【作者】 张超；

【导师】 王立平；

【作者基本信息】 电子科技大学 ， 工程硕士（专业学位）， 2022， 硕士

【摘要】 轧辊是轧钢生产线的“牙齿”,在轧辊磨损后,必须进行磨削修复,因此,轧辊磨削在轧钢生产中占有重要地位。轧辊磨削需要同时保证表面粗糙度、光泽度、辊形、圆度等多质量指标综合创成,是典型的复杂多序磨削,工艺参数制定难度大。目前轧辊磨削的工艺参数主要依赖磨削工人的经验,稳定性与智能化水平较差,同时部分工艺能保证磨削质量,但效率不足,无法满足钢铁行业高效发展的需求。针对上述问题,本文以轧辊磨削为对象,深入开展面向多指标创成的工艺智能优化方法研究,主要内容如下:开展了磨削表面质量(粗糙度、光泽度)与工艺参数间的关系研究,基于单因素试验法和Box-Behnken法,研究并讨论了磨削深度、砂轮转速、工件转速、托板速度等4项磨削工艺参数对表面质量的单因素影响和交互作用影响。进一步进行全因素试验和Box-Behnken试验,获得了多道次磨削下的表面质量演化模型,对演化模型进行方差分析、显著性检验、充分性检验和失拟检验,从而实现了对轧辊磨削表面质量的有效预测。开展了轧辊磨削形位精度(辊形、圆度)与工艺参数间的关系研究,通过试验发现形位精度与工艺参数间存在较强的非线性关系。由于线性回归难以对形位精度变化进行拟合,建立了基于改进Elman神经网络的辊形误差和圆度误差预测模型,在网络训练过程中采用Sine混沌映射使得种群分布更均匀,同时利用麻雀搜索算法获得Elman神经网络的最优参数,从而实现了对轧辊磨削形位精度的有效预测。基于前述表面质量和形位精度的预测模型,提出了基于改进粒子群算法的轧辊磨削多指标工艺参数优化方法,以材料去除量和加工时间为优化目标,以磨削工艺参数为决策变量,以表面质量为约束,以形位精度作为检验条件,同时利用动态权重学习策略和自适应网格策略提高收敛速度。验证结果显示,与现有最优经验工艺参数相比,利用优智能优化后的工艺参数,表面粗糙度提升24.26%,表面光泽度达到91.4GU,最大辊形误差降低17.5%,圆度误差降低8.5%,同时总加工时间减少33.46%,充分证明了所提出优化方法的有效性。上述研究内容和成果可为提升轧辊磨削的质量和效率提供工艺技术支撑。更多还原

【Abstract】 Roll is the "teeth" of the rolling line and must be repaired by grinding after the rolls are worn out,therefore,roll grinding occupies an important position in steel rolling production.Roll grinding is a typical complex multi-step grinding process,which is difficult to establish the process parameters.At present,the process parameters of roll grinding mainly rely on the experience of grinding workers,and the stability and intelligence level are poor,while part of the process can guarantee the grinding quality but the efficiency is insufficient to meet the demand of efficient development of the steel industry.In view of the above problems,this paper carries out in-depth research on the process intelligent optimization method oriented to the creation of multiple indicators with roll grinding as the object,and the main contents are as follows.A study on the relationship between surface quality(roughness and gloss)of roll grinding and process parameters was carried out.Based on the single-factor test method and Box-Behnken response surface method,the single-factor effects and interactive effects of four grinding process parameters,including grinding depth,grinding wheel speed,workpiece speed and pallet speed,on surface quality were studied and discussed.Further full-factor tests and Box-Behnken tests were conducted to obtain the evolution model of surface quality under multiple passes of grinding,and the analysis of variance,significance test,adequacy test and misfit test were performed on the evolution model to achieve effective prediction of surface quality of roll grinding.A study on the relationship between roll grinding form accuracy(roll shape and roundness)and process parameters was carried out,and a strong non-linear relationship between form accuracy and process parameters was found through experiments.Since linear regression is difficult to fit the variation of profile accuracy,a roll shape error and roundness error prediction model based on the improved Elman neural network was established,and the Sine chaos mapping was used in the training process to make the population distribution more uniform.Based on the aforementioned prediction models of surface quality and form accuracy,an improved particle swarm algorithm-based multi-indicator process parameter optimization method for roll grinding is proposed,with material removal and processing time as the optimization objectives,grinding process parameters as the decision variables,surface quality as the constraint,and form accuracy as the test condition,while using dynamic weight learning strategy and adaptive grid strategy to improve the convergence speed.The validation results show that compared with the existing optimal empirical process parameters,the surface roughness is improved by 24.26%,the surface gloss reaches 91.4 GU,the roll shape error is reduced by 17.5%,and the roundness error is reduced by 8.5%,while the total machining time is reduced by 33.46%,which fully proves the effectiveness of the proposed optimization method.The above research contents and results can provide process technology support for improving the quality and efficiency of roll grinding.更多还原