Proposal of a decentralized peristaltic movement generation based on actual intestines and verification by content transfer experiment

Matsui, Daiki; OSHINO, Sana; Terayama, Iori; Nishihama, Rie; Okui, Manabu; Nakamura, Taro

doi:10.1109/sii52469.2022.9708865

Cited by 4 publications

(4 citation statements)

References 12 publications

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“…In our previous study, we aimed to develop a system to control a peristaltic mixing conveyor by sensing stimuli caused by changes in the contents of the device, like the enteric nervous system. In this paper, we developed a sensing function using a distributed arrangement like that of an intestinal tube [11] and verified two issues: estimation of the mixing state of contents [12][13] and automatic switching of driving patterns according to the state of contents [14], respectively. For the estimation of the mixing state, logistic regression was used to successfully estimate the mixing state of the powder and liquid in the device [12][13].…”

Section: Our Approachmentioning

confidence: 99%

“…Both studies were able to estimate mixing completion at some level under certain conditions. In a study on switching the drive pattern [14], the volume of the rigid rod in the unit was detected from the air flow sensor data mounted on the device, and the operation was successfully switched from random drive to peristaltic movement. However, this method makes it difficult to detect fluid contents and the conveyance of fluid contents has not yet been investigated.…”

Section: Our Approachmentioning

confidence: 99%

“…Therefore, we partially developed a sensing function [11] and system with a distributed arrangement like that of the intestinal tract. The two issues of estimating the mixing state of the contents [12][13] and automatic switching of the drive pattern according to the state of the contents [14] were verified, respectively. For the estimation of the mixing state, machine learning was used to successfully estimate the mixing state of powder and liquid in the device [12] [13].…”

Section: Introductionmentioning

confidence: 99%

“…Both studies were able to estimate mixing completion at some level under certain conditions. In a study on switching the drive pattern [14], a rigid rod was detected based on sensor data mounted on the device, and the movement was successfully switched from random drive to peristaltic movement. Currently, a system that simultaneously estimates the mixing state and switches driving patterns for highly fluid contents has not yet been developed.…”

Section: Introductionmentioning

confidence: 99%

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Construction of Motion Mode Switching System by Machine Learning for Peristaltic Mixing Conveyor Based on Intestinal Movement

Terayama,

Oshino,

Nishihama

et al. 2024

IEEE Access

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The high frequency of rocket launches requires low-cost solid rocket fuel. Currently, the fuel manufacturing process faces increased launch costs caused by the risk of ignition from rotary mixers and increased equipment and labor costs from batch processes in which mixing and conveying are separated. Therefore, this paper proposes and verifies an automatic switching system between mixing and conveying modes for a peristaltic mixing conveyor that enables safe and continuous mixing and conveying of solid fuel. In a previous study, peristaltic mixing conveyor with low shear force was developed and successfully produced solid fuel. However, there was room for improvement for more efficient fuel production because the device was controlled by pre-determined driving pattern. The actual intestine generates movement autonomously by enteric nerves. Therefore, the development of a sensing function that imitates the enteric nervous system and generates movement patterns based on the acquired data is expected to improve manufacturing efficiency. In this study, the sensor data of a mixed solid fuel simulant packaged in a bag were acquired, and the degree of mixing (unmixed and mixed completely) was discriminated using supervised learning (the k-nearest neighbor method). Furthermore, a system was constructed to continue the mixing mode when unmixing and automatically switch the motion to the conveying mode when the mixing was complete. The experiment showed that the motion mode automatically switched to the conveying mode at almost the same time as the labeled training data, and mixing and conveying of the simulated material was successfully performed.

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Section: Our Approachmentioning

confidence: 99%

Section: Our Approachmentioning

confidence: 99%