Analysis and compensation of shrinkage and distortion in wire-arc additive manufacturing of thin-walled curved hollow sections

Nguyen, Lam M.; Buhl, Johannes; Israr, Rameez; Bambach, Markus

doi:10.1016/j.addma.2021.102365

Cited by 18 publications

(8 citation statements)

References 37 publications

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“…The literature proposes a variety of activation criteria. Most commonly, the activation temperature [8], activation time [28] or Gauss point evaluated heat source [29] model-based criteria are implemented. While the activation time criterion is convenient for DED modelling, the activation temperature criterion offers a better representation of the LPBF process.…”

Section: Pre-processing Of Activation Criterionmentioning

confidence: 99%

See 1 more Smart Citation

Towards an Automated Framework for the Finite Element Computational Modelling of Directed Energy Deposition

Kovšca¹,

Starman²,

Klobčar³

et al. 2022

SSRN Journal

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Section: Pre-processing Of Activation Criterionmentioning

confidence: 99%

“…Similarly, Nguyen et al [8] developed a method to correct the central axis distortion of a thin-wall hollow section by applying a virtual bending moment. Furthermore, state-of-the-art numerical simulations can be employed to compensate and mitigate the distortions of the produced part in the first place.…”

Section: Introductionmentioning

confidence: 99%

Towards an Automated Framework for the Finite Element Computational Modelling of Directed Energy Deposition

Kovšca¹,

Starman²,

Klobčar³

et al. 2022

SSRN Journal

View full text Add to dashboard Cite

“…Some of these studies develop strategies to predict the shrinkage that takes place during the curing stage in a Stereolithography process (Hur and Youn, 1998; Huang and Jiang, 2003; Huang & Lan, 2005; Yaghi et al , 2019). Others describe geometric compensation strategies by reversing FEM-simulated deviations on different AM techniques: powder bed fusion (Yaghi et al , 2019; Biegler et al , 2020; Frigioescu et al , 2020), direct energy deposition (Nguyen et al , 2021), selective laser melting (Afazov et al , 2017a) or wire-arc additive manufacturing (Afazov et al , 2017b). In the case of Afazov et al (2021), they also apply geometric compensations by reversing the deformations detected by digitising with a 3D scanner.…”

Section: Introductionmentioning

confidence: 99%

A layerwise geometric error compensation procedure for additive manufacturing

Peña,

Rico,

Zapico

et al. 2024

RPJ

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Purpose The purpose of this paper is to provide a new procedure for in-plane compensation of geometric errors that often appear in the layers deposited by an additive manufacturing (AM) process when building a part, regardless of the complexity of the layer geometry. Design/methodology/approach The procedure is based on comparing the real layer contours to the nominal ones extracted from the STL model of the part. Considering alignment and form deviations, the compensation algorithm generates new compensated contours that match the nominal ones as closely as possible. To assess the compensation effectiveness, two case studies were analysed. In the first case, the parts were not manufactured, but the distortions were simulated using a predictive model. In the second example, the test part was actually manufactured, and the distortions were measured on a coordinate measuring machine. Findings The geometric deviations detected in both case studies, as evaluated by various quality indicators, reduced significantly after applying the compensation procedure, meaning that the compensated and nominal contours were better matched both in shape and size. Research limitations/implications Although large contours showed deviations close to zero, dimensional overcompensation was observed when applied to small contours. The compensation procedure could be enhanced if the applied compensation factor took into account the contour size of the analysed layer and other geometric parameters that could have an influence. Originality/value The presented method of compensation is applicable to layers of any shape obtained in any AM process.

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“…White-box models are helpful for better understanding the effects of the underlying dynamics and, in this way, improving the processes (Xiong et al, 2018). As shown in Nguyen et al (2021); Graf et al (2018), which focused on the effects of the thermal fields on the resulting part distortions, these approaches also allow modeling the interactions between coupled physical phenomena. However, finite element methods in additive manufacturing are very computationally expensive and require many hours, even days, to execute.…”

Section: Introductionmentioning

confidence: 99%

GPyro: uncertainty-aware temperature predictions for additive manufacturing

2022

Self Cite

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In additive manufacturing, process-induced temperature profiles are directly linked to part properties, and their prediction is crucial for achieving high-quality products. Temperature predictions require an accurate process model, which is usually either a physics-based or a data-driven simulator. Although many high-performance models have been developed, they all suffer from disadvantages such as long execution times, the need for large datasets, and error accumulation in long prediction horizons. These caveats undermine the utility of such modeling approaches and pose problems in their integration within iterative optimization and closed-loop control schemes. In this work, we introduce GPyro, a generative model family specifically designed to address these issues and enable fast probabilistic temperature predictions. GPyro combines physics-informed and parametric regressors with a set of smooth attention mechanisms and learns the evolution of the dynamics inherent to a system by employing Gaussian processes. The model predictions are equipped with confidence intervals quantifying the uncertainty at each timestep. We applied GPyro to Wire-arc additive manufacturing and learned an accurate model from a single experiment on a real welding cell, almost in real-time. Our model can be easily integrated within existing loop-shaping and optimization frameworks.

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Analysis and compensation of shrinkage and distortion in wire-arc additive manufacturing of thin-walled curved hollow sections

Cited by 18 publications

References 37 publications

Towards an Automated Framework for the Finite Element Computational Modelling of Directed Energy Deposition

Towards an Automated Framework for the Finite Element Computational Modelling of Directed Energy Deposition

A layerwise geometric error compensation procedure for additive manufacturing

GPyro: uncertainty-aware temperature predictions for additive manufacturing

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