Machine learning based screw-type material distributor controller

Abstract

The invention discloses a machine learning based screw-type material distributor controller which comprises a signal acquisition module, a processing module, a neural network module, an iterative learning module, a storage module, a first connecting array, a second connecting array and an output module. An adopted dynamic recurrent Elman neural network maps the material level of a blanking bin, falling difference in the air, blanking rate, material density and the spiral blade diameter, thread pitch and maximum screw rod rotation speed of a helical conveyor into material aerial amounts, the iterative learning module during off-line training adjusts weights according to a gradient descent method, and the processing module conducts advanced closing control on the helical conveyor through theoutput module according to prediction values of the aerial amounts in the online blanking control process. The controller adopts a nonlinear network to model the blanking process, the trained networkcan accurately predict the aerial amounts in different blanking states, accordingly direct and accurate blanking can be achieved, the machine learning based screw-type material distributor controlleris suitable for small-batch production, and the blanking efficiency is improved due to the fact that a screw can keep high operating speed.

Description

technical field [0001] The invention relates to the field of quantitative cutting, in particular to a machine learning-based screw-type material batching machine controller. Background technique [0002] In industrial and agricultural manufacturing and commodity packaging, there are a large number of powder materials, such as coal powder and other iron-making raw materials, polypropylene, polystyrene, polyvinyl chloride, light methyl cellulose, polypropylene nitrile, epoxy resin powder coating Chemical raw materials such as quartz sand, cement and other building materials, household chemical products such as washing powder, agricultural products such as millet and soybeans, grains and beans, or powder, slag, granular processed food, agricultural production materials such as feed, chemical fertilizers, pesticides, and powder Granular health care products, Chinese and Western medicines, condiments, etc. all require automatic quantitative packaging or ingredient manufacturing. ...

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] The weighing type quantification is based on the quality of the material to measure, fill or feed. It needs to be weighed continuously during the feeding process, and the feeding amount is controlled according to the feedback of the weighing result. Since the weighing is greatly affected by the impact of the feeding and the lagging material in the air, the feeding speed and accuracy face many difficulties

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