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Applied Sciences
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Advances in Additive Manufacturing and Topology Optimization
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Advances in Additive Manufacturing and Topology Optimization

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: closed (30 October 2021) | Viewed by 13405

Special Issue Editors

Prof. Jean-Michel Bergheau

E-Mail Website1 Website2
Guest Editor
Université de Lyon, Ecole Centrale de Lyon, LTDS, UMR5513 CNRS, 58 rue Jean Parot, 42023 Saint-Etienne, CEDEX 2, France
Interests: computational mechanics; computational manufacturing; finite element method; mechanics of materials; thermo-mechanical processes; welding; additive manufacturing; thermal stresses; multi-physics modelling
Special Issues, Collections and Topics in MDPI journals
Dr. John A. Turner

E-Mail Website1 Website2 Website3
Co-Guest Editor
Oak Ridge National Laboratory, Oak Ridge, TN, USA
Interests: HPC; computational science; computational physics; supercomputing; parallel computing; electrical energy storage; nuclear energy; fluid flow; battery safety; CFD; linear algebra; Krylov methods; coupled nonlinear systems; radiation transport; computational modeling; simulation

Special Issue Information

Dear Colleagues,

We are pleased to announce a forthcoming Special Issue of Applied Sciences titled “Advances in Additive Manufacturing and Topology Optimization”.

Additive manufacturing processes have been attracting increasing interest among manufacturers for several years. Initially dedicated to the manufacture of prototypes, the processes then evolved to meet the demand for functional mechanical parts. Additive manufacturing permits the production of custom-made parts within shorter timeframes and with less waste, along with complex geometries that could not be realized with more traditional manufacturing processes. However, beyond the technological aspects, several issues remain unaddressed, including the design of the parts, the optimization of the process, the mechanical consequences induced by the process and the lifespan of the manufactured parts. Numerical tools have been developed during the last few years, but the quality of the obtained results is unknown. An additional question which must be addressed is how to obtain the data to feed multi-physics models.

This Special Issue collects original articles addressing the design methods, monitoring and modelling of additive manufacturing processes and the mechanical behavior of the manufactured parts. Both theoretical and experimental contributions are welcome, as well as research papers or reviews.

Prof. Jean-Michel Bergheau
Dr. John A. Turner
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • additive manufacturing
  • advanced materials
  • process monitoring
  • process–microstructure–mechanical properties relationships
  • residual stresses and distortions
  • advanced process modelling and numerical simulations
  • simplified models
  • new design methods
  • topology optimization

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Published Papers (4 papers)

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Research

20 pages, 2909 KiB  
Article
Calibration Technique of Thermal Analysis Model for Metal Additive Manufacturing Process Simulation by Nonlinear Regression and Optimization
by Eun Gyo Park, Jae Won Kang, Jin Yeon Cho and Jeong Ho Kim
Appl. Sci. 2021, 11(24), 11647; https://doi.org/10.3390/app112411647 - 8 Dec 2021
Cited by 4 | Viewed by 2414
Abstract
A numerical analysis model that can accurately predict the physical characteristics of the actually additive manufactured products can significantly reduce time and costs for experimental builds and tests. Thermal analysis for the metal AM process simulation requires a lot of analysis parameters and [...] Read more.
A numerical analysis model that can accurately predict the physical characteristics of the actually additive manufactured products can significantly reduce time and costs for experimental builds and tests. Thermal analysis for the metal AM process simulation requires a lot of analysis parameters and conditions. However, their accuracy and reliability are not clear, and the current understanding of their influence on the analysis results is very insufficient. Therefore, in this study, the influence of uncertain analysis parameters on the thermal analysis results is estimated, and a procedure to calibrate these analysis parameters is proposed. By using the thermal analysis results for parameter cases determined by a design of experiments, a regression analysis model is constructed to estimate the sensitivity of the analysis parameters to the thermal analysis results. Additionally, it is used to determine the optimal values of analysis parameters that can produce the thermal analysis results closest to the given reference data from actual builds. By using the melt pool size computed from a numerical model as reference data, the proposed procedure is validated. From this result, it is confirmed that a high-fidelity thermal analysis model that can predict the characteristics of actual builds from minimal experimental builds can be constructed efficiently. Full article
(This article belongs to the Special Issue Advances in Additive Manufacturing and Topology Optimization)
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18 pages, 2259 KiB  
Article
A Temperature-Dependent Heat Source for Simulating Deep Penetration in Selective Laser Melting Process
by Yabo Jia, Yassine Saadlaoui and Jean-Michel Bergheau
Appl. Sci. 2021, 11(23), 11406; https://doi.org/10.3390/app112311406 - 2 Dec 2021
Cited by 6 | Viewed by 2270
Abstract
Numerical methods for simulating selective laser melting (SLM) have been widely carried out to understand the physical behaviors behind the process. Numerical simulation at the macroscale allows the relationship between input parameters (laser power, scanning speed, powder layer thickness, etc.) and output results [...] Read more.
Numerical methods for simulating selective laser melting (SLM) have been widely carried out to understand the physical behaviors behind the process. Numerical simulation at the macroscale allows the relationship between input parameters (laser power, scanning speed, powder layer thickness, etc.) and output results (distortion, residual stress, etc.) to be investigated. However, the macroscale thermal models solved by the finite element method cannot predict the melt pool depth correctly as they ignore the effect of fluid flow in the melting pool, especially in the case of the presence of deep penetration. To remedy this limitation, an easy-implemented temperature-dependent heat source is proposed. This heat source can adjust its parameters during the simulation to compensate for these neglected thermal effects related to the fluid flow and keyhole, and the heat source’s parameters become fixed once the temperatures of the points of interest become stable. Contrary to the conventional heat source model, parameters of the proposed heat source do not require a calibration with experiments for each process parameter. The proposed model is validated by comparing its results with those of the anisotropic thermal conductivity method and experimental measurements. Full article
(This article belongs to the Special Issue Advances in Additive Manufacturing and Topology Optimization)
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17 pages, 4510 KiB  
Article
Learning the Parametric Transfer Function of Unitary Operations for Real-Time Evaluation of Manufacturing Processes Involving Operations Sequencing
by Tanguy Loreau, Victor Champaney, Nicolas Hascoët, Philippe Mourgue, Jean-Louis Duval and Francisco Chinesta
Appl. Sci. 2021, 11(11), 5146; https://doi.org/10.3390/app11115146 - 1 Jun 2021
Viewed by 2667
Abstract
For better designing manufacturing processes, surrogate models were widely considered in the past, where the effect of different material and process parameters was considered from the use of a parametric solution. The last contains the solution of the model describing the system under [...] Read more.
For better designing manufacturing processes, surrogate models were widely considered in the past, where the effect of different material and process parameters was considered from the use of a parametric solution. The last contains the solution of the model describing the system under study, for any choice of the selected parameters. These surrogate models, also known as meta-models, virtual charts or computational vademecum, in the context of model order reduction, were successfully employed in a variety of industrial applications. However, they remain confronted to a major difficulty when the number of parameters grows exponentially. Thus, processes involving trajectories or sequencing entail a combinatorial exposition (curse of dimensionality) not only due to the number of possible combinations, but due to the number of parameters needed to describe the process. The present paper proposes a promising route for circumventing, or at least alleviating that difficulty. The proposed technique consists of a parametric transfer function that, as soon as it is learned, allows for, from a given state, inferring the new state after the application of a unitary operation, defined as a step in the sequenced process. Thus, any sequencing can be evaluated almost in real time by chaining that unitary transfer function, whose output becomes the input of the next operation. The benefits and potential of such a technique are illustrated on a problem of industrial relevance, the one concerning the induced deformation on a structural part when printing on it a series of stiffeners. Full article
(This article belongs to the Special Issue Advances in Additive Manufacturing and Topology Optimization)
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14 pages, 1762 KiB  
Article
Generalized Optimality Criteria Method for Topology Optimization
by Nam H. Kim, Ting Dong, David Weinberg and Jonas Dalidd
Appl. Sci. 2021, 11(7), 3175; https://doi.org/10.3390/app11073175 - 2 Apr 2021
Cited by 19 | Viewed by 4552
Abstract
In this article, a generalized optimality criteria method is proposed for topology optimization with arbitrary objective function and multiple inequality constraints. This algorithm uses sensitivity information to update both the Lagrange multipliers and design variables. Different from the conventional optimality criteria method, the [...] Read more.
In this article, a generalized optimality criteria method is proposed for topology optimization with arbitrary objective function and multiple inequality constraints. This algorithm uses sensitivity information to update both the Lagrange multipliers and design variables. Different from the conventional optimality criteria method, the proposed method does not satisfy constraints at every iteration. Rather, it improves the Lagrange multipliers and design variables such that the optimality criteria are satisfied upon convergence. The main advantages of the proposed method are its capability of handling multiple constraints and computational efficiency. In numerical examples, the proposed method was found to be more than 100 times faster than the optimality criteria method and more than 1000 times faster than the method of moving asymptotes. Full article
(This article belongs to the Special Issue Advances in Additive Manufacturing and Topology Optimization)
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