A knowledge representation for unit manufacturing processes

Zhang, Yingzhong; Luo, Xiangfeng; Zhang, Hong; Sutherland, John W.

doi:10.1007/s00170-014-5864-x

Cited by 40 publications

(12 citation statements)

References 17 publications

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“…In [27] the authors develop a model where the production process is presented using a black box, which includes input/output flows as well as the core of the black box -the operation. Operations are further represented by equipment, tools, workpiece (i.e.…”

Section: Ontology For Modelling Product Production Process and Equipment (Conceptual)mentioning

confidence: 99%

Interconnecting Product and Manufacturing Domains: A Literature Review

Wlazak

Andersen

Raudberget

2020

Advances in Transdisciplinary Engineering

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Research on product-process modelling has been significant over the last decade. In this paper, we present a literature review of 13 papers published in journals and conference proceedings between 2012–2019. The purpose of this paper is to review and classify the literature on integrated product-process modelling utilizing ontologies. Specifically, the objectives of the paper are; (1) to develop a classification framework that is based on the existing research on integrated product-process modelling; (2) to use the classification framework to synthesize what is known in this research area (qualitative issues that have been raised that are useful for both researchers and practitioners); (3) to use the classification framework to propose future avenues in this research area. The classification framework consists of three major categories; namely, (1) integrated product-process model’s application; (2) approaches to modelling; and (3) practical challenges for implementation of integrated product-process models. The classification of the published literature and the analysis provides insights for practitioners and researchers on the creation and accumulation of knowledge in the product-process modelling area and interconnecting of product and manufacturing domains. This paper is intended to highlight the importance of integrated product-process models utilizing ontologies and identify areas for future research areas.

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Section: Ontology For Modelling Product Production Process and Equipment (Conceptual)mentioning

confidence: 99%

Interconnecting Product and Manufacturing Domains: A Literature Review

Wlazak

Andersen

Raudberget

2020

Advances in Transdisciplinary Engineering

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“…Formal knowledge models provide a shared vocabulary between distributed systems thus enabling knowledge sharing and reuse. Furthermore, they support the expression of domain specific rules that allows carrying out automated reasoning procedures on the specified knowledge [3], [24], [41], [46]. As such, they are more powerful tools than the conceptual schemas defined in industry standards (e.g.…”

Section: Related Workmentioning

confidence: 99%

A Rule-Based Approach Founded on Description Logics for Industry 4.0 Smart Factories

Kourtis

Kavakli

Sakellariou

2019

IEEE Trans. Ind. Inf.

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This paper develops a formal framework, founded on description logics, to assist decision making in relation to the manufacturing operation and control in modern enterprises that stand to benefit from the transition to Industry 4.0. The objective is to provide sophisticated support to individuals making decisions in the area of production operations management and in particular production scheduling and material requirements planning. Using this framework, the paper demonstrates an approach to encode the domain knowledge of human experts managing the production as sets of formal rules. These rules can be implemented in an intelligent system that can assist and empower human experts, reducing difficulty when making decisions in complex manufacturing environments.

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“…For example, Zhan et al [22] presented a semantic approach that uses ontologies to share knowledge related to product data in CAD/CAE applications. Zhang et al [23] presented an ontology-based knowledge representation for unit manufacturing processes. Matsokis and Kiritsis [24] developed an ontology-based approach for product lifecycle management.…”

Section: Ontology-based Knowledge Systems For Materials Selectionmentioning

confidence: 99%

“…The interface implementation between the querying applications and the SQWRL engine can be found in [23].…”

Section: Overview Of the Frameworkmentioning

confidence: 99%

“…In this case, the knowledge base resource is at -http://www.jhcad.com/kbs/materials.owl‖. An SQWRL query engine is created by executing the constructor function of the SQWRLQueryEngineFactory class [23]. Then, all named mold product classes and their product instances in the knowledge base are listed in the product class tree window by their hierarchical relationships, as shown on the left side of the user interface.…”

Section: Starting the Materials Selectormentioning

confidence: 99%

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An ontology-based knowledge framework for engineering material selection

Zhang

Luo

Zhao

et al. 2015

Advanced Engineering Informatics

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Engineering material selection intensively depends on domain knowledge.In the face of the large number and wide variety of engineering materials, it is very necessary to research and develop an open, shared, and scalable knowledge framework for implementing domain-oriented and knowledge-based material selection. In this paper, the fundamental concepts and relationships involved in all aspects of material selection are analyzed in detail. A novel ontology-based knowledge framework is presented. The ontology-based Semantic Web technology is introduced into the semantic representation of material selection knowledge. The implicit material selection knowledge is represented as a set of labeled instances and RDF instance graphs in terms of the concept model, which provides a formal approach to organizing the captured material selection knowledge. A knowledge retrieval and reasoning approach integrating ontology concepts, instances, knowledge rules, and semantic queries encoded with Query-enhanced Web Rule Language (SQWRL) is proposed. The presented knowledge framework can provide powerful knowledge services for material selection. Finally, based on this knowledge framework, a case study on constructing a mold material selection knowledge system is provided. This work is a new attempt to build an open and shared knowledge framework for engineering material selection. IntroductionWith the development of technologies, the available set of engineering materials is rapidly growing in both type and number. It is estimated that there are more than 80,000 engineering materials available in the world, and new materials are emerging constantly [1]. Engineering materials are usually coupled with series of manufacturing processes. It is estimated that there are at least 1000 different manufacturing processes that can convert engineering materials into desired products[1]. In the material selection process, design engineers have to take into account a large number of factors, such as physical properties, mechanical properties, thermal properties, material cost, and impact on the environment. Hence, the vast number of materials and processes and the complex relationships between the different selection parameters often make the selection of materials for a given component a difficult task [2].Information on engineering materials presents in two categories: data and knowledge. Data is defined as the result of measurements that can be presented as numerical values, whereas knowledge represents the connections between the items of data [3].As the materials involve a large amount of data, it is necessary to employ database information systems to effectively manage, retrieve, and update the material data. A large number of material databases have been built, most of which can be accessed online [4]. Due to their data-oriented representation mode, the material databases still lack a knowledge inference mechanism and are not able to associate the data with facts.Material selection is a highly knowledge-intensive activity, involving knowle...

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A knowledge representation for unit manufacturing processes

Cited by 40 publications

References 17 publications

Interconnecting Product and Manufacturing Domains: A Literature Review

Interconnecting Product and Manufacturing Domains: A Literature Review

A Rule-Based Approach Founded on Description Logics for Industry 4.0 Smart Factories

An ontology-based knowledge framework for engineering material selection

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