4.7 Article

Electrical energy consumption and mechanical properties of selective-laser-melting-produced 316L stainless steel samples using various processing parameters

期刊

JOURNAL OF CLEANER PRODUCTION
卷 208, 期 -, 页码 77-85

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.jclepro.2018.10.109

关键词

Selective laser melting; Mechanical property; Electrical energy; Laser power; Exposure time

资金

  1. National Natural Science Foundation of China [51775486, 51505423]
  2. Fundamental Research Funds for the Central Universities [2017FZA4001, 2016QNA4002]
  3. National Basic Research Program of China [2015CB058100]

向作者/读者索取更多资源

Various processing parameters in selective laser melting (SLM) affect power profile and scanning time, which directly relates to electrical energy consumption. These processing parameters also control the microstructure of materials, which further influence the mechanical properties of the fabricated parts. In this paper, we investigate the correlation between electrical energy consumption and mechanical properties, and study whether electrical energy can be effectively reduced without significantly compromising mechanical properties by optimizing processing parameters. 316 L stainless steel was used as powder materials. Two key parameters, laser power and exposure time, were selected, and several mechanical properties, including density, hardness, wear resistance, tensile strength, flexural strength, and torsional strength, were tested. The results of electrical energy consumption and mechanical properties were jointly analyzed using growth rate comparison. It was found that the improvement of various mechanical properties with increased electrical energy consumption differs greatly. Density can be effectively increased without significantly increasing the electrical energy, but the electrical energy needs to be greatly increased in order to achieve a high flexural strength. Growth rate three-dimensional maps of mechanical properties and electrical energy consumption are presented as a reference for processing parameter optimization. (C) 2018 Elsevier Ltd. All rights reserved.

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