CAI Gaipin^a，b, YU Cheng^a，HAO Shuhao^a, XUAN Lyuwei^a

(a.School of Mechanical and Electrical Engineering；b.Jiangxi Province Engineering Research Center for Mechanical and Electrical of Mining and Metallurgy, Jiangxi University of Science and Technology,Jiangxi 341000, China)

Abstract: According to the difference of the mineral interface of different components, the box dimension method was used to describe the geometric characteristics of the mineral interface, the in-situ loading experiment was used to describe the mechanical characteristics of the mineral interface, and the back propagation(BP)neural network algorithm was used to describe mapping prediction of the geometric and mechanical characteristics of the mineral interface.The results show that the fractal dimension value of the quartz-tungsten interface in the studied wolframite is larger than the quartz-siliceous interface.The fractal dimension of the quartz-tungsten interface is distributed from 1.855 1 to 1.936 8, and quartz-tungsten’s is from 1.163 3 to 1.371.The mechanical properties of the bonding interface of different components are different and related to the physical properties and morphological characteristics of the constituent minerals.Among them, the minimum crushing stress range of the interface of the quartz-siliceous rock is from 1.178 5 to 1.482 6 GPa.The minimum crushing stress range of the quartz-tungsten interface is from 1.335 5 to 1.542 03 GPa.The BP neural network can realize the effective prediction of the geometric characteristics of the mineral interface on the mechanical characteristics, and the maximum error in the early stage of the prediction is only 4.14%.With the increasing of data, the prediction accuracy is getting higher and higher, and the minimum error in the late prediction period is only 0.011%.

Keywords: mineral interface; back propagation; neural network; geometric characteristics; mechanical characteristics; prediction of fracture characteristics

中图分类号：TU45                              文献标志码：A

文章编号：1008-5548(2022)01-0024-11

DOI:10.13732/j.issn.1008-5548.2022.01.003

收稿日期： 2021-09-06，修回日期：2021-09-17，在线出版时间：2021-11-09。

基金项目：国家自然科学基金项目，编号：51464017；江西省重点研发计划项目，编号：20181ACE50034。

第一作者简介：蔡改贫(1964—)，男，教授，博士，博士生导师，研究方向为智能矿山装备技术，智能监控与工业机器人。E-mail：1123615286@qq.com。