Wicaksono, Hendro; Ovtcharova, Jivka Energy Consumption Regulation and Optimization in Discrete Manufacturing through Semi-automatic Knowledge Generation using Data Mining Inproceedings Proceeding 10th Global Conference of Sustainable Manufacturing (GCSM), 2012. Abstract | BibTeX | Tags: data mining, discrete manufacturing, energy efficiency, knowledge capturing, knowledge management, machine learning @inproceedings{Wicaksono2012b,
title = {Energy Consumption Regulation and Optimization in Discrete Manufacturing through Semi-automatic Knowledge Generation using Data Mining},
author = {Hendro Wicaksono and Jivka Ovtcharova },
year = {2012},
date = {2012-11-02},
booktitle = {Proceeding 10th Global Conference of Sustainable Manufacturing (GCSM)},
abstract = {The rapid growth of industrialization has led to a significant increase of energy demand that results in a constantly increasing of energy prices. Meanwhile, the changes of social, technical, and economic conditions in the market have challenged manufacturers to deal with the requirements for various and complex products. This has made production processes more sophisticated and energy intensive thus it leads to expensive production costs. This paper discusses a knowledge based approach to regulate the energy consumption in processing the customer orders in discrete manufacturing. The knowledge base consists of a rule set, which determines the choices of machines to process the products based on the given characteristics. Generally, the construction of such a knowledge base is a time-consuming task. This paper presents a semi-automatic rule generation using data mining. It extracts the energy consumption pattern based on relation of different parameter, such as product properties, machine profile, production processes, and surrounding variables.},
keywords = {data mining, discrete manufacturing, energy efficiency, knowledge capturing, knowledge management, machine learning},
pubstate = {published},
tppubtype = {inproceedings}
}
The rapid growth of industrialization has led to a significant increase of energy demand that results in a constantly increasing of energy prices. Meanwhile, the changes of social, technical, and economic conditions in the market have challenged manufacturers to deal with the requirements for various and complex products. This has made production processes more sophisticated and energy intensive thus it leads to expensive production costs. This paper discusses a knowledge based approach to regulate the energy consumption in processing the customer orders in discrete manufacturing. The knowledge base consists of a rule set, which determines the choices of machines to process the products based on the given characteristics. Generally, the construction of such a knowledge base is a time-consuming task. This paper presents a semi-automatic rule generation using data mining. It extracts the energy consumption pattern based on relation of different parameter, such as product properties, machine profile, production processes, and surrounding variables. |
Wicaksono, Hendro; Rogalski, Sven; Ovtcharova, Jivka Ontology Driven Approach for Intelligent Energy Management in Discrete Manufacturing Inproceedings Proceedings of the International Conference on Knowledge Engineering and Ontology Development, pp. 108-114, INSTICC SciTePress, 2012, ISBN: 978-989-8565-30-3. Abstract | Links | BibTeX | Tags: energy efficiency, energy management, knowledge acquisition, knowledge capturing, machine learning, manufacturing, Ontology @inproceedings{Wicaksono2012c,
title = {Ontology Driven Approach for Intelligent Energy Management in Discrete Manufacturing},
author = {Hendro Wicaksono and Sven Rogalski and Jivka Ovtcharova},
url = {http://www.scitepress.org/PublicationsDetail.aspx?ID=VdNAwL50fGw=&t=1},
doi = {10.5220/0004141601080114},
isbn = {978-989-8565-30-3},
year = {2012},
date = {2012-10-07},
booktitle = {Proceedings of the International Conference on Knowledge Engineering and Ontology Development},
volume = {1},
pages = {108-114},
publisher = {SciTePress},
organization = {INSTICC},
abstract = {In recent years ontologies have been used for knowledge representation in different domains, such as energy management and manufacturing. Researchers have developed approaches in applying ontologies for intelligent energy management in households. In the manufacturing domain, ontologies have been used for knowledge management in order to provide a common formal understanding between the stakeholders, who have different background knowledge. Energy management in a manufacturing company involves different organizational entities and technical processes. This paper proposes an approach to applying ontology for intelligent energy management in discrete manufacturing companies. The ontology provides a formal knowledge representation that is accessible by different human stakeholders as well as machines in the company. This paper also demonstrates the methods used to construct and to process the ontology.},
keywords = {energy efficiency, energy management, knowledge acquisition, knowledge capturing, machine learning, manufacturing, Ontology},
pubstate = {published},
tppubtype = {inproceedings}
}
In recent years ontologies have been used for knowledge representation in different domains, such as energy management and manufacturing. Researchers have developed approaches in applying ontologies for intelligent energy management in households. In the manufacturing domain, ontologies have been used for knowledge management in order to provide a common formal understanding between the stakeholders, who have different background knowledge. Energy management in a manufacturing company involves different organizational entities and technical processes. This paper proposes an approach to applying ontology for intelligent energy management in discrete manufacturing companies. The ontology provides a formal knowledge representation that is accessible by different human stakeholders as well as machines in the company. This paper also demonstrates the methods used to construct and to process the ontology. |