EAGER: Cybermanufacturing: Predictive Analytics Models and Techniques for Intelligent Cybermanufacturing

使信息基础设施是新兴的面向服务的网络制造模式的主要元素。质量和成本是3D打印服务提供商（即制造商）面临的主要问题，因为它们是保持其服务竞争力和负担得起的关键措施。制造商或3D打印服务提供商对提交的工作的投标价格在市场竞争中至关重要。EARLY概念探索性研究资助（EAGER）项目是在一个试点原型系统中开发三种数据分析方法，以提供3D打印工作的准确成本估算。它们是形状挖掘，基于用户产品使用的分析，以及将形状挖掘与面向用户的数据的统计挖掘相结合以增强成本预测的混合方法。数据分析模型和面向服务的框架将提供一种通用和系统的方法，以提高下一代网络制造基础设施的效率，使制造商能够根据从大数据深度学习中获得的证据和洞察力做出及时和合理的决策。这种基于数据科学的方法将使人们更好地理解网络制造在提高制造效率和决策方面的力量和潜力。

Enabling information infrastructure is the major element of the emerging service-oriented cybermanufacturing paradigm. Quality and cost are the major issues that 3D printing service providers (i.e. manufacturers) are facing as they are the critical measures for keeping their services competitive and affordable. The bidding price from a manufacturer or 3D printing service provider for a submitted job is crucial in market competition. This EArly-concept Grant for Exploratory Research (EAGER) project is the development of three data analytics approaches in a pilot prototype system to provide accurate cost estimation of 3D printing jobs. They are shape mining, user-product usage based analytics, and a hybrid approach combining shape mining with statistical mining of user-oriented data for enhanced cost prediction. The data analytics models and service-oriented framework will provide a generic and systematic approach to improve the efficiency of next-generation cybermanufacturing infrastructure, enabling manufacturers to make timely and sound decisions based on evidence and insight derived from deep learning from big data. This data science-based approach will enable better understanding of the power and the potential of cybermanufacturing in manufacturing efficiency enhancement and decision making.