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.

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.

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.

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.

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.

Mapping the path to certification of metal laser powder bed fusion for aerospace applications

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Duncan William Gibbons ◽  
Jean-Pierre Louis Serfontein ◽  
André Francois van der Merwe
Keyword(s):  
Quality Control ◽  
Systems Engineering ◽  
Feasibility Studies ◽  
Point Of View ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Regulatory Oversight ◽  
Content Type ◽  
Powder Bed ◽  
Aerospace Applications

Purpose The purpose of this paper is to identify and define the certification lifecycle of laser powder bed fusion for aerospace applications from equipment acquisition and installation to production, part acceptance and continuous improvement activities. Design/methodology/approach A top–down systems engineering approach is performed consisting of concept development, requirements engineering and systems architecting. This approach is taken from the perspective of a production organization. Findings A certification roadmap is proposed that references industry requirements at the relevant phases of the roadmap. Each phase of the roadmap acts as a decision gate for progression to the next. Originality/value Qualification and certification of metal laser powder bed fusion is currently a challenge within the aerospace industry. From an aerospace point of view, the qualification and certification of this relatively new manufacturing process should not have to be any different from traditional manufacturing processes, although with extensive quality control and regulatory oversight. This paper proposes a means for fulfilling these requirements chronologically and provides guidance on ensuring such quality control throughout the manufacturing system lifecycle. This roadmap provides insight into the qualification and certification of laser powder bed fusion for aerospace applications and provides value for future industrial feasibility studies.

Microstructure and mechanical properties of hierarchical porous parts of Ti-6Al-4V alloy obtained by powder bed fusion techniques

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Edwin Sallica-Leva ◽  
Fernando Henrique da Costa ◽  
Cláudio Teodoro Dos Santos ◽  
André Luiz Jardini ◽  
Jorge Vicente Lopes da Silva ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Deformation Behavior ◽  
Orthopedic Implants ◽  
Experimental Methodology ◽  
Mechanical Resistance ◽  
Powder Bed Fusion ◽  
Content Type ◽  
Powder Bed ◽  
Hierarchical Arrangement ◽  
Hierarchical Porous

Purpose This paper aims to describe the obtainment of Ti-6Al-4V parts with a hierarchical arrangement of pores by additive manufacturing, aiming at designing orthopedic implants. Design/methodology/approach The experimental methodology compares microstructural and mechanical properties of Menger pre-fractal sponges of Ti-6Al-4V alloy, manufactured by laser powder bed fusion (LPBF) and electron beam powder bed fusion (EBPBF), with three different porosity volumes. The pore arrangement followed the formation sequence of the Menger sponge, with hierarchical order from 1 to 3. Findings The LPBF parts presented a martensitic microstructure, while the EBPBF parts presented an α + ß microstructure, independently of its wall thickness. The LPBF parts presented higher mechanical resistance and effective stiffness than the EBPBF parts with similar porosity volume. The stiffness values of the Menger pre-fractal sponges of Ti-6Al-4V alloy, between 4 and 29 GPa, are comparable to those of the cortical bone. Furthermore, the deformation behavior presented by the Menger pre-fractal sponges of Ti-6Al-4V alloy did not follow the Gibson and Ashby model's prediction. Originality/value To the best of the authors' knowledge, this is the first study to obtain Menger pre-fractal sponges of Ti-6Al-4V alloy by LPBF and EBPBF. The deformation behavior of the obtained porous parts was contrasted with the Gibson and Ashby model's prediction.

scholarly journals Machine Learning-enabled feedback loops for metal powder bed fusion additive manufacturing

2020 ◽  
Vol 176 ◽  
pp. 2586-2595
Author(s):  
Chao Liu ◽  
Léopold Le Roux ◽  
Ze Ji ◽  
Pierre Kerfriden ◽  
Franck Lacan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Additive Manufacturing ◽  
Metal Powder ◽  
Feedback Loops ◽  
Powder Bed Fusion ◽  
Powder Bed
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