Dimensional errors in selective laser melting products related to different orientations and processing parameters

Pal, Snehashis; Kokol, Vanja; Gubeljak, Nenad; Hadžistević, Miodrag; Hudák, Radovan; Drstvenšek, Igor

doi:10.17222/mit.2018.156

Cited by 11 publications

(5 citation statements)

References 19 publications

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“…The second option is to control the laser/beam power, scanning speed, scanning type, and layer thickness using KBS so that we can obtain the desired products [33]. It could also be possible to control the metal bath and thermal shrinkage by increasing or decreasing the thermal conductivity [12], which can be controlled by increasing or decreasing the support conditions to obtain the desired dimensions [24].…”

Section: Product Quality Controlmentioning

confidence: 99%

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Metallurgical and Geometric Properties Controlling of Additively Manufactured Products using Artificial Intelligence

Pal¹,

Drstvenšek²

2021

AMS

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This article has presented a technical concept for producing precisely desired Additive Manufactured (AM) metallic products using Artificial Intelligence (AI). Due to the stochastic nature of the metallic AM process, which causes a greater variance in product properties compared to traditional manufacturing processes, significant inaccuracies in metallurgical properties, as well as geometry, occur. The physics behind these phenomena are related to the melting process, bonding, cooling rate, shrinkage, support condition, part orientation. However, by controlling these phenomena, a wide range of product features can be achieved using the fabricating parameters. A variety of fabricating parameters are involved in the metal AM process, but an appropriate combination of these parameters for a given material is required to obtain an accurate and desired product. Zero defect product can be achieved by controlling these parameters by implementing Knowledge-Based System (KBS). A suitable combination of manufacturing parameters can be determined using mathematical tools with AI, considering the manufacturing time and cost. The knowledge required to integrate AM manufacturing characteristics and constraints into the design and fabricating process is beyond the capabilities of any single engineer. Concurrent Engineering enables the integration of design and manufacturing to enable trades based not only on product performance, but also on other criteria that are not easily evaluated, such as production capability and support. A decision support system or KBS that can guide manufacturing issues during the preliminary design process would be an invaluable tool for system designers. The main objective of this paper is to clearly describe the metal AM manufacturing process problem and show how to develop a KBS for manufacturing process determination.

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Section: Product Quality Controlmentioning

confidence: 99%

“…The problem of dimensions is one of the major problems with an AM product [12]. The product dimensions are only slightly different from those of the model CAD.…”

Section: Introductionmentioning

confidence: 99%

Metallurgical and Geometric Properties Controlling of Additively Manufactured Products using Artificial Intelligence

Pal¹,

Drstvenšek²

2021

AMS

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“…Xia et al (2018) had such results that Co–Cr cubic specimens built with a 550-mm/s scan speed and 115-W laser power had more minor height errors than those built with other levels of these two process parameters. Pal et al (2019) concluded that the linear energy density, or the ratio of laser power to scan speed and hatch space, affects the side and height dimensional errors of Ti–6Al–4V cubic specimens. It is found out of practice that the distortion of maxillary RPD frameworks is more common than that of mandibular RPD frameworks.…”

Section: Introductionmentioning

confidence: 99%

“…The effects of laser process parameters on the accuracy of RPD frameworks have not been thoroughly investigated. Laser process parameters indeed affect not merely the dimensional accuracy of parts (Vandenbroucke and Kruth, 2007; Pal et al , 2019) but the warpage and distortion of parts. Xia et al (2018) had such results that Co–Cr cubic specimens built with a 550-mm/s scan speed and 115-W laser power had more minor height errors than those built with other levels of these two process parameters.…”

Section: Introductionmentioning

confidence: 99%

Effects of selective laser melting process parameters on the accuracy of the intaglio surface of maxillary removable partial denture frameworks

Chen

Zhou

et al. 2022

RPJ

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Purpose This in vitro study aims to explore the effects of selective laser melting (SLM) process parameters on the accuracy of the intaglio surface of cobalt–chromium alloy (Co–Cr), commercially pure titanium (CP Ti) and titanium alloy (Ti–6Al–4V) maxillary removable partial denture (RPD) frameworks and optimize these process parameters. Design/methodology/approach Maxillary RPD framework specimens designed on a benchmark model were built. The process parameters, including contour scan speed and laser power, infill scan speed and laser power, hatch space, build orientation and metallic powder type, were arranged through the Taguchi design. Three-dimensional deviations of the clasps area, connector area and overall area of maxillary RPD frameworks were analyzed by using root mean square (RMS) as a metric. One-way analyses of variance with the above RMSs as the dependent variable were carried out (α = 0.05). Findings Maxillary RPD frameworks built horizontally had a more accurate intaglio surface than those built at other orientation angles; CP Ti or Ti–6Al–4V maxillary RPD frameworks had a more accurate intaglio surface than Co–Cr ones; the Maxillary RPD framework built with a higher infill scan speed and lower infill laser power had the more accurate intaglio surface than the one built with other levels of these two process parameters. Originality/value A novel benchmark model for evaluating the accuracy of the intaglio surface of maxillary RPD frameworks manufactured by SLM is proposed. The accuracy of the intaglio surface of maxillary RPD frameworks can be improved by adjusting SLM process parameters. The optimal setting of process parameters concerning the accuracy of the intaglio surface of maxillary RPD frameworks was given.

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“…Several studies have been carried out on the properties of products made with the powder bed additive manufacturing processes, in particular the selective laser melting (SLM) process [1][2][3]. However, these investigations were based on employing different processing parameters [4][5][6], scanning strategies [7], and different build-up directions due to different angular orientations of the part [8,9].…”

Section: Introductionmentioning

confidence: 99%

The effects of locations on the build tray on the quality of specimens in powder bed additive manufacturing

Pal

Gubeljak

Bončina

et al. 2021

Int J Adv Manuf Technol

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In this study, the effect of powder spreading direction was investigated on selectively laser-melted specimens. The results showed that the metallurgical properties of the specimens varied during fabrication with respect to their position on the build tray. The density, porosity, and tensile properties of the Co–Cr–W–Mo alloy were investigated on cuboid and tensile specimens fabricated at different locations. Two different significant positions on the tray were selected along the powder spreading direction. One set of specimens was located near the start line of powder spreading, and the other set was located near the end of the building tray. The main role in the consequences of powder layering was played by the distribution of powder particle sizes and the packing density of the layers. As a result, laser penetration, melt pool formation, and fusion characteristics varied. To confirm the occurrence of variations in sample density, an additional experiment was performed with a Ti–6Al–4V alloy. Furthermore, the powders were collected at two different fabricating locations and their size distribution for both materials was investigated.

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Dimensional errors in selective laser melting products related to different orientations and processing parameters

Cited by 11 publications

References 19 publications

Metallurgical and Geometric Properties Controlling of Additively Manufactured Products using Artificial Intelligence

Metallurgical and Geometric Properties Controlling of Additively Manufactured Products using Artificial Intelligence

Effects of selective laser melting process parameters on the accuracy of the intaglio surface of maxillary removable partial denture frameworks

The effects of locations on the build tray on the quality of specimens in powder bed additive manufacturing

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