Additive Manufacturing (AM) is becoming data-intensive. The ability to identify Data Analytics (DA) opportunities for effective use of AM data becomes a critical factor in the success of AM. To successfully identify high-potential DA opportunities in AM requires a set of distinctive interdisciplinary knowledge. This paper proposes a methodology that enables collaborative knowledge management for identifying and prioritizing DA opportunities in AM. The framework of the proposed methodology has three components: a team of experts, a DA Opportunity Knowledge Base (DOKB), and a prioritization tool. The team of experts provides diverse knowledge that can be used to identify and prioritize DA opportunities. The DOKB, developed by using the Web Ontology Language (OWL), captures diverse knowledge from the experts to identify DA opportunities. The prioritization tool ranks the identified DA opportunities by using the Fuzzy integrated Technique of Order Preference Similarity to the Ideal Solution (Fuzzy-TOPSIS). A case study, in which National Institute of Standards and Technology (NIST) researchers participated, demonstrates our methodology. As a result, 264 DA opportunities for AM’s Laser-Powder Bed Fusion (L-PBF) process are identified and prioritized. The prioritized DA opportunities help set a DA direction for L-PBF AM. Our methodology keeps knowledge sharable, reusable, revisable, and extendable. Thus, this methodology can continue to facilitate collaboration within the AM community to identify high potential and high impact DA opportunities in AM.

增材制造 (AM) 正变得数据密集型。识别数据分析 (DA) 机会以有效使用 AM 数据的能力成为 AM 成功的关键因素。要在 AM 中成功识别高潜力的 DA 机会，需要一套独特的跨学科知识。本文提出了一种方法，使协作知识管理能够识别和优先考虑 AM 中的 DA 机会。所提议方法的框架包含三个组成部分：专家团队、DA 机会知识库 (DOKB) 和优先排序工具。专家团队提供多种知识，可用于识别发展议程机会并确定其优先级。DOKB 使用 Web Ontology Language (OWL) 开发，从专家那里获取各种知识以识别 DA 机会。优先级工具通过使用与理想解决方案的顺序偏好相似性的模糊集成技术 (Fuzzy-TOPSIS) 对已识别的 DA 机会进行排序。美国国家标准与技术研究院 (NIST) 研究人员参与的案例研究展示了我们的方法论。因此，确定并优先考虑了 AM 的激光粉末床融合 (L-PBF) 工艺的 264 个 DA 机会。优先考虑的 DA 机会有助于为 L-PBF AM 设定 DA 方向。我们的方法论使知识可共享、可重用、可修改和可扩展。因此，这种方法可以继续促进 AM 社区内的合作，以确定 AM 中具有高潜力和高影响力的 DA 机会。