【摘要】：采用激光表面仿生強化技術(shù)提高模具的使用壽命是近幾年新興的一個課題，是仿生學(xué)在模具工業(yè)領(lǐng)域中的重要應(yīng)用，為模具行業(yè)帶來了巨大的經(jīng)濟效益。將激光仿生強化工藝規(guī)范化更有助于其在未來的發(fā)展與推廣。 文中以人工神經(jīng)網(wǎng)絡(luò)理論為依據(jù)，將激光熔凝單元體形態(tài)和激光加工工藝之間存在的高度非線性關(guān)系視為“黑箱”，把熔凝單元體橫截面尺寸作為神經(jīng)網(wǎng)絡(luò)的輸入，激光工藝參數(shù)作為神經(jīng)網(wǎng)絡(luò)的輸出，建立了激光熔凝參數(shù)反求模型。采用結(jié)構(gòu)化分析方法對壓鑄模具激光仿生強化工藝決策系統(tǒng)進行了功能模塊劃分和結(jié)構(gòu)設(shè)計。從系統(tǒng)總體功能出發(fā)，設(shè)計了壓鑄模具材料表、激光工藝參數(shù)表、熔凝結(jié)果表、仿生形態(tài)表、熱疲勞性能表和摩擦磨損性能表以及表與表之間的關(guān)系，建立了壓鑄模具激光仿生強化工藝數(shù)據(jù)庫。以Visual C++6.0為開發(fā)平臺，采用模塊化編程設(shè)計人機交互界面，，開發(fā)出具有自主知識產(chǎn)權(quán)的壓鑄模具激光仿生強化工藝決策系統(tǒng)。 最后，以DIEVAR模具鋼為例，從激光熔凝單元體的橫截面尺寸、組織形態(tài)、顯微硬度和熱疲勞性能四個方面對壓鑄模具激光仿生強化工藝決策系統(tǒng)的決策精度和整體性能進行了測試。結(jié)果顯示：系統(tǒng)穩(wěn)定性良好；決策精度能夠達到1.33%；經(jīng)反求的激光工藝參數(shù)熔凝加工后，材料硬度提高了40-100HV，熱疲勞性能也顯著提高。將該系統(tǒng)應(yīng)用于實際模具強化工藝制定中，經(jīng)生產(chǎn)運行顯示，強化后的模具壽命提高約70%。
[Abstract]:The application of laser surface bionic strengthening technology to improve the service life of mould is a new topic in recent years. It is an important application of bionics in the field of mould industry and brings great economic benefits to die industry. The standardization of laser bionic strengthening technology is helpful to its development and popularization in the future. Based on the theory of artificial neural network, the highly nonlinear relationship between the shape of laser coagulation unit and the laser processing technology is regarded as a "black box", and the cross section size of the fusion unit is regarded as the input of neural network. Laser process parameters are used as the output of neural network, and the inverse model of laser melting parameters is established. The structural analysis method is used to divide the function modules and design the structure of the process decision system for the laser bionic strengthening of die casting die. Based on the overall function of the system, the material table of die casting die, the parameter table of laser process, the result table of melting, the bionic form table, the table of thermal fatigue performance and the table of friction and wear performance, and the relationship between the table and the table are designed. The database of laser bionic strengthening process for die casting die was established. With Visual C 6.0 as the development platform, the man-machine interactive interface is designed by modular programming, and a die casting mould laser bionic strengthening process decision system with independent intellectual property rights is developed. Finally, taking DIEVAR die steel as an example, the cross section size and microstructure of laser solidifying unit are analyzed. Four aspects of microhardness and thermal fatigue performance of the die casting die laser bionic strengthening process decision-making system decision-making accuracy and overall performance were tested. The results show that the stability of the system is good, the decision accuracy can reach 1.33, and the hardness of the material is increased by 40 ~ 100 HVV, and the thermal fatigue property is also improved significantly after the reverse laser processing parameters are fused and solidified. The system is applied to the actual die strengthening process. The operation shows that the service life of the strengthened die is increased by about 70%.
【學(xué)位授予單位】：太原科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TG665;TG233.1

【參考文獻】

相關(guān)期刊論文 前10條

1 王大康,劉永峰,石亞寧;智能化CAPP系統(tǒng)的工藝決策[J];北京工業(yè)大學(xué)學(xué)報;2003年02期

2 李紅良;;智能決策支持系統(tǒng)的發(fā)展現(xiàn)狀及應(yīng)用展望[J];重慶工學(xué)院學(xué)報(自然科學(xué)版);2009年10期

3 路曉;管自生;;自清潔仿生表面的濕潤性及其制備的進展[J];材料導(dǎo)報;2006年06期

4 周宏,張志輝,任露泉,宋起飛,陳莉;仿生非光滑表面45~#鋼模具的熱疲勞性能[J];材料科學(xué)與工藝;2004年06期

5 潘清躍,宋仁國,張奇志,黃衛(wèi)東,周堯和;基于人工神經(jīng)網(wǎng)絡(luò)-遺傳算法的1Cr18Ni9Ti鋼激光表面熔凝工藝優(yōu)化[J];材料研究學(xué)報;1998年03期

6 胡巧玲,李曉東,沈家驄;仿生結(jié)構(gòu)材料的研究進展[J];材料研究學(xué)報;2003年04期

7 劉曉芳,劉會金,陳允平;人工智能技術(shù)在電能質(zhì)量分析中的應(yīng)用[J];電工技術(shù)雜志;2004年12期

8 劉白林;劉震;;基于VC6.0的武器系統(tǒng)故障診斷專家系統(tǒng)實現(xiàn)[J];彈箭與制導(dǎo)學(xué)報;2006年03期

9 萬魯河;王繼富;臧淑英;崔金香;;基于空間數(shù)據(jù)挖掘技術(shù)的森林防火智能決策支持系統(tǒng)研究[J];地理科學(xué);2009年03期

10 湯素麗;羅宇鋒;;人工神經(jīng)網(wǎng)絡(luò)技術(shù)的發(fā)展與應(yīng)用[J];電腦開發(fā)與應(yīng)用;2009年10期

本文編號：2209278