On the use of 2D moment invariants in the classification of additive manufacturing powder feedstock

2019 ◽  
Vol 149 ◽  
pp. 255-263 ◽  
Author(s):  
Ryan Harrison ◽  
Elizabeth A. Holm ◽  
Marc De Graef
Metals ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 720
Author(s):  
Benjamin Young ◽  
Joseph Heelan ◽  
Sean Langan ◽  
Matthew Siopis ◽  
Caitlin Walde ◽  
...  

Additive manufacturing is a rapidly expanding field, encompassing many methods to manufacture parts and coatings with a wide variety of feedstock. Metal powders are one such feedstock, with a range of compositions and morphologies. Understanding subtle changes in the feedstock is critical to ensure successful consolidation and quality control of both the feedstock and manufactured part. Current standards lack the ability to finely distinguish almost acceptable powders from barely acceptable ones. Here, novel means of powder feedstock characterization for quality control are demonstrated for the solid-state AM process of cold spray, though similar methods may be extrapolated to other additive methods as well. These characterization methods aim to capture the physics of the process, which in cold spray consists of high strain rate deformation of solid-state feedstock. To capture this, in this effort powder compaction was evaluated via rapidly applied loads, flowability of otherwise non-flowable powders was evaluated with the addition of vibration, and powder electrical resistivity was evaluated through compaction between two electrodes. Several powders, including aluminum alloys, chromium, and cermet composites, were evaluated in this effort, with each case study demonstrating the need for non-traditional characterization metrics as a means of quality control and classification of these materials.


2021 ◽  
Vol 11 (13) ◽  
pp. 6086
Author(s):  
Nils Ellendt ◽  
Fabian Fabricius ◽  
Anastasiya Toenjes

Additive manufacturing processes offer high geometric flexibility and allow the use of new alloy concepts due to high cooling rates. For each new material, parameter studies have to be performed to find process parameters that minimize microstructural defects such as pores or cracks. In this paper, we present a system developed in Python for accelerated image analysis of optical microscopy images. Batch processing can be used to quickly analyze large image sets with respect to pore size distribution, defect type, contribution of defect type to total porosity, and shape accuracy of printed samples. The open-source software is independent of the microscope used and is freely available for use. This framework allows us to perform such an analysis on a circular area with a diameter of 5 mm within 10 s, allowing detailed process maps to be obtained for new materials within minutes after preparation.


2018 ◽  
Vol 31 (2) ◽  
pp. 375-386 ◽  
Author(s):  
Ohyung Kwon ◽  
Hyung Giun Kim ◽  
Min Ji Ham ◽  
Wonrae Kim ◽  
Gun-Hee Kim ◽  
...  

2021 ◽  
Vol 1045 ◽  
pp. 157-178
Author(s):  
Onuchukwu Godwin Chike ◽  
Norhayati Binti Ahmad ◽  
Uday M. Basheer Al-Naib

Material engineers continuously make every effort for the evolution of novel and prevailing production performances to supply our biosphere with resource-proficient, economical, and hygienic substances with superior package operation. The mitigation of energy depletion and gas releases as an utmost significance worldwide is a renowned datum; which also needs the improvement of delicate substances employing budget-proficient and ecologically pleasant methods. Consequently, copious exploration has been aimed in the study of methods retaining a potential to wrestle these widespread essentials. Material engineering processes have advanced as a feasible substitute for conventional steel fragment construction methods. CE has experienced an extraordinary advancement throughout the previous three decades. It was originally utilised uniquely as a state-of-the-art reserve of the paradigm. Referable to the expertise development which permits merging countless engineering procedures for the output of a modified portion that employed intricate configurations, CE expertise has got cumulative responsiveness. As such, this article intends to furnish a comprehensive appraisal of chemical fabrication progressions for steel substance evolution utilised in different applications. The inspection encompasses the current advancement of CE know-hows, a detailed taxonomy and classification of manufacturing operations. The focal point of the upcoming perspective of CE in substance investigation and application is further deliberated


Author(s):  
Albert Bruce Chu ◽  
Maxwell Murialdo ◽  
James P. Lewicki ◽  
Jennifer N. Rodriguez ◽  
Mitchell K. Shiflett ◽  
...  

Metals ◽  
2018 ◽  
Vol 8 (9) ◽  
pp. 729 ◽  
Author(s):  
Wei Chen ◽  
Guangfu Yin ◽  
Zai Feng ◽  
Xiaoming Liao

Additive manufacturing by selective laser melting (SLM) was used to investigate the effect of powder feedstock on 316L stainless steel properties include microstructure, relative density, microhardness and mechanical properties. Gas atomized SS316L powders of three different particle size distribution were used in this study. Microstructural investigations were done by scanning electron microscopy (SEM). Tensile tests were performed at room temperatures. Microstructure characterization revealed the presence of hierarchical structures consisting of solidified melt pools, columnar grains and multiform shaped sub-grains. The results showed that the SLM sample from the fine powder obtained the highest mechanical properties with ultimate tensile strength (UTS) of 611.9 ± 9.4 MPa and yield strength (YS) of 519.1 ± 5.9 MPa, and an attendant elongation (EL) of 14.6 ± 1.9%, and a maximum of 97.92 ± 0.13% and a high microhardness 291 ± 6 HV0.1. It has been verified that the fine powder (~16 μm) could be used in additive manufacturing with proper printing parameters.


2020 ◽  
Vol 7 ◽  
pp. 6
Author(s):  
Vladimir V. Popov ◽  
Alexander Fleisher

Hybrid additive manufacturing is a relatively modern trend in the integration of different additive manufacturing techniques in the traditional manufacturing production chain. Here the AM-technique is used for producing a part on another substrate part, that is manufactured by traditional manufacturing like casting or milling. Such beneficial combination of additive and traditional manufacturing helps to overcome well-known issues, like limited maximum build size, low production rate, insufficient accuracy, and surface roughness. The current paper is devoted to the classification of different approaches in the hybrid additive manufacturing of steel components. Additional discussion is related to the benefits of Powder Bed Fusion (PBF) and Direct Energy Deposition (DED) approaches for hybrid additive manufacturing of steel components.


2015 ◽  
Vol 137 (12) ◽  
Author(s):  
Floriane Laverne ◽  
Frédéric Segonds ◽  
Nabil Anwer ◽  
Marc Le Coq

Additive manufacturing (AM) is emerging as an important manufacturing process and a key technology for enabling innovative product development. Design for additive manufacturing (DFAM) is nowadays a major challenge to exploit properly the potential of AM in product innovation and product manufacturing. However, in recent years, several DFAM methods have been developed with various design purposes. In this paper, we first present a state-of-the-art overview of the existing DFAM methods, then we introduce a classification of DFAM methods based on intermediate representations (IRs) and product's systemic level, and we make a comparison focused on the prospects for product innovation. Furthermore, we present an assembly based DFAM method using AM knowledge during the idea generation process in order to develop innovative architectures. A case study demonstrates the relevance of such approach. The main contribution of this paper is an early DFAM method consisting of four stages as follows: choice and development of (1) concepts, (2) working principles, (3) working structures, and (4) synthesis and conversion of the data in design features. This method will help designers to improve their design features, by taking into account the constraints of AM in the early stages.


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