Thermomechanical Modeling of Additive Manufacturing Large Parts

Denlinger, Erik R.; Irwin, J. David; Michaleris, P.

doi:10.1115/1.4028669

Cited by 265 publications

(133 citation statements)

References 37 publications

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“…Whatever the method chosen, accuracy of the thermal and residual stress distributions is fulfilled and computational time is saved. It is moreover possible to increase the gain of CPU time saving by implementing an adaptive coarsening method [51,63].…”

Section: Metal Deposition Modelingmentioning

confidence: 99%

“…Moreover, the model is set up in the Lagrangian framework, which is more convenient for distortion modeling of large parts. The mechanical analysis is also non-linear (because of the non-linearity of the material behavior) but considered as a quasi-static incremental analysis [56,57,63]. The governing stress equation can be expressed as [63,75] follows:…”

Section: Mechanical Analysismentioning

confidence: 99%

“…The scarcity of thermo-metallurgical properties in the literature often obliges researchers to disregard the phase dependency of the thermal properties. Conductivity, density, and specific heat are temperature dependent and are assumed constant above 800°C for most materials (IN718 [58], Ti-6Al-4V [63]) or are obtained from a library such as JMatPro® for the metallurgical composition (X4CrNiCuNb 16-4 hardening stainless steel [64] or 316L stainless steel [54]). …”

Section: Metallurgical Phase Transformations and Materials Propertiesmentioning

confidence: 99%

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Metal additive-manufacturing process and residual stress modeling

Megahed

Mindt

N’Dri

et al. 2016

Integr Mater Manuf Innov

225

113

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Additive manufacturing (AM), widely known as 3D printing, is a direct digital manufacturing process, where a component can be produced layer by layer from 3D digital data with no or minimal use of machining, molding, or casting. AM has developed rapidly in the last 10 years and has demonstrated significant potential in cost reduction of performance-critical components. This can be realized through improved design freedom, reduced material waste, and reduced post processing steps. Modeling AM processes not only provides important insight in competing physical phenomena that lead to final material properties and product quality but also provides the means to exploit the design space towards functional products and materials. The length-and timescales required to model AM processes and to predict the final workpiece characteristics are very challenging. Models must span length scales resolving powder particle diameters, the build chamber dimensions, and several hundreds or thousands of meters of heat source trajectories. Depending on the scan speed, the heat source interaction time with feedstock can be as short as a few microseconds, whereas the build time can span several hours or days depending on the size of the workpiece and the AM process used. Models also have to deal with multiple physical aspects such as heat transfer and phase changes as well as the evolution of the material properties and residual stresses throughout the build time. The modeling task is therefore a multi-scale, multi-physics endeavor calling for a complex interaction of multiple algorithms. This paper discusses models required to span the scope of AM processes with a particular focus towards predicting as-built material characteristics and residual stresses of the final build. Verification and validation examples are presented, the over-spanning goal is to provide an overview of currently available modeling tools and how they can contribute to maturing additive manufacturing.

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Section: Metal Deposition Modelingmentioning

confidence: 99%

Section: Mechanical Analysismentioning

confidence: 99%

Section: Metallurgical Phase Transformations and Materials Propertiesmentioning

confidence: 99%

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Metal additive-manufacturing process and residual stress modeling

Megahed

Mindt

N’Dri

et al. 2016

Integr Mater Manuf Innov

225

113

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“…However, the existing researches on 3D FEA simulation are typically applied to simple geometries like a three-layer component [55] or a small cuboid [48,56]. Although a thermomechanical modeling of large parts in AM processes were investigated in [57], it was restricted to the specific component geometry, which cannot be widely applied to geometry of freeform.…”

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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“…49 Here, as a representative example, we describe the use of HPC simulations of heat and mass transfer for predicting the spatial and temporal variation of the melt-pool shape during electronbeam AM of Ti-6Al-4V alloys.…”

Section: Approachmentioning

confidence: 99%

Additive manufacturing of materials: Opportunities and challenges

et al. 2015

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Figure 1. Development stages of big-area additive manufacturing (BAAM). (a) Gantrystyle robotic automation system, without any heating environment, was developed and integrated with a single-screw extrusion nozzle. (b) Initial material trials led to extensive distortion with acrylonitrile butadiene styrene (ABS) pellets, whereas use of pellets with ABS and carbon fi ber (CF) reduced the distortion. (c) Aerial view of the industrial-scale BAAM system developed by Local Motors. (d) The industrial-scale system was verifi ed by printing out a chassis of a small car, which was then integrated with an electric drive train. (e) The same technology was coupled with traditional manufacturing to produce a prototype similar to the Ford Shelby Cobra, which was also powered by an electric drive train. (f) The same technology has also been used to make molds for use in prototyping during traditional metal-forming processes. Reproduced with permission from Reference 10.

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Thermomechanical Modeling of Additive Manufacturing Large Parts

Cited by 265 publications

References 37 publications

Metal additive-manufacturing process and residual stress modeling

Metal additive-manufacturing process and residual stress modeling

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

Additive manufacturing of materials: Opportunities and challenges

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