Physics-Based Multivariable Modeling and Feedback Linearization Control of Melt-Pool Geometry and Temperature in Directed Energy Deposition

Wang, Qian; Li, Jianyi; Gouge, Michael; Nassar, Abdalla R.; Michaleris, P.; Reutzel, Edward W.

doi:10.1115/1.4034304

Cited by 62 publications

(28 citation statements)

References 44 publications

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“…The investigated geometry is common in additive manufacturing, where the physics within the thin topmost layer of the problem (Ω − ) is often different than in the remainder of the domain. We note that such problems from are an important potential application for the proposed method, since additive manufacturing is an active area of research [3,10,11,12,14,15,22,23,24,27].…”

Section: Steady Additive-type Problemmentioning

confidence: 99%

“…Problems with localized nonhomogeneous material properties are of great interest in engineering [3,4,5,8,9,10,11,12,14,15,16,22,23,24,25,26,27]. In such problems, the varying physical properties between the different regions result in potentially large modeling differences.…”

Section: Introductionmentioning

confidence: 99%

“…In many instances, a material which comprises a relatively small portion of the domain may feature significantly more complex physics and, therefore, it may require a higher mesh resolution than in the remainder of the domain. Common examples of such problems with high industrial interest are thermal problems in additive manufacturing (where the nonlinear phase transformation phenomena occur only along a thin strip on the top of the domain) [3,10,11,12,14,15,16,22,23,24,25,26,27], or fluid flow problems through immersed membranes (where the flow properties change inside a subregion of the domain) [17,18,19,21]. A simple diagram of such a physical situation is shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

“…In contrast, a mesh that is coarse in one region and fine in another, while computationally more attractive, may be difficult to generate and is also known to cause difficulties with preconditioning [5,6,7,13]. Additionally, in some instances the boundary of the subregion may be geometrically complicated, or may change in time, requiring frequent remeshing or the use of complicated local refinement and derefinement techniques [12,15,22,23,24,27].…”

Section: Introductionmentioning

confidence: 99%

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A Fat boundary-type method for localized nonhomogeneous material problems

Viguerie

Bertoluzza²,

Auricchio

2020

Computer Methods in Applied Mechanics and Engineering

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Problems with localized nonhomogeneous material properties arise frequently in many applications and are a well-known source of difficulty in numerical simulations. In certain applications (including additive manufacturing), the physics of the problem may be considerably more complicated in relatively small portions of the domain, requiring a significantly finer local mesh compared to elsewhere in the domain. This can make the use of a uniform mesh numerically unfeasible. While nonuniform meshes can be employed, they may be challenging to generate (particularly for regions with complex boundaries) and more difficult to precondition. The problem becomes even more prohibitive when the region requiring a finer-level mesh changes in time, requiring the introduction of refinement and derefinement techniques. To address the aforementioned challenges, we employ a technique related to the Fat boundary method [1,2,20] as a possible alternative. We analyze the proposed methodology from a mathematical point of view and validate our findings on two-dimensional numerical tests.

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Section: Steady Additive-type Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Fat boundary-type method for localized nonhomogeneous material problems

Viguerie

Bertoluzza²,

Auricchio

2020

Computer Methods in Applied Mechanics and Engineering

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“…Figure 2. Finite element analysis steps in additive manufacturing processes [67][68][69][70][71][72][73][74][75][76].…”

Section: Finite Element Analysismentioning

confidence: 99%

Integration of physically-based and data-driven approaches for thermal field prediction in additive manufacturing

Jin

2018

Materials & Design

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A quantitative understanding of thermal field evolution is vital for quality control in additive manufacturing (AM). Because of the unknown material parameters, high computational costs, and imperfect understanding of the underlying science, physically-based approaches alone are insufficient for component-scale thermal field prediction. Here, I present a new framework that integrates physically-based and data-driven approaches with quasi in situ thermal imaging to address this problem. The framework consists of (i) thermal modeling using 3D finite element analysis (FEA), (ii) surrogate modeling using functional Gaussian process, and (iii) Bayesian calibration using the thermal imaging data. Based on heat transfer laws, I first investigate the transient thermal behavior during AM using 3D FEA. A functional Gaussian process-based surrogate model is then constructed to reduce the computational costs from the high-fidelity, physically-based model. I finally employ a Bayesian calibration method, which incorporates the surrogate model and thermal measurements, to enable layer-to-layer thermal field prediction across the whole component. A case study on fused deposition modeling is conducted for components with 7 to 16 layers. The cross-validation results show that the proposed framework allows for accurate and fast thermal field prediction for components with different process settings and geometric designs. Integration of Physically-based and Data-driven Approaches for Thermal Field Prediction in Additive ManufacturingJingran Li GENERAL AUDIENCE ABSTRACT This paper aims to achieve the layer to layer temperature monitoring and consequently predict the temperature distribution for any new freeform geometry. An engineering statistical synergistic model is proposed to integrate the pure statistical methods and finite element modeling (FEM), which is physically meaningful as well as accurate for temperature prediction. Besides, this proposed synergistic model contains geometry information, which can be applied to any freeform geometry. This paper serves to enable a holistic cyber physical systems-based approach for the additive manufacturing (AM) not only restricted in fused deposition modeling (FDM) process but also can be extended to powder-based process like laser engineered net shaping (LENS) and selective laser sintering (SLS). This paper as well as the scheduled future works will make it affordable for customized AM including customized geometries and materials, which will greatly accelerate the transition from rapid prototyping to rapid manufacturing. This article demonstrates a first evaluation of engineering statistical synergistic model in AM technology, which gives a perspective on future researches about online quality monitoring and control of AM based data fusion principles. iv ACKNOWLEDGEMENTS

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Numerical solution of additive manufacturing problems using a two‐level method

Viguerie

Auricchio

2021

Numerical Meth Engineering

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Additive manufacturing techniques have grown significantly in recent years due to their ability to produce designs almost impossible to obtain with standard production technologies. This growth has in turn led to increased demand for numerical simulation of additive processes. Unfortunately, such simulations are difficult from the computational point of view, since additive problems are characterized by the presence of highly local nonlinearities requiring fine mesh resolutions on small portions of the domain. These difficulties are compounded by the fact that such regions of local high nonlinearities move in the domain with time, and further, by the fact that the physical domain itself evolves with time. Accordingly, the present article addresses these complex computational issues by employing a two‐level technique. The two‐level method provides a natural framework for the resolution of localized nonlinearities while avoiding the need for complex meshing operations, such as refinement‐and‐derefinement algorithms. The proposed approach is validated on a series of one‐ and two‐dimensional problems to establish the viability of the introduced two‐level method for additive manufacturing problems.

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Physics-Based Multivariable Modeling and Feedback Linearization Control of Melt-Pool Geometry and Temperature in Directed Energy Deposition

Cited by 62 publications

References 44 publications

A Fat boundary-type method for localized nonhomogeneous material problems

A Fat boundary-type method for localized nonhomogeneous material problems

Integration of physically-based and data-driven approaches for thermal field prediction in additive manufacturing

Numerical solution of additive manufacturing problems using a two‐level method

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