PREMΛP: Knowledge Driven Design of Materials and Engineering Process

Bhat, M. Seetharama; Shah, Sapan; Das, Prasenjit; Kumar, Pankaj; Kulkarni, Nagesh; Ghaisas, Smita; Reddy, Sreedhar

doi:10.1007/978-81-322-1050-4_105

Cited by 12 publications

(5 citation statements)

References 10 publications

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“…As an example, in a development processes, data is generated throughout the organization's systems in such as the Product Lifecycle Management (PLM) system in product development (Stark, 2016) or the development of maintenance operations in a Computerized Maintenance Management System (CMMS). But transforming such raw data into something useful for decision making is a big task (Bhat et al, 2013;Chilamkurti et al, 2014;Coussement & Benoit, 2021;Ghasemaghaei & Turel, 2022;Hodkiewicz & Ho, 2016;Janssen et al, 2017;Stark, 2016), making it a generally underutilized resource (Hodkiewicz & Ho, 2016). Streams in data science tend to focus on pre-processing methods rather than how to bring the data to the decision makers (Coussement & Benoit, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

Modular Maintenance Decision Architecture

Agergaard,

Sigsgaard,

Mortensen

et al. 2023

Proc. Des. Soc.

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The operation of large production assets requires many decisions from the acquisition and design of new assets to the choice of lubricant for a specific piece of equipment. The decisions made in maintenance have a direct effect on the management of the production process, making it important to ensure correct maintenance decision making. However, studies on maintenance decision making tend to focus on smaller areas of decisions being made in a process, but rarely the entire process. To introduce more studies that consider the entire maintenance process, this paper proposes using a modular Maintenance Decision Architecture. The paper introduces a framework for structuring information sources into standardized information modules and mapping them to maintenance decisions made across the entire organization. The application of approaches from product, system, and service engineering are used to support the management of the complexities of maintenance of large production facilities.

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Section: Literature Reviewmentioning

confidence: 99%

Modular Maintenance Decision Architecture

Agergaard,

Sigsgaard,

Mortensen

et al. 2023

Proc. Des. Soc.

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“…Most ontologies focus on specific sub-domains of the materials field (e.g., metals, ceramics, thermal properties, nanotechnology) and have been developed with a specific use in mind (e.g., search, data integration, discovery). Some examples of ontologies are the Materials Ontology (Ashino 2010) for data exchange among thermal property databases, PREMΛP ontology (Bhat et al 2013) for steel mill products, MatOnto ontology (Cheung et al 2008) for oxygen ion conducting materials in the fuel cell domain, and the FreeClassOWL ontology (Radinger et al 2013) for the construction and building materials domain. An ontology design pattern regarding material transformations was proposed in (Vardeman II et al 2017).…”

Section: Ontologies In Materials Domainmentioning

confidence: 99%

A Method for Extending Ontologies with Application to the Materials Science Domain

Armiento

Lambrix

2019

Data Science Journal

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In the materials science domain the data-driven science paradigm has become the focus since the beginning of the 2000s. A large number of research groups and communities are building and developing data-driven workflows. However, much of the data and knowledge is stored in different heterogeneous data sources maintained by different groups. This leads to a reduced availability of the data and poor interoperability between systems in this domain. Ontologybased techniques are an important way to reduce these problems and a number of efforts have started. In this paper we investigate efforts in the materials science, and in particular in the nanotechnology domain, and show how such ontologies developed by domain experts, can be improved. We use a phrase-based topic model approach and formal topical concept analysis on unstructured text in this domain to suggest additional concepts and axioms for the ontology that should be validated by a domain expert. We describe the techniques and show the usefulness of the approach through an experiment where we extend two nanotechnology ontologies using approximately 600 titles and abstracts.

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“…A new set of product representation was developed that is aimed to promote knowledge reuse in design [32]. With respect to material selection in design, PreMAP, a material-driven knowledge database, allows for simulation of sets of unobserved design variables based on existing analyses [33]. This has significant implications to the detailed design stage for optimizing design features based on existing product knowledge.…”

Section: State Of the Art Reviewmentioning

confidence: 99%

Toward Knowledge Management for Smart Manufacturing

Feng

Bernstein

Hedberg

et al. 2017

Journal of Computing and Information Science in Engineering

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The need for capturing knowledge in the digital form in design, process planning, production, and inspection has increasingly become an issue in manufacturing industries as the variety and complexity of product lifecycle applications increase. Both knowledge and data need to be well managed for quality assurance, lifecycle-impact assessment, and design improvement. Some technical barriers exist today that inhibit industry from fully utilizing design, planning, processing, and inspection knowledge. The primary barrier is a lack of a well-accepted mechanism that enables users to integrate data and knowledge. This paper prescribes knowledge management to address a lack of mechanisms for integrating, sharing, and updating domain-specific knowledge in smart manufacturing. Aspects of the knowledge constructs include conceptual design, detailed design, process planning, material property, production, and inspection. The main contribution of this paper is to provide a methodology on what knowledge manufacturing organizations access, update, and archive in the context of smart manufacturing. The case study in this paper provides some example knowledge objects to enable smart manufacturing.

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PREMΛP: Knowledge Driven Design of Materials and Engineering Process

Cited by 12 publications

References 10 publications

Modular Maintenance Decision Architecture

Modular Maintenance Decision Architecture

A Method for Extending Ontologies with Application to the Materials Science Domain

Toward Knowledge Management for Smart Manufacturing

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