表面技术2025，Vol.54Issue(11)：83-99,172,18.DOI:10.16490/j.cnki.issn.1001-3660.2025.11.007
露天矿重型卡车制动摩擦温升特性及其智能预测模型
Frictional Braking Temperature Rise Characteristics and Intelligent Prediction Model of Heavy-duty Mining Trucks in Open-pit Mines
摘要
Abstract
This research focuses on the frictional braking temperature rise characteristics and intelligent prediction model of heavy-duty mining trucks in open-pit mines,aiming to address the potential safety risks resulting from unpredictable braking-induced temperature increase. The study is centered around the XG105 three-bridge rigid mining truck of XCMG Group.Initially,the three main braking modes including emergency braking,continuous braking,and cyclic multiple braking are thoroughly analyzed.A thermal-mechanical coupling model of the drum brake is then developed with the direct coupling method.In this process,the vehicle mass is equivalently distributed onto the brake drum to simplify the complex vehicle dynamics model.During braking,the energy conversion occurs,and a significant portion of the mechanical energy of the vehicle is transformed into heat in the braking system.A correction coefficient of 0.9 is adopted,signifying that approximately 90％of the mechanical energy is converted into heat absorbed by the brakes.This coefficient is crucial as it more accurately reflects the actual heat-generating situation in the braking process. Subsequently,a simulation test system for the brake temperature rise of mining trucks is established in the laboratory.In accordance with relevant standards,parameters such as load,longitudinal slope gradient,and initial braking speed are precisely set.The load conditions include no-load(35 t),full-load(105 t),and 30％over-load(126 t),while the longitudinal slope gradient values are set at 6％,7％,and 8％.These parameter settings cover a wide range of actual operating conditions in open-pit mines.Under continuous braking and cyclic multiple braking modes,27 sets of orthogonal experiments are conducted,generating a total of 8 876 sample data.This large-scale data collection is essential for comprehensively understanding the temperature rise characteristics under different braking conditions. Finally,an intelligent prediction model for brake temperature rise is constructed based on the CNN-LSTM-Attention algorithm.By means of Pearson correlation analysis,input features with a correlation coefficient exceeding 0.8 with the maximum braking temperature are carefully selected.These features,such as travel time,longitudinal slope gradient,initial braking speed,total quality,and braking frequency,are highly relevant to the temperature rise phenomenon.After a series of data preprocessing steps,including data cleaning,division,normalization,and transformation,the model is trained.The trained model shows remarkable prediction accuracy.In the continuous braking mode,under three new working conditions,the average prediction error is 5.2℃,and the relative error of the percentage does not exceed 7％.In the cyclic multiple braking mode,under three new working conditions,the average prediction error is 4.79℃,and the relative error of the percentage is within 5％. In summary,the thermal-mechanical coupling model effectively simulates different braking scenarios,providing a reliable way for analyzing the temperature rise process.The intelligent prediction model based on the CNN-LSTM-Attention algorithm can accurately forecast the maximum braking temperature rise.However,there are areas for improvement.Future research should concentrate on optimizing the friction coefficient and refining simulation parameters to make the model more adaptable to the complex and variable real-world conditions in open-pit mines.This study offers a solid theoretical basis for enhancing the driving safety of mining trucks and paves the way for further research in this field.关键词
重型矿用卡车/制动温升特性/热-力耦合/预测模型Key words
heavy-duty mining truck/temperature rise characteristics/thermal-mechanical coupling/prediction model分类
计算机与自动化引用本文复制引用
张航,鲍久圣,阴妍,曹婷,胡德平,苏文胜,朱晨钟,曹靖雨,顾永华..露天矿重型卡车制动摩擦温升特性及其智能预测模型[J].表面技术,2025,54(11):83-99,172,18.基金项目
江苏省科技成果转化专项资金(BA2023035) （BA2023035）
徐州市重点研发计划(KC22419) （KC22419）
江苏高校优势学科建设工程资助项目(PAPD)Jiangsu Province Scientific and Technological Achievements Transformation Special Fund Project(BA2023035) （PAPD）
Xuzhou Key Research and Development Plan Project(KC22419) （KC22419）
Jiangsu University Superior Discipline Construction Project(PAPD) （PAPD）
