A peristaltic mixing and conveyor that imitates the intestines of living organisms is used to mix powders and highly viscous fluids at low shear force in the manufacturing processes of foods, pharmaceuticals, paints, cosmetics, and other products. Although the device uses sequence control to mix and convey the contents, the actual intestine controls its motion autonomously according to the state of the contents. This research aims to establish an autonomous control method that can change between mixing and conveying according to the state of the mixture. In this paper, we propose a method to construct a learning model that is not affected by the content input conditions, in which samples with different total input amounts and mixing ratios are fed into the device, and sensor values acquired during the device operation are used as training data. The generalizability of the learning model was verified, and it was shown that mixing state estimation was possible up to 20 min from the start of mixing for mixtures with different input amounts and input order.

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