A repository for DFM problems using description logics

Abstract

Purpose -The purpose of this paper is to present an information model (ontology) for design-for-manufacturing (DFM) problems, where parts are to be manufactured using an additive manufacturing process. DFM problem formulation is often challenging since the formulation step requires both design and manufacturing process knowledge. The ontology also captures some relationships that model how that manufacturing knowledge applies to part designs. The ontology is implemented and serves as a repository of DFM problems that are available for reuse. Design/methodology/approach -The ontology is encoded using a description logic (DL) known as ALE. Using this ontology, a designer can retrieve archived DFM problems that are similar to a problem being formulated. DLs are a subset of first-order logic that have been used for information modeling in several application areas, including engineering information management. They are used typically to construct classification hierarchies that can be efficiently searched. Findings -The paper demonstrates that the DL model is correct by showing that the classification hierarchies that are computed match our DFM ontology. Retrieval of DFM problems is demonstrated using a prototype implementation of our ontology. Examples are taken from the area of design for manufacture using the stereolithography process.Research limitations/implications -The domain of the ontology is limited to additive manufacturing processes. Only DFM problems related to the determination of design parameters (e.g. dimensions) were within the scope of this work. Originality/value -No ontology for DFM problems has been presented previously. Implementation of the ontology using DL is also original.

The impacts of product design changes on supply chain risk: a case study

“…Without the application of design for manufacturing (Yim and Rosen, 2008) and concurrent engineering (Anumba et al , 2002), there is less communications and collaboration between the R&D and production departments, which cannot ensure the changed design is manufacturable in the assembly line. Losing the customer is one of the potential risks because the SPV manufacturer cannot meet the customer's design change or cannot complete the redesign within the time window.…”

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…”

“…As can be seen from the description above, each ontology/approach has its specific usage in AM. The ontologies/approaches in [8][9][10]12,14,[16][17][18][19][20][21][22]28] are targeted at general AM processes, while each of the remaining ones is presented for one specific AM process, including laser powder bed fusion, wire arc AM, or lithography-based ceramic manufacturing. Among those targeted at general AM processes, the ontologies/approaches in [8,10,16,17,19,20] are related to part orientation for FDM.…”