Transactions of Nonferrous Metals Society of China The Chinese Journal of Nonferrous Metals

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中國(guó)有色金屬學(xué)報(bào)

ZHONGGUO YOUSEJINSHU XUEBAO

第30卷    第7期    總第256期    2020年7月

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文章編號(hào):1004-0609(2020)-07-1644-09
隨機(jī)森林回歸分析在激光熔覆形貌預(yù)測(cè)中的應(yīng)用
梁旭東,王 煒,趙 凱,郝云波,楊 萍,朱忠良

(上海航天設(shè)備制造總廠有限公司,上海 200245)

摘 要: 激光熔覆修復(fù)過(guò)程中單道熔覆層形貌極大地影響修復(fù)效果,但多工藝參數(shù)對(duì)熔覆層影響的耦合作用機(jī)制尚未被研究清楚,因此,獲得不同工藝參數(shù)組合與熔覆層尺寸的定量關(guān)系是亟待解決的難題。以Inconel 625合金的激光熔覆修復(fù)為背景,采用隨機(jī)森林(Random Forest,RF)算法構(gòu)建了激光熔覆工藝參數(shù)(激光功率、掃描速度、送粉速率)到單道熔覆層尺寸的回歸模型,將模型用于特定熔覆參數(shù)組下單道尺寸的預(yù)測(cè);同時(shí)在給定期望的單道熔覆層尺寸參數(shù)時(shí),基于Gini不純度選擇強(qiáng)關(guān)聯(lián)因子構(gòu)建了工藝參數(shù)預(yù)測(cè)模型。結(jié)果表明,激光熔覆工藝參數(shù)預(yù)測(cè)模型的預(yù)測(cè)誤差小于4%,能夠準(zhǔn)確地估計(jì)加工特定單道熔覆層截面幾何形狀所需的激光熔覆工藝參數(shù)。

 

關(guān)鍵字: 隨機(jī)森林;激光熔覆;特征篩選;參數(shù)預(yù)測(cè)

Application of random forest regression analysis in trace geometry prediction of laser cladding
LIANG Xu-dong, WANG Wei, ZHAO Kai, HAO Yun-bo, YANG Ping, ZHU Zhong-liang

Shanghai Aerospace Equipments Manufacturer Co., Ltd., Shanghai 200245, China

Abstract:The shape of the single cladding layer in the laser cladding repair process greatly affects the quality of the repair. It is necessary to control the morphology of the cladding layer to achieve high-quality repair. However, the coupling mechanism of multi-process parameters on the cladding layer was studied clearly. Therefore, obtaining the quantitative relationship between different process parameter combinations and the size of the cladding layer is an urgent problem to be solved. Based on the laser cladding repair of Inconel 625 alloy, a random forest (RF) algorithm was used to construct a regression model of laser cladding process parameter set (laser power, scanning speed, powder feeding rate) to single pass cladding size. The model was used to predict the single track size of a specific cladding parameter group. At the same time, the strong correlation factors were selected based on the Gini impurity and used to build a process parameter prediction model. The results show that the prediction error of the laser cladding process parameter prediction model is less than 4%, which can accurately estimate the laser cladding process parameters required to process the specific single-pass cladding cross-section geometry.

 

Key words: random forest; laser cladding; feature selection; parameter prediction

ISSN 1004-0609
CN 43-1238/TG
CODEN: ZYJXFK

ISSN 1003-6326
CN 43-1239/TG
CODEN: TNMCEW

主管:中國(guó)科學(xué)技術(shù)協(xié)會(huì) 主辦:中國(guó)有色金屬學(xué)會(huì) 承辦:中南大學(xué)
湘ICP備09001153號(hào) 版權(quán)所有:《中國(guó)有色金屬學(xué)報(bào)》編輯部
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