Topic 1: Robotic machining system
Recently, multi degree of freedom robot arm is applied to various mechanical machining processes such as milling, drilling, and grinding. Robotic machining system has advantages compared to conventional 3-axes or 5-axes machining system in respect of space efficiency and flexibility. However, robotic machining system still has limitations because of its low position accuracy and low stiffness. We are challenging to improve the position accuracy of the robotic machining system.
？Topic 2: Machine learning
Machine learning is a field of computer science that enables computer learn without being explicitly programmed. Machining learning becomes a mainstream technology in manufacturing field with development of deep learning (artificial neural networks containing hidden layers). We are challenging to diagnose and optimize the machining process in real-time based on the monitoring data by using the machine learning technology.
？Topic 3: Friction compensation control
Friction is a dominant factor that deteriorates positioning accuracy and stability of feed drive system. Friction compensation control is one of the effective ways to improve the position control performance by applying the additional driving force for counterbalancing the friction force. We are challenging to improve the accuracy of friction estimation by considering the influence of circumstance such as pressure and temperature on friction characteristics.
Cyber physical system is integration of cyber (simulation) and physical processes. Remarkable development in processing speed and simulation technology enables real-time interaction between cyber and physical system. Digital twin established based on the real machine tool can be utilized to prognostic and optimization of the machining process. We are challenging to improve machine tool performance based on the cyber physical system.
？Topic 4: Cyber physical system based machine tool optimization
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