On the surface roughness of 316L stainless steel fabricated using L-PBF additive manufacturing
Keywords:
Additive manufacturing, Metal, Surface roughness, Tensile characteristics, Surface finishAbstract
Additive manufacturing (AM) offers numerous advantages over traditional fabrication methods such as manufacturing complex parts. However, a significant limitation lies in the restricted surface quality, hindering its widespread use. While parts produced through conventional manufacturing techniques such as milling and grinding typically have an average roughness (Ra) value of less than 1–2 μm, those manufactured using laser powder bed fusion (LPBF) AM usually fall within the range of 10 to 30 μm. Surface roughness plays a critical role in various applications, as certain uses necessitate superior surface quality to prevent premature failure due to surface-induced cracking. Subpar surface quality not only compromises the strength, wear resistance, and corrosion resistance of parts but also impacts the precision of the fabricated components. Therefore, it is imperative to optimize the fabrication process and enhance the surface quality of metal parts. Moreover, the surface quality of each layer dictates the bonding strength between adjacent layers and process stability, as a high-quality preceding surface is essential for ensuring the integrity of subsequent layers. Consequently, surface roughness significantly influences process stability and the properties of metal parts produced through LPBF. This work aims at evaluating surface roughness of as printed 316L stainless steel parts made using LPBF AM process and their effects on tensile properties of the produced samples. Microscopic analyses are done to evaluate the roughness (including Ra, Rq, and Sa parameters) at different locations to evaluate the effects of different printing parameters on their size distributions. In addition, the macro-mechanical behaviour of the as printed samples is compared with the ones with polished surface.
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Copyright (c) 2024 Rasid Ahmed Yildiz, Cansin Ozdogan , Mohammad Malekan
This work is licensed under a Creative Commons Attribution 4.0 International License.