金属增材制造中的缺陷检测本体
An Ontology for Defect Detection in Metal Additive Manufacturing
摘要：
工业4.0应用的一个关键挑战是为自动化制造服务开发控制系统，这些系统能够解决数据集成和语义互操作性问题，以及监控和决策任务。为了解决先进制造系统中的这一问题，基于形式本体的原则性知识表示方法已经被提出作为异构数据源存在下的信息管理和维护的基础。此外，本体提供推理和查询能力，以帮助领域专家和最终用户在约束验证和决策的上下文中。最后，基于本体的先进制造服务的方法可以支持基于黑盒机器学习算法的监视、控制和仿真系统的行为的可解释性和可解释性。在这项工作中，我们提供了一种新的本体论的金属增材制造文献中已知的工艺引起的缺陷的分类。再加上一个正式表示的特征和来源的缺陷，我们整合了我们的知识库与国家的最先进的本体在该领域。我们的知识库旨在通过添加进一步的缺陷分析术语和诊断推理功能来增强增材制造本体的建模能力。
英文摘要：
A key challenge for Industry 4.0 applications is to develop control systems for automated manufacturing services that are capable of addressing both data integration and semantic interoperability issues, as well as monitoring and decision making tasks. To address such an issue in advanced manufacturing systems, principled knowledge representation approaches based on formal ontologies have been proposed as a foundation to information management and maintenance in presence of heterogeneous data sources. In addition, ontologies provide reasoning and querying capabilities to aid domain experts and end users in the context of constraint validation and decision making. Finally, ontology-based approaches to advanced manufacturing services can support the explainability and interpretability of the behaviour of monitoring, control, and simulation systems that are based on black-box machine learning algorithms. In this work, we provide a novel ontology for the classification of process-induced defects known from the metal additive manufacturing literature. Together with a formal representation of the characterising features and sources of defects, we integrate our knowledge base with state-of-the-art ontologies in the field. Our knowledge base aims at enhancing the modelling capabilities of additive manufacturing ontologies by adding further defect analysis terminology and diagnostic inference features.
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