Approach to an optimized printing path for additive manufacturing in construction utilizing FEM modeling

Lachmayer, Lukas; Ekanayaka, Virama; Hürkamp, André; Raatz, Annika

doi:10.1016/j.procir.2021.11.101

Cited by 12 publications

(7 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Details on the mesh generation can be found in refs. [5, 6]. Hence, the current configuration

\begin{equation} \mathbf {x} (\mathbf {X}, t, \mathbf {p}) = \mathbf {X}(\mathbf {p},\vec{s}_t) + \mathbf {u}(\mathbf {X}, t) \end{equation}

is described by the initial configuration

\mathbf {X}(\mathbf {p},\vec{s}_t)

and a displacement field

\mathbf {u}(\mathbf {X}_0, t)

describing the deviation of the actual geometry from the planned geometry.…”

Section: Process Based Finite Element Analysis For Ammentioning

confidence: 99%

“…Hence, the physical behaviour of fresh concrete needs to be considered during path planning and online-control. To estimate the plastic deformations and process stability during the additive manufacturing (AM) process, a path and process-based finite element simulation has been developed [5][6][7]. Since this simulation is quite time consuming (minuts-hours, depending on the complexity) it can only be used during the initial path planning so far, to support the process design.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A novel surrogate modelling approach for additive manufacturing processes

Hürkamp,

Ekanayaka

2023

Proc Appl Math and Mech

Self Cite

View full text Add to dashboard Cite

Additive manufacturing (AM) enables the production of complex and customised components with minimal waste. However, to achieve the desired geometry and quality, a lot of process optimisation is needed (often by experimental trial and error). Especially, in the case of AM with concrete materials, the time‐dependent behaviour of the fresh concrete is a large challenge to achieve process stability. Simulations taking into account the time‐dependent material are able to predict the printing result. Since these simulations are very time consuming, a proper orthogonal decomposition (POD)‐based reduced order model for AM is presented. Based on snapshots along the build‐up process, a surrogate model is constructed that is able to predict the printing result with respect to the underlying process and materials. Inspired by the POD approximation, a novel surrogate modelling approach using the initial waypoints of the printing trajectory as input is investigated. In this case, it is possible to analyse the manufacturing process for arbitrary geometries. The proposed method is demonstrated for the use case of additive manufacturing in construction.

show abstract

“…Details on the mesh generation can be found in refs. [5, 6]. Hence, the current configuration

\begin{equation} \mathbf {x} (\mathbf {X}, t, \mathbf {p}) = \mathbf {X}(\mathbf {p},\vec{s}_t) + \mathbf {u}(\mathbf {X}, t) \end{equation}

is described by the initial configuration

\mathbf {X}(\mathbf {p},\vec{s}_t)

and a displacement field

\mathbf {u}(\mathbf {X}_0, t)

describing the deviation of the actual geometry from the planned geometry.…”

Section: Process Based Finite Element Analysis For Ammentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel surrogate modelling approach for additive manufacturing processes

Hürkamp,

Ekanayaka

2023

Proc Appl Math and Mech

Self Cite

View full text Add to dashboard Cite

show abstract

“…The following are the most cited Industry 4.0 technologies (TI4.0) in the scientific literature in the CI, to support this study. Additive manufacturing (AM) – Also called 3D printing, by depositing material layer-upon-layer, this technology can build complex structures, using different materials, if necessary (Kanyilmaz et al , 2022; Lachmayer et al , 2021; Tahmasebinia et al , 2020). IoT – Provides connectivity between humans, mobile devices, machines, actuators and sensors, making ubiquitous data and information more accessible (Kim et al , 2017; Lucato et al , 2019; Pacchini et al , 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

The DAWN readiness model to assess the level of use of Industry 4.0 technologies in the construction industry in Brazil

Ribeiro

Coutinho

Sátyro

et al. 2022

View full text Add to dashboard Cite

Purpose Construction industry (CI) has great prominence for the world economy, and it is expected that, with the use of the innovative technologies and approaches of Industry 4.0 (I4.0), the new industrial paradigm, construction can reach higher levels of productivity. This study aims to develop a model (readiness model) to assess the level of use of I4.0 technologies by the construction sector in Brazil and its most relevant applications. Design/methodology/approach The methodology used was bibliographic research, design-science research and a survey to validate the model, carried out with 162 companies, considered among the main ones in the sector in Brazil. The literature review revealed 13 technologies of I4.0 applied to construction; hence, the views of industry experts were based on these technologies. Findings The Digital Advancement Within CoNstruction (DAWN) readiness model was proposed, showing that among the 13 evaluated technologies of I4.0 and their applications, the Brazilian construction companies had a low level of utilization; both high and middle-income companies presented this low level of use; some technologies with a greater number of scientific publications were less used in practice in the Brazilian construction. Originality/value The originality and theoretical contribution are to present a readiness model to assess the level of use of I4.0 technologies and their most relevant applications in the CI in countries with an economy similar to Brazil’s, making it possible to measure the level of adoption of these technologies.

show abstract

“…To systematically examine the deformations occurring during the printing process, a novel path and process-based finite element simulation has been developed [5]. This simulation incorporates time stamps assigned to the mesh elements, allowing for the numerical implementation of the time-dependent material behavior [6,7]. Physics-informed neural networks (PINNs) have emerged as a powerful tool in applied mathematics and engineering [8,9], finding successful applications across a diverse range of problems in recent years [10].…”

Section: Introductionmentioning

confidence: 99%

Modeling of additive manufacturing processes with time‐dependent material properties using physics‐informed neural networks

Ekanayaka,

Hürkamp

2023

Proc Appl Math and Mech

Self Cite

View full text Add to dashboard Cite

Recently, physics‐informed neural networks (PINNs) have been effectively utilized in a wide range of problems within the domains of applied mathematics and engineering. In PINNs, the governing physical equations are directly incorporated into the loss function of the network and a conventional labeled dataset is not required for its training. In order to successfully simulate the additive manufacturing processes with concrete, a novel process‐based FE‐simulation has been developed where the Drucker–Prager plasticity model is used as the material model. In this work, we will examine the deployment of a PINN to substitute the Newton–Raphson iterations that occur in the return‐mapping algorithm of the Drucker–Prager plasticity model.

show abstract

Approach to an optimized printing path for additive manufacturing in construction utilizing FEM modeling

Cited by 12 publications

References 16 publications

A novel surrogate modelling approach for additive manufacturing processes

A novel surrogate modelling approach for additive manufacturing processes

The DAWN readiness model to assess the level of use of Industry 4.0 technologies in the construction industry in Brazil

Modeling of additive manufacturing processes with time‐dependent material properties using physics‐informed neural networks

Contact Info

Product

Resources

About