制造过程的观测数据驱动建模与Optimization
Observational Data-Driven Modeling and Optimization of Manufacturing Processes
摘要：
各行业观测数据的急剧增加为数据驱动的决策和管理提供了前所未有的机会，包括制造业。在生产背景下，数据驱动的方法可以利用观测数据来建模，控制和改进过程性能。当由观测数据提供足够的覆盖范围，以告知真实的过程性能动态，它们可以克服与侵入性控制设计实验相关的成本，并可用于监测和提高过程质量。我们提出了一种新的综合方法，使用观测数据的过程参数设计，同时确定显着的控制变量。我们评估我们的方法使用模拟实验，并将其应用到现实世界的情况下，从轮胎制造公司设置。
英文摘要：
The dramatic increase of observational data across industries provides unparalleled opportunities for data-driven decision making and management, including the manufacturing industry. In the context of production, data-driven approaches can exploit observational data to model, control and improve the process performance. When supplied by observational data with adequate coverage to inform the true process performance dynamics, they can overcome the cost associated with intrusive controlled designed experiments and can be applied for both monitoring and improving process quality. We propose a novel integrated approach that uses observational data for process parameter design while simultaneously identifying the significant control variables. We evaluate our method using simulated experiments and also apply it to a real-world case setting from a tire manufacturing company.
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