in WAAM Aluminum

Effect of the Metal Transfer Mode on the Symmetry of Bead Geometry in WAAM Aluminum

Bead Geometry Prediction in Laser-Wire Additive Manufacturing Process Using Machine Learning: Case of Study

Numerical Estimation of the Geometry of the Deposited Layers during Direct Laser Deposition of Multi-Pass Walls

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