研究目的
Investigating the dynamic behavior and modeling of the single axis galvanometer motor actuator for Selective Laser Melting (SLM) additive manufacturing process to improve existing models and extend the frequency domain validity.
研究成果
The proposed galvanometer motor model presents admissible error values compared to experimental data in terms of both frequency and time responses. It extends the frequency domain validity and can be further used for performance improvement of the SLM process. The model will be the entry point for advanced control structures investigation aiming to improve axis dynamics.
研究不足
The model's accuracy can be further improved with higher resonance mode order and advanced friction model functions. The study is limited to a commercial device, meaning the galvanometer is inserted in a controlled loop and cannot be disconnected, which entails open-loop model parameters identification from closed-loop acquired data.
1：Experimental Design and Method Selection:
The study focuses on modeling the galvanometer motor actuator with physical considerations to improve basic linear and simplified existing models. It includes the development of an enhanced model with flexible modes and friction considerations.
2：Sample Selection and Data Sources:
Experimental data from a commercial three-axis scan head is used for model validation.
3：List of Experimental Equipment and Materials:
A commercial three-axis scan head with galvanometers X and Y with mounted mirrors and a Dynamic Focus Module (DFM) element.
4：Experimental Procedures and Operational Workflow:
The study involves comparing the frequency responses of the developed models to experimental data, tuning flexible modes parameters by hand according to experimental frequency responses, and using a Particle Swarm Optimisation (PSO) algorithm for friction model parameters tuning.
5：Data Analysis Methods:
The Mean Square Error (MSE) between the experimental time response of the galvanometer and the time response of the model to the experimental voltage input signal is used as a cost function for optimization.
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