Prediction and optimization of cutting quality is an important method to improve the cutting quality. Aiming at the prediction of quality characteristic parameters for pulsed Nd: YAG laser cutting, a prediction algorithm based on pareto genetic algorithm is used in this paper. KW(Kerf Width) and MRR(Material removal rate) are selected as the optimization objective, and the multi-objective optimization model is established in this paper. The theoretical analysis and experimental results show that the algorithm can be used for KW and MRR prediction in pulsed Nd: YAG laser cutting. A large number of forecast data show the rules as follows. The effects of three types of combined parameters( gas pressure and pulse width, pulse width and pulse frequency, pulse width and cutting speed) on KW are obvious, while the effects of combined parameters, pulse width and pulse frequency, pulse frequency and cutting speed are more obvious on MRR. The study in this paper can provide theoretical guidance and parameters for prediction and optimization of quality in laser cutting.
The demand for health services for the elderly has become an urgent problem to be solved. In order to solve the problems of elderly people who are prone to sudden disease during sleep, difficulty in night care, and the unconstrained requirements for health monitoring, an unconstrained vital signs mattress monitoring system was proposed. An intelligent mattress with built-in thin film piezoelectric sensors was developed to realize the real-time monitoring of vital signs such as heart rate, respiratory rate and body movement state. At the same time, medical and health resources are integrated to provide personalized and precision health care services for the elderly including safety monitoring, health management, etc. and build a smart health and elderly care service system. The experimental application shows that the system can effectively prevent accidents, improve both the efficiency of care and the level of health care services.
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