两种难加工材料的切削试验研究及其工艺参数优化
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摘要
本文在低碳经济战略思想指导下,针对我国高端装备制造业常用的塑性难切削加工材料奥氏体不锈钢(022Cr19Ni10)和钛合金(Ti6A14V),切削加工中的切削状态、表面完整性、环保节能、高效切削加工等问题进行了深入的试验研究。针对两种被切削材料分别在干式、环保型湿式、低温冷风微油雾三种清洁切削加工条件下的各种切削参数进行了对比切削试验研究和参数优化,获得了控制切削加工状态、零件表面完整性和切削精度保证的基本方法,以利于指导生产。本文开展了以下研究工作：
(1)用均匀设计法设计切削试验方案,引入伪变量A表达冷却降温切削条件,实现含定性因素混合水平切削试验,提升了因素水平数和因素取值范围,减少了切削试验次数,降低了试验成本,建立的各因变量回归模型的回归效果非常显著,并且BP神经网络各因变量模型训练结果的总误差很小,使低成本全面清洁切削试验变为可行。
(2)针对奥氏体不锈钢切削和钛合金切削,分别建立了对应两种不同材料的二次回归切削力模型、表面粗糙度模型、刀具前刀面温度模型和已切削加工面残余应力模型,同时还建立了BP神经网络6-7-5-1结构的表面粗糙度模型、切削温度模型,以及BP神经网络6-7-1结构的切削温度模型和已切削加工面表面残余应力模型。并分析了各自变量及自变量交互作用对切削力、表面粗糙度、切削温度和表面残余应力的影响。
(3)针对奥氏体不锈钢切削,建立了刀具寿命SCL、S二次回归模型,和刀具寿命SCL、S的BP神经网络6-7-1模型,并进行了各自变量及自变量交互作用对刀具寿命SCL、S的影响分析；同时建立了切削参数多目标优化模型并进行了验证。
本论文基于两种难切削加工材料、三种清洁冷却降温切削条件,在试验设计、因变量模型建立、切削参数多目标优化模型建立、因变量影响因素分析等方面进行了积极的探索,解决了清洁切削加工推广应用进程中出现的一系列问题,尤其为低成本实现含定性因素混合水平切削试验研究探索了一种新的思路,丰富和完善了切削试验研究方法手段。
Under the guidence of Low-Carbon Economic Strategy(LCES) thought, aimed at the problems of energy-saving, environmental protection and high efficiency cutting for hard-to-cut martieral,022Cr19Ni10Austenitic stainless steel (ASS) and Ti6A14V Titanium alloy (TA), which are common used in high level equipment manufacture in our country, a lot of experiment researches are carried out. Comparison tests are done for the two materials in three cooling conditions of dry, clean liquid and low temperature wind with fine oil and the cutting parameters obtained are optimazed. The optimatic cutting parameters, which are useful for instructing manufacture, are obtained.
Main works in this thesis as following:
(1) Cutting experimental scheme is designed by uniform design method and a pseudo-variable A is introduced to express cooling cutting conditions, and then mix horizontal cutting experiments including qualitative factors is carry out, the provided factor horizontal number and the factors range, therefore, are promoted, the number and the cost of cutting experiments are reduced. By means of this scheme, the regression effect of the dependent variable regression models built is very significant and the result differences of the variable model training in BP neural network are very fine, therefore it makes that posible to carry low-cost clean cutting experiment.
(2) Aimed at the cutting process of ASS and TA, cutting force model, workpiece surface roughness model, cutting temperature pattern model and workpiece surface residual stress model of quadratic regression for the two materials are built up. At the meantime, workpiece surface roughness model and cutting temperature pattern model of BP neural network's6-7-5-1structure as well as cutting temperature pattern model, workpiece surface residual stress model of6-7-1structure for the two materials are also built up. And the then, effects on the cutting force, workpiece surface roughness, cutting temperature and workpiece surface residual stress due to the respective variables and the independent variable interaction are analyzed respectively.
(3) SCL^S tool life models of quadratic regression and BP neural network's6-7-1for the ASS cutting are built respectively, and effects on the tool life SCL, S due to respective variables and the independent variable interaction are analyzed. Meanwhile, a Multi-objective Integrated Optimization model of cutting parameter is built and the model is tested and verified.
Aimed at two type of hard-to-cut materials in three clean cooling cutting conditions, the active researches for the experimental design, the modeling dependent variable, modeling cutting parameter multi-objective optimization and the dependent variable factor analysis etc are carried in this thesis. A series of problems which appeared in the clean cutting promotion and application process, therefor, as solved based on the researches, and also a new approach for carrying out mix horizontal cutting experiments including qualitative factors at low cost are explored, the research ways are enriched and cutting tests means are completed.
(1)用均匀设计法设计切削试验方案,引入伪变量A表达冷却降温切削条件,实现含定性因素混合水平切削试验,提升了因素水平数和因素取值范围,减少了切削试验次数,降低了试验成本,建立的各因变量回归模型的回归效果非常显著,并且BP神经网络各因变量模型训练结果的总误差很小,使低成本全面清洁切削试验变为可行。
(2)针对奥氏体不锈钢切削和钛合金切削,分别建立了对应两种不同材料的二次回归切削力模型、表面粗糙度模型、刀具前刀面温度模型和已切削加工面残余应力模型,同时还建立了BP神经网络6-7-5-1结构的表面粗糙度模型、切削温度模型,以及BP神经网络6-7-1结构的切削温度模型和已切削加工面表面残余应力模型。并分析了各自变量及自变量交互作用对切削力、表面粗糙度、切削温度和表面残余应力的影响。
(3)针对奥氏体不锈钢切削,建立了刀具寿命SCL、S二次回归模型,和刀具寿命SCL、S的BP神经网络6-7-1模型,并进行了各自变量及自变量交互作用对刀具寿命SCL、S的影响分析；同时建立了切削参数多目标优化模型并进行了验证。
本论文基于两种难切削加工材料、三种清洁冷却降温切削条件,在试验设计、因变量模型建立、切削参数多目标优化模型建立、因变量影响因素分析等方面进行了积极的探索,解决了清洁切削加工推广应用进程中出现的一系列问题,尤其为低成本实现含定性因素混合水平切削试验研究探索了一种新的思路,丰富和完善了切削试验研究方法手段。
Under the guidence of Low-Carbon Economic Strategy(LCES) thought, aimed at the problems of energy-saving, environmental protection and high efficiency cutting for hard-to-cut martieral,022Cr19Ni10Austenitic stainless steel (ASS) and Ti6A14V Titanium alloy (TA), which are common used in high level equipment manufacture in our country, a lot of experiment researches are carried out. Comparison tests are done for the two materials in three cooling conditions of dry, clean liquid and low temperature wind with fine oil and the cutting parameters obtained are optimazed. The optimatic cutting parameters, which are useful for instructing manufacture, are obtained.
Main works in this thesis as following:
(1) Cutting experimental scheme is designed by uniform design method and a pseudo-variable A is introduced to express cooling cutting conditions, and then mix horizontal cutting experiments including qualitative factors is carry out, the provided factor horizontal number and the factors range, therefore, are promoted, the number and the cost of cutting experiments are reduced. By means of this scheme, the regression effect of the dependent variable regression models built is very significant and the result differences of the variable model training in BP neural network are very fine, therefore it makes that posible to carry low-cost clean cutting experiment.
(2) Aimed at the cutting process of ASS and TA, cutting force model, workpiece surface roughness model, cutting temperature pattern model and workpiece surface residual stress model of quadratic regression for the two materials are built up. At the meantime, workpiece surface roughness model and cutting temperature pattern model of BP neural network's6-7-5-1structure as well as cutting temperature pattern model, workpiece surface residual stress model of6-7-1structure for the two materials are also built up. And the then, effects on the cutting force, workpiece surface roughness, cutting temperature and workpiece surface residual stress due to the respective variables and the independent variable interaction are analyzed respectively.
(3) SCL^S tool life models of quadratic regression and BP neural network's6-7-1for the ASS cutting are built respectively, and effects on the tool life SCL, S due to respective variables and the independent variable interaction are analyzed. Meanwhile, a Multi-objective Integrated Optimization model of cutting parameter is built and the model is tested and verified.
Aimed at two type of hard-to-cut materials in three clean cooling cutting conditions, the active researches for the experimental design, the modeling dependent variable, modeling cutting parameter multi-objective optimization and the dependent variable factor analysis etc are carried in this thesis. A series of problems which appeared in the clean cutting promotion and application process, therefor, as solved based on the researches, and also a new approach for carrying out mix horizontal cutting experiments including qualitative factors at low cost are explored, the research ways are enriched and cutting tests means are completed.
引文
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