Development and experimental study of an automated laser-foil-printing additive manufacturing system

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Chia-Hung Hung ◽  
Tunay Turk ◽  
M. Hossein Sehhat ◽  
Ming C. Leu
Keyword(s):  
Mechanical Properties ◽  
Experimental Study ◽  
Additive Manufacturing ◽  
Mechanical Strength ◽  
Automated System ◽  
304L Stainless Steel ◽  
Powder Bed Fusion ◽  
Content Type ◽  
Powder Bed ◽  
Manufacturing Methods

Purpose This paper aims to present the development and experimental study of a fully automated system using a novel laser additive manufacturing technology called laser foil printing (LFP), to fabricate metal parts layer by layer. The mechanical properties of parts fabricated with this novel system are compared with those of comparable methodologies to emphasize the suitability of this process. Design/methodology/approach Test specimens and parts with different geometries were fabricated from 304L stainless steel foil using an automated LFP system. The dimensions of the fabricated parts were measured, and the mechanical properties of the test specimens were characterized in terms of mechanical strength and elongation. Findings The properties of parts fabricated with the automated LFP system were compared with those of parts fabricated with the powder bed fusion additive manufacturing methods. The mechanical strength is higher than those of parts fabricated by the laser powder bed fusion and directed energy deposition technologies. Originality/value To the best knowledge of authors, this is the first time a fully automated LFP system has been developed and the properties of its fabricated parts were compared with other additive manufacturing methods for evaluation.

A printability assessment framework for fabricating low variability nickel-niobium parts using laser powder bed fusion additive manufacturing

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Bing Zhang ◽  
Raiyan Seede ◽  
Austin Whitt ◽  
David Shoukr ◽  
Xueqin Huang ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Additive Manufacturing ◽  
Uncertainty Quantification ◽  
Process Optimization ◽  
Powder Bed Fusion ◽  
Assessment Framework ◽  
Laser Powder Bed Fusion ◽  
Content Type ◽  
Powder Bed ◽  
Analytical Thermal Model

Purpose There is recent emphasis on designing new materials and alloys specifically for metal additive manufacturing (AM) processes, in contrast to AM of existing alloys that were developed for other traditional manufacturing methods involving considerably different physics. Process optimization to determine processing recipes for newly developed materials is expensive and time-consuming. The purpose of the current work is to use a systematic printability assessment framework developed by the co-authors to determine windows of processing parameters to print defect-free parts from a binary nickel-niobium alloy (NiNb5) using laser powder bed fusion (LPBF) metal AM. Design/methodology/approach The printability assessment framework integrates analytical thermal modeling, uncertainty quantification and experimental characterization to determine processing windows for NiNb5 in an accelerated fashion. Test coupons and mechanical test samples were fabricated on a ProX 200 commercial LPBF system. A series of density, microstructure and mechanical property characterization was conducted to validate the proposed framework. Findings Near fully-dense parts with more than 99% density were successfully printed using the proposed framework. Furthermore, the mechanical properties of as-printed parts showed low variability, good tensile strength of up to 662 MPa and tensile ductility 51% higher than what has been reported in the literature. Originality/value Although many literature studies investigate process optimization for metal AM, there is a lack of a systematic printability assessment framework to determine manufacturing process parameters for newly designed AM materials in an accelerated fashion. Moreover, the majority of existing process optimization approaches involve either time- and cost-intensive experimental campaigns or require the use of proprietary computational materials codes. Through the use of a readily accessible analytical thermal model coupled with statistical calibration and uncertainty quantification techniques, the proposed framework achieves both efficiency and accessibility to the user. Furthermore, this study demonstrates that following this framework results in printed parts with low degrees of variability in their mechanical properties.

Effect of post-processing on microstructure and mechanical properties of Alloy 718 fabricated using powder bed fusion additive manufacturing processes

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sareh Götelid ◽  
Taoran Ma ◽  
Christophe Lyphout ◽  
Jesper Vang ◽  
Emil Stålnacke ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Additive Manufacturing ◽  
Residual Stresses ◽  
Design Methodology ◽  
Hot Isostatic Pressing ◽  
Powder Bed Fusion ◽  
Content Type ◽  
Powder Bed ◽  
Nickel Based Superalloy ◽  
Compressive Stresses

Purpose This study aims to investigate additive manufacturing of nickel-based superalloy IN718 made by powder bed fusion processes: powder bed fusion laser beam (PBF-LB) and powder bed fusion electron beam (PBF-EB). Design/methodology/approach This work has focused on the influence of building methods and post-fabrication processes on the final part properties, including microstructure, surface quality, residual stresses and mechanical properties. Findings PBF-LB produced a much smoother surface. Blasting and shot peening (SP) reduced the roughness even more but did not affect the PBF-EB surface finish as much. As-printed PBF-EB parts have low residual stresses in all directions, whereas it was much higher for PBF-LB. However, heat treatment removed the stresses and SP created compressive stresses for samples from both PBF processes. The standard Arcam process parameter for PBF-EB for IN718 is not fully optimized, which leads to porosity and inferior mechanical properties. However, impact toughness after hot isostatic pressing was surprisingly high. Originality/value The two processes gave different results and also responses to post-treatments, which could be of advantage or disadvantage for different applications. Suggestions for improving the properties of parts produced by each method are presented.

scholarly journals Understanding Laser Powder Bed Fusion Surface Roughness

2020 ◽  
Vol 142 (7) ◽  
Author(s):  
Jacob C. Snyder ◽  
Karen A. Thole
Keyword(s):  
Experimental Study ◽  
Surface Roughness ◽  
Additive Manufacturing ◽  
Process Parameters ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Comprehensive Understanding ◽  
Powder Bed ◽  
Manufacturing Methods ◽  
New Framework

Abstract Surface roughness is a well-known consequence of additive manufacturing methods, particularly powder bed fusion processes. To properly design parts for additive manufacturing, a comprehensive understanding of the inherent roughness is necessary. While many researchers have measured different surface roughness resultant from a variety of parameters in the laser powder bed fusion process, few have succeeded in determining causal relationships due to the large number of variables at play. To assist the community in understanding the roughness in laser powder bed fusion processes, this study explored several studies from the literature to identify common trends and discrepancies amongst roughness data. Then, an experimental study was carried out to explore the influence of certain process parameters on surface roughness. Through these comparisons, certain local and global roughness trends have been identified and discussed, as well as a new framework for considering the effect of process parameters on surface roughness.

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.

3D printing of clay paste enhanced by scrap polymer from powder bed processes

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mattia Mele ◽  
Michele Ricciarelli ◽  
Giampaolo Campana
Keyword(s):  
Additive Manufacturing ◽  
Volumetric Flow Rate ◽  
The Body ◽  
Fused Deposition Modelling ◽  
Powder Bed Fusion ◽  
Dimensional Error ◽  
Content Type ◽  
Powder Bed ◽  
Fused Deposition ◽  
Polymer Fraction

Purpose Powder bed additive manufacturing processes are widespread due to their many technical and economic advantages. Nevertheless, the disposal of leftover powder poses a problem in terms of process sustainability. The purpose of this paper is to provide an alternative solution to recycle waste PA12 powder from HP multi jet fusion. In particular, the opportunity to use this material as a dispersion in three-dimensional (3D) printed clay is investigated. Design/methodology/approach A commercial fused deposition modelling printer was re-adapted to extrude a viscous paste composed of clay, PA12 and water. Once printed, parts were dried and then put in an oven to melt the polymer fraction. Four compositions with different PA12 concentration were studied. First, the extrudability of the paste was observed by testing different extrusion lengths. Then, the surface porosities were evaluated through microscopical observations of the manufactured parts. Finally, benchmarks with different geometries were digitalised via 3D scanning to analyse the dimensional alterations arising at each stage of the process. Findings Overall, the feasibility of the process is demonstrated. Extrusion tests revealed that the composition of the paste has a minor influence on the volumetric flow rate, exhibiting a better consistency in the case of long extrusions. The percentage of surface cavities was proportional to the polymer fraction contained in the mix. From dimensional analyses, it was possible to conclude that PA12 reduced the degree of shrinkage during the drying phase, while it increased dimensional alterations occurring in the melting phase. The results showed that the dimensional error measured on the z-axis was always higher than that of the XY plane. Practical implications The method proposed in this paper provides an alternative approach to reuse leftover powders from powder bed fusion processes via another additive manufacturing process. This offers an affordable and open-source solution to companies dealing with polymer powder bed fusion, allowing them to reduce their environmental impacts while expanding their production. Originality/value The paper presents an innovative additive manufacturing solution for powder reuse. Unlike the recycling methods in the body of literature, this solution does not require any intermediate transformation process, such as filament fabrication. Also, the cold material deposition enables the adoption of very inexpensive extrusion equipment. This preliminary study demonstrates the feasibility and the benefits of this process, paving the way for numerous future studies.

On the manufacturability of Inconel 718 thin-walled honeycomb structures by laser powder bed fusion

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
José M. Zea Pérez ◽  
Jorge Corona-Castuera ◽  
Carlos Poblano-Salas ◽  
John Henao ◽  
Arturo Hernández Hernández
Keyword(s):  
Mechanical Properties ◽  
Inconel 718 ◽  
Dimensional Accuracy ◽  
Honeycomb Lattice ◽  
Lattice Structures ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Content Type ◽  
Powder Bed ◽  
Thin Walled

Purpose The purpose of this paper is to study the effects of printing strategies and processing parameters on wall thickness, microhardness and compression strength of Inconel 718 superalloy thin-walled honeycomb lattice structures manufactured by laser powder bed fusion (L-PBF). Design/methodology/approach Two printing contour strategies were applied for producing thin-walled honeycomb lattice structures in which the laser power, contour path, scanning speed and beam offset were systematically modified. The specimens were analyzed by optical microscopy for dimensional accuracy. Vickers hardness and quasi-static uniaxial compression tests were performed on the specimens with the least difference between the design wall thickness and the as built one to evaluate their mechanical properties and compare them with the counterparts obtained by using standard print strategies. Findings The contour printing strategies and process parameters have a significant influence on reducing the fabrication time of thin-walled honeycomb lattice structures (up to 50%) and can lead to improve the manufacturability and dimensional accuracy. Also, an increase in the young modulus up to 0.8 times and improvement in the energy absorption up to 48% with respect to those produced by following a standard strategy was observed. Originality/value This study showed that printing contour strategies can be used for faster fabrication of thin-walled lattice honeycomb structures with similar mechanical properties than those obtained by using a default printing strategy.

Quality assurance in metal powder bed fusion via deep-learning-based image classification

2019 ◽  
Vol 26 (2) ◽  
pp. 259-266 ◽  
Author(s):  
Maximilian Hugo Kunkel ◽  
Andreas Gebhardt ◽  
Khumbulani Mpofu ◽  
Stephan Kallweit
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Quality Control ◽  
Metal Powder ◽  
Monitoring Data ◽  
Powder Bed Fusion ◽  
Destructive Testing ◽  
Deep Convolutional Neural Networks ◽  
Content Type ◽  
Powder Bed

Purpose This paper aims to establish a standardized, quick, reliable and cost-efficient method of quality control (QC) in metal powder bed fusion (PBFM) based on process monitoring data. Design/methodology/approach Based on destructive testing results that emerged from a statistical investigation on powder bed fusion process exceeding reproducibility of mechanical properties, it was investigated if the generated monitoring data from a concept laser machine allows reliable deductions on resulting mechanical properties of the manufactured specimens. Findings The application of machine learning on generated melt pool images, under-recognition of destructive testing results, enables enhanced pattern recognition. The generated computational model successfully classified 9,280 unseen layer images by 98.9 per cent accuracy. This finding offers an automated approach to quality control within PBFM. Originality/value To the authors knowledge, it is the first time that machine learning has been applied for the purpose of QC in additive manufacturing. The ability of deep convolutional neural networks to recognize patterns, which are imperceptible to the human eye, shows high potential to facilitate activities of QC and to minimize QC-related costs and throughput times. The achieved processing speed for image analyses also points a way for future developments of self-corrective PBFM systems.

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