New Hammerstein Modeling and Analysis for Controlling Melt Pool Width in Powder Bed Fusion Additive Manufacturing

2021 ◽  
pp. 1-7
Author(s):  
Dan Wang ◽  
Xinyu Zhao ◽  
Xu Chen
Keyword(s):  
Finite Element ◽  
Additive Manufacturing ◽  
Process Variations ◽  
Element Model ◽  
Hammerstein Model ◽  
Powder Bed Fusion ◽  
Modeling And Analysis ◽  
Powder Bed ◽  
Melt Pool ◽  
Linearized Model

Abstract Despite the advantages and emerging applications, broader adoption of powder bed fusion (PBF) additive manufacturing is challenged by insufficient reliability and in-process variations. Finite element modeling and control-oriented modeling have been identified fundamental for predicting and engineering part qualities in PBF. This paper first builds a finite element model (FEM) of the thermal fields to look into the convoluted thermal interactions during the PBF process. Using the FEM data, we identify a novel surrogate system model from the laser power to the melt pool width. Linking a linearized model with a memoryless nonlinear submodel, we develop a physics-based Hammerstein model that captures the complex spatiotemporal thermomechanical dynamics. We verify the accuracy of the Hammerstein model using the FEM and prove that the linearized model is only a representation of the Hammerstein model around the equilibrium point. Along the way, we conduct the stability and robustness analyses and formalize the Hammerstein model to facilitate the subsequent control designs.

Closed-Loop Simulation Integrating Finite Element Modeling With Feedback Controls in Powder Bed Fusion Additive Manufacturing

2020 ◽  
Author(s):  
Dan Wang ◽  
Xu Chen
Keyword(s):  
Finite Element ◽  
Additive Manufacturing ◽  
Finite Element Modeling ◽  
Closed Loop ◽  
Agile Manufacturing ◽  
Powder Bed Fusion ◽  
Powder Bed ◽  
Element Modeling ◽  
Melt Pool ◽  
Wide Range

Abstract Powder bed fusion (PBF) additive manufacturing has enabled unmatched agile manufacturing of a wide range of products from engine components to medical implants. While high-fidelity finite element modeling and feedback control have been identified key for predicting and engineering part qualities in PBF, existing results in each realm are developed in opposite computational architectures wildly different in time scale. Integrating both realms, this paper builds a first-instance closed-loop simulation framework by utilizing the output signals retrieved from the finite element model (FEM) to directly update the control signals sent to the model. The proposed closed-loop simulation enables testing the limits of advanced controls in PBF and surveying the parameter space fully to generate more predictable part qualities. Along the course of formulating the framework, we verify the FEM by comparing its results with experimental and analytical solutions and then use the FEM to understand the melt-pool evolution induced by the in-layer thermomechanical interactions. From there, we build a repetitive control algorithm to greatly attenuate variations of the melt pool width.

Modeling and analysis of the on-demand spare parts supply using additive manufacturing

2019 ◽  
Vol 25 (3) ◽  
pp. 473-487 ◽  
Author(s):  
Yuan Zhang ◽  
Stefan Jedeck ◽  
Li Yang ◽  
Lihui Bai
Keyword(s):  
Supply Chain ◽  
Additive Manufacturing ◽  
Spare Parts ◽  
Production Optimization ◽  
Arrival Process ◽  
Powder Bed Fusion ◽  
Content Type ◽  
Modeling And Analysis ◽  
Powder Bed ◽  
On Demand

PurposeDespite the widespread expectation that additive manufacturing (AM) will become a disruptive technology to transform the spare parts supply chain, very limited research has been devoted to the quantitative modeling and analysis on how AM could fulfill the on-demand spare parts supply. On the other hand, the choice of using AM as a spare parts supply strategy over traditional inventory is a rising decision faced by manufacturers and requires quantitative analysis for their AM-or-stock decisions. The purpose of this paper is to develop a quantitative performance model for a generic powder bed fusion AM system in a spare parts supply chain, thus providing insights into this less-explored area in the literature.Design/methodology/approachIn this study, analysis based on a discrete event simulation was carried out for the use of AM in replacement of traditional warehouse inventory for an on-demand spare parts supply system. Generic powder bed fusion AM system was used in the model, and the same modeling approach could be applied to other types of AM processes. Using this model, the impact of both spare parts demand characteristics (e.g. part size attributes, demand rates) and the AM operations characteristics (e.g. machine size and postpone strategy) on the performance of using AM to supply spare parts was studied.FindingsThe simulation results show that in many cases the AM operation is not as cost competitive compared to the traditional warehouse-based spare parts supply operation, and that the spare parts size characteristics could significantly affect the overall performance of the AM operations. For some scenarios of the arrival process of spare parts demand, the use of the batched AM production could potentially result in significant delay in parts delivery, which necessitates further investigations of production optimization strategies.Originality/valueThe findings demonstrate that the proposed simulation tool can not only provide insights on the performance characteristics of using AM in the spare parts supply chain, especially in comparison to the traditional warehousing system, but also can be used toward decision making for both the AM manufacturers and the spare parts service providers.

scholarly journals An Overview of Additive Manufacturing Technologies—A Review to Technical Synthesis in Numerical Study of Selective Laser Melting

Materials ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 3895 ◽  
Author(s):  
Abbas Razavykia ◽  
Eugenio Brusa ◽  
Cristiana Delprete ◽  
Reza Yavari
Keyword(s):  
Numerical Simulation ◽  
Additive Manufacturing ◽  
Selective Laser Melting ◽  
Powder Bed Fusion ◽  
Laser Melting ◽  
Powder Bed ◽  
Part Quality ◽  
Melt Pool ◽  
Wide Range ◽  
Am Processes

Additive Manufacturing (AM) processes enable their deployment in broad applications from aerospace to art, design, and architecture. Part quality and performance are the main concerns during AM processes execution that the achievement of adequate characteristics can be guaranteed, considering a wide range of influencing factors, such as process parameters, material, environment, measurement, and operators training. Investigating the effects of not only the influential AM processes variables but also their interactions and coupled impacts are essential to process optimization which requires huge efforts to be made. Therefore, numerical simulation can be an effective tool that facilities the evaluation of the AM processes principles. Selective Laser Melting (SLM) is a widespread Powder Bed Fusion (PBF) AM process that due to its superior advantages, such as capability to print complex and highly customized components, which leads to an increasing attention paid by industries and academia. Temperature distribution and melt pool dynamics have paramount importance to be well simulated and correlated by part quality in terms of surface finish, induced residual stress and microstructure evolution during SLM. Summarizing numerical simulations of SLM in this survey is pointed out as one important research perspective as well as exploring the contribution of adopted approaches and practices. This review survey has been organized to give an overview of AM processes such as extrusion, photopolymerization, material jetting, laminated object manufacturing, and powder bed fusion. And in particular is targeted to discuss the conducted numerical simulation of SLM to illustrate a uniform picture of existing nonproprietary approaches to predict the heat transfer, melt pool behavior, microstructure and residual stresses analysis.

Hybrid Modeling Approach for Melt Pool Prediction in Laser Powder Bed Fusion Additive Manufacturing

2020 ◽  
Author(s):  
Tesfaye Moges ◽  
Zhuo Yang ◽  
Kevontrez Jones ◽  
Shaw Feng ◽  
Paul Witherell ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Hybrid Model ◽  
Hybrid Models ◽  
Hybrid Modeling ◽  
Data Driven ◽  
Powder Bed Fusion ◽  
Cfd Model ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Melt Pool

Abstract Multi-scale, multi-physics, computational models are a promising tool to provide detailed insights to understand the process-structure-property-performance relationships in additive manufacturing (AM) processes. To take advantage of the strengths of both physics-based and data-driven models, we propose a novel, hybrid modeling framework for laser powder bed fusion (L-PBF) processes. Our unbiased, model integration method combines physics-based data and measurement data for approaching more accurate prediction of melt-pool width. Both a high-fidelity computational fluid dynamics (CFD) model and experiments utilizing optical images are used to generate a combined dataset of melt-pool widths. From this aggregated dataset, a hybrid model is developed using data-driven modeling techniques, including polynomial regression and Kriging methods. The performance of the hybrid model is evaluated by computing the average relative error and comparing it with the results of the simulations and surrogate models constructed from the original CFD model and experimental measurements. It is found that the proposed hybrid model performs better in terms of prediction accuracy and computational time. Future work includes a conceptual introduction on the use of an AM ontology to support improved model and data selection when constructing hybrid models. This study can be viewed as a significant step towards the use of hybrid models as predictive models with improved accuracy and without the sacrifice of speed.

Simulation of metallic powder bed additive manufacturing processes with the finite element method: A critical review

2016 ◽  
Vol 231 (1) ◽  
pp. 96-117 ◽  
Author(s):  
Babis Schoinochoritis ◽  
Dimitrios Chantzis ◽  
Konstantinos Salonitis
Keyword(s):  
Finite Element ◽  
Additive Manufacturing ◽  
Experimental Validation ◽  
Metallic Powder ◽  
Manufacturing Processes ◽  
Powder Bed ◽  
Modeling Methodology ◽  
Melt Pool ◽  
Element Simulation ◽  
Melt Pool Characteristics

This article provides a literature review of finite element simulation studies for metallic powder bed additive manufacturing processes. The various approaches in the numerical modeling of the processes and the selection of materials properties are presented in detail. Simulation results are categorized according to three major findings’ groups (i.e. temperature field, residual stresses and melt pool characteristics). Moreover, the means used for the experimental validation of the simulation findings are described. Looking deeper into the studies reviewed, a number of future directions are identified in the context of transforming simulation into a powerful tool for the industrial application of additive manufacturing. Smart modeling approaches should be developed, materials and their properties should be further characterized and standardized, commercial packages specialized in additive manufacturing simulation have to be developed and simulation needs to become part of the modern digital production chains. Finally, the reviewed studies are organized in a table and characterized according to the process and material studied, the modeling methodology and the experimental validation method used in each of them. The key findings of the reviewed studies are also summarized.

Powder-Scale Meshfree Simulations of Powder Bed Fusion Based Additive Manufacturing Processes

2019 ◽  
Author(s):  
Zongyue Fan ◽  
Hao Wang ◽  
Bo Li
Keyword(s):  
Additive Manufacturing ◽  
Optimal Transportation ◽  
The Novel ◽  
Powder Bed Fusion ◽  
Computational Framework ◽  
Powder Bed ◽  
Materials Behaviors ◽  
Melt Pool ◽  
Powder Particles ◽  
Am Processes

Abstract We present a powder-scale meshfree direct numerical simulation (DNS) capability for the powder bed fusion (PBF) based additive manufacturing (AM) processes using the novel Hot Optimal Transportation Meshfree (HOTM) method. The HOTM method is an incremental Lagrangian meshfree computational framework for materials behaviors under extreme thermomechanical loading conditions, which combines the Optimal Transportation Meshfree (OTM) method and the variational thermomechanical constitutive updates. The realistic multi-layer powder bed geometry is modeled explicitly in the HOTM simulations based on experimental data. A phase-aware constitutive model is developed to predict the phase change and multiphase mixing during the PBF AM processes automatically. The governing equations including the linear momentum and energy conservation equations are solved for the multiphase flow simultaneously to predict the deformation, temperature and local state of the powder particles. The powder-scale DNS is employed to study the influence of various laser powers on the melt pool thermodynamics.

Ultrasonics for monitoring melt pool dynamics and in situ sensing of microstructure during powder bed fusion additive manufacturing

2021 ◽  
Vol 150 (4) ◽  
pp. A307-A307
Author(s):  
Christopher M. Kube ◽  
Nathan Kizer ◽  
Abdalla Nassar ◽  
Edward Reutzel ◽  
Haifeng Zhang ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Powder Bed Fusion ◽  
Powder Bed ◽  
Melt Pool ◽  
In Situ Sensing

Hybrid Modeling of Melt Pool Geometry in Additive Manufacturing Using Neural Networks

2021 ◽  
Author(s):  
Kevontrez Jones ◽  
Zhuo Yang ◽  
Ho Yeung ◽  
Paul Witherell ◽  
Yan Lu
Keyword(s):  
Neural Networks ◽  
Finite Element ◽  
Additive Manufacturing ◽  
Real Time ◽  
Hybrid Model ◽  
Computational Models ◽  
Single Layer ◽  
Element Model ◽  
Additive Process ◽  
Melt Pool

Abstract Laser powder-bed fusion is an additive manufacturing (AM) process that offers exciting advantages for the fabrication of metallic parts compared to traditional techniques, such as the ability to create complex geometries with less material waste. However, the intricacy of the additive process and extreme cyclic heating and cooling leads to material defects and variations in mechanical properties; this often results in unpredictable and even inferior performance of additively manufactured materials. Key indicators for the potential performance of a fabricated part are the geometry and temperature of the melt pool during the building process, due to its impact upon the underlining microstructure. Computational models, such as those based on the finite element method, of the AM process can be used to elucidate and predict the effects of various process parameters on the melt pool, according to physical principles. However, these physics-based models tend to be too computationally expensive for real-time process control. Hence, in this work, a hybrid model utilizing neural networks is proposed and demonstrated to be an accurate and efficient alternative for predicting melt pool geometries in AM, which provides a unified description of the melting conditions. The results of both a physics-based finite element model and the hybrid model are compared to real-time experimental measurements of the melt pool during single-layer AM builds using various scanning strategies.

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