CN113118458A - Prediction method for tensile property of metal component formed by selective laser melting - Google Patents

Prediction method for tensile property of metal component formed by selective laser melting Download PDF

Info

Publication number
CN113118458A
CN113118458A CN202110421256.XA CN202110421256A CN113118458A CN 113118458 A CN113118458 A CN 113118458A CN 202110421256 A CN202110421256 A CN 202110421256A CN 113118458 A CN113118458 A CN 113118458A
Authority
CN
China
Prior art keywords
tensile property
vth
metal
laser
tensile
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202110421256.XA
Other languages
Chinese (zh)
Other versions
CN113118458B (en
Inventor
郭慧
孙文海
白凤霞
贾晓东
孙庆辉
陈新贵
胡强
张友亮
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Institute of Applied Physics of Jiangxi Academy of Sciences
Original Assignee
Institute of Applied Physics of Jiangxi Academy of Sciences
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Institute of Applied Physics of Jiangxi Academy of Sciences filed Critical Institute of Applied Physics of Jiangxi Academy of Sciences
Priority to CN202110421256.XA priority Critical patent/CN113118458B/en
Publication of CN113118458A publication Critical patent/CN113118458A/en
Application granted granted Critical
Publication of CN113118458B publication Critical patent/CN113118458B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B33—ADDITIVE MANUFACTURING TECHNOLOGY
    • B33Y—ADDITIVE MANUFACTURING, i.e. MANUFACTURING OF THREE-DIMENSIONAL [3-D] OBJECTS BY ADDITIVE DEPOSITION, ADDITIVE AGGLOMERATION OR ADDITIVE LAYERING, e.g. BY 3-D PRINTING, STEREOLITHOGRAPHY OR SELECTIVE LASER SINTERING
    • B33Y50/00—Data acquisition or data processing for additive manufacturing
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P10/00—Technologies related to metal processing
    • Y02P10/25—Process efficiency

Landscapes

  • Chemical & Material Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Manufacturing & Machinery (AREA)
  • Materials Engineering (AREA)
  • Investigating Strength Of Materials By Application Of Mechanical Stress (AREA)
  • Powder Metallurgy (AREA)

Abstract

A prediction method for tensile property of a laser selective melting formed metal component comprises the following steps: (1) 3D printing is carried out on the metal powder in a selective laser area to obtain metal components with different process parameters of laser power P, scanning speed v, scanning interval t and powder layer spreading thickness h; (2) the obtained different metal components are subjected to tensile property test to obtain the yield strength sigma thereofyAnd an elongation δ; (3) with vth/P value as abscissa and sigma respectivelyyAnd δ is the ordinate plotting σy-vth/P and δ -vth/P relationship maps; and the slope k of the fitting curve is obtained by linear fitting1、k2And intercept b of fitted curve1、b2(ii) a (4) Curve fitting by stepLine further predicts the maximum value of tensile properties sigma of metal memberyAnd δ; and calculating to obtain a predicted value of the tensile property. The 3D printing formed part obtained by the method is uniform and compact, and has high strong plasticity; the method can simply and conveniently realize the high-efficiency prediction of the tensile property.

Description

Prediction method for tensile property of metal component formed by selective laser melting
Technical Field
The invention relates to a prediction method for tensile property of a metal component formed by selective laser melting, belonging to the technical field of 3D printing.
Background
The 316L stainless steel has good corrosion resistance and oxidation resistance, and is widely applied to the fields of petroleum pipelines, nuclear power stations, marine equipment and the like. The selective laser melting technology is one of the main means of metal additive manufacturing, and has the advantage of finely forming metal parts with complex structures, but the process parameters in the selective laser melting forming process influence internal defects, thereby having great influence on the performance of formed parts. Wherein, the tensile property (including yield strength, elongation and the like) is the basic property of the structural material, which not only affects other static mechanical properties such as hardness, toughness and the like, but also directly affects the service performance of the material in final use.
In view of the fundamental importance of tensile properties, on the one hand, the evaluation of tensile properties during the development of engineering materials is indispensable and, on the other hand, the combined improvement of tensile strength and plasticity is often targeted. Therefore, accurate evaluation of tensile properties plays an important role in engineering material development and application. However, at present, the prediction of the tensile property of 3D printing metal materials is very deficient, so that a direct quantitative relationship between the tensile property and laser process parameters is established, the rapid prediction of the tensile property is realized, and the problem to be solved in the field of 3D printing at present is urgently solved.
Disclosure of Invention
The invention aims to establish the relation between a laser process and tensile property and improve the strong plasticity of the molten and formed stainless steel in a laser selection area.
The technical scheme of the invention is as follows, a prediction method of tensile property of a metal component formed by selective laser melting comprises the following steps:
(1) 3D printing is carried out on the metal powder in a selective laser area to obtain metal components with different process parameters of laser power P, scanning speed v, scanning interval t and powder layer spreading thickness h;
(2) the obtained different metal components are subjected to tensile property test to obtain the yield strength sigma thereofyAnd an elongation δ;
(3) by using process and performance data, with vth/P value as abscissa and sigma respectivelyyAnd δ is the ordinate plotting σy-vth/P and δ -vth/P relationship maps; and the parameter k is obtained through linear fitting1，k2，b1And b2Wherein k is1And k2As the slope of the fitted curve, b1And b2Is the intercept of the fitted curve;
(4) further predicting the maximum value sigma of the tensile property of the metal member through the curve fitted in the step (3)yAnd δ; or the process parameters P, v, t, h and the parameter value k1，k2，b1And b2Directly into the following equation:
σy＝k1(vth/P)+b1
δ＝k2(vth/P)+b2
and calculating to obtain a predicted value of the tensile property.
The metal powder is 316 stainless steel, and comprises, by mass, 16.0-18.0% of Cr, 10.0-14.0% of Ni, 2.0-3.0% of Mo, 2.00% of Mn, 1.00% of Si, and the balance of Fe.
The protective gas in the 3D printing process in the step (1) is high-purity argon; pre-laying a layer of stainless steel powder before printing, wherein the particle size of the powder is controlled to be 25-53 mu m; preheating the substrate to 80 ℃; the scraper is a steel scraper.
In the step (1), the laser power is 200-300W, the scanning speed is 800-1000mm/s, the scanning distance is 0.1mm, and the thickness of the powder layer is 0.02-0.04 mm.
And (2) carrying out tensile property test on the series metal members under the same experimental conditions.
And (3) fitting the data by adopting professional data processing software, wherein the restriction correlation degree is not lower than 0.95.
The method has the beneficial effects that the 3D printing formed part obtained by the method is uniform and compact, and has high strong plasticity; the method can simply and conveniently realize the high-efficiency prediction of the tensile property.
Drawings
FIG. 1 is a flow chart of a method for predicting tensile properties of a laser selective melting formed metal component;
FIG. 2 is a graph of tensile engineering stress-strain curves of selective laser melting 316L stainless steel under different laser process parameters eta ═ vth/P;
FIG. 3 is ay- (vth/P) linear relationship diagram;
FIG. 4 is a graph of the delta- (vth/P) linear relationship;
FIG. 5 shows selective laser melting at 0.009mm3Tensile fracture morphology scan at/J;
FIG. 6 shows selective laser melting at 0.012mm3Tensile fracture morphology scan at/J;
FIG. 7 shows selective laser melting at 0.013mm3Tensile fracture morphology scan at/J;
FIG. 8 shows selective laser melting at 0.016mm3Tensile fracture morphology scan at/J;
FIG. 9 shows selective laser melting at 0.018mm3Tensile fracture morphology scan at/J;
FIG. 10 is the result of prediction of tensile properties, and the selective laser melting is 0.020mm3Tensile fracture morphology scan at/J.
Detailed Description
A specific embodiment of the present invention is shown in fig. 1.
The embodiment of the method for predicting the tensile property of the laser selective melting forming metal component comprises the following steps:
(1) melting metal powder in a laser selective area for 3D printing to obtain metal components with different process parameters such as laser power P, scanning speed v, scanning interval t, powder layer spreading thickness h and the like;
the protective gas in the 3D printing process is high-purity argon; pre-laying a layer of stainless steel powder before printing, wherein the particle size of the powder is controlled to be 25-53 mu m; preheating the substrate to 80 ℃; the scraper is a steel scraper.
The tensile engineering stress-strain curve of the selective laser melting 316L stainless steel of the embodiment under different laser process parameters eta ═ vth/P is shown in FIG. 2.
(2) The tensile property of the metal component is tested to obtain the yield strength sigmayAnd an elongation δ.
(3) By using process and performance data, with vth/P value as abscissa and sigma respectivelyyAnd δ is the ordinate plotting σy- (vth/P) and delta- (vth/P) maps, and by linear fitting, ay2151.96 × vth/P +629.98 and δ 1782.50 × vth/P + 12.27. As shown in fig. 3 and 4.
The larger η ═ vth/P is, the smaller the melt pool size becomes, the lower the internal defects become, and the more excellent the tensile properties become, where η represents the energy dispersion.
When the formula is substituted by P200W, the scanning speed v 1000mm/s, the scanning distance t 0.1mm and the powder layer thickness h 0.04mm, eta 0.020 mm/P3J, can yield σy673MPa and δ 47.9%, which is very close to the tensile test results, with minimal fracture defects, as shown in fig. 10.
FIG. 5 shows the selective laser melting at 0.009mm for this example3Tensile fracture morphology scan at/J; the laser power P is 300W, the scanning speed v is 900mm/s, the scanning distance t is 0.1mm and the powder layer thickness h is 0.03mm, at this time, the yield strength is 652MPa, the plasticity is 28.54%, and at this time, the fracture defects are the most.
FIG. 6 shows that the laser selective melting is performed at 0.012mm in the present embodiment3Tensile fracture morphology scan at/J; the laser power P is 300W, the scanning speed v is 900mm/s,the scanning distance t was 0.1mm and the powder coating thickness h was 0.04mm, and the yield strength was 653MPa and the plasticity was 33.8%.
FIG. 7 shows that the laser of this embodiment is selectively melted to 0.013mm3Tensile fracture morphology scan at/J; the laser power P was 250W, the scanning speed v was 800mm/s, the scanning pitch t was 0.1mm, and the powder coating thickness h was 0.04mm, at which time the yield strength was 658MPa and the plasticity was 35.9%.
FIG. 8 shows that the laser selective melting is performed at 0.016mm in the present embodiment3Tensile fracture morphology scan at/J; the laser power P is 250W, the scanning speed v is 1000mm/s, the scanning distance t is 0.1mm and the powder layer thickness h is 0.04mm, at which time the yield strength is 669MPa and the plasticity is 39.8%.
FIG. 9 shows the selective laser melting at 0.018mm for this example3Tensile fracture morphology scan at/J; the laser power P is 200W, the scanning speed v is 900mm/s, the scanning distance t is 0.1mm and the powder coating thickness h is 0.04mm, the yield strength is 670MPa and the plasticity is 45.2%.

Claims (6)

1. A method for predicting tensile properties of a laser selective melting formed metal member, the method comprising the steps of:
(1) 3D printing is carried out on the metal powder in a selective laser area to obtain metal components with different process parameters of laser power P, scanning speed v, scanning interval t and powder layer spreading thickness h;
(2) the obtained different metal components are subjected to tensile property test to obtain the yield strength sigma thereofyAnd an elongation δ;
(3) by using process and performance data, with vth/P value as abscissa and sigma respectivelyyAnd δ is the ordinate plotting σy-vth/P and δ -vth/P relationship maps; and the parameter k is obtained through linear fitting1，k2，b1And b2Wherein k is1And k2As the slope of the fitted curve, b1And b2Is the intercept of the fitted curve;
(4) further predicting the maximum value sigma of the tensile property of the metal member through the curve fitted in the step (3)yAnd δ; or the process parameters P, v,t, h and parameter value k1，k2，b1And b2Directly into the following equation:
σy＝k1(vth/P)+b1
δ＝k2(vth/P)+b2
and calculating to obtain a predicted value of the tensile property.
2. The method of claim 1, wherein the powdered metal is 316 stainless steel, and the composition comprises, by mass, 16.0-18.0% of Cr, 10.0-14.0% of Ni, 2.0-3.0% of Mo, 2.00% of Mn, 1.00% of Si, and the balance Fe.
3. The method for predicting the tensile property of a laser selective melting formed metal component according to claim 1, wherein the protective gas in the 3D printing process is high-purity argon gas; pre-laying a layer of stainless steel powder before printing, wherein the particle size of the powder is controlled to be 25-53 mu m; preheating the substrate to 80 ℃; the scraper is a steel scraper.
4. The method as claimed in claim 1, wherein the laser power is 200-300W, the scanning speed is 800-1000mm/s, the scanning distance is 0.1mm, and the thickness of the powder layer is 0.02-0.04 mm.
5. The method of claim 1, wherein the step (2) of performing the tensile property test on the series of metal members under the same experimental conditions.
6. The method for predicting the tensile property of a laser selective melting formed metal member according to claim 1, wherein the step (3) is performed by fitting the data with professional data processing software, and the constraint correlation degree is not lower than 0.95.
CN202110421256.XA 2021-04-20 2021-04-20 Prediction method for tensile property of metal component formed by selective laser melting Active CN113118458B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110421256.XA CN113118458B (en) 2021-04-20 2021-04-20 Prediction method for tensile property of metal component formed by selective laser melting

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110421256.XA CN113118458B (en) 2021-04-20 2021-04-20 Prediction method for tensile property of metal component formed by selective laser melting

Publications (2)

Publication Number Publication Date
CN113118458A true CN113118458A (en) 2021-07-16
CN113118458B CN113118458B (en) 2023-04-07

Family

ID=76778201

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110421256.XA Active CN113118458B (en) 2021-04-20 2021-04-20 Prediction method for tensile property of metal component formed by selective laser melting

Country Status (1)

Country Link
CN (1) CN113118458B (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115415542A (en) * 2022-07-19 2022-12-02 闽都创新实验室 Method for predicting performance of duplex stainless steel 3D printing piece based on response surface method
CN117110346A (en) * 2023-10-23 2023-11-24 中国空气动力研究与发展中心设备设计与测试技术研究所 Analysis method for microstructure of laser selective melting plate

Citations (23)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1253491A2 (en) * 2001-04-24 2002-10-30 Bayer Aktiengesellschaft Hybrid model and method for determining the mechanical properties and processing properties of an injection moulded article
CN101975704A (en) * 2010-10-08 2011-02-16 中国船舶重工集团公司第七二五研究所 Method for predicting vibration fatigue performance of viscoelastic material
CN104245982A (en) * 2012-04-18 2014-12-24 日本发条株式会社 Magnesium alloy member and method for manufacturing same
CN105930553A (en) * 2016-04-11 2016-09-07 南京航空航天大学 Method for predicting tensile strength of nickel-base superalloy gap
CN106694883A (en) * 2017-02-10 2017-05-24 江西省科学院应用物理研究所 Synchronous powder spreading type metal laser 3D printing powder feeding device
CN106735205A (en) * 2016-12-08 2017-05-31 鑫精合激光科技发展（北京）有限公司 A kind of technological parameter of metal material 3D printing determines method
CN107356479A (en) * 2017-07-12 2017-11-17 南方科技大学 Metal material tensile property evaluation method based on selective laser melting technology
CN108197384A (en) * 2017-12-30 2018-06-22 大族激光科技产业集团股份有限公司 The parameter optimization method of powder laser 3D printing moulding process
CN108256179A (en) * 2017-12-29 2018-07-06 沈阳工业大学 A kind of method for predicting material creep curve
CN109492277A (en) * 2018-10-25 2019-03-19 哈尔滨工程大学 A method of estimation metal increasing material manufacturing ultrasonic implement treatment acts on layer depth
CN109781531A (en) * 2019-03-15 2019-05-21 本钢板材股份有限公司 A method of the stress-strain diagram under prediction material at high temperature high strain rate
CN109855959A (en) * 2017-11-30 2019-06-07 中国科学院金属研究所 A kind of prediction technique of Metal Material Fatigue intensity
CN109855958A (en) * 2017-11-30 2019-06-07 中国科学院金属研究所 A kind of prediction technique of material during tensile performance
EP3495904A1 (en) * 2017-12-07 2019-06-12 Siemens Aktiengesellschaft Method and apparatus for predicting manufacturing parameters of a product to be manufactured in a 3d-printing process
CN110184599A (en) * 2019-06-06 2019-08-30 南方科技大学 Laser powder feeding cladding system and laser powder feeding cladding control method
CN110434330A (en) * 2019-06-28 2019-11-12 上海电气集团股份有限公司 A kind of technological parameter development approach of powdering formula increasing material manufacturing target metal materials
CN110472355A (en) * 2019-08-20 2019-11-19 南京航空航天大学 A kind of 3D printing method for previewing solved based on multi- scenarios method modeling and simulation
CN110560685A (en) * 2018-06-05 2019-12-13 福士瑞精密工业(郑州)有限公司 Metal 3D printing process parameter optimization method
CN111222256A (en) * 2020-02-26 2020-06-02 天津理工大学 Numerical simulation method for predicting selective laser melting single-melting-channel molding defects
US20200257933A1 (en) * 2019-02-05 2020-08-13 Imagars Llc Machine Learning to Accelerate Alloy Design
CN112100745A (en) * 2020-09-15 2020-12-18 东北大学 Automobile girder steel mechanical property prediction method based on LDA theory
CN112453395A (en) * 2019-09-09 2021-03-09 天津大学 Preparation method of graphene-316L stainless steel based on selective laser melting
CN112580233A (en) * 2020-11-25 2021-03-30 华东理工大学 Metal toughness performance prediction method considering grain size

Patent Citations (23)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1253491A2 (en) * 2001-04-24 2002-10-30 Bayer Aktiengesellschaft Hybrid model and method for determining the mechanical properties and processing properties of an injection moulded article
CN101975704A (en) * 2010-10-08 2011-02-16 中国船舶重工集团公司第七二五研究所 Method for predicting vibration fatigue performance of viscoelastic material
CN104245982A (en) * 2012-04-18 2014-12-24 日本发条株式会社 Magnesium alloy member and method for manufacturing same
CN105930553A (en) * 2016-04-11 2016-09-07 南京航空航天大学 Method for predicting tensile strength of nickel-base superalloy gap
CN106735205A (en) * 2016-12-08 2017-05-31 鑫精合激光科技发展（北京）有限公司 A kind of technological parameter of metal material 3D printing determines method
CN106694883A (en) * 2017-02-10 2017-05-24 江西省科学院应用物理研究所 Synchronous powder spreading type metal laser 3D printing powder feeding device
CN107356479A (en) * 2017-07-12 2017-11-17 南方科技大学 Metal material tensile property evaluation method based on selective laser melting technology
CN109855959A (en) * 2017-11-30 2019-06-07 中国科学院金属研究所 A kind of prediction technique of Metal Material Fatigue intensity
CN109855958A (en) * 2017-11-30 2019-06-07 中国科学院金属研究所 A kind of prediction technique of material during tensile performance
EP3495904A1 (en) * 2017-12-07 2019-06-12 Siemens Aktiengesellschaft Method and apparatus for predicting manufacturing parameters of a product to be manufactured in a 3d-printing process
CN108256179A (en) * 2017-12-29 2018-07-06 沈阳工业大学 A kind of method for predicting material creep curve
CN108197384A (en) * 2017-12-30 2018-06-22 大族激光科技产业集团股份有限公司 The parameter optimization method of powder laser 3D printing moulding process
CN110560685A (en) * 2018-06-05 2019-12-13 福士瑞精密工业(郑州)有限公司 Metal 3D printing process parameter optimization method
CN109492277A (en) * 2018-10-25 2019-03-19 哈尔滨工程大学 A method of estimation metal increasing material manufacturing ultrasonic implement treatment acts on layer depth
US20200257933A1 (en) * 2019-02-05 2020-08-13 Imagars Llc Machine Learning to Accelerate Alloy Design
CN109781531A (en) * 2019-03-15 2019-05-21 本钢板材股份有限公司 A method of the stress-strain diagram under prediction material at high temperature high strain rate
CN110184599A (en) * 2019-06-06 2019-08-30 南方科技大学 Laser powder feeding cladding system and laser powder feeding cladding control method
CN110434330A (en) * 2019-06-28 2019-11-12 上海电气集团股份有限公司 A kind of technological parameter development approach of powdering formula increasing material manufacturing target metal materials
CN110472355A (en) * 2019-08-20 2019-11-19 南京航空航天大学 A kind of 3D printing method for previewing solved based on multi- scenarios method modeling and simulation
CN112453395A (en) * 2019-09-09 2021-03-09 天津大学 Preparation method of graphene-316L stainless steel based on selective laser melting
CN111222256A (en) * 2020-02-26 2020-06-02 天津理工大学 Numerical simulation method for predicting selective laser melting single-melting-channel molding defects
CN112100745A (en) * 2020-09-15 2020-12-18 东北大学 Automobile girder steel mechanical property prediction method based on LDA theory
CN112580233A (en) * 2020-11-25 2021-03-30 华东理工大学 Metal toughness performance prediction method considering grain size

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
HAMED KHOSRAVI: "复合铸造工艺参数对A356-SiC_p复合材料显微组织和拉伸性能的影响", 《中国有色金属学报：英文版》 *
吴思炜: "基于大数据的力学性能预测与工艺参数筛选", 《钢铁研究学报》 *
张旺峰: "钛合金组织特征参数与拉伸性能预测", 《稀有金属材料与工程》 *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115415542A (en) * 2022-07-19 2022-12-02 闽都创新实验室 Method for predicting performance of duplex stainless steel 3D printing piece based on response surface method
CN115415542B (en) * 2022-07-19 2024-05-03 闽都创新实验室 Prediction method for performance of duplex stainless steel 3D printing piece based on response surface method
CN117110346A (en) * 2023-10-23 2023-11-24 中国空气动力研究与发展中心设备设计与测试技术研究所 Analysis method for microstructure of laser selective melting plate

Also Published As

Publication number Publication date
CN113118458B (en) 2023-04-07

Similar Documents

Publication Publication Date Title
CN113118458B (en) Prediction method for tensile property of metal component formed by selective laser melting
Feifei et al. Effects of rare earth oxide on hardfacing metal microstructure of medium carbon steel and its refinement mechanism
CN108359902B (en) A kind of high performance alloys steel flange and forging and its manufacturing method
EP2639323B1 (en) Wear-resistant cobalt-based alloy and engine valve coated with same
CN110004392B (en) High-temperature corrosion-resistant wear-resistant amorphous thermal spraying material
CN114480893B (en) Method for reducing additive manufacturing cracks of nickel-based superalloy and nickel-based superalloy
Liu et al. Activated flux tungsten inert gas welding of 8 mm-thick AISI 304 austenitic stainless steel
EP2639324A1 (en) High-toughness cobalt-based alloy and engine valve coated with same
Chen et al. Laser surface modification of ductile iron: Part 1 microstructure
Ning et al. Assessment of the universality of duplex stainless steel powder in laser additive repair based on Schaeffler diagram
Fan et al. Microstructure evolution, thermal and mechanical property of Co alloyed Sn-0.7 Cu lead-free solder
JURCHELA et al. Considerations on the Effects of Carbon Content on the Cavitation Erosion Resistance of Stainless Steels with Controled Content of Chromium and Carbon
CN111441049A (en) Laser cladding powder for remanufacturing 12CrNi3 camshaft and preparation method thereof
Guo et al. High-strength wire+ arc additive manufactured steel
CN110484916A (en) A kind of high speed and ultrahigh speed laser melting coating Co-based alloy powder
Den Uijl et al. Prediction of post weld hardness of advanced high strength steels for automotive application using a dedicated carbon equivalent number
CN113234962A (en) Plasma cladding modified nickel-based high-temperature alloy coating for repairing surface and preparation method thereof
CN110195179A (en) A kind of magnesium-rare earth for silk material 3D printing
Eremin et al. The properties of chromium steel overlaying used as a hardening coating for stop valve sealing surface
CN112024869A (en) SMTGH5188 spherical powder for 3D printing and preparation method and application thereof
CN113699415B (en) Corrosion-resistant and high-temperature oxidation-resistant Co-based high-temperature alloy coating and preparation method thereof
Pascal Development of high temperature vacuum brazed WC-Co-NiP functional composite coatings
Renheng et al. High-Temperature Oxidation Performance of 4Cr4Mo2NiMnSiV Hot Die Steel
Jordan et al. Influence of precipitation and segregation on hydrogen embrittlement in aged 9Cr–1Mo steel
Zherebtsov et al. Centrifugal electroslag casting of annular flange blanks

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant