Industry
Iceland's Euler Achieves LPBF Failure Prediction Using Existing Layer Images: Saving Over $115,000 per Machine Annually
2026-11-01
Icelandic software startup Euler has released a purely software-based, hardware-agnostic laser powder bed fusion (LPBF) process monitoring platform that predicts defects based on layer image data already captured by modern LPBF systems during each build. The company claims it can connect to existing equipment within 5 minutes and display results instantly. In a joint study with the Danish Technological Institute (DTI) lasting 47 days, Euler monitored 54 builds across 4 Nikon SLM systems, covering six metal alloys including 316L stainless steel and Ti6Al4V titanium alloy. The study found that 27.8% of total print time was spent on builds that ultimately failed, and 77% of that time could be saved by stopping production at predicted failure points, yielding potential annual savings of over $115,000 per machine. The solution requires no additional sensor hardware and can deliver failure warnings using existing layer images, offering LPBF users a low-cost path to reduce trial-and-error and powder waste while improving equipment utilization—and signaling that powder bed monitoring is shifting from vendor-proprietary systems toward platform-based software services.
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冰岛Euler以设备已有层图像实现LPBF失效预测：单台年省超11.5万美元
冰岛软件初创公司Euler发布纯软件、硬件无关的激光粉末床熔融（LPBF）过程监控平台，基于现代LPBF系统每次构建已采集的层图像数据预测缺陷，声称可在5分钟内接入现有设备并即时显示结果。Euler与丹麦技术研究院（DTI）联合研究历时47天，监控4台Nikon SLM系统上的54次构建，覆盖316L不锈钢与Ti6Al4V钛合金等六种金属合金。研究发现27.8%的总打印时间花在最终失败的构建上，其中77%可通过在预测失效点停机而节省，单台设备年潜在节省超11.5万美元。该方案无需新增传感器硬件，以现有层图像即可实现失效预警，为金属粉末LPBF用户降低试错与粉末浪费、提升设备稼动率提供了一条低成本落地路径，也提示粉床监控正在从设备商专属走向平台化软件服务。