A Methodology for Predicting Porosity From Thermal Imaging of Melt Pools in Additive Manufacturing Thin Wall Sections

2017 ◽  
Author(s):  
Mojtaba Khanzadeh ◽  
Sudipta Chowdhury ◽  
Linkan Bian ◽  
Mark A. Tschopp
Keyword(s):  
Additive Manufacturing ◽  
Prediction Method ◽  
Pool Analysis ◽  
Industrial Applications ◽  
In Situ Monitoring ◽  
Thin Wall ◽  
Structure Property ◽  
Thermal Profile ◽  
Self Organizing Maps ◽  
Melt Pool

The microstructure and mechanical properties of Laser Based Additive Manufacturing (LBAM) are often inconsistent and unreliable for many industrial applications. One of the key technical challenges is the lack of understanding of the underlying process-structure-property relationship. The objective of the present research is to use the melt pool thermal profile to predict porosity within the LBAM process. Herein, we propose a novel porosity prediction method based on morphological features and the temperature distribution of the top surface of the melt pool as the LBAM part is being built. Self-organizing maps (SOM) are then used to further analyze the 2D melt pool dataset to identify similar and dissimilar melt pools. The performance of the proposed method of porosity prediction uses X-Ray tomography characterization, which identified porosity within the Ti-6Al-4V thin wall specimen. The experimentally identified porosity locations were compared to the porosity locations predicted based on the melt pool analysis. Results show that the proposed method is able to predict the location of porosity almost 85% of the time when the appropriate SOM model is selected. The significance of such a methodology is that this may lead the way towards in situ monitoring and on-the-fly modification of melt pool thermal profile to minimize or eliminate pores within LBAM parts.

ExaAM: Metal additive manufacturing simulation at the fidelity of the microstructure

2022 ◽  
Vol 36 (1) ◽  
pp. 13-39
Author(s):  
John A Turner ◽  
James Belak ◽  
Nathan Barton ◽  
Matthew Bement ◽  
Neil Carlson ◽  
...  
Keyword(s):  
Mechanical Property ◽  
Additive Manufacturing ◽  
Test Problems ◽  
Structure Property ◽  
Manufacturing Simulation ◽  
Melt Pool ◽  
Multi Stage ◽  
Polycrystal Plasticity ◽  
Topology And Shape Optimization ◽  
Computational Systems

Additive manufacturing (AM), or 3D printing, of metals is transforming the fabrication of components, in part by dramatically expanding the design space, allowing optimization of shape and topology. However, although the physical processes involved in AM are similar to those of welding, a field with decades of experimental, modeling, simulation, and characterization experience, qualification of AM parts remains a challenge. The availability of exascale computational systems, particularly when combined with data-driven approaches such as machine learning, enables topology and shape optimization as well as accelerated qualification by providing process-aware, locally accurate microstructure and mechanical property models. We describe the physics components comprising the Exascale Additive Manufacturing simulation environment and report progress using highly resolved melt pool simulations to inform part-scale finite element thermomechanics simulations, drive microstructure evolution, and determine constitutive mechanical property relationships based on those microstructures using polycrystal plasticity. We report on implementation of these components for exascale computing architectures, as well as the multi-stage simulation workflow that provides a unique high-fidelity model of process–structure–property relationships for AM parts. In addition, we discuss verification and validation through collaboration with efforts such as AM-Bench, a set of benchmark test problems under development by a team led by the National Institute of Standards and Technology.

scholarly journals A New Approach For Calculating Inherent Strain and Distortion in Additive Manufacturing of Metal Parts

2021 ◽  
Author(s):  
Hong-Seok Park ◽  
Hwa Seon Shin ◽  
Ngoc-Hien TRAN
Keyword(s):  
Additive Manufacturing ◽  
Prediction Method ◽  
Industrial Applications ◽  
Scale Model ◽  
Design Stage ◽  
Design Engineers ◽  
Part Distortion ◽  
Metallic Parts ◽  
Inherent Strain ◽  
Is Value

Abstract Additive manufacturing (AM) of metallic parts is widely utilized for industrial applications. However, quality issues of the printed parts, including part distortion and cracks caused by high temperature and fast cooling, result in high residual stress. This is a challenge that limits the industry acceptance of AM. To overcome this challenge, a numerical modeling method for predicting part distortion at the design stage plays an important role, and enables design engineers to remove failures before printing, as well as determine the optimal printing process parameters to minimize part deformation. This research proposes an inherent strain-based part deformation prediction method. To determine the inherent strain (IS) value, a micro-scale model for analyzing the temperature distribution is constructed. The IS value is calculated from the temperature gradient. Then, the IS value is used for determining the part deformation. The proposed methodology has been developed and evaluated, using a 316L stainless steel cantilever beam, in both simulations and experimental results.

In-situ monitoring of optical emission spectra for microscopic pores in metal additive manufacturing Submitted for Publication

2021 ◽  
pp. 1-21
Author(s):  
Wenbo Sun ◽  
Zhenhao Zhang ◽  
Wenjing Ren ◽  
Jyoti Mazumder ◽  
Jionghua (Judy) Jin
Keyword(s):  
Additive Manufacturing ◽  
Emission Spectra ◽  
Optical Emission ◽  
In Situ Monitoring ◽  
Melt Pool ◽  
Multiple Processes ◽  
Melt Pool Temperature ◽  
Optical Emission Spectra

Abstract Quality assurance techniques are increasingly demanded in additive manufacturing. Going beyond most of the existing research that focuses on the melt pool temperature monitoring, we develop a new method that monitors the in-situ optical emission spectra signals. Optical emission spectra signals have been showing a potential capability of detecting microscopic pores. The concept is to extract features from the optical emission spectra via deep auto-encoders, and then cluster the features into two quality groups to consider both unlabelled and labelled samples in a semi-supervised manner. The method is integrated with multitask learning to make it adaptable for the samples collected from multiple processes. Both a simulation example and a case study are performed to demonstrate the effectiveness of the proposed method.

scholarly journals Wire Arc Additive Manufacturing: Review on Recent Findings and Challenges in Industrial Applications and Materials Characterization

Metals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 939
Author(s):  
Mukti Chaturvedi ◽  
Elena Scutelnicu ◽  
Carmen Catalina Rusu ◽  
Luigi Renato Mistodie ◽  
Danut Mihailescu ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Structural Integrity ◽  
Industrial Applications ◽  
Raw Material ◽  
Continuous Increase ◽  
Control Optimization ◽  
Modern Manufacturing ◽  
Directed Energy ◽  
Manufacturing Technologies ◽  
Wire Arc Additive Manufacturing

Wire arc additive manufacturing (WAAM) is a fusion manufacturing process in which the heat energy of an electric arc is employed for melting the electrodes and depositing material layers for wall formation or for simultaneously cladding two materials in order to form a composite structure. This directed energy deposition-arc (DED-arc) method is advantageous and efficient as it produces large parts with structural integrity due to the high deposition rates, reduced wastage of raw material, and low consumption of energy in comparison with the conventional joining processes and other additive manufacturing technologies. These features have resulted in a constant and continuous increase in interest in this modern manufacturing technique which demands further studies to promote new industrial applications. The high demand for WAAM in aerospace, automobile, nuclear, moulds, and dies industries demonstrates compatibility and reflects comprehensiveness. This paper presents a comprehensive review on the evolution, development, and state of the art of WAAM for non-ferrous materials. Key research observations and inferences from the literature reports regarding the WAAM applications, methods employed, process parameter control, optimization and process limitations, as well as mechanical and metallurgical behavior of materials have been analyzed and synthetically discussed in this paper. Information concerning constraints and enhancements of the wire arc additive manufacturing processes to be considered in terms of wider industrial applicability is also presented in the last part of this paper.

scholarly journals Low-Roughness-Surface Additive Manufacturing of Metal-Wire Feeding with Small Power

Materials ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4265
Author(s):  
Bobo Li ◽  
Bowen Wang ◽  
Greg Zhu ◽  
Lijuan Zhang ◽  
Bingheng Lu
Keyword(s):  
Additive Manufacturing ◽  
High Surface ◽  
Deposition Process ◽  
High Energy ◽  
Width Ratio ◽  
Thin Wall ◽  
Small Power ◽  
High Surface Quality ◽  
Wall Structures ◽  
Wire Feed

Aiming at handling the contradiction between power constraint of on-orbit manufacturing and the high energy input requirement of metal additive manufacturing (AM), this paper presents an AM process based on small-power metal fine wire feed, which produces thin-wall structures of height-to-width ratio up to 40 with core-forming power only about 50 W. In this process, thermal resistance was introduced to optimize the gradient parameters which greatly reduces the step effect of the typical AM process, succeeded in the surface roughness (Ra) less than 5 μm, comparable with that obtained by selective laser melting (SLM). After a 10 min electrolyte-plasma process, the roughness of the fabricated specimen was further reduced to 0.4 μm, without defects such as pores and cracks observed. The ultimate tensile strength of the specimens measured about 500 MPa, the relative density was 99.37, and the Vickers hardness was homogeneous. The results show that the proposed laser-Joule wire feed-direct metal deposition process (LJWF-DMD) is a very attractive solution for metal AM of high surface quality parts, particularly suitable for rapid prototyping for on-orbit AM in space.

scholarly journals Additive Manufacturing of Bulk Nanocrystalline FeNdB Based Permanent Magnets

Micromachines ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 538
Author(s):  
Dagmar Goll ◽  
Felix Trauter ◽  
Timo Bernthaler ◽  
Jochen Schanz ◽  
Harald Riegel ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Melt Spinning ◽  
Permanent Magnets ◽  
Maximum Energy ◽  
Laser Melting ◽  
Powder Bed ◽  
Melt Pool ◽  
Bulk Nanocrystalline ◽  
Ribbon Structures ◽  
Nanocrystalline Ribbon

Lab scale additive manufacturing of Fe-Nd-B based powders was performed to realize bulk nanocrystalline Fe-Nd-B based permanent magnets. For fabrication a special inert gas process chamber for laser powder bed fusion was used. Inspired by the nanocrystalline ribbon structures, well-known from melt-spinning, the concept was successfully transferred to the additive manufactured parts. For example, for Nd16.5-Pr1.5-Zr2.6-Ti2.5-Co2.2-Fe65.9-B8.8 (excess rare earth (RE) = Nd, Pr; the amount of additives was chosen following Magnequench (MQ) powder composition) a maximum coercivity of µ0Hc = 1.16 T, remanence Jr = 0.58 T and maximum energy density of (BH)max = 62.3 kJ/m3 have been achieved. The most important prerequisite to develop nanocrystalline printed parts with good magnetic properties is to enable rapid solidification during selective laser melting. This is made possible by a shallow melt pool during laser melting. Melt pool depths as low as 20 to 40 µm have been achieved. The printed bulk nanocrystalline Fe-Nd-B based permanent magnets have the potential to realize magnets known so far as polymer bonded magnets without polymer.

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