Knowledge-Driven Manufacturability Analysis for Additive Manufacturing

Abstract

Additive Manufacturing (AM) evolved recently from a rapid prototyping process to a standard manufacturing tool. Nevertheless, it is still not a widely used method due to different process-related challenges. In recent years printer technologies and possible printable materials emerged but there are still challenging demands on the printing process. Hence, it is of vital importance to inspect the manufacturability of the designed parts. This work focuses on the not yet widely researched ceramic printing with the Lithography-based Ceramic Manufacturing (LCM) processes. It presents a knowledge-driven framework able to automatically examine geometric properties of a part and compare it to AM guidelines. As a knowledge base, an ontology is used which contains information about the capabilities of AM processes, printers and materials. The manufacturability system uses triangle-based mesh processing algorithms to recognize features and check the guidelines necessary for LCM. The evaluation shows the feasibility of manufacturability analysis with the developed framework and its limitations. INDEX TERMSAdditive manufacturing, computer-aided design, manufacturability analysis, knowledge representation, ontology.

Knowledge-Driven Design for Additive Manufacturing: A Framework for Design Adaptation

“…As a result, two databases were created, one for the designer and one for the machine operator which have been linked to CAD software for identifying features violating design guidelines (Formentini et al, 2022). For the analysis of manufacturability of parts produced via Lithography-based ceramic manufacturing, a knowledge-driven framework was proposed in (Mayerhofer et al, 2021) whereby an ontology is employed as a knowledge base for representing design guidelines for the respective manufacturing technology. As a result, the parts mesh is annotated and highlighted at the critical areas with the respective guideline (Mayerhofer et al, 2021).…”

“…For the analysis of manufacturability of parts produced via Lithography-based ceramic manufacturing, a knowledge-driven framework was proposed in (Mayerhofer et al, 2021) whereby an ontology is employed as a knowledge base for representing design guidelines for the respective manufacturing technology. As a result, the parts mesh is annotated and highlighted at the critical areas with the respective guideline (Mayerhofer et al, 2021).…”

Description Logic Ontology-Supported Part Orientation for Fused Deposition Modelling

“…During the past two decades, the application of DL ontologies in AM has gained importance and popularity. Many researchers developed ontologies or ontology-supported approaches to assist certain tasks in AM: Yim and Rosen [8] presented an ontology-supported case-based reasoning approach to assist AM process planning; Yim and Rosen [9] developed a ontology-based repository for AM design problems; Liu and Rosen [10] proposed an ontology-supported knowledge modelling and reuse approach for AM process planning; Witherell et al [11] constructed an ontology-based metamodel for composable and reusable laser powder bed fusion process; Eddy et al [12] developed an ontology-based intelligent tool for AM knowledge management; Roh et al [13] constructed an ontologybased laser and thermal metamodel for laser powder bed fusion; Lu et al [14] presented a set of ontology-supported digital solutions for integrated and collaborative AM; Assouroko et al [15] proposed an ontology-supported approach for characterising model fidelity in laser powder bed fusion; Dinar and Rosen [16] developed a design for AM ontology; Kim et al [17] proposed an ontology-based approach to link AM design to AM process planning; Hagedorn et al [18] presented an ontology-supported approach for innovative design for AM; Liang [19] proposed an ontology-oriented knowledge methodology for AM process planning; Kim et al [20] developed a design for AM ontology to support manufacturability analysis; Sanfilippo et al [21] constructed an ontology to represent the data and knowledge in the AM value chain; Ali et al [22] developed a product life cycle ontology for AM; Xiong et al [23] established an ontology-supported process planning framework for wire arc AM; Ko et al [24] studied machine learning and ontology based design rule construction for laser powder bed fusion; Chen et al [25] studied ontology-driven learning of Bayesian network for causal inference and quality assurance in laser powder bed fusion; Roh et al [26] established an ontology-based process map for laser powder bed fusion; Mayerhofer et al [27] studied ontology-driven manufacturability analysis for lithography-based ceramic manufacturing; Jarrar et al [28] presented an ontology-based approach for a decision support system in AM; Park et al [29] studied ontology-supported collaborative knowledge management to identify data analytics opportunities in laser powder bed fusion; Li et al…”

Evaluation of design support tools for additive manufacturing and conceptualisation of an integrated knowledge management framework

“…Furthermore, the solution is integrated into a cloud platform. [48] In a consecutive publication, the results of the implemented framework are presented [49].…”