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3 pages, 187 KiB

Open AccessEditorial

Modeling, Simulation and Data Processing for Additive Manufacturing

by Mika Salmi

Materials 2021, 14(24), 7755; https://doi.org/10.3390/ma14247755 - 15 Dec 2021

Cited by 2 | Viewed by 1971

Abstract

Additive manufacturing or, more commonly, 3D printing is one of the fundamental elements of Industry 4 [...] Full article

(This article belongs to the Special Issue Modeling, Simulation and Data Processing for Additive Manufacturing)

Research

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27 pages, 3350 KiB

Open AccessArticle

Nanoparticle Additivation Effects on Laser Powder Bed Fusion of Metals and Polymers—A Theoretical Concept for an Inter-Laboratory Study Design All Along the Process Chain, Including Research Data Management

by Ihsan Murat Kusoglu, Florian Huber, Carlos Doñate-Buendía, Anna Rosa Ziefuss, Bilal Gökce, Jan T. Sehrt, Arno Kwade, Michael Schmidt and Stephan Barcikowski

Materials 2021, 14(17), 4892; https://doi.org/10.3390/ma14174892 - 27 Aug 2021

Cited by 7 | Viewed by 3026

Abstract

In recent years, the application field of laser powder bed fusion of metals and polymers extends through an increasing variability of powder compositions in the market. New powder formulations such as nanoparticle (NP) additivated powder feedstocks are available today. Interestingly, they behave differently [...] Read more.

In recent years, the application field of laser powder bed fusion of metals and polymers extends through an increasing variability of powder compositions in the market. New powder formulations such as nanoparticle (NP) additivated powder feedstocks are available today. Interestingly, they behave differently along with the entire laser powder bed fusion (PBF-LB) process chain, from flowability over absorbance and microstructure formation to processability and final part properties. Recent studies show that supporting NPs on metal and polymer powder feedstocks enhances processability, avoids crack formation, refines grain size, increases functionality, and improves as-built part properties. Although several inter-laboratory studies (ILSs) on metal and polymer PBF-LB exist, they mainly focus on mechanical properties and primarily ignore nano-additivated feedstocks or standardized assessment of powder feedstock properties. However, those studies must obtain reliable data to validate each property metric’s repeatability and reproducibility limits related to the PBF-LB process chain. We herein propose the design of a large-scale ILS to quantify the effect of nanoparticle additivation on powder characteristics, process behavior, microstructure, and part properties in PBF-LB. Besides the work and sample flow to organize the ILS, the test methods to measure the NP-additivated metal and polymer powder feedstock properties and resulting part properties are defined. A research data management (RDM) plan is designed to extract scientific results from the vast amount of material, process, and part data. The RDM focuses not only on the repeatability and reproducibility of a metric but also on the FAIR principle to include findable, accessible, interoperable, and reusable data/meta-data in additive manufacturing. The proposed ILS design gives access to principal component analysis (PCA) to compute the correlations between the material–process–microstructure–part properties. Full article

(This article belongs to the Special Issue Modeling, Simulation and Data Processing for Additive Manufacturing)

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20 pages, 9925 KiB

Open AccessArticle

Towards Automatic Detection of Precipitates in Inconel 625 Superalloy Additively Manufactured by the L-PBF Method

by Piotr Macioł, Jan Falkus, Paulina Indyka and Beata Dubiel

Materials 2021, 14(16), 4507; https://doi.org/10.3390/ma14164507 - 11 Aug 2021

Cited by 4 | Viewed by 1896

Abstract

In our study, the comparison of the automatically detected precipitates in L-PBF Inconel 625, with experimentally detected phases and with the results of the thermodynamic modeling was used to test their compliance. The combination of the complementary electron microscopy techniques with the microanalysis [...] Read more.

In our study, the comparison of the automatically detected precipitates in L-PBF Inconel 625, with experimentally detected phases and with the results of the thermodynamic modeling was used to test their compliance. The combination of the complementary electron microscopy techniques with the microanalysis of chemical composition allowed us to examine the structure and chemical composition of related features. The possibility of automatic detection and identification of precipitated phases based on the STEM-EDS data was presented and discussed. The automatic segmentation of images and identifying of distinguishing regions are based on the processing of STEM-EDS data as multispectral images. Image processing methods and statistical tools are applied to maximize an information gain from data with low signal-to-noise ratio, kee** human interactions on a minimal level. The proposed algorithm allowed for automatic detection of precipitates and identification of interesting regions in the Inconel 625, while significantly reducing the processing time with acceptable quality of results. Full article

(This article belongs to the Special Issue Modeling, Simulation and Data Processing for Additive Manufacturing)

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22 pages, 9242 KiB

Open AccessArticle

Investigation of Compressive and Tensile Behavior of Stainless Steel/Dissolvable Aluminum Bimetallic Composites by Finite Element Modeling and Digital Image Correlation

by **uhui Li, Morteza Ghasri-Khouzani, Abdoul-Aziz Bogno, **g Liu, Hani Henein, Zengtao Chen and Ahmed Jawad Qureshi

Materials 2021, 14(13), 3654; https://doi.org/10.3390/ma14133654 - 30 Jun 2021

Cited by 15 | Viewed by 3737

Abstract

This study reports fabrication, mechanical characterization, and finite element modeling of a novel lattice structure based bimetallic composite comprising 316L stainless steel and a functional dissolvable aluminum alloy. A net-shaped 316L stainless steel lattice structure composed of diamond unit cells was fabricated by [...] Read more.

This study reports fabrication, mechanical characterization, and finite element modeling of a novel lattice structure based bimetallic composite comprising 316L stainless steel and a functional dissolvable aluminum alloy. A net-shaped 316L stainless steel lattice structure composed of diamond unit cells was fabricated by selective laser melting (SLM). The cavities in the lattice structure were then filled through vacuum-assisted melt infiltration to form the bimetallic composite. The bulk aluminum sample was also cast using the same casting parameters for comparison. The compressive and tensile behavior of 316L stainless steel lattice, bulk dissolvable aluminum, and 316L stainless steel/dissolvable aluminum bimetallic composite is studied. Comparison between experimental, finite element analysis (FEA), and digital image correlation (DIC) results are also investigated in this study. There is no notable difference in the tensile behavior of the lattice and bimetallic composite because of the weak bonding in the interface between the two constituents of the bimetallic composite, limiting load transfer from the 316L stainless steel lattice to the dissolvable aluminum matrix. However, the aluminum matrix is vital in the compressive behavior of the bimetallic composite. The dissolvable aluminum showed higher Young’s modulus, yield stress, and ultimate stress than the lattice and composite in both tension and compression tests, but much less elongation. Moreover, FEA and DIC have been demonstrated to be effective and efficient methods to simulate, analyze, and verify the experimental results through juxtaposing curves on the plots and comparing strains of critical points by checking contour plots. Full article

(This article belongs to the Special Issue Modeling, Simulation and Data Processing for Additive Manufacturing)

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29 pages, 10225 KiB

Open AccessArticle

Tri-Planar Geometric Dimensioning and Tolerancing Characteristics of SS 316L Laser Powder Bed Fusion Process Test Artifacts and Effect of Base Plate Removal

by Baltej Singh Rupal, Tegbir Singh, Tonya Wolfe, Marc Secanell and Ahmed Jawad Qureshi

Materials 2021, 14(13), 3575; https://doi.org/10.3390/ma14133575 - 26 Jun 2021

Cited by 2 | Viewed by 2024

Abstract

The precision of LPBF manufactured parts is quantified by characterizing the geometric tolerances based on the ISO 1101 standard. However, there are research gaps in the characterization of geometric tolerance of LPBF parts. A literature survey reveals three significant research gaps: (1) systematic [...] Read more.

The precision of LPBF manufactured parts is quantified by characterizing the geometric tolerances based on the ISO 1101 standard. However, there are research gaps in the characterization of geometric tolerance of LPBF parts. A literature survey reveals three significant research gaps: (1) systematic design of benchmarks for geometric tolerance characterization with minimum experimentation; (2) holistic geometric tolerance characterization in different orientations and with varying feature sizes; and (3) a comparison of results, with and without the base plate. This research article focuses on addressing these issues by systematically designing a benchmark that can characterize geometric tolerances in three principal planar directions. The designed benchmark was simulated using the finite element method, manufactured using a commercial LPBF process using stainless steel (SS 316L) powder, and the geometric tolerances were characterized. The effect of base plate removal on the geometric tolerances was quantified. Simulation and experimental results were compared to understand tolerance variations using process variations such as base plate removal, orientation, and size. The tolerance zone variations not only validate the need for systematically designed benchmarks, but also for tri-planar characterization. Simulation and experimental result comparisons provide quantitative information about the applicability of numerical simulation for geometric tolerance prediction for the LPBF process. Full article

(This article belongs to the Special Issue Modeling, Simulation and Data Processing for Additive Manufacturing)

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19 pages, 9896 KiB

Open AccessEditor’s ChoiceArticle

Methodology for the Quality Control Process of Additive Manufacturing Products Made of Polymer Materials

by Grzegorz Budzik, Joanna Woźniak, Andrzej Paszkiewicz, Łukasz Przeszłowski, Tomasz Dziubek and Mariusz Dębski

Materials 2021, 14(9), 2202; https://doi.org/10.3390/ma14092202 - 25 Apr 2021

Cited by 41 | Viewed by 4918

Abstract

The objective of this publication is to present a quality control methodology for additive manufacturing products made of polymer materials, where the methodology varies depending on the intended use. The models presented in this paper are divided into those that are manufactured for [...] Read more.

The objective of this publication is to present a quality control methodology for additive manufacturing products made of polymer materials, where the methodology varies depending on the intended use. The models presented in this paper are divided into those that are manufactured for the purpose of visual presentation and those that directly serve the needs of the manufacturing process. The authors also a propose a comprehensive control system for the additive manufacturing process to meet the needs of Industry 4.0. Depending on the intended use of the models, the quality control process is divided into three stages: data control, manufacturing control, and post-processing control. Research models were made from the following materials: RGD 720 photopolymer resin (PolyJet method), ABS M30 thermoplastic (FDM method), E-Partial photopolymer resin (DLP method), PLA thermoplastic (FFF method), and ABS thermoplastic (MEM method). The applied measuring tools had an accuracy of at least an order of magnitude higher than that of the manufacturing technologies used. The results show that the PolyJet method is the most accurate, and the MEM method is the least accurate. The findings also confirm that the selection of materials, 3D printing methods, and measurement methods should always account not only for the specificity and purpose of the model but also for economic aspects, as not all products require high accuracy and durability. Full article

(This article belongs to the Special Issue Modeling, Simulation and Data Processing for Additive Manufacturing)

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41 pages, 12557 KiB

Open AccessArticle

Design and Additive Manufacturing of Porous Sound Absorbers—A Machine-Learning Approach

by Sebastian Kuschmitz, Tobias P. Ring, Hagen Watschke, Sabine C. Langer and Thomas Vietor

Materials 2021, 14(7), 1747; https://doi.org/10.3390/ma14071747 - 1 Apr 2021

Cited by 19 | Viewed by 3701

Abstract

Additive manufacturing (AM), widely known as 3D-printing, builds parts by adding material in a layer-by-layer process. This tool-less procedure enables the manufacturing of porous sound absorbers with defined geometric features, however, the connection of the acoustic behavior and the material’s micro-scale structure is [...] Read more.

Additive manufacturing (AM), widely known as 3D-printing, builds parts by adding material in a layer-by-layer process. This tool-less procedure enables the manufacturing of porous sound absorbers with defined geometric features, however, the connection of the acoustic behavior and the material’s micro-scale structure is only known for special cases. To bridge this gap, the work presented here employs machine-learning techniques that compute acoustic material parameters (Biot parameters) from the material’s micro-scale geometry. For this purpose, a set of test specimens is used that have been developed in earlier studies. The test specimens resemble generic absorbers by a regular lattice structure based on a bar design and allow a variety of parameter variations, such as bar width, or bar height. A set of 50 test specimens is manufactured by material extrusion (MEX) with a nozzle diameter of

0.2

mm and a targeted under extrusion to represent finer structures. For the training of the machine learning models, the Biot parameters are inversely identified from the manufactured specimen. Therefore, laboratory measurements of the flow resistivity and absorption coefficient are used. The resulting data is used for training two different machine learning models, an artificial neural network and a k-nearest neighbor approach. It can be shown that both models are able to predict the Biot parameters from the specimen’s micro-scale with reasonable accuracy. Moreover, the detour via the Biot parameters allows the application of the process for application cases that lie beyond the scope of the initial database, for example, the material behavior for other sound fields or frequency ranges can be predicted. This makes the process particularly useful for material design and takes a step forward in the direction of tailoring materials specific to their application. Full article

(This article belongs to the Special Issue Modeling, Simulation and Data Processing for Additive Manufacturing)

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20 pages, 9263 KiB

Open AccessArticle

Thermo-Mechanical Modeling of Wire-Fed Electron Beam Additive Manufacturing

by Fatih Sikan, Priti Wanjara, Javad Gholipour, Amit Kumar and Mathieu Brochu

Materials 2021, 14(4), 911; https://doi.org/10.3390/ma14040911 - 15 Feb 2021

Cited by 13 | Viewed by 3078

Abstract

The primary objective of this research was to develop a finite element model specifically designed for electron beam additive manufacturing (EBAM) of Ti-6Al-4V to understand metallurgical and mechanical aspects of the process. Multiple single-layer and 10-layer build Ti-6Al-4V samples were fabricated to validate [...] Read more.

The primary objective of this research was to develop a finite element model specifically designed for electron beam additive manufacturing (EBAM) of Ti-6Al-4V to understand metallurgical and mechanical aspects of the process. Multiple single-layer and 10-layer build Ti-6Al-4V samples were fabricated to validate the simulation results and ensure the reliability of the developed model. Thin wall plates of 3 mm thickness were used as substrates. Thermocouple measurements were recorded to validate the simulated thermal cycles. Predicted and measured temperatures, residual stresses, and distortion profiles showed that the model is quite reliable. The thermal predictions of the model, when validated experimentally, gave a low average error of 3.7%. The model proved to be extremely successful for predicting the cooling rates, grain morphology, and the microstructure. The maximum deviations observed in the mechanical predictions of the model were as low as 100 MPa in residual stresses and 0.05 mm in distortion. Tensile residual stresses were observed in the deposit and the heat-affected zone, while compressive stresses were observed in the core of the substrate. The highest tensile residual stress observed in the deposit was approximately 1.0 σ_ys (yield strength). The highest distortion on the substrate was approximately 0.2 mm. Full article

(This article belongs to the Special Issue Modeling, Simulation and Data Processing for Additive Manufacturing)

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19 pages, 3824 KiB

Open AccessEditor’s ChoiceArticle

A Comparative Analysis of Laser Additive Manufacturing of High Layer Thickness Pure Ti and Inconel 718 Alloy Materials Using Finite Element Method

by Sapam Ningthemba Singh, Sohini Chowdhury, Yadaiah Nirsanametla, Anil Kumar Deepati, Chander Prakash, Sunpreet Singh, Linda Yongling Wu, Hongyu Y. Zheng and Catalin Pruncu

Materials 2021, 14(4), 876; https://doi.org/10.3390/ma14040876 - 12 Feb 2021

Cited by 22 | Viewed by 3597

Abstract

Investigation of the selective laser melting (SLM) process, using finite element method, to understand the influences of laser power and scanning speed on the heat flow and melt-pool dimensions is a challenging task. Most of the existing studies are focused on the study [...] Read more.

Investigation of the selective laser melting (SLM) process, using finite element method, to understand the influences of laser power and scanning speed on the heat flow and melt-pool dimensions is a challenging task. Most of the existing studies are focused on the study of thin layer thickness and comparative study of same materials under different manufacturing conditions. The present work is focused on comparative analysis of thermal cycles and complex melt-pool behavior of a high layer thickness multi-layer laser additive manufacturing (LAM) of pure Titanium (Ti) and Inconel 718. A transient 3D finite-element model is developed to perform a quantitative comparative study on two materials to examine the temperature distribution and disparities in melt-pool behaviours under similar processing conditions. It is observed that the layers are properly melted and sintered for the considered process parameters. The temperature and melt-pool increases as laser power move in the same layer and when new layers are added. The same is observed when the laser power increases, and opposite is observed for increasing scanning speed while kee** other parameters constant. It is also found that Inconel 718 alloy has a higher maximum temperature than Ti material for the same process parameter and hence higher melt-pool dimensions. Full article

(This article belongs to the Special Issue Modeling, Simulation and Data Processing for Additive Manufacturing)

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17 pages, 8916 KiB

Open AccessArticle

Numerical Study on Thermodynamic Behavior during Selective Laser Melting of 24CrNiMo Alloy Steel

by **angpeng Luo, Minghuang Zhao, Jiayi Li and Chenghong Duan

Materials 2020, 13(1), 45; https://doi.org/10.3390/ma13010045 - 20 Dec 2019

Cited by 10 | Viewed by 3142

Abstract

In this paper, a multi-layer and multi-track finite element model of 24CrNiMo alloy steel by selective laser melting (SLM) is established by using the ABAQUS software. The distribution and evolution of temperature field and stress field and the influence of process parameters on [...] Read more.

In this paper, a multi-layer and multi-track finite element model of 24CrNiMo alloy steel by selective laser melting (SLM) is established by using the ABAQUS software. The distribution and evolution of temperature field and stress field and the influence of process parameters on them are systematically studied. The results show that the peak temperature increases from 2153 °C to 3105 °C and the residual stress increases from 335 MPa to 364 MPa with increasing laser power from 200 W to 300 W; the peak temperature decreases from 2905 °C to 2405 °C and the residual stress increases from 327 MPa to 363 MPa with increasing scanning speed from 150 mm/s to 250 mm/s; the peak temperature increases from 2621 °C to 2914 °C and the residual stress decreases from 354 MPa to 300 MPa with increasing preheating temperature from 25 °C to 400 °C. Far away from scanning area, far away from starting point, and the adjacent areas with vertical scanning direction, resulting in a uniform temperature distribution, help to reduce the residual stress. Due to the remelting effect, the interlayer scanning angle changing helps to release the residual stress of the former layer causing a smaller residual stress after redistribution. Full article

(This article belongs to the Special Issue Modeling, Simulation and Data Processing for Additive Manufacturing)

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Review

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16 pages, 3596 KiB

Open AccessReview

Additive Manufacturing Processes in Medical Applications

by Mika Salmi

Materials 2021, 14(1), 191; https://doi.org/10.3390/ma14010191 - 3 Jan 2021

Cited by 230 | Viewed by 17372

Abstract

Additive manufacturing (AM, 3D printing) is used in many fields and different industries. In the medical and dental field, every patient is unique and, therefore, AM has significant potential in personalized and customized solutions. This review explores what additive manufacturing processes and materials [...] Read more.

Additive manufacturing (AM, 3D printing) is used in many fields and different industries. In the medical and dental field, every patient is unique and, therefore, AM has significant potential in personalized and customized solutions. This review explores what additive manufacturing processes and materials are utilized in medical and dental applications, especially focusing on processes that are less commonly used. The processes are categorized in ISO/ASTM process classes: powder bed fusion, material extrusion, VAT photopolymerization, material jetting, binder jetting, sheet lamination and directed energy deposition combined with classification of medical applications of AM. Based on the findings, it seems that directed energy deposition is utilized rarely only in implants and sheet lamination rarely for medical models or phantoms. Powder bed fusion, material extrusion and VAT photopolymerization are utilized in all categories. Material jetting is not used for implants and biomanufacturing, and binder jetting is not utilized for tools, instruments and parts for medical devices. The most common materials are thermoplastics, photopolymers and metals such as titanium alloys. If standard terminology of AM would be followed, this would allow a more systematic review of the utilization of different AM processes. Current development in binder jetting would allow more possibilities in the future. Full article

(This article belongs to the Special Issue Modeling, Simulation and Data Processing for Additive Manufacturing)

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