航空發動機阻燃鈦合金力學性能預測及成分優化

李雅迪; 弭光寶; 李培杰; 曹京霞; 黃旭

doi:10.13374/j.issn2095-9389.2020.10.12.001

航空發動機阻燃鈦合金力學性能預測及成分優化

doi: 10.13374/j.issn2095-9389.2020.10.12.001

李雅迪^{1, 2},
弭光寶^{2, 3, ,},
李培杰¹,
曹京霞^{2, 3},
黃旭^{2, 3}

1.
清華大學新材料國際研發中心，北京 100084
2.
中國航發北京航空材料研究院鈦合金研究所，北京 100095
3.
中國航發先進鈦合金重點實驗室，北京 100095

基金項目: 國家自然科學基金資助項目（51471155，U2141222）；國家科技重大專項資助項目（J2019-Ⅷ-0003-0165）

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通訊作者:
E-mail: miguangbao@163.com

中圖分類號: TG146.2
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- 被引次數: 0
出版歷程
- 收稿日期: 2020-10-12
- 網絡出版日期: 2021-04-16
- 刊出日期: 2022-06-25

Predicting the mechanical properties and composition optimization of a burn-resistant titanium alloy for aero-engines

LI Ya-di^{1, 2},
MI Guang-bao^{2, 3
, ,},
LI Pei-jie¹,
CAO Jing-xia^{2, 3},
HUANG Xu^{2, 3}

1.
National Center of Novel Materials for International Research, Tsinghua University, Beijing 100084, China
2.
Titanium Alloys Research Institute, AECC Beijing Institute of Aeronautical Materials, Beijing 100095, China
3.
Aviation Key Laboratory of Science and Technology on Advanced Titanium Alloys, AECC Beijing Institute of Aeronautical Materials, Beijing 100095, China

More Information

Corresponding author: E-mail: miguangbao@163.com

摘要

摘要: 采用支持向量機算法，在實驗數據的基礎上，建立航空發動機阻燃鈦合金的合金化元素與力學性能關系模型，分析合金化元素對力學性能的影響規律。模型的輸入參數為V、Al、Si和C元素，輸出參數為室溫拉伸性能（抗拉強度、屈服強度、延伸率和斷面收縮率）。結果表明：各個力學性能支持向量機模型的線性相關系數均在0.975以上，具有較高的預測能力；各個力學性能測試樣本實驗值與模型預測值的絕對百分誤差均在5%以內，具有良好的泛化能力，能夠有效地反映出阻燃鈦合金的合金化元素與力學性能之間的定量關系，進而實現對該合金的成分優化。對于Ti?35V?15Cr阻燃鈦合金，可以通過加入質量分數為0～0.1%的Si元素和質量分數為0.05%～0.125%的C元素，并減少質量分數為2%～5%的V元素，來提高力學性能；對于Ti?25V?15Cr阻燃鈦合金，可以通過加入質量分數為1.5%～1.8%的Al元素和質量分數為0.15%～0.2%的C元素，來提高力學性能。
- 阻燃鈦合金 /
- 支持向量機算法 /
- 合金化元素 /
- 力學性能 /
- 成分優化
Abstract: Lightweight high-temperature titanium alloys are a key material for aero-engines. With the increasing use of new titanium alloys in aero-engines, titanium fire has become a typical catastrophic fault that plagues material design and selection. A burn-resistant titanium alloy is a special material developed to deal with the problem of titanium fire. Its application in aero-engines has become one of the key technologies for the prevention and control of titanium fire. Therefore, explaining the influence of the alloying elements of burn-resistant titanium alloys on mechanical properties is important to provide an important theoretical basis for the design and application of these alloys. Based on the experimental data, the relationship model between the alloying elements and mechanical properties of a burn-resistant titanium alloy was established using a support vector machine algorithm, and the effect of the alloying elements on the mechanical properties was analyzed. The input parameters of the model were V, Al, Si, and C elements, and the output parameters were the room temperature tensile properties (tensile strength, yield strength, elongation, and the reduction of area). Results show that the linear correlation coefficient of each mechanical property of the SVM model is above 0.975, which signifies good prediction ability. The absolute percentage error between the predicted and experimental values of each mechanical property test sample is within 5%, indicating good generalization ability and an effective reflection of the quantitative relationship between the alloying elements and mechanical properties of the burn-resistant titanium alloy for optimizing the composition of the alloy. The mechanical properties of the Ti–35V–15Cr alloy can be improved by adding 0–0.1% Si element and 0.05%–0.125% C element and reducing 2%–5% V element. Meanwhile, the mechanical properties of the Ti–25V–15Cr alloy can be improved by adding 1.5%–1.8% Al element and 0.15%–0.2% C element.
- burn-resistant titanium alloy /
- support vector machine algorithm /
- alloying elements /
- mechanical properties /
- composition optimization

HTML全文

圖 1 力學性能實驗值與模型預測值的線性相關性分析.(a)抗拉強度; (b)屈服強度; (c)伸長率; (d)斷面收縮率

Figure 1. Linear correlation analysis between the experimental and predicted values using SVM: (a) tensile strength; (b) yield strength; (c) elongation; (d) reduction of area

下載: 全尺寸圖片幻燈片

圖 2 V元素含量對Ti?V?Cr系阻燃鈦合金力學性能的影響.(a)強度; (b)塑性

Figure 2. Influence of the V element content on the mechanical properties of the Ti?V?Cr burn-resistant titanium alloy: (a) strength；(b) ductility

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圖 3 Al元素含量對Ti?V?Cr系阻燃鈦合金力學性能的影響。（a）強度；（b）塑性

Figure 3. Influence of the Al element content on the properties of the Ti?V?Cr burn-resistant titanium alloy: (a) strength; (b) ductility

下載: 全尺寸圖片幻燈片

圖 4 Si元素含量對Ti?V?Cr系阻燃鈦合金力學性能的影響。（a）強度；（b）塑性

Figure 4. Influence of the Si element content on the properties of the Ti?V?Cr burn-resistant titanium alloy: (a) strength; (b) ductility

下載: 全尺寸圖片幻燈片

圖 5 C元素含量對Ti?V?Cr系阻燃鈦合金力學性能的影響。（a）強度；（b）塑性

Figure 5. Influence of the C element content on the properties of the Ti?V?Cr burn-resistant titanium alloy: (a) strength; (b) ductility

下載: 全尺寸圖片幻燈片

表 1 Ti?V?Cr系阻燃鈦合金實驗值與支持向量機模型預測值的誤差比較　　　　　　　　　　　　

Table 1. Error comparison of the mechanical properties of the experimental data with the predicted values using SVM

Sample	Mass fraction/%				Comparison	Mechanical properties
Sample	V	Al	Si	C	Comparison	Tensile strength/MPa	Yield strength/MPa	Elongation/%	Reduction of area/%
1	35.00	0	0	0	Experimental	1042	1028	10.0	15.0
					Predicted	1042.10	1028.10	10.10	15.10
					Absolute error/%	0.01	0.01	1.00	0.67
2	35.00	0	0.25	0	Experimental	1060	1032	15.1	19.5
					Predicted	1059.90	1031.90	15.00	19.40
					Absolute error/%	0.01	0.01	0.66	0.52
3	35.00	0	0.50	0	Experimental	1111	1080	7.3	11.0
					Predicted	1110.90	1079.90	7.40	11.10
					Absolute error/%	0.01	0.01	1.37	0.91
4	35.00	0	0	0.08	Experimental	1071	1005	18.4	33.0
					Predicted	1060.60	997.32	18.30	32.90
					Absolute error%	0.97	0.76	0.55	0.30
5	35.00	0	0.50	0.08	Experimental	1065	1005	19.0	33.5
					Predicted	1065.10	1005.10	18.90	33.40
					Absolute error/%	0.01	0.01	0.52	0.30
6	35.00	0	0	0.15	Experimental	1034	952	21.0	38.1
					Predicted	1034.10	952.10	21.10	38.20
					Absolute error/%	0.01	0.01	0.47	0.26
7^*	25.50	2.60	0	0.27	Experimental	1070	1050	16.0	22.5
					Predicted	1069.90	1049.90	14.66	23.11
					Absolute error/%	0.01	0.01	8.39	2.70
8^*	20.00	0	0.20	0	Experimental	960	933	24.5	46.0
					Predicted	960.10	933.10	24.40	45.90
					Absolute error/%	0.01	0.01	0.41	0.22
9^*	30.00	0	0.20	0	Experimental	1025	973	20.0	38.5
					Predicted	1024.90	973.10	19.90	32.84
					Absolute error/%	0.01	0.01	0.50	14.70
10	35.00	0	0.30	0.10	Experimental	1025	964	17.2	33.0
					Predicted	1024.90	963.90	17.30	33.10
					Absolute error/%	0.01	0.01	0.58	0.30
11	35.20	0	0.17	0.07	Experimental	1026	963	16.5	29.4
					Predicted	1026.10	970.16	16.60	29.50
					Absolute error/%	0.01	0.74	0.61	0.34
12^**	25.20	0	0.21	0	Experimental	969	942	18.5	30.4
					Predicted	986.21	936.34	19.18	31.78
					Absolute error/%	1.78	0.60	3.68	4.54
Note: *is test set and the rest are training sets; are the data from references [12,16].

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