Abstract
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Today, due to the hard competition in global manufactures, manufacturing systems are demanded to expose an increased flexibility and adaptability in order to cope with dynamical changes of manufacturing condition, objectives and an increasing number of product variants within industrial turbulent en...

Today, due to the hard competition in global manufactures, manufacturing systems are demanded to expose an increased flexibility and adaptability in order to cope with dynamical changes of manufacturing condition, objectives and an increasing number of product variants within industrial turbulent environment. To cope with these challengers, a concept of self-optimizing manufacturing system was proposed as a new trend of smart manufacturing systems. Self-optimizing manufacturing systems enables engineering systems to react autonomously and flexibly on changes of operation conditions. The self-optimization can execute as a process of the three actions “analysis of the current system states”, “determination of the system’s new goals” and “adjusting the system’s behavior”. They perform this series of actions as the “Self-Optimization Process”. In order to implement an ideal manufacturing system, a virtual prototype was used as a novel method which significantly reduces manufacturing cost and time compared to the traditional build-and-test approach. For further clarification of the self-optimizing manufacturing system concept, this thesis presents two study cases, including “self-optimizing excavator” and “self-optimizing pulley forming system”. Self-optimizing systems were implemented and verified on the virtual prototype model to prove the efficiency of the proposed strategy.

주제어

#Self-optimizing system modeling and controlling advanced control autonomous excavator adaptive behavior virtual prototyping forming process FE numerical simulation artificial neural network;

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