Thermal confinement effects in laser-polished AM 316L slots: The role of geometry in internal surface response

Aswin Karkadakattil

doi:10.59400/mea4103

Thermal confinement effects in laser-polished AM 316L slots: The role of geometry in internal surface response

Aswin Karkadakattil
Independent Researcher, Kasaragod 671314, India

Article ID: 4103

Keywords: laser polishing, additive manufacturing (AM) 316L, thermal confinement, depth-dependent microhardness, slot and channel geometries, microstructure–property relationship, biomedical and aerospace components

Abstract

Laser polishing (LP) is widely employed to enhance the surface quality of additively manufactured (AM) metals; however, its behaviour within deep or confined internal geometries remains insufficiently understood. Many high-performance AM components, such as biomedical implants, turbine cooling channels, and metal microfluidic systems, incorporate narrow internal features where heat-transfer conditions differ significantly from open surfaces. In this study, laser powder bed fusion (LPBF)-fabricated 316L stainless steel specimens containing ~10 mm deep slots with widths ranging from 1 to 5 mm were subjected to laser polishing using a continuous-wave fibre laser (power: 80–120 W, scan speed: 450–750 mm/s, spot size: ~80–100 µm, ~60–70% track overlap, single-pass strategy). The influence of internal geometric confinement on microstructural evolution and mechanical response was systematically investigated. A pronounced depth-dependent microhardness gradient was observed along the slot wall, with hardness decreasing from approximately 270 HV in the lower region to ~210 HV near the slot opening, with more significant gradients in narrower geometries. Quantitative grain-size analysis revealed finer grains (~8–12 µm) in the lower region and coarser grains (~18–25 µm) toward the upper region, indicating progressive grain coarsening with increasing height. These variations are attributed to geometry-dependent thermal boundary conditions, where enhanced conductive coupling to the bulk substrate in the lower region promotes higher cooling rates, while reduced thermal extraction near the slot opening results in slower solidification. The results provide clear experimental evidence that internal geometric confinement can significantly influence microstructure–property evolution during laser polishing, even under constant processing parameters. This study highlights the importance of incorporating geometric effects into post-processing strategies for AM components and offers practical insights for achieving more predictable and uniform mechanical performance in confined internal features.

Published

2026-02-06

How to Cite

Karkadakattil, A. (2026). Thermal confinement effects in laser-polished AM 316L slots: The role of geometry in internal surface response. Mechanical Engineering Advances, 4(1). https://doi.org/10.59400/mea4103

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Issue

Vol. 4 No. 1 (2026)

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Article

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

[1]Siddiqui AA, Dubey AK. Recent trends in laser cladding and surface alloying. Optics & Laser Technology. 2021; 134: 106619. doi: 10.1016/j.optlastec.2020.106619

[2]Khan SA, Shamail S, Anwar S, et al. Wear performance of surface treated drills in high speed drilling of AISI 304 stainless steel. Journal of Manufacturing Processes. 2020; 58: 223–235. doi: 10.1016/j.jmapro.2020.08.022

[3]Guo J, Xiang C, Rossiter J. A soft and shape-adaptive electroadhesive composite gripper with proprioceptive and exteroceptive capabilities. Materials & Design. 2018; 156: 586–587. doi: 10.1016/j.matdes.2018.07.027

[4]DebRoy T, Wei HL, Zuback JS, et al. Additive manufacturing of metallic components – Process, structure and properties. Progress in Materials Science. 2018; 92: 112–224. doi: 10.1016/j.pmatsci.2017.10.001

[5]Chandran AM, Varun S, Bhargavi PVB, et al. h-BN and graphene oxide/epoxy nanocomposite—A comparative study of mechanical, electrical and thermal properties. In: Proceedings of the Fracture and Damage Mechanics: Theory, Simulation and Experiment; 15–17 September 2020; Mallorca, Spain, p. 020002. doi: 10.1063/5.0028259

[6]King WE, Barth HD, Castillo VM, et al. Observation of keyhole-mode laser melting in laser powder-bed fusion additive manufacturing. Journal of Materials Processing Technology. 2014; 214(12): 2915–2925. doi: 10.1016/j.jmatprotec.2014.06.005

[7]Li W, Yamasaki S, Mitsuhara M, et al. In situ EBSD study of deformation behavior of primary α phase in a bimodal Ti-6Al-4V alloy during uniaxial tensile tests. Materials Characterization. 2020; 163: 110282. doi: 10.1016/j.matchar.2020.110282

[8]Schneider CA, Rasband WS, Eliceiri KW. NIH Image to ImageJ: 25 years of image analysis. Nature Methods. 2012; 9(7): 671–675. doi: 10.1038/nmeth.2089

[9]ASTM. Standard Test Methods for Determining Average Grain Size. ASTM International; 2004.

[10]Hamza HM, Deen KM, Khaliq A, et al. Microstructural, corrosion and mechanical properties of additively manufactured alloys: A review. Critical Reviews in Solid State and Materials Sciences. 2022; 47(1): 46–98. doi: 10.1080/10408436.2021.1886044

[11]Chen L, Richter B, Zhang X, et al. Modification of surface characteristics and electrochemical corrosion behavior of laser powder bed fused stainless-steel 316L after laser polishing. Additive Manufacturing. 2020; 32: 101013. doi: 10.1016/j.addma.2019.101013

[12]Obeidi MA, McCarthy E, O’Connell B, et al. Laser Polishing of Additive Manufactured 316L Stainless Steel Synthesized by Selective Laser Melting. Materials. 2019; 12(6): 991. doi: 10.3390/ma12060991

[13]Jiang Z, Song B, Zhou X, et al. On-machine measurement of location errors on five-axis machine tools by machining tests and a laser displacement sensor. International Journal of Machine Tools and Manufacture. 2015; 95: 1–12. doi: 10.1016/j.ijmachtools.2015.05.004

[14]Lamikiz A, Sánchez JA, López De Lacalle LN, et al. Laser polishing of parts built up by selective laser sintering. International Journal of Machine Tools and Manufacture. 2007; 47(12–13): 2040–2050. doi: 10.1016/j.ijmachtools.2007.01.013

[15]Manco E, Cozzolino E, Astarita A. Laser polishing of additively manufactured metal parts: A review. Surface Engineering. 2022; 38(3): 217–233. doi: 10.1080/02670844.2022.2072080

[16]Adebiyi DI, Popoola API, Pityana SL. Phase constituents and microhardness of laser alloyed Ti–6Al–4 V alloy. Journal of Laser Applications. 2015; 27(S2): S29104. doi: 10.2351/1.4906388

[17]Lin X, Huang WD. Laser additive manufacturing of high-performance metal components. Chinese Science: Information Science. 2015; 45(9): 1111–1126.

[18]Chen J, Hou W, Wang X, et al. Microstructure, porosity and mechanical properties of selective laser melted AlSi10Mg. Chinese Journal of Aeronautics. 2020; 33(7): 2043–2054. doi: 10.1016/j.cja.2019.08.017

[19]Song J, Wu W, Zhang L, et al. Role of scanning strategy on residual stress distribution in Ti-6Al-4V alloy prepared by selective laser melting. Optik. 2018; 170: 342–352. doi: 10.1016/j.ijleo.2018.05.128

[20]Galy C, Le Guen E, Lacoste E, et al. Main defects observed in aluminum alloy parts produced by SLM: From causes to consequences. Additive Manufacturing. 2018; 22: 165–175. doi: 10.1016/j.addma.2018.05.005

[21]Karkadakattil A. AI and Metaheuristic Optimization in Additive Manufacturing of Lightweight Alloys: A Critical Review. Journal of The Institution of Engineers (India): Series C. 2026; 107(2): 1063–1085. doi: 10.1007/s40032-026-01355-4

[22]Satterlee N, Torresani E, Olevsky E, et al. Comparison of machine learning methods for automatic classification of porosities in powder-based additive manufactured metal parts. The International Journal of Advanced Manufacturing Technology. 2022; 120(9–10): 6761–6776. doi: 10.1007/s00170-022-09141-z

[23]Babamiri BB, Indeck J, Demeneghi G, et al. Quantification of porosity and microstructure and their effect on quasi-static and dynamic behavior of additively manufactured Inconel 718. Additive Manufacturing. 2020; 34: 101380. doi: 10.1016/j.addma.2020.101380

[24]Ge J, Pillay S, Ning H. Post-Process Treatments for Additive-Manufactured Metallic Structures: A Comprehensive Review. Journal of Materials Engineering and Performance. 2023; 32(16): 7073–7122. doi: 10.1007/s11665-023-08051-9

[25]Dalpadulo E, Pini F, Leali F. Powder bed fusion integrated product and process design for additive manufacturing: a systematic approach driven by simulation. The International Journal of Advanced Manufacturing Technology. 2024; 130(11–12): 5425–5440. doi: 10.1007/s00170-024-13042-8

[26]Ding R, Yao J, Du B, et al. Effect of Shielding Gas Volume Flow on the Consistency of Microstructure and Tensile Properties of 316L Manufactured by Selective Laser Melting. Metals. 2021; 11(2): 205. doi: 10.3390/met11020205

[27]Ren L, Wang N, Wang X, et al. Modeling and analysis of material removal depth contour for curved-surfaces abrasive belt grinding. Journal of Materials Processing Technology. 2023; 316: 117945. doi: 10.1016/j.jmatprotec.2023.117945

[28]Du Plessis A, Yadroitsava I, Yadroitsev I. Effects of defects on mechanical properties in metal additive manufacturing: A review focusing on X-ray tomography insights. Materials & Design. 2020; 187: 108385. doi: 10.1016/j.matdes.2019.108385

[29]Martin AA, Calta NP, Khairallah SA, et al. Dynamics of pore formation during laser powder bed fusion additive manufacturing. Nature Communications. 2019; 10(1): 1987. doi: 10.1038/s41467-019-10009-2

[30]Yonehara M, Kato C, Ikeshoji T-T, et al. Correlation between surface texture and internal defects in laser powder-bed fusion additive manufacturing. Scientific Reports. 2021; 11(1): 22874. doi: 10.1038/s41598-021-02240-z

[31]Srivastava AK, Kumar A, Kumar P, et al. Research Progress in Metal Additive Manufacturing: Challenges and Opportunities. International Journal on Interactive Design and Manufacturing (IJIDeM). 2023; doi: 10.1007/s12008-023-01661-6

[32]Karkadakattil A. A Physics-Stabilized Self-Updating Digital Twin Framework Using Physics-Informed Neural Networks for Thermal Field Prediction. Applied Computer Systems. 2026; 31(1): 30–40. doi: 10.2478/acss-2026-0003

[33]Karkadakattil A. Geometry aware laser polishing of LPBF AlSi10Mg defence components with physics inspired neural network based surface roughness prediction. Discover Mechanical Engineering. 2025; 4(1): 52. doi: 10.1007/s44245-025-00144-0

[34]Karkadakattil A. Physics-informed machine learning framework for grain size prediction in laser-processed 316L stainless steel: integration of experimental microstructure and surrogate data. Canadian Metallurgical Quarterly. 2025; 1–14. doi: 10.1080/00084433.2025.2593051

[35]Karkadakattil A. AI-Driven prediction of surface roughness in laser-polished LPBF Ti6Al4V: a sustainable proof-of-concept. Australian Journal of Multi-Disciplinary Engineering. 2025; 1–13. doi: 10.1080/14488388.2025.2570030

[36]Grasso M, Colosimo BM. Process defects and in situ monitoring methods in metal powder bed fusion: a review. Measurement Science and Technology. 2017; 28(4): 044005. doi: 10.1088/1361-6501/aa5c4f

[37]Li R, Zhu J, Zhou W, et al. Thermal properties of sodium nitrate-expanded vermiculite form-stable composite phase change materials. Materials & Design. 2016; 104: 190–196. doi: 10.1016/j.matdes.2016.05.039

[38]Möller F, Grden M, Thomy C, et al. Combined Laser Beam Welding and Brazing Process for Aluminium Titanium Hybrid Structures. Physics Procedia. 2011; 12: 215–223. doi: 10.1016/j.phpro.2011.03.028

[39]Shin WS, Lee T, Sohn H, et al. Microstructural and mechanical properties degradation of the acceleration/deceleration zones in stainless steel 316L fabricated by selective laser melting. Journal of Materials Research and Technology. 2025; 35: 2215–2225. doi: 10.1016/j.jmrt.2025.01.183

[40]Jurca M, Langer H-J. Temperature field measurement as quality assurance measure in case of laser material processing. Physics Procedia. 2010; 5: 473–481. doi: 10.1016/j.phpro.2010.08.075

[41]Zhang D, Qiu D, Gibson MA, et al. Additive manufacturing of ultrafine-grained high-strength titanium alloys. Nature. 2019; 576(7785): 91–95. doi: 10.1038/s41586-019-1783-1

[42]Gu D, Shen Y. Balling phenomena in direct laser sintering of stainless steel powder: Metallurgical mechanisms and control methods. Materials & Design. 2009; 30(8): 2903–2910. doi: 10.1016/j.matdes.2009.01.013

[43]Childs THC, Hauser C, Badrossamay M. Selective laser sintering (melting) of stainless and tool steel powders: Experiments and modelling. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture. 2005; 219(4): 339–357. doi: 10.1243/095440505X8109

[44]Amini S, Amiri MR. Study of ultrasonic vibrations’ effect on friction stir welding. The International Journal of Advanced Manufacturing Technology. 2014; 73(1–4): 127–135. doi: 10.1007/s00170-014-5806-7

[45]Xiao X, Lu C, Fu Y, et al. Progress on Experimental Study of Melt Pool Flow Dynamics in Laser Material Processing. In: Liquid Metals. IntechOpen; 2021. doi: 10.5772/intechopen.97205

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