Foundation Ontology for Distributed Manufacturing Process Planning

Sarkar, Arkopaul; Šormaz, Dušan

doi:10.1115/detc2016-60159

Cited by 5 publications

(4 citation statements)

References 16 publications

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“…This increases reusability of MSDL. Sarkar and Sormaz [27] map CAD product features to manufacturing processes via SWRL rules to derive manufacturing processes. They also present the reference ontology Semantically Integrated Manufacturing Planning Model (SIMPM) to formalize knowledge regarding manufacturing processes, but neglect resources.…”

Section: Domain-specific Knowledge Basesmentioning

confidence: 99%

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Applying knowledge bases to make factories smarter

Ocker

Paredis

Vogel‐Heuser

2019

At - Automatisierungstechnik

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Knowledge Bases (KBs) enable engineers to capture knowledge in a formalized way. This formalization allows us to combine knowledge, thus creating the basis for smart factories while also supporting product and production system design. Building comprehensive and reusable KBs is still a challenge, though, especially for knowledge-intensive domains like engineering and production. To cope with the sheer amount of knowledge, engineers should reuse existing KBs. This paper presents a comprehensive overview of domain-specific KBs for production and engineering, as well as generic top-level ontologies. The application of such top-level ontologies offers new insights by integrating knowledge from various domains, stakeholders, and companies. To bridge the gap between top-level ontologies and existing domain KBs, we introduce an Intermediate Engineering Ontology (IEO).

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Section: Domain-specific Knowledge Basesmentioning

confidence: 99%

“…In this section, we compare the IEO with the domainspecific KBs presented. Due to a lack of information, we neglected MaSDeM [20,19], Process Ontology (PrOnto) [36], and SIMPM [27]. The foci of these domain-specific KBs is mirrored in the more specific classes intentionally neglected by the IEO, cp.…”

Section: Comparing the Ieo With Domain Specific Kbsmentioning

confidence: 99%

Applying knowledge bases to make factories smarter

Ocker

Paredis

Vogel‐Heuser

2019

At - Automatisierungstechnik

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“…A promising method for organizing and retrieving relevant information about manufacturing processes is through ontologies. Some relevant examples include work in distributed manufacturing process planning [65], capability-based process selection for supplier discovery [66], and domain specific ontologies, such as for additive manufacturing [67]. Deploying more complete information models, such as those described in ASTM E3012-16 [21] form, would enable more comprehensive comparisons of environmental impacts related to manufacturing processes and systems [23, 68].…”

Section: Studying Phases Of the Sima Reference Architecturementioning

confidence: 99%

Analyzing environmental sustainability methods for use earlier in the product lifecycle

Brundage

Bernstein

Hoffenson

et al. 2018

Journal of Cleaner Production

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Environmental sustainability information in the manufacturing industry is not easily shared between stages in the product lifecycle. In particular, reliable manufacturing-related information for assessing the sustainability of a product is often unavailable at the design stage. Instead, designers rely on aggregated, often outdated information or make decisions by analogy (e.g., a similar manufacturing process for a similar product yielded X and Y results). However, smart manufacturing and the Internet of Things have potential to bridge the gap between design and manufacturing through data and knowledge sharing. This paper analyzes environmental sustainability assessment methods to enable more accurate decisions earlier in design. The techniques and methods are categorized based on the stage they apply to in the product lifecycle, as described by the Systems Integration of Manufacturing Applications (SIMA) reference architecture. Furthermore, opportunities for aligning standard data representation to promote sustainability assessment during design are identified.

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“…More broadly, ontological representations of existing design knowledge have been used to support decision-making (Sarkar, 2016;Ming et al, 2018;Zhang et al, 2018). Other relevant ontologies include the taxonomy of error types proposed by (Sutcliffe & Rugg, 1998), which covers operator errors as well as those stemming from social/organizational challenges and design errors, and the taxonomy of information technology project failures proposed by Al-Ahmad et al (2009).…”

Section: Introductionmentioning

confidence: 99%

Detecting and Characterizing Patterns of Failure in Complex Engineered Systems: An Ontology Development and Clustering Approach

et al. 2023

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While the causes of failures in complex engineered systems are often clear in hindsight, it can be challenging to predict failures proactively during the design of novel engineered products or systems. Identifying patterns can be useful for capturing common characteristics that may lead to failure. In this paper, we present a methodology for identifying patterns of failure from NASA's publicly available Lessons Learned Information System (LLIS). We apply an ontology development and clustering approach to identify representative patterns leading to failures in historical lessons learned. A joint inductive-deductive approach reveals the key themes in lessons that lead to failure, which are formalized and recorded as an ontology of complex systems failure causes. Documents from the LLIS are manually tagged with relevant characteristics from the ontology. From the tagged set, clustering is used to capture co-occurring sets of characteristics that lead to failure. The primary contribution of this work is a method for extracting a set of generic failure patterns in complex engineered systems and characteristics for these patterns that can be identified at design time, knowledge of which can be used to plan mitigation strategies.

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Foundation Ontology for Distributed Manufacturing Process Planning

Cited by 5 publications

References 16 publications

Applying knowledge bases to make factories smarter

Applying knowledge bases to make factories smarter

Analyzing environmental sustainability methods for use earlier in the product lifecycle

Detecting and Characterizing Patterns of Failure in Complex Engineered Systems: An Ontology Development and Clustering Approach

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