Long-term process stability in additive manufacturing

夏比冲击试验 材料科学 选择性激光烧结 选择性激光熔化 极限抗拉强度 吞吐量 过程(计算) 韧性 复合材料 灵敏度(控制系统) 过程变量 原材料 微观结构 工艺工程 机械工程 计算机科学 烧结 工程类 电信 化学 有机化学 无线 电子工程 操作系统
作者
Scott Jensen,Jay Carroll,Priya Pathare,David J. Saiz,Jonathan Pegues,Brad Boyce,Bradley Howell Jared,Michael Heiden
出处
期刊:Additive manufacturing [Elsevier BV]
卷期号:61: 103284-103284 被引量:8
标识
DOI:10.1016/j.addma.2022.103284
摘要

Laser powder bed fusion (L-PBF), also known as selective laser sintering or direct laser melting, is an additive manufacturing process in which part geometries are formed simultaneously with the underlying material. The microstructure, defect content, and surface quality are all synthesized conjointly with the part shape. While the geometric design freedom allowed by this process enables new complex features and parts with small (∼1 mm) features, challenges associated with process qualification can deter wider adoption. Furthermore, a lack of historical performance data for statistical process control of witness coupons, for either bulk material or for small features, makes the barrier to entry more difficult. Here, we demonstrate long-term, property-based process monitoring and variability assessment using both small-featured (1 mm) and larger, bulk-representative material witness coupons. Over a one-year period, more than 550 tensile bars and 80 Charpy impact bars were printed alongside 316 L stainless steel parts built using L-PBF and tested to detect shifts in the process over time. Miniature tensile bars with a 1 mm2 gage area were tested using a high throughput mechanical testing system. In parallel, a larger test coupon was used to monitor density, hardness, and Charpy impact toughness. This collection of measurements was used to determine detectable property shifts correlated to L-PBF process changes including powder feedstock, machine hardware, software versioning, and machine parameter settings. The benefits of using small featured, high-throughput samples are discussed based on process sensitivity and the number of repeat tests possible for each build. This study not only reveals the utility of property-based process monitoring but illustrates the sensitivity of these measurements to detect process changes and provides further evidence for property stability in modern L-PBF.

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