Tribological behavior investigation of 316L stainless steel samples processed by selective laser melting. (April 2023)

• Record Type: Journal Article
• Authors: Pant, Meena; Nagdeve, Leeladhar; Moona, Girija; Kumar, Harish; Sharma, Anuj
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 237:Number 4(2023)
• Page Start: 718
• Page End: 731
• Publication Date: 2023-04
• Subjects: Selective laser melting -- microstructure analysis -- process parameter -- tribological behavior -- optimization; Tribology -- Periodicals; 621.89
• DOI: 10.1177/13506501221101797
• Languages: English
• ISSNs: 1350-6501

Abstract

Selective Laser Melting (SLM) is a widely used process-based on melting powder material through a high laser beam. Process parameters significantly influence the microstructure, surface texture, and mechanical properties of materials in SLM. In this experimental investigation, we have used SLM core parameters, including scan speed, layer thickness, hatch spacing, and laser power, to observe their influence on the tribological behavior of 316L Stainless Steel (SS) samples. Samples were fabricated using SLM, and the wear test was performed according to the ASTM G-99 test standard on a dry sliding wear testing machine, keeping the process parameters (load, speed, wear track diameter, and time) constant for all samples. The Scanning Electron Microscopic (SEM) analysis showed columnar and cellular microstructure of the samples before the wear. In contrast, shallow plowing grooves, delamination marks, and cracks were observed in worn-out samples caused due to formation of abrasive and adhesive wear. Taguchi's statistical design of experiment was used to optimize the selected parameters for response variables like density and specific wear rate. From the Taguchi analysis the high density and minimum specific wear rate values were achieved at a scan speed of 900 mm/s, a laser power of 300 W, and a hatch spacing of 90 µm.The validation of obtained parameters has been done through Analysis of variance (ANOVA) to develop a linear correlation between the chosen parameters.