Process Defects Knowledge Modeling in Laser Powder Bed Fusion Additive Manufacturing: An Ontological Framework

Abstract

Comprehensive knowledge of the laser powder bed fusion (LPBF) process defects, their causal factors, and relationships can enable proactive prevention and/or mitigation of those defects to ensure the production of high-quality products. However, this knowledge is scattered in a plethora of research articles, and there is a need for a formal and structured knowledge base to document the LPBF process defects knowledge and model the complex network of relationships among those defects and their causal factors. In response, this paper proposes an ontological framework to systematically structure and represent the knowledge of LPBF defects in a sustainable, reusable, and extensible way. In doing so, we first conducted a detailed literature review and analysis of current LPBF defect surveys to systematically develop a consistent and comprehensive classification of defects and potential causal factors. Then, to effectively represent the gathered knowledge, the ontological framework was designed to: (1) organize and formalize knowledge on LPBF defects and the causal factors, (2) model the complex network of causal links and cascading effects among the defects and causal factors, (3) enable easy querying of the stored knowledge, and (4) include ontological entities that are suitable for extension and reuse. A prototype LPBF defects ontology was developed in Web Ontology Language (OWL)/Resource Description Framework (RDF) formalism using the Protégé tool to effectively realize those design requirements. The developed ontology covers thirty-one unique defects and knowledge of their causal factors, including defect-to-defect causal relationships and hierarchical categorization under four major categories of high-level defect classes. Similarly, forty-five unique causal factors were categorized under twelve major categories. The proposed ontological framework and knowledge model offer a pathway to (1) provide a comprehensive knowledge base on LPBF defects for in-depth tutoring and training of novice researchers and practitioners; (2) help investigators to identify root causes of detected defects for informed corrective action; (3) guide process planning tasks from a defect control perspective; and (4) support future application and reuse of the knowledge. This study also provides several examples to illustrate the modeling of cascading effects in causal relationships, discovering knowledge using an ontology reasoner, visualizing the complex network of causal links using OntoGraph, and retrieving stored knowledge by answering competency questions using SPARQL queries. In the future, the reasoning capabilities of our proposed ontology can also be leveraged to develop expert systems for optimizing the AM workflow and quantitatively predict and diagnose LPBF defects.© 2023 Society of Manufacturing Engineers (SME). Published by Elsevier Ltd. All rights reserved.This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)Peer-review under responsibility of the Scientific Committee of the NAMRI/SME.