TY - JOUR
T1 - Process Derivation Methodology for Reconfigurable Smart Factory
AU - Choi, Seunghyun
AU - Youm, Sekyoung
AU - Kang, Yong Shin
N1 - Publisher Copyright:
© The Author(s), under exclusive licence to Korean Society for Precision Engineering 2023.
PY - 2024/3
Y1 - 2024/3
N2 - Due to the development of computing technology, various data are collected and analyzed to extract meaningful information and are utilized in various industries. In particular, in the manufacturing industry, collected data are analyzed and used for process management, monitoring, quality prediction, etc., to establish smart factories and Reconfigurable Smart Factories. To continuously manage the processes of a Reconfigurable Smart Factory, it is necessary to derive a process model that includes factors affecting the process and to compare the actual execution process with the designed process and make improvements. The conventional methods for deriving process model are methods that apply to processes with a fixed start and end and are difficult to apply directly to the processes of a Reconfigurable Smart Factory. Furthermore, the conventional methods are sequential control flow-oriented, and a process derivation method that includes factors affecting the process is required. Therefore, it is necessary to consider various factors (e.g., time attributes, task time) that affect the process, not just the sequence, to solve this. In this study, we propose a novel method to derive a process that includes sequential flow and time conditions. The proposed methodology focuses on the execution time and order of instances to derive the process. Experiments were conducted by changing the conditions of the methodology and measuring the execution time to validate the proposed methodology.
AB - Due to the development of computing technology, various data are collected and analyzed to extract meaningful information and are utilized in various industries. In particular, in the manufacturing industry, collected data are analyzed and used for process management, monitoring, quality prediction, etc., to establish smart factories and Reconfigurable Smart Factories. To continuously manage the processes of a Reconfigurable Smart Factory, it is necessary to derive a process model that includes factors affecting the process and to compare the actual execution process with the designed process and make improvements. The conventional methods for deriving process model are methods that apply to processes with a fixed start and end and are difficult to apply directly to the processes of a Reconfigurable Smart Factory. Furthermore, the conventional methods are sequential control flow-oriented, and a process derivation method that includes factors affecting the process is required. Therefore, it is necessary to consider various factors (e.g., time attributes, task time) that affect the process, not just the sequence, to solve this. In this study, we propose a novel method to derive a process that includes sequential flow and time conditions. The proposed methodology focuses on the execution time and order of instances to derive the process. Experiments were conducted by changing the conditions of the methodology and measuring the execution time to validate the proposed methodology.
KW - Autonomous Reconfigurable Smart Factory
KW - Formalism
KW - Pattern mining
KW - Process management
KW - Sequential Pattern Mining
KW - Time perspective
UR - http://www.scopus.com/inward/record.url?scp=85182700741&partnerID=8YFLogxK
U2 - 10.1007/s12541-023-00820-9
DO - 10.1007/s12541-023-00820-9
M3 - Article
AN - SCOPUS:85182700741
SN - 2234-7593
VL - 25
SP - 497
EP - 508
JO - International Journal of Precision Engineering and Manufacturing
JF - International Journal of Precision Engineering and Manufacturing
IS - 3
ER -