SBPar scanning: Toward a complete optimal skeleton scan strategy for Additive Manufacturing
Keywords:
Additive Manufacturing; Hatching strategy; Skeleton-Based Perpendicular scanning; Skeleton-based Parallel scanning; Scan Optimization
Abstract
In a previous work (Prog Addit Manuf 6:93–118, 2021), a novel Additive Manufacturing scan strategy was designed; the Skeleton-Based Perpendicular (SBP) scanning should show minimal trajectory series compared to classical exiting hatching patterns used in the literature. In contrast, this pattern should lead to mechanical anisotropy due to the one-way oriented printing if it is applied in all part’s layers; a complementary scan strategy must be designed to balance the SBP orientations. This important constraint led the author of this paper to develop the “Skeleton-Based Parallel” (SBPar) strategy as a SBP’s complementary scan for avoiding such issues. Subsequently, the present work details the design of the SBPar pattern and the corresponding scan length; analytical formulations are drawn-up for a simple rectangle as a proof of the concept. Therefore, the superposition of SBP and SBPar constitutes the total skeletal scanning (SB). Results emphasized two conflictual interests: apart from stripe scan, the proposed SBPar scan exhibits a maximized trajectory compared to the other scan strategies; thus, it seems lastly compromising the minimization objective targeted by SBP scan. On the other hand, according to this maximization aspect, the second interest is regarded in terms of surface control which requires maximizing matter spreading and thereafter offering higher densification to the processed surfaces. Furthermore, The SB and the classical scan strategies showed degrees of length-similarities according to decision variables adopted herein. Further works will be dedicated to the implementation of the Skeletal-Based trajectory within real 3D-parts and then to the associated mechanical characterization.References
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[43] Schmidt, J., Fügenschuh, A. (2024) Trajectory optimization for arbitrary layered geometries in wire-arc additive manufacturing. Optim Eng 25, 529–553. https://doi.org/10.1007/s11081-023-09813-z
[2] K Fri A Laazizi M Bensada et al (2022) Microstructural and heat treatment analysis of 316L elaborated by SLM additive manufacturing process. Int J Adv Manuf Technol (124) 2289–2297. https://doi.org/10.1007/s00170-022-10622-4
[3] K Fri I Akhrif A Laazizi et al (2023) Experimental investigation of the effects of processing parameters and heat treatment on SS 316L manufactured by laser powder bed fusion. Prog Addit Manuf. https://doi.org/10.1007/s40964-023-00538-0
[4] Alzahrani, H.M. (2024) In silico study of the electronic portal imaging devices sensitivity to parotid glands shrinkage during radiotherapy for nasopharyngeal cases. J.Umm Al-Qura Univ. Appll. Sci. 10, 279–289. https://doi.org/10.1007/s43994-023-00103-z
[5] S Roux Le M Salem A Hor (2018) Improvement of the bridge curvature method to assess residual stresses in selective laser melting. Addit Manuf (22) 320-329. https://doi.org/10.1016/j.addma.2018.05.025
[6] M Yakout MA Elbestawi SC Veldhuis 2018 Density and mechanical properties in selective laser melting of Invar 36 and stainless steel 316L J Mater Process Technol. https://doi.org/10.1016/j.jmatprotec.2018.11.006
[7] D Pitassi et al (2018) Finite element thermal analysis of metal parts additively manufactured via selective laser melting. In: P Răzvan (ed) Finite element method: simulation, numerical analysis and solution technique. IntechOpen, 123–156.
[8] J Suryawanshi KG Prashanth U Ramamurty (2017) Mechanical behavior of selective laser melted 316L stainless steel Mater Sci Eng A 696 113 121. https://doi.org/10.1016/j.msea.2017.04.058
[9] J Damon S Dietrich (2019) Process porosity and mechanical performance of fused filament fabricated 316L stainless steel. Rapid Prot J (25) N°7 1319-1327. https://doi.org/10.1108/RPJ-01-2019-0002
[10] A Kudzal (2017) Effect of scan pattern on the microstructure and mechanical properties of Powder Bed Fusion additive manufactured 17–4 stainless steel. Mater Design (133) 205-215 https://doi.org/10.1016/j.matdes.2017.07.047
[11] S Catchpole-Smith (2017) Fractal scan strategies for selective laser melting of ‘unweldable’ nickel superalloys. Add Man (15) 113-122. https://doi.org/10.1016/j.addma.2017.02.002
[12] T Mishurova K Artzt J Haubrich (2018) New aspects about the search for the most relevant parameters optimizing SLM materials. Addit Manuf (25) 325-334. https://doi.org/10.1016/j.addma.2018.11.023
[13] Bähr, M., Buhl, J., Radow, G. et al. Stable honeycomb structures and temperature based trajectory optimization for wire-arc additive manufacturing. Optim Eng 22, 913–974 (2021). https://doi.org/10.1007/s11081-020-09552-5
[14] D Gu and H Chen (2018) Selective laser melting of high strength and toughness stainless steel parts: the roles of laser hatch style and part placement strategy. Mater Sci Eng A (725) 419-427. https://doi.org/10.1016/j.msea.2018.04.046
[15] M El Jai I Akhrif & N Saidou (2021) Skeleton-based perpendicularly scanning: a new scanning strategy for additive manufacturing, modeling and optimization. Prog Addit Manuf (6) 781–820. https://doi.org/10.1007/s40964-021-00197-z
[16] EO Olakanmi RF Cochrane KW Dalgarno (2011) Densification mechanism and microstructural evolution in selective laser sintering of Al–12Si powders. J Mater Process Technol (211) 113-121. https://doi.org/10.1016/j.jmatprotec.2010.09.003
[17] H Gong (2015) Influence of defects on mechanical properties of Ti–6Al–4 V components produced by selective laser melting and electron beam melting. Mater Design (86) 545-554. https://doi.org/10.1016/j.matdes.2015. 07.147
[18] M Yakout MA Elbestawi SC Veldhuis (2018) A study of thermal expansion coefficients and microstructure during selective laser melting of Invar 36 and stainless steel 316L. Addit Manuf (24) 405-418. https://doi.org/10.1016/j.addma.2018.09.035
[19] K Easterling (1992) Introduction to the Physical Metallurgy of Welding. Butterworth-Heinemann, 2nd Edition, Great Britain.
[20] C Wu S Li C Zhang X Wang (2016) Microstructural evolution in 316LN austenitic stainless steel during solidification process under different cooling rates J Mater Sci (51) 2529-2539. https://doi.org/10.1007/s10853-015-9565-0
[21] A F Padilha and P R Rios (2002) Decomposition of Austenite in Austenitic Stainless Steels. ISIJ International (42) N°4 325–337. https://doi.org/10.2355/isijinternational.42.325
[22] D Kong (2018) Heat treatment effect on the microstructure and corrosion behavior of 316L stainless steel fabricated by selective laser melting for proton exchange membrane fuel cells. Electrochim Acta https://doi.org/10.1016/j.electacta.2018.04.188
[23] D Kong (2019) Anisotropy in the microstructure and mechanical property for the bulk and porous 316L stainless steel fabricated via selective laser melting. Mat Lett (235) 1-5. https://doi.org/10.1016/j.matlet.2018.09.152
[24] J-C Lippold D-J Kotecki (2005) Welding metallurgy and weldability of stainless steels. John Wiley & Sons Publication, New Jersey, USA.
[25] E Liverani (2017) Effect of Selective Laser Melting (SLM) process parameters on microstructure and mechanical properties of 316L austenitic stainless steel. J Mater Process Technol (249) 255-263 https://doi.org/10.1016/j.jmatprotec.2017.05.042
[26] KG Prashanth S Scudino HJ Klauss et al (2014) Microstructure and mechanical properties of Al-12Si produced by selective laser melting: effect of heat treatment Mater Sci Eng A (590) 153-160. https://doi.org/10.1016/j.msea.2013.10.023
[27] A Pathania AK Subramaniyan N Bommanahalli Kenchappa (2023) Densification behaviour of laser powder bed fusion processed Ti6Al4V: Effects of customized heat treatment and build direction. Proceedings of the Institution of Mechanical Engineers. Part E: Journal of Process Mechanical Engineering. https://doi.org/10.1177/09544089231190483
[28] C Qiu NJE Adkins MM Attallah MM (2013) Microstructure and tensile properties of selectively laser-melted and of HIPed laser-melted Ti-6Al-4V. Mater Sci Eng A (578) 230–239. http://dx.doi.org/10.1016/j.msea.2013.04.099
[29] S Tammas-Williams PJ Withers I Todd et al (2016) Porosity regrowth during heat treatment of hot isostatically pressed additively manufactured titanium components. Scr Mater (122) 72–76. https://doi.org/10.1016/j.scriptamat.2016.05.002
[30] K Ouazzani M El Jai I Akhrif et al (2023) An experimental study of FDM parameter effects on ABS surface quality: roughness analysis. Int J Adv Manuf Technol (127) 151–178. https://doi.org/10.1007/s00170-023-11435-9
[31] MS Duval-Chaneac (2018) Experimental study on finishing of internal laser melting (SLM) surface with abrasive flow machining (AFM). Precis Eng https://doi.org/10.1016/j.precisioneng.2018.03.006
[32] AH Mary T Kara (2016) Robust Proportional Control for Trajectory Tracking of a Nonlinear Robotic Manipulator: LMI Optimization Approach. Arab J Sci Eng 41, 5027–5036. https://doi.org/10.1007/s13369-016-2221-4
[33] X Ji S Feng Q Han et al (2021) Improvement and Fusion of A* Algorithm and Dynamic Window Approach Considering Complex Environmental Information. Arab J Sci Eng 46, 7445–7459. https://doi.org/10.1007/s13369-021-05445-6
[34] C Rousseau and Y Saint-Aubin (2001) Mathematiques and technologie, SUMAT Springer.
[35] P Felkel S Obderzalek (1998) Straight skeleton implementation. Reprinted proceedings of spring conference on computer graphics 210–218. Budmerice, Slovakia.
[36] W Zizhao C Xingyu Y Lingyun et al (2020) Co-skeletons: Consistent curve skeletons for shape families. Computers & Graphics (90) 62-72. https://doi.org/10.1016/j.cag.2020.05.006
[37] RL Blanding GM Turkiyyah DW Storti MA Ganter (2000) Skeleton-based three-dimensional geometric morphing, Computational Geometry (15) 129–148. https://doi.org/10.1016/S0925-7721(99)00050-4
[38] Yuchen He, Sung Ha Kang, and Luis Álvarez (2021) Finding the Skeleton of 2D Shape and Contours: Implementation of Hamilton-Jacobi Skeleton, Image Processing On Line, 11 18–36. https://doi.org/10.5201/ipol.2021.296
[39] QJ Wu and JD Bourland (1999) A morphology-guided radiosurgery treatment planning and optimization for multiple isocenters Medic Phy (26) 2151-2160. https://doi.org/10.1118/1.598731
[40] QJ Wu (2000) Sphere packing using morphological analysis. Ser Discr Math Theor Comput Sci (55) 45-54. https://doi.org/10.1090/dimacs/055
[41] MZ El Khattabi M El Jai Y Lahmadi et al (2023) Understanding the Interplay Between Metrics, Normalization Forms, and Data distribution in K-Means Clustering: A Comparative Simulation Study. Arab J Sci Eng. https://doi.org/10.1007/s13369-023-07741-9
[42] MZ El Khattabi M El Jai Y Lahmadi et al (2024) Geometry-Inference Based Clustering Heuristic: New k-means Metric for Gaussian Data and Experimental Proof of Concept. Oper. Res. Forum 5, 13. https://doi.org/10.1007/s43069-024-00291-2
[43] Schmidt, J., Fügenschuh, A. (2024) Trajectory optimization for arbitrary layered geometries in wire-arc additive manufacturing. Optim Eng 25, 529–553. https://doi.org/10.1007/s11081-023-09813-z
Published
2025-06-23
How to Cite
RIHANI, N., Akhrif, I., & El Jai, M. (2025). SBPar scanning: Toward a complete optimal skeleton scan strategy for Additive Manufacturing. Statistics, Optimization & Information Computing. https://doi.org/10.19139/soic-2310-5070-1917
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Research Articles
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