Analysis and Structural Optimization Test on the Collision Mechanical Model of Blade Jun-Cao Grinding Hammer

Shuhe Zheng, Chongcheng Chen and Yuming Guo ()
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Shuhe Zheng: College of Agriculture Engineering, Shanxi Agricultural University, Jinzhong 041399, China
Chongcheng Chen: College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
Yuming Guo: College of Agriculture Engineering, Shanxi Agricultural University, Jinzhong 041399, China

Agriculture, 2024, vol. 14, issue 3, 1-15

Abstract: Aiming at the problems found in grinding Jun-Cao, such as poor grinding effect and high grinding power of mill, this study proposes a blade Jun-Cao grinding hammer based on the traditional hammer mill. With dynamics model analysis, it had better performance than a traditional hammer. By simulating the operation process in the DEM, forces on Jun-Cao and their motions were analyzed. By optimizing the structural parameters of the hammer blade based on multiobjective optimization using the genetic algorithm, an optimal solution set was obtained as a reference for practical production. Meanwhile, a bench test was designed to compare the traditional rectangular hammer with the new blade hammer regarding the operation effect. The result proved the following: (1) cutting edge length, cutting edge thickness and hammer thickness had a significant influence on the grinding effect and grinding power; (2) a total of 22 optimal solution sets were obtained, based on which the blade hammer with a cutting edge length of 45 mm, a cutting edge thickness of 3 mm and a hammer thickness of 7 mm was finally selected in the bench test; (3) the bench test proved that the blade hammer was generally superior to the traditional rectangular hammer with the output per kilowatt-hour having been improved by 13.55% on average.

Keywords: hammer; Jun-Cao; EDEM; genetic algorithm; CCD test (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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