工业4.0开放科学的开放数据:增材制造质量的现场监测

IF 2.6 2区 工程技术 Q2 ENGINEERING, INDUSTRIAL Journal of Quality Technology Pub Date : 2022-08-11 DOI:10.1080/00224065.2022.2106910
M. Gronle, M. Grasso, Emidio Granito, F. Schaal, B. Colosimo
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引用次数: 3

摘要

开放科学具有促进创新解决方案和知识发展的能力,这要归功于对研究界和协作网络内共享的数据的透明访问。正因为如此,它已成为各种研发战略计划和路线图的政策重点,但其潜力在工业上的认识仍然有限。增材制造(AM)代表了一个开放科学计划可能产生巨大影响的领域,因为大型学术和工业团体都在同一领域工作,公司和研究中心每天都会产生大量数据,许多具有挑战性的问题仍然需要解决。本文介绍了一个基于开放式科学合作项目的案例研究,该项目由主要的增材制造系统开发商之一通快激光与系统技术有限公司与米兰理工大学合作。该案例研究依赖于一个开放的数据集,包括在工业机器上的铝零件样品激光粉末床熔化过程中收集的在线和原位信号。信号是通过安装在激光路径同轴的两个光电二极管获得的。设计这些标本是为了有意地介绍某些位置和某些层的异常情况。该数据集专门用于支持新型原位监测方法的开发,以便在零件建造过程中快速、稳健地进行异常检测。提出了一种分层统计监测方法,并提出了初步结果,但该问题仍有待进一步研究和探索新的解决方案。
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Open data for open science in Industry 4.0: In-situ monitoring of quality in additive manufacturing
Abstract Open science has the capacity of boosting innovative solutions and knowledge development thanks to a transparent access to data shared within the research community and collaborative networks. Because of this, it has become a policy priority in various research and development strategy plans and roadmaps, but the awareness if its potential is still limited in industry. Additive manufacturing (AM) represents a field where open science initiatives may have a great impact, as large academic and industrial communities are working in the same area, enormous quantities of data are generated on a daily basis by companies and research centers, and many challenging problems still need to be solved. This article presents a case study based on an open science collaboration project between TRUMPF Laser- und Systemtechnik GmbH, one of the major AM systems developers and Politecnico di Milano. The case study relies on an open data set including in-line and in-situ signals gathered during the laser powder bed fusion of specimens of aluminum parts on an industrial machine. The signals were acquired by means of two photodiodes installed co-axially to the laser path. The specimens were designed to introduce, on purpose, anomalies in certain locations and in certain layers. The data set is specifically designed to support the development of novel in-situ monitoring methodologies for fast and robust anomaly detection while the part is being built. A layerwise statistical monitoring approach is proposed and preliminary results are presented, but the problem is open to additional research and to the exploration of novel solutions.
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来源期刊
Journal of Quality Technology
Journal of Quality Technology 管理科学-工程:工业
CiteScore
5.20
自引率
4.00%
发文量
23
审稿时长
>12 weeks
期刊介绍: The objective of Journal of Quality Technology is to contribute to the technical advancement of the field of quality technology by publishing papers that emphasize the practical applicability of new techniques, instructive examples of the operation of existing techniques and results of historical researches. Expository, review, and tutorial papers are also acceptable if they are written in a style suitable for practicing engineers. Sample our Mathematics & Statistics journals, sign in here to start your FREE access for 14 days
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