本文關(guān)鍵詞： 激光輔助水射流 氮化硅陶瓷 材料去除機(jī)理 微細(xì)銑削 預(yù)測(cè)模型　出處：《山東大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：激光輔助水射流微細(xì)加工技術(shù)綜合了激光無(wú)切削力、無(wú)污染和水射流無(wú)熱損傷、加工范圍廣的優(yōu)點(diǎn)。為了研究激光輔助水射流加工氮化硅陶瓷的性能和拓展該技術(shù)的應(yīng)用范圍,本論文研究了激光輔助水射流微細(xì)加工氮化硅陶瓷的去除機(jī)理及工藝。實(shí)驗(yàn)研究了激光輔助水射流加工工藝參數(shù)對(duì)微細(xì)切槽性能的影響。揭示了工件原始表面形貌對(duì)激光輔助水射流加工表面形貌的影響和材料去除機(jī)理。建立了激光輔助水射流微細(xì)銑削氮化硅陶瓷的槽深和表面粗糙度的預(yù)測(cè)模型。構(gòu)建了基于BP神經(jīng)網(wǎng)絡(luò)的激光輔助水射流微細(xì)銑削氮化硅陶瓷微結(jié)構(gòu)尺寸的預(yù)測(cè)模型。研究了激光燒蝕、水輔助激光和激光輔助水射流等技術(shù)加工氮化硅陶瓷時(shí)的加工性能,探討了工藝參數(shù)對(duì)槽寬和槽深、表面粗糙度和熱影響區(qū)寬度的影響。結(jié)果表明,激光輔助水射流加工技術(shù)能獲得具有較大深寬比的槽形,其熱損傷小于激光燒蝕和水輔助激光加工的熱損傷。水射流壓力對(duì)槽寬和熱影響區(qū)寬度的影響最顯著,激光脈沖能量對(duì)槽深的影響最顯著,水射流沖擊角度對(duì)表面粗糙度的影響最顯著。研究了工件原始表面形貌對(duì)加工表面成形情況的影響。結(jié)果表明,加工氮化硅陶瓷的加工區(qū)存在熱解區(qū)、氧化損傷區(qū)和軟化變形區(qū)。工件原始表面的加工紋理不利于改善表面加工質(zhì)量,在原始光滑表面上形成的熱影響區(qū)小于在原始粗糙表面上形成的熱影響區(qū)。研究了激光輔助水射流加工氮化硅陶瓷時(shí)的材料去除機(jī)理。結(jié)果表明,激光輔助水射流加工氮化硅陶瓷的材料去除機(jī)理是水射流沖擊去除被激光加熱軟化的材料和激光照射核心區(qū)熱解的材料。建立了槽深和表面粗糙度的預(yù)測(cè)模型,該模型的預(yù)測(cè)結(jié)果與實(shí)驗(yàn)結(jié)果基本吻合,誤差較小。研究了工藝參數(shù)的交互作用對(duì)槽深和表面粗糙度的影響。橫向偏置距離對(duì)槽深影響不顯著,但對(duì)加工表面粗糙度影響顯著;激光脈沖能量對(duì)槽深的影響最顯著;水射流壓力對(duì)表面粗糙度的影響最顯著。激光脈沖能量與水射流壓力的交互作用對(duì)槽深和表面粗糙度的影響最顯著。實(shí)驗(yàn)證明激光輔助水射流微細(xì)加工技術(shù)微細(xì)銑削氮化硅陶瓷的微結(jié)構(gòu)和微結(jié)構(gòu)陣列是可行的。單個(gè)微槽的邊界線平直完整,沒(méi)有出現(xiàn)崩邊和邊緣熱損傷。微槽陣列分布均勻,相鄰微槽之間沒(méi)有干涉。建立了基于BP人工神經(jīng)網(wǎng)絡(luò)的微結(jié)構(gòu)尺寸預(yù)測(cè)模型,并進(jìn)行了實(shí)驗(yàn)驗(yàn)證。結(jié)果表明,預(yù)測(cè)值和實(shí)驗(yàn)值之間具有較好的一致性,整體預(yù)測(cè)平均誤差小于10%。
[Abstract]:The laser-assisted water jet micro-machining technology has the advantages of no cutting force, no pollution, no heat damage and wide range of processing. In order to study the properties of laser assisted water jet machining silicon nitride ceramics and expand its application scope, In this paper, the removal mechanism and process of laser assisted water jet micromachining of silicon nitride ceramics are studied. The effect of laser assisted water jet processing parameters on the performance of the micro-groove is experimentally studied. The original surface shape of the workpiece is revealed. Effect of appearance on surface morphology and material removal mechanism of laser assisted water jet machining. A prediction model of groove depth and surface roughness for laser assisted water jet micro-milling of silicon nitride ceramics was established, and a BP neural network was constructed. The prediction model of microstructural size of silicon nitride ceramics by laser-assisted water jet micro-milling was developed. Laser ablation was studied. The machining properties of silicon nitride ceramics by water-assisted laser and laser-assisted water jet are discussed. The effects of process parameters on groove width, groove depth, surface roughness and heat affected zone width are discussed. The laser-assisted water jet machining technology can obtain the groove shape with a larger ratio of depth to width, and its thermal damage is smaller than that of laser ablation and water-assisted laser processing. The water jet pressure has the most significant effect on the groove width and the width of the heat-affected zone. The influence of laser pulse energy on groove depth and the impact angle of water jet on the surface roughness is most significant. The effect of the original surface morphology of the workpiece on the shaping of the machined surface is studied. There are pyrolysis zone, oxidation damage zone and softening deformation zone in the processing zone of silicon nitride ceramics. The processing texture of the original surface of the workpiece is not conducive to improving the surface processing quality. The heat affected zone formed on the original smooth surface is smaller than that formed on the original rough surface. The material removal mechanism of laser assisted water jet processing of silicon nitride ceramics is studied. The material removal mechanism of laser assisted water jet machining silicon nitride ceramics is water jet impingement removal of materials heated and softened by laser and pyrolysis of core region irradiated by laser. A prediction model of groove depth and surface roughness is established. The prediction results of the model are in good agreement with the experimental results and the error is small. The influence of the interaction of process parameters on the groove depth and surface roughness is studied. The influence of transverse offset distance on the groove depth is not significant, but on the machined surface roughness is significant. The effect of laser pulse energy on groove depth is most significant. The influence of water jet pressure on surface roughness is most significant. The interaction between laser pulse energy and water jet pressure has the most significant effect on groove depth and surface roughness. Milling silicon nitride ceramic microstructures and microstructural arrays are feasible. The boundaries of single microgrooves are straight and complete, There is no flanging and edge thermal damage. The microgroove array is uniformly distributed and there is no interference between adjacent microgrooves. A prediction model of microstructural size based on BP artificial neural network is established and verified by experiments. There is good consistency between the predicted value and the experimental value, and the average error of the whole prediction is less than 10%.
【學(xué)位授予單位】：山東大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TQ174.6

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