• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.1
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• pp.163-170
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• 2023

This paper introduces the semantic modeling technology for autonomous control of manufacturing facilities and status definition algorithm. With the development of digital twin technology and various ICT technologies of the smart factory, a new production management model is being built in the manufacturing industry. Based on the advanced smart manufacturing technology, the status determination algorithm was presented as a methodology to quickly identify and respond to problems with autonomous control and facilities in the factory. But the existing status determination algorithm informs the user or administrator of error information through the grid map and is presented as a model for coping with it. However, the advancement and direction of smart manufacturing technology is diversifying into flexible production and production tailored to consumer needs. Accordingly, in this paper, a technology that can design and build a factory using a semantic-based Linked List data structure and provide only necessary information to users or managers through graph-based information is introduced to improve management efficiency. This methodology can be used as a structure suitable for flexible production and small-volume production of various types.

• Smart Media Journal
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• v.10 no.3
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• pp.23-30
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• 2021

Although the manufacturing time series data clustering technique is an important grouping solution in the field of detecting and improving manufacturing large data-based equipment and process defects, it has a disadvantage of low accuracy when applying the existing static data target clustering technique to time series data. In this paper, an evolutionary computation-based time series cluster analysis approach is presented to improve the coherence of existing clustering techniques. To this end, first, the image shape resulting from the manufacturing process is converted into one-dimensional time series data using linear scanning, and the optimal sub-clusters for hierarchical cluster analysis and split cluster analysis are derived based on the Pearson distance metric as the target of the transformation data. Finally, by using a genetic algorithm, an optimal cluster combination with minimal similarity is derived for the two cluster analysis results. And the performance superiority of the proposed clustering is verified by comparing the performance with the existing clustering technique for the actual manufacturing process image.