Content Detection for Continuous and Efficient Production of Solid Rocket Fuel by Peristaltic Mixer

Wakamatsu, Kota; Hagiwara, Daiki; Adachi, Haruka; Ashigaki, Kyota; Iwasaki, Akira; Yamada, Yasuyuki; Habu, Hiroto; Nakamura, Taro

doi:10.1109/sii46433.2020.9025893

Cited by 9 publications

(6 citation statements)

References 10 publications

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“…In this experiment, the powder and liquid components were determined as Table 1. These are the same as those in the previous study [12]. The powder concentration, which is a criterion for the object to be mixed and the actual degree of mixing, is determined as follows:…”

Section: A Device Drive Methods and Contents To Be Fedmentioning

confidence: 99%

“…The flow rate at air supply showed a similar trend, although the directions of increase and decrease were opposite to that of pressure. From the previous study [12], it is known that when the contents are only powder, they are not pushed out by the pump drive and tend to remain in the unit, resulting in a rapid rise in pressure and a rapid decrease in the air flow rate. As the flowability of the contents increased, the pressure rise slowed and the air flow rate decreased faster.…”

Section: Considerationmentioning

confidence: 99%

“…Therefore, reproducing intestinal motility via the device functioning similar to the intestine can lead to a better understanding of intestinal function. Our system is designed to determine the mixing state in each unit from the sensor values installed in the device [12,13]. In the future, we aim to control the driving pattern of the device autonomously based on the estimated mixing degree to achieve efficient device driving.…”

Section: Introductionmentioning

confidence: 99%

“…As mechanical stimuli for intestinal input, pressure and dilatation of the intestinal wall due to the contents have been suggested [14]. Therefore, a sensing system using the pressure and flow rate sensors was developed, focusing on the air pressure applied to the device [12]. The system could estimate the degree of mixing via machine learning from the time-series information of the sensors [13].…”

Section: Introductionmentioning

confidence: 99%

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Generalization Capability of Mixture Estimation Model for Peristaltic Continuous Mixing Conveyor

et al. 2021

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We propose herein a method for estimating the mixing state of the contents of a peristaltic continuous mixing conveyor simulating the intestine, developed for mixing and conveying powders and liquids. This study serves to improve a previously proposed method for estimating the mixing state using a logistic regression model with the pressure and flow rate sensors installed in the device as inputs. Moreover, the estimation accuracy of the proposed method is better than that of the previous method. The generalizability of the proposed method is evaluated for four conditions in which the feeding order of the contents, powder, and liquid are changed. The feeding order is as follows: powder first, liquid first, and powder and liquid alternately. As a result, a highly accurate estimation of mixing is achieved under the condition wherein the powder component is in the unit adjacent to the lid, but not under the condition wherein the liquid component is fed first. It is speculated that this is because the movement of the powder component inside the device is more easily reflected by the pressure and flow rate sensors installed in the device than in the liquid component.

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Section: A Device Drive Methods and Contents To Be Fedmentioning

confidence: 99%

Section: Considerationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Generalization Capability of Mixture Estimation Model for Peristaltic Continuous Mixing Conveyor

et al. 2021

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“…On the other hand, to reproduce a control system that autonomously determines the appropriate motion for the contents, such as an enteric nervous system, it is necessary to develop a system that detects the mixing state of the contents based on information obtained from the sensors mounted on the device and automatically generates a driving pattern according to that state. 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.…”

Section: Introductionmentioning

confidence: 99%

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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Construction of Distributed Sensing System for Peristaltic Continuous Mixing Conveyor Imitating Intestines: Internal State Measurement Using Accelerometer

OSHINO

Wakamatsu

Matsui

et al. 2021

2021 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)

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Content Detection for Continuous and Efficient Production of Solid Rocket Fuel by Peristaltic Mixer

Cited by 9 publications

References 10 publications

Generalization Capability of Mixture Estimation Model for Peristaltic Continuous Mixing Conveyor

Generalization Capability of Mixture Estimation Model for Peristaltic Continuous Mixing Conveyor

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

Construction of Distributed Sensing System for Peristaltic Continuous Mixing Conveyor Imitating Intestines: Internal State Measurement Using Accelerometer

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