【摘要】：15-5PH不銹鋼作為一種新型不銹鋼材料,以其優(yōu)良的機(jī)械性能、耐高溫和耐腐蝕性能,被廣泛應(yīng)用在飛機(jī)驅(qū)動(dòng)裝置、機(jī)身主梁及起落架等關(guān)鍵結(jié)構(gòu)件。其優(yōu)良力學(xué)性能也預(yù)示著該材料的難加工性。在15-5PH不銹鋼加工過(guò)程中,由于其導(dǎo)熱系數(shù)低和彈性模量小等,易出現(xiàn)變形抗力大、切削溫度高、刀具磨損破損快以及加工硬化等現(xiàn)象,從而導(dǎo)致15-5PH不銹鋼的加工質(zhì)量和加工效率低下、加工成本過(guò)高。因此,研究15-5PH不銹鋼的切削性能,優(yōu)化其工藝參數(shù),開(kāi)發(fā)具有自主知識(shí)產(chǎn)權(quán)的專用數(shù)據(jù)庫(kù)對(duì)于提高加工效率、控制加工成本和加工質(zhì)量具有重要意義。本文針對(duì)企業(yè)對(duì)于切削數(shù)據(jù)庫(kù)系統(tǒng)的功能需求,選取粗加工、圓弧面和腹板三類特征開(kāi)展基礎(chǔ)實(shí)驗(yàn)研究、切削結(jié)果建模研究和切削參數(shù)優(yōu)化研究。再將所有實(shí)驗(yàn)數(shù)據(jù)和實(shí)驗(yàn)結(jié)果錄入到切削參數(shù)庫(kù)系統(tǒng),并調(diào)試運(yùn)行。針對(duì)15-5PH不銹鋼粗加工過(guò)程中,刀具磨損和破損嚴(yán)重以及刀具壽命低下等問(wèn)題,選用單因素實(shí)驗(yàn)設(shè)計(jì)方法,開(kāi)展刀具磨損和刀具壽命實(shí)驗(yàn)。觀察和分析不同條件下刀具的磨損和失效形式,分析各切削用量對(duì)刀具壽命的影響機(jī)理和規(guī)律。采用BP神經(jīng)網(wǎng)絡(luò)算法,進(jìn)行刀具壽命建模,建立刀具壽命的預(yù)測(cè)模型,實(shí)現(xiàn)刀具壽命的經(jīng)驗(yàn)預(yù)測(cè)。并借助粒子群優(yōu)化算法,以切削效率為目標(biāo),進(jìn)行粗加工銑削參數(shù)優(yōu)化。針對(duì)曲面粗糙度,選用正交試驗(yàn)設(shè)計(jì)方法,開(kāi)展15-5PH不銹鋼圓弧面工件精加工實(shí)驗(yàn)。研究分析銑削參數(shù)對(duì)圓弧面表面粗糙度的影響程度和規(guī)律。同時(shí),采用偏最小二乘法和運(yùn)動(dòng)學(xué)分析法,分別進(jìn)行表面粗糙度的經(jīng)驗(yàn)建模和理論建模。通過(guò)對(duì)比預(yù)測(cè)精度更高的理論預(yù)測(cè)模型,以理論預(yù)測(cè)模型為基礎(chǔ),結(jié)合粒子群優(yōu)化算法,以切削效率為目標(biāo),進(jìn)行圓弧面精加工銑削參數(shù)優(yōu)化。針對(duì)腹板特征件,選用正交試驗(yàn)設(shè)計(jì)方法開(kāi)展腹板精加工實(shí)驗(yàn),研究分析切削參數(shù)對(duì)腹板表面粗糙度的影響程度和規(guī)律。其次,運(yùn)用BP神經(jīng)網(wǎng)絡(luò)算法構(gòu)建表面粗糙度的經(jīng)驗(yàn)預(yù)測(cè)模型。最后,基于粒子群優(yōu)化算法,結(jié)合預(yù)測(cè)模型,以切削效率為目標(biāo),進(jìn)行腹板精加工銑削參數(shù)優(yōu)化。最后,將各類特征件的實(shí)驗(yàn)參數(shù)和結(jié)果、預(yù)測(cè)模型以及優(yōu)化算法和優(yōu)化結(jié)果錄入到航空切削參數(shù)庫(kù)系統(tǒng),為數(shù)據(jù)庫(kù)系統(tǒng)的核心功能提供理論、數(shù)據(jù)和技術(shù)支撐。
[Abstract]:As a new type of stainless steel material, 15-5PH stainless steel has been widely used in aircraft driving device, fuselage main beam and landing gear for its excellent mechanical properties, high temperature resistance and corrosion resistance. Its excellent mechanical properties also indicate that the material is difficult to be machined. In the process of processing 15-5PH stainless steel, due to its low thermal conductivity and small elastic modulus, it is easy to appear such phenomena as high deformation resistance, high cutting temperature, fast tool wear and damage, and work hardening, etc. As a result, the processing quality and efficiency of 15-5PH stainless steel are low and the processing cost is too high. Therefore, it is of great significance to study the cutting performance of 15-5PH stainless steel, optimize its technological parameters and develop a special database with independent intellectual property rights to improve the processing efficiency, control the processing cost and process quality. In this paper, according to the functional requirements of the enterprise for cutting database system, we select three features of rough machining, arc surface and web to carry out basic experimental research, modeling of cutting results and optimization of cutting parameters. Then all the experimental data and experimental results are inputted into the cutting parameter database system and debugged and run. Aiming at the problems of serious tool wear and breakage and low tool life during rough machining of 15-5PH stainless steel, single factor experimental design method is used to carry out tool wear and tool life experiment. The wear and failure modes of cutting tools under different conditions were observed and analyzed, and the influence mechanism and law of cutting parameters on tool life were analyzed. The BP neural network algorithm is used to model the tool life and the prediction model of the tool life is established to realize the empirical prediction of the tool life. With the help of particle swarm optimization (PSO) algorithm and aiming at cutting efficiency, the milling parameters of rough machining are optimized. According to surface roughness, orthogonal design method is used to finish machining 15-5PH stainless steel round arc workpiece. The influence of milling parameters on the surface roughness of arc surface is studied and analyzed. At the same time, the empirical modeling and theoretical modeling of surface roughness are carried out by partial least square method and kinematics analysis method respectively. By comparing the theoretical prediction model with higher prediction precision, based on the theoretical prediction model, combining with particle swarm optimization algorithm and aiming at cutting efficiency, the milling parameters of arc finish machining are optimized. According to the characteristics of web, the orthogonal design method was used to carry out the experiments of web finishing, and the influence of cutting parameters on the surface roughness of web was studied. Secondly, an empirical prediction model of surface roughness is constructed by using BP neural network algorithm. Finally, based on particle swarm optimization (PSO) algorithm and combined with prediction model, the milling parameters of web finishing are optimized with cutting efficiency as the goal. Finally, the experimental parameters and results of all kinds of features, the prediction model, the optimization algorithm and the optimization results are inputted into the aeronautical cutting parameter database system, which provides theoretical, data and technical support for the core functions of the database system.
【學(xué)位授予單位】：南京理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：V261.23

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