Screw-type multi-component material blanking method based on variable rate learning

Abstract

The invention discloses a screw type multi-component material blanking method based on variable speed learning. Based on single or accumulated blanking error, a controller controls spiral conveyors through iterative learning; learning factors of the single or accumulated blanking error are dynamically adjusted based on the blanking error change in the iterating process; all the spiral conveyors are acted in sequence; after blanking of once formula quantity is finished, a blanking valve is opened; and when materials in a mixing hopper reach a set value, a push plate is opened after the materials are uniformly mixed, and the mixture is discharged. A distance sensor and a stirrer are adopted to detect and adjust accumulation of the materials in a blanking bin to guarantee compactness and stability of the materials; learning factors can be automatically adjusted and optimized; compared with the prior art, the device has no need to repeatedly adjust the parameters, can quickly obtain iterating expressions with better convergence performances, and is high in blanking efficiency; and the blanked materials in the iterative learning process can be effectively used.

Description

technical field [0001] The invention relates to the field of quantitative feeding, in particular to a screw type multi-component material feeding method based on variable rate learning. Background technique [0002] In industrial and agricultural manufacturing and commodity packaging, there are a large number of powder materials, such as polypropylene, polystyrene, polyvinyl chloride, light methyl cellulose, polypropylene nitrile, epoxy resin powder coating and other chemical raw materials, quartz sand, Raw materials for building materials such as cement, household chemical products such as washing powder, agricultural products such as millet and soybeans, grains and legumes, or processed food in powder, slag, and granular form, agricultural production materials such as feed, chemical fertilizers, and pesticides, as well as powdered and granular health care products, Chinese and Western medicines , condiments, etc. all need automatic quantitative packaging or ingredient manu...

AI Technical Summary

Problems solved by technology

In order to improve the feeding accuracy, various adjustment methods have appeared, such as the Chinese patent application number 201320001933.3, which adopts frequency conversion speed regulation for the screw, and gradually slows down the feeding speed when it is close to the target value, reducing the drop in the air; the application number is 201310234280.8 Chinese patent, in the three-speed frequency conversion feeding process of soda ash packaging machine, large and small screws are used to feed in multiple stages; the Chinese patent with application number 200920248298.2 considers that it is difficult to control the quantitative during fast feeding, and it is reduced by the method of fast first and then slow The impact of feeding drop; the final value of the blanking of these non-weighing schemes can only be close to the expected value, and the accuracy is not high

[0005] Weighing type quantification is used to measure, fill or feed according to the set amount of material at one time. It needs to be weighed continuously during the feeding process, and the feeding amount is controlled according to the feedback of the weighing result. Because the weighing is affected by the impact of the feeding and the lagging material in the air. Large, component feeding speed and accuracy are facing many difficulties

In order to compensate for the interference of airborne materials on the measurement accuracy, many schemes adopt the technology of closing the valve in advance. For example, the Chinese patent application number 201410230888.8 divides the ingredient weighing process into three stages, and uses iterative learning control method to calculate in the last stage Close the advance control amount, but the learning factor of iterative learning in this scheme needs to be optimized through trial and error and feedback observation, so long-term experiments and debugging are required, and this scheme can only improve the blanking accuracy after the learning is completed. The accumulative blanking accuracy in the learning process cannot be guaranteed

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