Six-Sigma Quality Management of Additive Manufacturing
Six-Sigma Quality Management of Additive Manufacturing
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DOI：
10.1109/jproc.2020.3034519
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发表时间：
2021-04
期刊：
影响因子：
--
通讯作者：
KUMARA S
中科院分区：
文献类型：
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作者：
YANG H;RAO P;SIMPSON T;LU Y;WITHERELL P;NASSAR AR;REUTZEL E;KUMARA S
质量是部署新工艺、产品或服务的关键决定因素，并影响新兴制造技术的采用。添加剂制造(AM)作为一种制造工艺的出现，有可能使从生产到供应链的许多与企业相关的职能发生革命性的变化。AM提供了前所未有的设计灵活性和扩展的功能，再加上极大地缩短了交货期，这可能为大规模定制铺平道路。然而，目前AM的广泛应用受到工艺重复性和质量管理方面的技术挑战的阻碍。六西格玛(6S)在传统制造业(如半导体和汽车行业)通过大量使用数据、统计和优化在质量计划、控制和改进方面的突破性效果已经得到证明。6S需要数据驱动的DMAIC方法，包括五个步骤-定义、测量、分析、改进和控制。尽管6S知识体系在从制造、医疗保健、物流等各种已建立的行业中持续取得成功，但在AM的背景下集中应用6S质量管理方法的情况很少在本文中，我们建议为AM的6S质量管理设计、开发和实施新的DMAIC方法首先，我们定义了AM LayerWise制造和大规模定制(甚至是独一无二的生产)带来的特定质量挑战。其次，我们介绍了AM计量和传感技术的回顾，从材料到设计、工艺和环境，再到建造后的检查。第三，我们为实现来自AM系统的数据的全部潜力提供了一个框架，并强调了分析方法和工具的必要性。我们提出并描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、过程可变性和最终构建质量之间的相互关系。第四，提出了本体分析、实验设计(DOE)和仿真分析等方法对AM系统进行改进。最后，讨论了新的过程控制方法，以优化行动计划，一旦检测到异常，具体考虑到准备时间和能源消耗。我们认为，这项工作将促进更深入的调查和多学科的研究努力，以加快6S质量管理在AM中的应用
Quality is a key determinant in deploying new processes, products, or services and influences the adoption of emerging manufacturing technologies. The advent of additive manufacturing (AM) as a manufacturing process has the potential to revolutionize a host of enterprise-related functions from production to the supply chain. The unprecedented level of design flexibility and expanded functionality offered by AM, coupled with greatly reduced lead times, can potentially pave the way for mass customization. However, widespread application of AM is currently hampered by technical challenges in process repeatability and quality management. The breakthrough effect of six sigma (6S) has been demonstrated in traditional manufacturing industries (e.g., semiconductor and automotive industries) in the context of quality planning, control, and improvement through the intensive use of data, statistics, and optimization. 6S entails a data-driven DMAIC methodology of five steps—define, measure, analyze, improve, and control. Notwithstanding the sustained successes of the 6S knowledge body in a variety of established industries ranging from manufacturing, healthcare, logistics, and beyond, there is a dearth of concentrated application of 6S quality management approaches in the context of AM. In this article, we propose to design, develop, and implement the new DMAIC methodology for the 6S quality management of AM. First, we define the specific quality challenges arising from AM layerwise 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, and environment, to postbuild 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. We posit that this work will catalyze more in-depth investigations and multidisciplinary research efforts to accelerate the application of 6S quality management in AM.