Surface roughness prediction model for Electron Beam Melting (EBM) processing Ti6Al4V

Galati, Manuela; Rizza, Giovanni; Defanti, Silvio; Denti, Lucia

doi:10.1016/j.precisioneng.2021.01.002

Cited by 44 publications

(14 citation statements)

References 40 publications

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“…Each EB-PBF supplier adopts a different preheating strategy. For Arcam machines, the preheating phase consists of a series of passages of a defocused electron beam that scans the powder bed at a high current (about 30 mA) and speed (about15000 mm/s) [53] in two steps: preheating one and preheating two. During preheating one, the preheated area corresponds to the maximum rectangular area, including all the areas to be melted.…”

Section: Sintering During Eb-pbfmentioning

confidence: 99%

“…During preheating one, the preheated area corresponds to the maximum rectangular area, including all the areas to be melted. During preheating two, the heated area is smaller and corresponds to an offset of the section to be melted [10,53,54]. The following main process parameters define the sintering degree: the beam speed, the beam current, the number of scan repetitions and the distance between adjacent scanned lines [10,55].…”

Section: Sintering During Eb-pbfmentioning

confidence: 99%

“…EB-PBF generally uses spherical particles with a diameter, in a virgin state, ranging between 45 and 105 μm [64,65]. Although the entire process is conducted under a vacuum [53], powder oxidation may occur on the surface of powder particles during handling, sieving and blasting [66]. The presence of oxide on the particle change locally the diffusion coefficients and the influence on the sintering degree depends on the considered alloy [67].…”

Section: Sintering During Eb-pbfmentioning

confidence: 99%

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A phase-field study of neck growth in electron beam powder bed fusion (EB-PBF) process of Ti6Al4V powders under different processing conditions

Rizza

Galati

Iuliano

2022

Int J Adv Manuf Technol

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Traditional sintering processes are carried out to achieve complete material densification. In an electron beam powder bed fusion (EB-PBF) process, the same sintering mechanisms occur but only with the aim to form small connections between the particles (necks). A proper neck formation is central for the EB-PBF process because, among other effects, ensures the thermal stability of the process and helps to avoid smoke phenomena. This work presents a numerical study of neck formation under the EB-PBF processing conditions. A new type of modelling is introduced for the temperature sintering load and included in a phase-field model, which simulates the neck growth during the EB-PBF process of Ti6Al4V powders. The model was validated with an ad-hoc experiment, which provided a deviation with respect to the estimated neck diameter of about 9%. The deviation was investigated by reasonably varying the processing conditions. The results showed that the thermal history, the process time scale (including also the cooling phase), and the geometrical characteristics of the particles significantly affected the sintering rate and neck radius.

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Section: Sintering During Eb-pbfmentioning

confidence: 99%

Section: Sintering During Eb-pbfmentioning

confidence: 99%

Section: Sintering During Eb-pbfmentioning

confidence: 99%

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A phase-field study of neck growth in electron beam powder bed fusion (EB-PBF) process of Ti6Al4V powders under different processing conditions

Rizza

Galati

Iuliano

2022

Int J Adv Manuf Technol

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“…Display of optimization results and surface topography: Through the operation, the interface will display the optimized parameters, which are compared with the original parameters. In the interface operation bar, the surface topography simulation function is set, and the optimal parameters can be used as input to obtain the surface topography of the part, and the related information such as the surface processing quality of the part can be more intuitively displayed [15][16].…”

Section: Figure 1 Simulation System Structure Diagrammentioning

confidence: 99%

Roughness Prediction Model Based on Neural Network

2021

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In order to meet the needs of people's consumption quality upgrade, major manufacturers have put forward higher requirements for the machining accuracy, surface roughness and material properties of the workpiece. By building a prediction model (PM) to predict the surface of the formed parts with different process parameters Roughness, using NN to build a roughness prediction model (PM), adjust the process parameters through the model, and ensure the surface quality of the parts. Therefore, this paper discusses and studies the roughness PM based on neural network (NN). This paper firstly introduces the basic situation of the surface topography simulation system and the workflow of the PM in detail, and then builds the PM from the surface roughness measurement, data processing and overall parameter design, and finally compares the model results. Analysis and evaluation index analysis.

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“…Electron Beam Melting (EBM) represents an excellent alternative, with fewer geometrical difficulties, 15 but it's very rarely applied to aluminum alloys because the peculiar thermal properties of this material make the EBM process extremely slow and complicated. PBF-LB/M allows the production of extremely complex hollow shapes with even lower costs than in the case of simple bulky geometries.…”

Section: Introductionmentioning

confidence: 99%

Effective Mechanical Properties of AlSi7Mg Additively Manufactured Cubic Lattice Structures

Mantovani

Giacalone

Merulla

et al. 2022

3D Printing and Additive Manufacturing

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Lattice structures, whose manufacturing has been enabled by additive technologies, are gaining growing popularity in all the fields where lightweighting is imperative. Since the complexity of the lattice geometries stretches the technological boundaries even of additive processes, the manufactured structures can be significantly different from the nominal ones, in terms of expected dimensions but also of defects. Therefore, the successful use of lattices needs the combined optimization of their design, structural modeling, build orientation, and setup. The article reports the results of quasi-static compression tests performed on BCCxyz lattices manufactured in a AlSi7Mg alloy using additive manufacturing. The results are compared with numerical simulations using two different approaches. The findings show the influence of the relative density on stiffness, strength, and on the energy absorption properties of the lattice. The correlation with the technological feasibility points out credible improvements in the choice of a unit cell with fewer manufacturing issues, lower density, and possibly equal mechanical properties.

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Surface roughness prediction model for Electron Beam Melting (EBM) processing Ti6Al4V

Cited by 44 publications

References 40 publications

A phase-field study of neck growth in electron beam powder bed fusion (EB-PBF) process of Ti6Al4V powders under different processing conditions

A phase-field study of neck growth in electron beam powder bed fusion (EB-PBF) process of Ti6Al4V powders under different processing conditions

Roughness Prediction Model Based on Neural Network

Effective Mechanical Properties of AlSi7Mg Additively Manufactured Cubic Lattice Structures

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