Mixing Determination for Solid Rocket Fuel Production by Peristaltic Mixing Pump Using Packing Method

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

doi:10.1109/iecon49645.2022.9968625

Cited by 2 publications

(2 citation statements)

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“…Furthermore, a machine learning model is required to estimate mixing was constructed from the acquired sensor values and evaluated, and a learning model was selected for use in the mixing and conveying switching system. Previous studies [12][13] [15] have shown that mixing degree discrimination is possible in certain environments. Therefore, in the next phase, a simple machine learning model that enables highly accurate mixture discrimination in this environment was applied to the device in the same way as in the previous study to construct a motion-switching system.…”

Section: Mixture Discrimination By Supervised Learningmentioning

confidence: 99%

“…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]. In addition, the state of mixing of fuel simulants packed in bags has been successfully estimated [15]. Both studies were able to estimate mixing completion at some level under certain conditions.…”

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: Mixture Discrimination By Supervised Learningmentioning

confidence: 99%