Investigation into the effect of energy density on densification, surface roughness and loss of alloying elements of 7075 aluminium alloy processed by laser powder bed fusion
“…Further, Langmuir's evaporation equation can estimate MAM components’ compositional variation due to alloying elements’ vaporisation [37,274]. For instance, using a thermal model coupled with Langmuir's equation, loss of alloying elements has been characterised for LPBF of Al7075 alloy (Figure 28(a)) [297]. The quantifiable estimation of molten spatter ejection using computational modelling is difficult, as many melt ejecta is released from the fusion zone.…”
Section: Process Induced Non-crystallographic Defectsmentioning
confidence: 99%
“…Although some advancements have been made in the prediction of entrained powder ejection for LDED and LPBF process with the help of the coupled DEM-CFD model [18,24,26], one such computational prediction of powder spattering in the LPBF process is illustrated in Figure 28(b) [298,299]. Likewise, accurate surface roughness prediction during the fusion-based process is problematic due to the interaction of unmelted powder particles with melt pool, as this aspect has not yet been incorporated into the thermo-fluidic modelling framework. Figure 28. (a) prediction of loss of alloying elements in LPBF [297], (b) Powder spattering in LPBF [298,299], (c) side surface roughness evolution in EBPF [300], (d) schematic representation of Weld pool, mushy zone, and partially melted zone of an alloy laser fusion [301], and (e) Susceptibility of wrought Al alloys to solidification cracking as per Kou's cracking index [301]. …”
Section: Process Induced Non-crystallographic Defectsmentioning
This review article provides a critical assessment of the progress made in computational modelling of metal-based additive manufacturing (AM) with emphasis on its ability to predict physical phenomena, concepts of microstructural evolution, residual stresses, role of multiple thermal cycles, and formation of multi-dimensional defects along with the achieved degree of experimental validation. The uniqueness of this article stems from the inclusion of comprehensive information on computational progress in the field of fusion-based, sintering-based, and mechanical deformation-based AM. A computational model's role in determining the process framework for the desired outcome of the set properties of the AM components is recognised while presenting the process-microstructure maps, thereby appraising computational ability towards the qualification of products. The inclusion of a detailed discussion on the bi-directional coupling of machine learning and physics-based computational models provides a futuristic roadmap for the digital twin of metal-based AM.
“…Further, Langmuir's evaporation equation can estimate MAM components’ compositional variation due to alloying elements’ vaporisation [37,274]. For instance, using a thermal model coupled with Langmuir's equation, loss of alloying elements has been characterised for LPBF of Al7075 alloy (Figure 28(a)) [297]. The quantifiable estimation of molten spatter ejection using computational modelling is difficult, as many melt ejecta is released from the fusion zone.…”
Section: Process Induced Non-crystallographic Defectsmentioning
confidence: 99%
“…Although some advancements have been made in the prediction of entrained powder ejection for LDED and LPBF process with the help of the coupled DEM-CFD model [18,24,26], one such computational prediction of powder spattering in the LPBF process is illustrated in Figure 28(b) [298,299]. Likewise, accurate surface roughness prediction during the fusion-based process is problematic due to the interaction of unmelted powder particles with melt pool, as this aspect has not yet been incorporated into the thermo-fluidic modelling framework. Figure 28. (a) prediction of loss of alloying elements in LPBF [297], (b) Powder spattering in LPBF [298,299], (c) side surface roughness evolution in EBPF [300], (d) schematic representation of Weld pool, mushy zone, and partially melted zone of an alloy laser fusion [301], and (e) Susceptibility of wrought Al alloys to solidification cracking as per Kou's cracking index [301]. …”
Section: Process Induced Non-crystallographic Defectsmentioning
This review article provides a critical assessment of the progress made in computational modelling of metal-based additive manufacturing (AM) with emphasis on its ability to predict physical phenomena, concepts of microstructural evolution, residual stresses, role of multiple thermal cycles, and formation of multi-dimensional defects along with the achieved degree of experimental validation. The uniqueness of this article stems from the inclusion of comprehensive information on computational progress in the field of fusion-based, sintering-based, and mechanical deformation-based AM. A computational model's role in determining the process framework for the desired outcome of the set properties of the AM components is recognised while presenting the process-microstructure maps, thereby appraising computational ability towards the qualification of products. The inclusion of a detailed discussion on the bi-directional coupling of machine learning and physics-based computational models provides a futuristic roadmap for the digital twin of metal-based AM.
“…The third type of defects pointed out by Galy et al [ 82 ] concerns the surface finishing of PBF-LB/M specimens. Li et al [ 103 ] demonstrated that there is a correlation between surface roughness and processing parameters. In particular, they showed that the higher the laser power, the lower the surface roughness, when the scanning speed value is fixed.…”
Their high strength-to-weight ratio, good corrosion resistance and excellent thermal and electrical conductivity have exponentially increased the interest in aluminium alloys in the context of laser-based powder bed fusion (PBF-LB/M) production. Although Al-based alloys are the third most investigated category of alloys in the literature and the second most used in industry, their processing by PBF-LB/M is often hampered by their considerable solidification shrinkage, tendency to oxidation, high laser reflectivity and poor powder flowability. For these reasons, high-strength Al-based alloys traditionally processed by conventional procedures have often proved to be unprintable with additive technology, so the design and development of new tailored Al-based alloys for PBF-LB/M production is necessary. The aim of the present work is to explore all the challenges encountered before, during and after the PBF-LB/M processing of Al-based alloys, in order to critically analyse the solutions proposed in the literature and suggest new approaches for addressing unsolved problems. The analysis covers the critical aspects in the literature as well as industrial needs, industrial patents published to date and possible future developments in the additive market.
“…LPBF process is known to cause many defects such as LoF or gas pores in the as-printed specimens. 7,34,36,41,50 In order to determine the proportion of defects in the microstructure, density measurements were carried out using Archimedes' method and X-ray tomography, and the results are shown in Table 3.…”
Section: Defect Analysismentioning
confidence: 99%
“…[28][29][30][31] The control of the microstructure allowed by the laser-scan strategy provides an interesting way to study the microstructural influence on fatigue. Numerous studies have reported the influence of energetic parameters on defect occurrence, 22,[32][33][34][35] and have investigated the impact of LPBF defects on fatigue behavior [36][37][38][39][40][41] ; however only few studies have investigated the microstructure-defect interplay using the laser-scan strategy as the controlling parameter. This new approach is full of interest because it allows to study the impact of specific microstructural characteristics such as grain shape and organization on the microstructure-defect interaction, which is not possible by varying the energetic parameters.…”
The particular roles of grain morphology and defects, controlled using laser‐scan strategies, on the mechanical properties and the fatigue behavior of 316L stainless steel are investigated. Microstructural characterization and X‐ray tomography analysis was performed to understand the genesis of polycrystalline microstructure and defects. Tensile and fatigue tests were performed to analyze the effect of defect population and microstructural properties on plasticity and damage mechanisms during monotonic and cyclic loading. The effect of the grain‐size and shape and type of defect was carefully investigated to evaluate the mechanisms driving the mechanical behavior under quasi‐static and fatigue loading. It is shown that the laser‐scan strategy determines the anisotropy in the plane perpendicular to the building direction. Moreover, contrary to the existing literature, for 316L obtained by AM, the grain size and shape does not affect the mechanical properties, and LoF defects drive the fatigue life, independent of the defect/grain size ratio.
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