Pub Date : 2024-04-18DOI: 10.1007/s10035-024-01430-6
Wei Zhang, Weijian Xiao, Chuanniu Yuan, Xu Gong, Bozhan Hai, Rongxin Chen, Kun Liu
Based on the discrete element method, a 3D particle size model including MoS2 lubricant and iron powder particles has been used to simulate the powder compaction process. The percolation behavior of sidewall lubricant particles and the influence of lubricant percolation on the powder densification and force chain parameters (quantity, average length, average strength, and angle) has been studied. Results indicated that the degree of percolation increased with the increase in pressure. Lubricants located at the top of the model are more prone to percolation. The lubricant percolation behavior causes the pores in the compact to become larger, and minimize the coordination number and compactness of the compact. Although the percolation behavior can generate more high-strength short force chains, it can lead to a high concentration of spatial angles of the force chains, hindering the formation of cross force chain networks.
{"title":"3D DEM investigation on percolation of lubricant particles during uniaxial metal powder compaction","authors":"Wei Zhang, Weijian Xiao, Chuanniu Yuan, Xu Gong, Bozhan Hai, Rongxin Chen, Kun Liu","doi":"10.1007/s10035-024-01430-6","DOIUrl":"10.1007/s10035-024-01430-6","url":null,"abstract":"<p>Based on the discrete element method, a 3D particle size model including MoS<sub>2</sub> lubricant and iron powder particles has been used to simulate the powder compaction process. The percolation behavior of sidewall lubricant particles and the influence of lubricant percolation on the powder densification and force chain parameters (quantity, average length, average strength, and angle) has been studied. Results indicated that the degree of percolation increased with the increase in pressure. Lubricants located at the top of the model are more prone to percolation. The lubricant percolation behavior causes the pores in the compact to become larger, and minimize the coordination number and compactness of the compact. Although the percolation behavior can generate more high-strength short force chains, it can lead to a high concentration of spatial angles of the force chains, hindering the formation of cross force chain networks.</p>","PeriodicalId":49323,"journal":{"name":"Granular Matter","volume":"26 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140625180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-12DOI: 10.1007/s10035-024-01427-1
Dandan Han, Yang Zhou, Junshan Nie, Qiqiang Li, Lin Chen, Qi Chen, Lihua Zhang
The corn variety “Zhenghong 507”, which is widely cultivated in hilly and mountainous areas of Southwest China, was assigned as the research object. The discrete element model of the mid-section of the corn ear that can be threshed was established by integrating the Hertz-Mindlin with the bonding V2 contact model, and the crucial bonding parameters were simulated and calibrated. With the measured normal threshing force (6.34 N) and tangential threshing force (4.75 N) of a single kernel as target values, the parameters of bonding characteristics between the kernel and the cob of corn ear were screened and optimized for significance via the Placket-Burman test, steepest ascent test, and the central composite design. The results indicate that the optimal parameter combinations for the normal stiffness and shear stiffness per unit area, normal strength, shear strength, contact radius between kernels, contact radius between cobs, and bonded disk scale were 3.4 × 108 N·m−3, 2.238 × 108 N·m−3, 0.6 × 106 Pa, 0.364 × 106 Pa, 1.87 mm, 16.5 mm and 1.321. Finally, the accuracy of the corn ear DEM model was validated by comparing the simulation to the physical test using the threshing rate as an evaluation index combined with the quality distribution of kernels after threshing.