Additive manufacturing and post-processing simulation: laser cladding followed by high speed machining

Salonitis, Konstantinos; D’Alvise, L.; Schoinochoritis, Babis; Chantzis, Dimitrios

doi:10.1007/s00170-015-7989-y

Cited by 88 publications

(18 citation statements)

References 29 publications

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“…Only the layers below deform due to the thermal gradients, exhibiting bulging in the direction of the curve's center points. Similar bulging of a cylindrical geometry was also observed by Salonitis [25] for their numerical studies and by Papadakis [2] for experimental and numerical investigations. In these studies, only closed cylinders were investigated that bulged inward -i.e.…”

Section: Bulging Predictionsupporting

confidence: 81%

Finite element analysis of in-situ distortion and bulging for an arbitrarily curved additive manufacturing directed energy deposition geometry

Biegler

Marko

Graf

et al. 2018

Additive Manufacturing

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With the recent rise in the demand for additive manufacturing (AM), the need for reliable simulation tools to support experimental efforts grows steadily. Computational welding mechanics approaches can simulate the AM processes but are generally not validated for AM-specific effects originating from multiple heating and cooling cycles. To increase confidence in the outcomes and to use numerical simulation reliably, the result quality needs to be validated against experiments for in-situ and post-process cases. In this article, a validation is demonstrated for a structural thermomechanical simulation model on an arbitrarily curved Directed Energy Deposition (DED) part: at first, the validity of the heat input is ensured and subsequently, the model's predictive quality for in-situ deformation and the bulging behaviour is investigated. For the in-situ deformations, 3D-Digital Image Correlation measurements are conducted that quantify periodic expansion and shrinkage as they occur. The results show a strong dependency of the local stiffness of the surrounding geometry. The numerical simulation model is set up in accordance with the experiment and can reproduce the measured 3-dimensional insitu displacements. Furthermore, the deformations due to removal from the substrate are quantified via 3D-scanning, exhibiting considerable distortions due to stress relaxation. Finally, the prediction of the deformed shape is discussed in regards to bulging simulation: to improve the accuracy of the calculated final shape, a novel extension of the model relying on the modified stiffness of inactive upper layers is proposed and the experimentally observed bulging could be reproduced in the finite element model.

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Section: Bulging Predictionsupporting

confidence: 81%

Finite element analysis of in-situ distortion and bulging for an arbitrarily curved additive manufacturing directed energy deposition geometry

Biegler

Marko

Graf

et al. 2018

Additive Manufacturing

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“…Scanner speed, stand-off distance, diameter ratio of the clad to powder stream for Gaussian mode distribution [84] Layer/melt pool dimensions Laser power, powder mass flux [85] Shapes of manufactured structures, thermal loads [86] Local temperature history, track profile, microstructure scale [87] Spreading, cooling and solidification processes of droplets Substrate velocity [88] Thickness of the deposition layer, the depth of the molten pool, the penetration of the substrate or previous deposited layer Scanning speed, powder feeding rate, input electric current [89] Residual stresses, distortion High speed machining post-process [90] Thermal modelling, residual stresses, thermal distortions…”

Section: Kpimentioning

confidence: 99%

“…More specifically, modelling of surface roughness is presented in [78] (analytically) and in [79] (numerically). Modelling of topology and dimensional accuracy takes place in [80][81][82][83][84] using analytical, in [85,86] analytical-numerical, in [17][18][19]32,79,[87][88][89][90][91][92][93][94] numerical, whereas in [95] those issues have been empirically modelled utilizing artificial neural networks (ANN). Modelling of the mechanical properties and microstructure has been conducted in [80,81] using analytical, in [86,96] analyticalnumerical and in [94,[97][98][99][100][101] using numerical approaches.…”

Section: Directed Energy Depositionmentioning

confidence: 99%

Modelling of additive manufacturing processes: a review and classification

Stavropoulos

Foteinopoulos

2018

Manufacturing Rev.

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Abstract. Additive manufacturing (AM) is a very promising technology; however, there are a number of open issues related to the different AM processes. The literature on modelling the existing AM processes is reviewed and classified. A categorization of the different AM processes in process groups, according to the process mechanism, has been conducted and the most important issues are stated. Suggestions are made as to which approach is more appropriate according to the key performance indicator desired to be modelled and a discussion is included as to the way that future modelling work can better contribute to improving today's AM process understanding.

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“…Therefore, only sanding and polishing are performed here. First of all, the purpose of the sanding process is to compensate for the errors caused by the extrusion nozzle wire width and to eliminate the "Step Effect" of each formed parts so as to meet the assembling accuracy requirements [27,28]. Secondly, in order to further improve the surface 10 Complexity roughness and smoothness of the polished parts, mechanical polishing is used to polish the surface of the forming parts.…”

Section: Analysis Of Process Parameters Of Spur Face Gear Mastermentioning

confidence: 99%

FDM Rapid Prototyping Technology of Complex‐Shaped Mould Based on Big Data Management of Cloud Manufacturing

et al. 2018

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In order to solve the problem of high cost and long cycle in the process of traditional subtractive material manufacturing of a complex-shaped mould, the technology of FDM rapid prototyping is used in combination with the global service idea of cloud manufacturing, where the information of various kinds of heterogeneous-forming process data produced in the process of FDM rapid prototyping is analysed. Meanwhile, the transfer and transformation relation of each forming process data information in the rapid manufacturing process with the digital model as the core is clarified, so that the FDM rapid manufacturing process is integrated into one, thus forming a digital and intelligent manufacturing system for a complex-shaped mould based on the cloud manufacturing big data management. This paper takes the investment casting mould of a spur gear as an example. Through research on the forming mechanism of jet wire, the factors affecting forming quality and efficiency is analysed from three stages: the pretreatment of the 3D model, the rapid prototyping, and the postprocessing of the forming parts. The relationship between the forming parameters and the craft quality is thus established, and the optimization schemes at each stage of this process are put forward through the study on the forming mechanism of jet wire. Through a rapid prototyping test, it is shown that the spur face gear master mould based on this technology can be quickly manufactured with a critical surface accuracy within a range of 0.036 mm–0.181 mm and a surface roughness within the range of 0.007–0.01 μm by only 1/3 the processing cycle of traditional subtractive material manufacturing. It lays a solid foundation for rapid intelligent manufacturing of products with a complex-shaped structure.

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Additive manufacturing and post-processing simulation: laser cladding followed by high speed machining

Cited by 88 publications

References 29 publications

Finite element analysis of in-situ distortion and bulging for an arbitrarily curved additive manufacturing directed energy deposition geometry

Finite element analysis of in-situ distortion and bulging for an arbitrarily curved additive manufacturing directed energy deposition geometry

Modelling of additive manufacturing processes: a review and classification

FDM Rapid Prototyping Technology of Complex‐Shaped Mould Based on Big Data Management of Cloud Manufacturing

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