A Holonic-Based Self-Learning Mechanism for Energy-Predictive Planning in Machining Processes

被引:21
作者
Shin, Seung-Jun [1 ]
Kim, Young-Min [2 ]
Meilanitasari, Prita [2 ]
机构
[1] Hanyang Univ, Div Interdisciplinary Ind Studies, Seoul 04763, South Korea
[2] Hanyang Univ, Grad Sch Technol & Innovat Management, Seoul 04763, South Korea
基金
新加坡国家研究基金会;
关键词
cyber-physical production systems; self-learning factory; holonic manufacturing systems; machine learning; transfer learning; predictive analytics; AGENT-BASED SYSTEMS; MANUFACTURING SYSTEMS; MONITORING DATA; ARCHITECTURE; METHODOLOGY; PERFORMANCE; CONSUMPTION; COMPLEXITY; MANAGEMENT; EFFICIENCY;
D O I
10.3390/pr7100739
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
The present work proposes a holonic-based mechanism for self-learning factories based on a hybrid learning approach. The self-learning factory is a manufacturing system that gains predictive capability by machine self-learning, and thus automatically anticipates the performance results during the process planning phase through learning from past experience. The system mechanism, including a modeling method, architecture, and operational procedure, is structured to agentize machines and manufacturing objects under the paradigm of Holonic Manufacturing Systems. This mechanism allows machines and manufacturing objects to acquire their data and model interconnection and to perform model-driven autonomous and collaborative behaviors. The hybrid learning approach is designed to obtain predictive modeling ability in both data-existent and even data-absent environments via accommodating machine learning (which extracts knowledge from data) and transfer learning (which extracts knowledge from existing knowledge). The present work also implements a prototype system to demonstrate automatic predictive modeling and autonomous process planning for energy reduction in milling processes. The prototype generates energy-predictive models via hybrid learning and seeks the minimum energy-using machine tool through the contract net protocol combined with energy prediction. As a result, the prototype could achieve a reduction of 9.70% with respect to energy consumption as compared with the maximum energy-using machine tool.
引用
收藏
页数:28
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