AI 中文总结
本文针对增材制造的六西格玛质量管理，提出新DMAIC方法。定义质量挑战，综述计量传感技术，构建数据框架，阐述新数据驱动分析方法，介绍相关改进方法，还讨论了检测异常时优化行动计划的过程控制方法。
AI 中文摘要
在本文中，我们提议设计、开发并实施用于增材制造六西格玛质量管理的新DMAIC方法。首先，我们定义了增材制造逐层制造和大规模定制（甚至单件生产）所带来的特定质量挑战。其次，我们对增材制造的计量和传感技术进行了综述，涵盖从材料到设计、工艺、环境以及后处理检查。第三，我们构建了一个框架以充分挖掘增材制造系统数据的潜力，并强调了分析方法和工具的必要性。我们提出并阐述了新的数据驱动分析方法的效用，包括深度学习、机器学习和网络科学，以表征和建模工程设计、机器设置、工艺变异性和最终制造质量之间的相互关系。第四，我们介绍了用于增材制造系统改进的本体分析、实验设计（DOE）和模拟分析方法。最后，讨论了新的过程控制方法，以便在检测到异常时优化行动计划，并特别考虑提前期和能源消耗。
英文摘要
In this paper, we propose to design, develop, and implement the new DMAIC methodology for Six-Sigma quality management of AM. First, we define the specific quality challenges arising from AM layer-wise fabrication and mass customization (even one-of-a-kind production). Second, we present a review of AM metrology and sensing techniques, from materials through design, process, environment, to post-build inspection. Third, we contextualize a framework for realizing the full potential of data from AM systems, and emphasize the need for analytical methods and tools. We propose and delineate the utility of new data-driven analytical methods, including deep learning, machine learning, and network science, to characterize and model the interrelationships between engineering design, machine setting, process variability and final build quality. Fourth, we present the methodologies of ontology analytics, design of experiments (DOE) and simulation analysis for AM system improvements. In closing, new process control approaches are discussed to optimize the action plans, once an anomaly is detected, with specific consideration of lead time and energy consumption.
Journal refProceedings of the IEEE, vol. 109, no. 4, pp. 347-376, April 2021
DOI：10.1109/JPROC.2020.3034519
