Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook
Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook
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DOI：
10.1115/1.4047855
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发表时间：
2020-11-01
影响因子：
4
通讯作者：
Zhang, Jianjing
中科院分区：
文献类型：
--
作者：
Arinez, Jorge F.;Chang, Qing;Zhang, Jianjing
当今的制造系统正变得越来越复杂、动态和互联。由于存在无数的不确定性和相互依赖性，工厂运营面临着高度非线性和随机活动的挑战。人工智能（AI），特别是机器学习（ML）的最新发展显示出巨大的潜力，可以通过先进的分析工具来处理生成的大量制造数据（称为大数据），从而改变制造领域。本文件的重点有三：（1）审查人工智能在代表性制造问题中的最新应用，（2）提供系统视图，用于分析人工智能必须理解的多个级别的数据和过程依赖关系，以及（3）确定挑战和机遇，不仅进一步利用人工智能进行制造，也影响着人工智能的未来发展，以更好地满足制造业的需求。为了满足这些目标，本文采用了分层组织广泛实行的制造工厂在检查从整体系统水平的相互依赖性，以更详细的颗粒级别的来料加工流程。在此过程中，本文考虑了从吞吐量和质量，人机协作中的监督控制，过程监控，诊断和预测，最后到材料工程的进展，以实现过程建模和控制中所需的材料性能等广泛的主题。
Today's manufacturing systems are becoming increasingly complex, dynamic, and connected. The factory operations face challenges of highly nonlinear and stochastic activity due to the countless uncertainties and interdependencies that exist. Recent developments in artificial intelligence (AI), especially Machine Learning (ML) have shown great potential to transform the manufacturing domain through advanced analytics tools for processing the vast amounts of manufacturing data generated, known as Big Data. The focus of this paper is threefold: (1) review the state-of-the-art applications of AI to representative manufacturing problems, (2) provide a systematic view for analyzing data and process dependencies at multiple levels that AI must comprehend, and (3) identify challenges and opportunities to not only further leverage AI for manufacturing, but also influence the future development of AI to better meet the needs of manufacturing. To satisfy these objectives, the paper adopts the hierarchical organization widely practiced in manufacturing plants in examining the interdependencies from the overall system level to the more detailed granular level of incoming material process streams. In doing so, the paper considers a wide range of topics from throughput and quality, supervisory control in human-robotic collaboration, process monitoring, diagnosis, and prognosis, finally to advances in materials engineering to achieve desired material property in process modeling and control.