A prediction model for finding the optimal laser parameters in additive manufacturing of NiTi shape memory alloy

Shape memory alloys (SMAs) have been applied for various applications in the fields of aerospace, automotive, and medical. Nickel-titanium (NiTi) is the most well-known alloy among the others due to its outstanding functional characteristics including superelasticity (SE) and shape memory effect (SM...

Full description

Saved in:

Bibliographic Details
Published in:	International journal of advanced manufacturing technology Vol. 105; no. 11; pp. 4691 - 4699
Main Authors:	Mehrpouya, Mehrshad, Gisario, Annamaria, Rahimzadeh, Atabak, Nematollahi, Mohammadreza, Baghbaderani, Keyvan Safaei, Elahinia, Mohammad
Format:	Journal Article
Language:	English
Published:	London Springer London 01-12-2019 Springer Nature B.V
Subjects:	Additive manufacturing Alloys Artificial neural networks Austenite Automotive parts CAE) and Design Computer-Aided Engineering (CAD Engineering Industrial and Production Engineering Intermetallic compounds Lasers Martensite Martensitic transformations Mathematical models Mechanical Engineering Mechanical properties Media Management Nickel base alloys Nickel titanides Original Article Parameters Prediction models Prototyping Shape effects Shape memory alloys Strain Superelasticity Surgical implants Three dimensional printing Transformation temperature Shape memory alloys Additive manufacturing Artificial neural network NiTi Modeling
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	Shape memory alloys (SMAs) have been applied for various applications in the fields of aerospace, automotive, and medical. Nickel-titanium (NiTi) is the most well-known alloy among the others due to its outstanding functional characteristics including superelasticity (SE) and shape memory effect (SME). These particular properties are the result of the reversible martensite-to-austenite and austenite-to-martensite transformations. In recent years, additive manufacturing (AM) has provided a great opportunity for fabricating NiTi products with complex shapes. Many researchers have been investigating the AM process to set the optimal operational parameters, which can significantly affect the properties of the end-products. Indeed, the functional and mechanical behavior of printed NiTi parts can be tailored by controlling laser power, laser scan speed, and hatch spacing having them a crucial role in properties of 3D-printed parts. In particular, the effect of the input parameters can significantly alter the mechanical properties such as strain recovery rates and the transformation temperatures; therefore, using suitable parameter combination is of paramount importance. In this framework, the present study develops a prediction model based on artificial neural network (ANN) to generate a nonlinear map between inputs and outputs of the AM process. Accordingly, a prototyping tool for the AM process, also useful for dealing with the settings of the optimal operational parameters, will be built, tested, and validated.
AbstractList	Shape memory alloys (SMAs) have been applied for various applications in the fields of aerospace, automotive, and medical. Nickel-titanium (NiTi) is the most well-known alloy among the others due to its outstanding functional characteristics including superelasticity (SE) and shape memory effect (SME). These particular properties are the result of the reversible martensite-to-austenite and austenite-to-martensite transformations. In recent years, additive manufacturing (AM) has provided a great opportunity for fabricating NiTi products with complex shapes. Many researchers have been investigating the AM process to set the optimal operational parameters, which can significantly affect the properties of the end-products. Indeed, the functional and mechanical behavior of printed NiTi parts can be tailored by controlling laser power, laser scan speed, and hatch spacing having them a crucial role in properties of 3D-printed parts. In particular, the effect of the input parameters can significantly alter the mechanical properties such as strain recovery rates and the transformation temperatures; therefore, using suitable parameter combination is of paramount importance. In this framework, the present study develops a prediction model based on artificial neural network (ANN) to generate a nonlinear map between inputs and outputs of the AM process. Accordingly, a prototyping tool for the AM process, also useful for dealing with the settings of the optimal operational parameters, will be built, tested, and validated.
Author	Gisario, Annamaria Nematollahi, Mohammadreza Mehrpouya, Mehrshad Elahinia, Mohammad Rahimzadeh, Atabak Baghbaderani, Keyvan Safaei
Author_xml	– sequence: 1 givenname: Mehrshad orcidid: 0000-0001-8939-7937 surname: Mehrpouya fullname: Mehrpouya, Mehrshad email: mehrshad.mehrpouya@uniroma3.it organization: Department of Mechanical and Industrial Engineering, The University of Roma Tre – sequence: 2 givenname: Annamaria surname: Gisario fullname: Gisario, Annamaria organization: Department of Mechanical and Aerospace Engineering, Sapienza University of Rome – sequence: 3 givenname: Atabak surname: Rahimzadeh fullname: Rahimzadeh, Atabak organization: Department of Mechanical and Aerospace Engineering, Sapienza University of Rome – sequence: 4 givenname: Mohammadreza surname: Nematollahi fullname: Nematollahi, Mohammadreza organization: Dynamic and Smart Systems Laboratory, Mechanical Industrial and Manufacturing Engineering Department, The University of Toledo – sequence: 5 givenname: Keyvan Safaei surname: Baghbaderani fullname: Baghbaderani, Keyvan Safaei organization: Dynamic and Smart Systems Laboratory, Mechanical Industrial and Manufacturing Engineering Department, The University of Toledo – sequence: 6 givenname: Mohammad surname: Elahinia fullname: Elahinia, Mohammad organization: Dynamic and Smart Systems Laboratory, Mechanical Industrial and Manufacturing Engineering Department, The University of Toledo
BookMark	eNp9kD1PwzAQQC0EEqXwB5gsMQfOsWMnY1XxJSFYymw58Zm6SuJgp0jl15NSJLZON9x7d9K7IKd96JGQawa3DEDdJQCmIANWZSCKSmbfJ2TGBOcZB1ackhnkssy4kuU5uUhpM-GSyXJGhgUdIlrfjD70tAsWW-pCpM731vcfdFwjDcPoO9PS1iSMdDDRdDhiTNT31FjrR_-FtDP91plm3Ma9Fhx99StP09oM0w67EHfUtG3YXZIzZ9qEV39zTt4f7lfLp-zl7fF5uXjJGgHFmPG6ql1d1DWrBGAhsTBWKWxAVoVzYKCRtrFOlcqo0om8ZhJdzmzBJeaCA5-Tm8PdIYbPLaZRb8I29tNLnYsKSsWYEEcpnkOlGEg-UfmBamJIKaLTQ5yKxJ1moPf99aG_nvrr3_76e5L4QUrDPgnG_9NHrB-puour
CitedBy_id	crossref_primary_10_1007_s00170_020_05071_w crossref_primary_10_1016_j_jallcom_2021_159791 crossref_primary_10_1007_s11665_022_06995_y crossref_primary_10_1115_1_4053181 crossref_primary_10_1177_09544089231215683 crossref_primary_10_1007_s11831_023_09969_y crossref_primary_10_1002_pat_5020 crossref_primary_10_1007_s00170_020_06378_4 crossref_primary_10_1007_s00521_022_07370_7 crossref_primary_10_1038_s41467_022_34122_x crossref_primary_10_1007_s11837_023_06134_5 crossref_primary_10_1007_s12540_023_01401_1 crossref_primary_10_1016_j_matdes_2021_109654 crossref_primary_10_1016_j_rcim_2023_102525 crossref_primary_10_1007_s10845_022_01957_6 crossref_primary_10_1016_j_actamat_2021_117017 crossref_primary_10_1557_jmr_2020_60 crossref_primary_10_1007_s10853_023_08931_4 crossref_primary_10_3390_app11178010 crossref_primary_10_1039_D0BM01369B crossref_primary_10_1007_s00170_021_08381_9 crossref_primary_10_1016_j_addma_2022_102691 crossref_primary_10_1007_s40436_020_00304_3 crossref_primary_10_1007_s40964_022_00387_3 crossref_primary_10_3390_ma17051006 crossref_primary_10_1007_s40964_021_00236_9 crossref_primary_10_3390_ma13092104 crossref_primary_10_1007_s00170_024_12974_5 crossref_primary_10_1016_j_optlastec_2020_106653 crossref_primary_10_1007_s00170_022_10522_7 crossref_primary_10_1016_j_jmrt_2022_10_015 crossref_primary_10_1088_2631_7990_acc7d9 crossref_primary_10_1007_s12046_021_01684_3 crossref_primary_10_3390_met10010151 crossref_primary_10_1016_j_matdes_2024_113086 crossref_primary_10_1007_s40192_022_00252_9 crossref_primary_10_1016_j_finmec_2021_100055 crossref_primary_10_3390_coatings10111104 crossref_primary_10_1016_j_jmrt_2023_04_195 crossref_primary_10_1016_j_mtcomm_2021_102022 crossref_primary_10_3390_met10111522 crossref_primary_10_1115_1_4054935 crossref_primary_10_1016_j_matlet_2021_129648 crossref_primary_10_1016_j_tibtech_2022_01_005
Cites_doi	10.1016/j.scriptamat.2017.11.047 10.1038/srep46707 10.1016/j.jmapro.2016.05.005 10.1243/09544100JAERO211 10.1016/j.jmsy.2019.08.005 10.1016/j.jmbbm.2017.01.047 10.1007/s00170-014-6679-5 10.1007/s40830-016-0083-y 10.1016/S0921-5093(01)01183-2 10.1007/s00170-018-03259-9 10.1016/S1359-6454(00)00153-1 10.1038/s41598-018-36641-4 10.1007/s00339-019-2782-7 10.1016/j.optlastec.2019.05.010 10.1080/10426914.2017.1279290 10.1002/9781118426913 10.1016/j.pmatsci.2016.08.001 10.1016/j.msea.2017.01.008 10.1002/adem.201300558 10.3390/ma12050808 10.1016/j.ins.2014.01.038 10.1016/j.jmapro.2019.06.013 10.4028/www.scientific.net/AMM.564.533 10.1201/9780203736166 10.1007/s40516-019-00084-0 10.1016/j.advengsoft.2008.12.001 10.1016/j.actamat.2017.10.072 10.1007/s10856-018-6044-6 10.1007/s11665-014-0958-z 10.1016/j.jmapro.2019.02.026 10.1557/mrs.2016.209 10.1115/MSEC2016-8784 10.1016/j.msea.2015.10.032 10.3390/bioengineering6020037 10.2174/1876402908666161102151453 10.1007/s00170-015-7576-2 10.1016/j.optlaseng.2016.09.004 10.1108/RPJ-11-2014-0149 10.1016/j.ijleo.2016.09.042 10.1088/0964-1726/23/10/104002 10.1016/j.rcim.2015.12.004 10.1016/j.addma.2016.07.001 10.1016/j.jmapro.2017.11.011 10.1177/1045389X16635848 10.1016/j.scriptamat.2017.10.016 10.1016/j.addma.2015.07.002 10.1111/ffe.12560 10.1115/1.4038293 10.1016/j.msea.2003.10.326 10.1007/s40830-019-00214-9 10.1117/12.2299817 10.3390/app9183865 10.1016/j.cad.2015.12.003
ContentType	Journal Article
Copyright	Springer-Verlag London Ltd., part of Springer Nature 2019 The International Journal of Advanced Manufacturing Technology is a copyright of Springer, (2019). All Rights Reserved. Springer-Verlag London Ltd., part of Springer Nature 2019.
Copyright_xml	– notice: Springer-Verlag London Ltd., part of Springer Nature 2019 – notice: The International Journal of Advanced Manufacturing Technology is a copyright of Springer, (2019). All Rights Reserved. – notice: Springer-Verlag London Ltd., part of Springer Nature 2019.
DBID	AAYXX CITATION 8FE 8FG ABJCF AFKRA BENPR BGLVJ CCPQU DWQXO HCIFZ L6V M7S PQEST PQQKQ PQUKI PRINS PTHSS
DOI	10.1007/s00170-019-04596-z
DatabaseName	CrossRef ProQuest SciTech Collection ProQuest Technology Collection Materials Science & Engineering Collection ProQuest Central UK/Ireland ProQuest Central Technology Collection ProQuest One Community College ProQuest Central SciTech Premium Collection ProQuest Engineering Collection Engineering Database ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China Engineering Collection
DatabaseTitle	CrossRef Engineering Database Technology Collection ProQuest One Academic Eastern Edition SciTech Premium Collection ProQuest One Community College ProQuest Technology Collection ProQuest SciTech Collection ProQuest Central China ProQuest Central ProQuest Engineering Collection ProQuest One Academic UKI Edition ProQuest Central Korea Materials Science & Engineering Collection ProQuest One Academic Engineering Collection
DatabaseTitleList	Engineering Database Engineering Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering
EISSN	1433-3015
EndPage	4699
ExternalDocumentID	10_1007_s00170_019_04596_z
GroupedDBID	-5B -5G -BR -EM -XW -XX -Y2 -~C .86 .VR 06D 0R~ 0VY 123 1N0 1SB 203 28- 29J 29~ 2J2 2JN 2JY 2KG 2KM 2LR 2P1 2VQ 2~H 30V 4.4 406 408 409 40D 40E 5GY 5QI 5VS 67Z 6NX 8FE 8FG 8TC 8UJ 95- 95. 95~ 96X 9M8 AAAVM AABHQ AABYN AAFGU AAHNG AAIAL AAJKR AANZL AARHV AARTL AATNV AATVU AAUYE AAWCG AAYFA AAYIU AAYQN AAYTO ABBBX ABBXA ABDBF ABDEX ABDZT ABECU ABFGW ABFTD ABFTV ABHQN ABJCF ABJNI ABJOX ABKAG ABKAS ABKCH ABKTR ABMNI ABMQK ABNWP ABPTK ABQBU ABSXP ABTAH ABTEG ABTHY ABTKH ABTMW ABULA ABWNU ABXPI ACBMV ACBRV ACBXY ACBYP ACGFS ACHSB ACHXU ACIGE ACIPQ ACIWK ACKNC ACMDZ ACMLO ACOKC ACOMO ACTTH ACVWB ACWMK ADHHG ADHIR ADINQ ADKNI ADKPE ADMDM ADMVV ADOXG ADQRH ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEEQQ AEFIE AEFTE AEGAL AEGNC AEJHL AEJRE AEKMD AEKVL AENEX AEOHA AEPYU AESKC AESTI AETLH AEVLU AEVTX AEXYK AFEXP AFGCZ AFKRA AFLOW AFNRJ AFQWF AFWTZ AFZKB AGAYW AGDGC AGGBP AGGDS AGJBK AGMZJ AGQMX AGWIL AGWZB AGYKE AHAVH AHBYD AHSBF AHYZX AIAKS AIIXL AILAN AIMYW AITGF AJBLW AJDOV AJRNO AJZVZ AKQUC ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMXSW AMYLF AMYQR AOCGG ARCEE ARMRJ ASPBG AVWKF AXYYD AYJHY AZFZN B-. B0M BA0 BBWZM BDATZ BENPR BGLVJ BGNMA CAG CCPQU COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DU5 EAD EAP EAS EBLON EBS EIOEI EJD EMK EPL ESBYG ESX FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNWQR GQ6 GQ7 GQ8 GXS HCIFZ HF~ HG5 HG6 HMJXF HQYDN HRMNR HVGLF HZ~ I-F I09 IHE IJ- IKXTQ ITM IWAJR IXC IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ KDC KOV KOW L6V LAS LLZTM M4Y M7S MA- ML~ N2Q N9A NB0 NDZJH NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J OAM P19 P9P PF0 PT4 PT5 PTHSS QOK QOS R4E R89 R9I RHV RIG RNI RNS ROL RPX RSV RZK S16 S1Z S26 S27 S28 S3B SAP SCLPG SCV SDH SDM SEG SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 T16 TN5 TSG TSK TSV TUC TUS U2A UG4 UNUBA UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WK8 YLTOR Z45 Z5O Z7R Z7S Z7V Z7W Z7X Z7Y Z7Z Z81 Z83 Z85 Z86 Z88 Z8M Z8N Z8P Z8Q Z8R Z8S Z8T Z8U Z8V Z8W Z8Z Z92 ZMTXR ZY4 _50 ~8M ~A9 ~EX AACDK AAEOY AAJBT AASML AAYXX AAYZH ABAKF ABYXP ACAOD ACDTI ACZOJ AEFQL AEMSY AFBBN AGQEE AGRTI AIGIU CITATION H13 DWQXO PQEST PQQKQ PQUKI PRINS
ID	FETCH-LOGICAL-c405t-3b9bfb5bb1940e56e5ad77ec0695ff0a0c6dcdf787a78f42b16ef21d536e24303
IEDL.DBID	AEJHL
ISSN	0268-3768
IngestDate	Mon Nov 04 11:08:59 EST 2024 Mon Nov 04 11:16:08 EST 2024 Fri Nov 22 00:18:30 EST 2024 Sat Dec 16 12:02:55 EST 2023
IsPeerReviewed	true
IsScholarly	true
Issue	11
Keywords	Shape memory alloys Additive manufacturing Artificial neural network NiTi Modeling
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c405t-3b9bfb5bb1940e56e5ad77ec0695ff0a0c6dcdf787a78f42b16ef21d536e24303
ORCID	0000-0001-8939-7937
PQID	2320971063
PQPubID	2044010
PageCount	9
ParticipantIDs	proquest_journals_2490871144 proquest_journals_2320971063 crossref_primary_10_1007_s00170_019_04596_z springer_journals_10_1007_s00170_019_04596_z
PublicationCentury	2000
PublicationDate	2019-12-01
PublicationDateYYYYMMDD	2019-12-01
PublicationDate_xml	– month: 12 year: 2019 text: 2019-12-01 day: 01
PublicationDecade	2010
PublicationPlace	London
PublicationPlace_xml	– name: London – name: Heidelberg
PublicationTitle	International journal of advanced manufacturing technology
PublicationTitleAbbrev	Int J Adv Manuf Technol
PublicationYear	2019
Publisher	Springer London Springer Nature B.V
Publisher_xml	– name: Springer London – name: Springer Nature B.V
References	ChenHZhaoYFProcess parameters optimization for improving surface quality and manufacturing accuracy of binder jetting additive manufacturing processRapid Prototyp J201622352753810.1108/RPJ-11-2014-01493458339 HartlDJLagoudasDCAerospace applications of shape memory alloysProceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering2007221453555210.1243/09544100JAERO211 DingDTowards an automated robotic arc-welding-based additive manufacturing system from CAD to finished partComput Aided Des201673667510.1016/j.cad.2015.12.003 FiratMGungorMGeneralized regression neural networks and feed forward neural networks for prediction of scour depth around bridge piersAdv Eng Softw200940873173710.1016/j.advengsoft.2008.12.0011167.62466 NingJAnalytical modeling of 3D temperature distribution in selective laser melting of Ti-6Al-4V considering part boundary conditionsJ Manuf Process20194431932610.1016/j.jmapro.2019.06.013 HaberlandCOn the development of high quality NiTi shape memory and pseudoelastic parts by additive manufacturingSmart Mater Struct2014231010400210.1088/0964-1726/23/10/104002 SaediSTexture, aging, and superelasticity of selective laser melting fabricated Ni-rich NiTi alloysMater Sci Eng A201768611010.1016/j.msea.2017.01.008 NematollahiMApplication of NiTi in assistive and rehabilitation devices: a reviewBioengineering2019623710.3390/bioengineering6020037 Elahinia MH (2016) Shape memory alloy actuators: design, fabrication, and experimental evaluation: John Wiley & Sons MehrpouyaMCheraghi BidsorkhiHMEMS applications of NiTi based shape memory alloys: a reviewMicro and Nanosystems201682799110.2174/1876402908666161102151453 GisarioAMetal additive manufacturing in the commercial aviation industry: a reviewJ Manuf Syst20195312414910.1016/j.jmsy.2019.08.005 SpeirsMOn the transformation behavior of NiTi shape-memory alloy produced by SLMShape Memory and Superelasticity20162431031610.1007/s40830-016-0083-y GisarioALaserOrigami (LO) of three-dimensional (3D) components: experimental analysis and numerical modellingJ Manuf Process20162324224810.1016/j.jmapro.2016.05.005 Mehrpouya M et al. (2019) Dissimilar laser welding of NiTi wires. Lasers in Manufacturing and Materials Processing: p. 1-14 Card SK (2018) The psychology of human-computer interaction: CRC Press. BikasHStavropoulosPChryssolourisGAdditive manufacturing methods and modelling approaches: a critical reviewInt J Adv Manuf Technol2016831-438940510.1007/s00170-015-7576-2 FrazierWEMetal additive manufacturing: a reviewJ Mater Eng Perform20142361917192810.1007/s11665-014-0958-z SaediSSaghaianSEJahadakbarAShayesteh MoghaddamNTaheri AndaniMSaghaianSMLuYCElahiniaMKaracaHEShape memory response of porous NiTi shape memory alloys fabricated by selective laser meltingJ Mater Sci Mater Med20182944010.1007/s10856-018-6044-6 Mehrpouya M (2017) Laser welding of NiTi shape memory sheets: experimental analysis and numerical modeling, in Department of Mechanical and Aerospace Engineering, Sapienza University of Rome. p. 134 DingDBead modelling and implementation of adaptive MAT path in wire and arc additive manufacturingRobot Comput Integr Manuf201639324210.1016/j.rcim.2015.12.004 SehitogluHCompressive response of NiTi single crystalsActa Mater200048133311332610.1016/S1359-6454(00)00153-1 DadbakhshSLaser additive manufacturing of bulk and porous shape-memory NiTi alloys: from processes to potential biomedical applicationsMRS Bull2016411076577410.1557/mrs.2016.209 VijayaraghavanVProcess characterisation of 3D-printed FDM components using improved evolutionary computational approachInt J Adv Manuf Technol2015785-878179310.1007/s00170-014-6679-5 DadbakhshSTexture and anisotropy in selective laser melting of NiTi alloyMater Sci Eng A201665022523210.1016/j.msea.2015.10.032 Nematollahi M et al. (2018) A two-way architectural actuator using NiTi SE wire and SME spring. in Active and Passive Smart Structures and Integrated Systems XII. International Society for Optics and Photonics AkbariMNumerical simulation and designing artificial neural network for estimating melt pool geometry and temperature distribution in laser welding of Ti6Al4V alloyOptik201612723111611117210.1016/j.ijleo.2016.09.042 Mehrpouya M et al. (2017) An investigation on the optimum machinability of NiTi based shape memory alloy. Materials and Manufacturing Processes: p. 1-8 ShamsaeiNAn overview of direct laser deposition for additive manufacturing; part II: mechanical behavior, process parameter optimization and controlAdditive Manufacturing20158123510.1016/j.addma.2015.07.002 HassanMehrpouyaMDawoodSReview of the machining difficulties of nickel-titanium based shape memory alloysAppl Mech Mater201456453310.4028/www.scientific.net/AMM.564.533 ElahiniaMAdditive manufacturing of NiTiHf high temperature shape memory alloyScr Mater2018145909410.1016/j.scriptamat.2017.10.016 AndaniMTMechanical and shape memory properties of porous Ni50. 1Ti49. 9 alloys manufactured by selective laser meltingJ Mech Behav Biomed Mater20176822423110.1016/j.jmbbm.2017.01.047 HuangYKhameseeMBToyserkaniEA comprehensive analytical model for laser powder-fed additive manufacturingAdditive Manufacturing201612909910.1016/j.addma.2016.07.001 Haykin S (1994) Neural networks: a comprehensive foundation. Prentice Hall PTR NingJAnalytical modeling of in-process temperature in powder bed additive manufacturing considering laser power absorption, latent heat, scanning strategy, and powder packingMaterials201912580810.3390/ma12050808 Chowdhury S (2016) Artificial neural network based geometric compensation for thermal deformation in additive manufacturing processes, University of Cincinnati MehrpouyaMThe potential of additive manufacturing in the smart factory industrial 4.0: a reviewAppl Sci2019918386510.3390/app9183865 MoghaddamNSAchieving superelasticity in additively manufactured NiTi in compression without post-process heat treatmentSci Rep2019914110.1038/s41598-018-36641-44031945 GisarioALaser-assisted bending of titanium grade-2 sheets: experimental analysis and numerical simulationOpt Lasers Eng20179211011910.1016/j.optlaseng.2016.09.004 MorganNMedical shape memory alloy applications—the market and its productsMater Sci Eng A20043781-2162310.1016/j.msea.2003.10.326 Lagoudas DC, Rediniotis OK, Khan MM (2000) Applications of shape memory alloys to bioengineering and biomedical technology, in Scattering Theory and Biomedical Engineering Modelling and Applications, World Scientific. p. 195-207 MirjaliliSMirjaliliSMLewisALet a biogeography-based optimizer train your multi-layer perceptronInf Sci201426918820910.1016/j.ins.2014.01.0383180809 MehrpouyaMNumerical study for prediction of optimum operational parameters in laser welding of NiTi alloyOpt Laser Technol201911815916910.1016/j.optlastec.2019.05.010 Namatollahi M et al. (2019) Additive manufacturing (AM), in Metals for Biomedical Devices, Elsevier. p. 331-353 WangXSelective laser melting produced layer-structured NiTi shape memory alloys with high damping properties and Elinvar effectScr Mater201814624625010.1016/j.scriptamat.2017.11.047 SaediSOn the effects of selective laser melting process parameters on microstructure and thermomechanical response of Ni-rich NiTiActa Mater201814455256010.1016/j.actamat.2017.10.072 NematollahiMAdditive manufacturing of Ni-rich NiTiHf 20: manufacturability, composition, density, and transformation behaviorShape Memory and Superelasticity20195111312410.1007/s40830-019-00214-9 NingJAnalytical modeling of transient temperature in powder feed metal additive manufacturing during heating and cooling stagesApplied Physics A2019125849610.1007/s00339-019-2782-7 GallKOn the mechanical behavior of single crystal NiTi shape memory alloys and related polycrystalline phenomenonMater Sci Eng A20013171-2859210.1016/S0921-5093(01)01183-2 ChowdhurySMhapsekarKAnandSPart build orientation optimization and neural network-based geometry compensation for additive manufacturing processJ Manuf Sci Eng2018140303100910.1115/1.4038293 Mehrpouya M et al. (2019) An artificial neural network model for laser transmission welding of biodegradable polyethylene terephthalate/polyethylene vinyl acetate (PET/PEVA) blends. Int J Adv Manuf Technol, p. 1-11 ElahiniaMFabrication of NiTi through additive manufacturing: a reviewProg Mater Sci20168363066310.1016/j.pmatsci.2016.08.001 MehrpouyaMGisarioAElahiniaMLaser welding of NiTi shape memory alloy: a reviewJ Manuf Process201820183116218610.1016/j.jmapro.2017.11.011 Mehrpouya M (2013) Modeling of machining perocess of nickel based shape memory alloy in Department of Mechanical and Manufacturing, Universiti Putra Malaysia. p. 130 MehrpouyaMLaserOrigami (LO) of three-dimensional (3D) components: experimental analysis and numerical modeling-part IIJ Manuf Process20193919219910.1016/j.jmapro.2019.02.026 WalkerJMProcess development and characterization of additively manufactured nickel–titanium shape memory partsJ Intell Mater Syst Struct201627192653266010.1177/1045389X16635848 MaJSpatial control of functional response in 4D-printed active metallic structuresSci Rep201774670710.1038/srep46707 DadbakhshSEffect of SLM parameters on transformation temperatures of shape memory nickel titanium partsAdv Eng Mater20141691140114610.1002/adem.201300558 FerganiOAnalytical modelling of residual stress in additive manufacturingFatigue Fract Eng Mater Struct201740697197810.1111/ffe.12560 A Gisario (4596_CR42) 2017; 92 A Gisario (4596_CR40) 2016; 23 S Saedi (4596_CR18) 2018; 29 Y Huang (4596_CR37) 2016; 12 Hassan (4596_CR11) 2014; 564 MT Andani (4596_CR19) 2017; 68 S Chowdhury (4596_CR45) 2018; 140 S Saedi (4596_CR58) 2017; 686 M Elahinia (4596_CR2) 2016; 83 M Nematollahi (4596_CR5) 2019; 6 4596_CR1 D Ding (4596_CR46) 2016; 39 K Gall (4596_CR55) 2001; 317 M Speirs (4596_CR30) 2016; 2 M Akbari (4596_CR54) 2016; 127 4596_CR8 M Mehrpouya (4596_CR3) 2018; 2018 4596_CR9 M Nematollahi (4596_CR23) 2019; 5 J Ning (4596_CR35) 2019; 125 4596_CR39 H Chen (4596_CR33) 2016; 22 D Ding (4596_CR47) 2016; 73 DJ Hartl (4596_CR7) 2007; 221 V Vijayaraghavan (4596_CR48) 2015; 78 4596_CR43 H Sehitoglu (4596_CR56) 2000; 48 S Dadbakhsh (4596_CR57) 2016; 650 C Haberland (4596_CR25) 2014; 23 M Elahinia (4596_CR24) 2018; 145 M Mehrpouya (4596_CR41) 2019; 39 4596_CR44 M Mehrpouya (4596_CR16) 2019; 9 N Morgan (4596_CR6) 2004; 378 WE Frazier (4596_CR15) 2014; 23 4596_CR50 X Wang (4596_CR29) 2018; 146 4596_CR51 4596_CR10 A Gisario (4596_CR17) 2019; 53 J Ma (4596_CR28) 2017; 7 M Mehrpouya (4596_CR52) 2019; 118 H Bikas (4596_CR36) 2016; 83 4596_CR12 4596_CR13 S Mirjalili (4596_CR49) 2014; 269 M Mehrpouya (4596_CR4) 2016; 8 4596_CR14 N Shamsaei (4596_CR31) 2015; 8 NS Moghaddam (4596_CR22) 2019; 9 S Dadbakhsh (4596_CR20) 2016; 41 JM Walker (4596_CR27) 2016; 27 J Ning (4596_CR32) 2019; 44 S Dadbakhsh (4596_CR26) 2014; 16 M Firat (4596_CR53) 2009; 40 O Fergani (4596_CR38) 2017; 40 S Saedi (4596_CR21) 2018; 144 J Ning (4596_CR34) 2019; 12
References_xml	– volume: 146 start-page: 246 year: 2018 ident: 4596_CR29 publication-title: Scr Mater doi: 10.1016/j.scriptamat.2017.11.047 contributor: fullname: X Wang – ident: 4596_CR12 – volume: 7 start-page: 46707 year: 2017 ident: 4596_CR28 publication-title: Sci Rep doi: 10.1038/srep46707 contributor: fullname: J Ma – volume: 23 start-page: 242 year: 2016 ident: 4596_CR40 publication-title: J Manuf Process doi: 10.1016/j.jmapro.2016.05.005 contributor: fullname: A Gisario – volume: 221 start-page: 535 issue: 4 year: 2007 ident: 4596_CR7 publication-title: Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering doi: 10.1243/09544100JAERO211 contributor: fullname: DJ Hartl – volume: 53 start-page: 124 year: 2019 ident: 4596_CR17 publication-title: J Manuf Syst doi: 10.1016/j.jmsy.2019.08.005 contributor: fullname: A Gisario – volume: 68 start-page: 224 year: 2017 ident: 4596_CR19 publication-title: J Mech Behav Biomed Mater doi: 10.1016/j.jmbbm.2017.01.047 contributor: fullname: MT Andani – volume: 78 start-page: 781 issue: 5-8 year: 2015 ident: 4596_CR48 publication-title: Int J Adv Manuf Technol doi: 10.1007/s00170-014-6679-5 contributor: fullname: V Vijayaraghavan – volume: 2 start-page: 310 issue: 4 year: 2016 ident: 4596_CR30 publication-title: Shape Memory and Superelasticity doi: 10.1007/s40830-016-0083-y contributor: fullname: M Speirs – volume: 317 start-page: 85 issue: 1-2 year: 2001 ident: 4596_CR55 publication-title: Mater Sci Eng A doi: 10.1016/S0921-5093(01)01183-2 contributor: fullname: K Gall – ident: 4596_CR50 doi: 10.1007/s00170-018-03259-9 – volume: 48 start-page: 3311 issue: 13 year: 2000 ident: 4596_CR56 publication-title: Acta Mater doi: 10.1016/S1359-6454(00)00153-1 contributor: fullname: H Sehitoglu – ident: 4596_CR51 – volume: 9 start-page: 41 issue: 1 year: 2019 ident: 4596_CR22 publication-title: Sci Rep doi: 10.1038/s41598-018-36641-4 contributor: fullname: NS Moghaddam – volume: 125 start-page: 496 issue: 8 year: 2019 ident: 4596_CR35 publication-title: Applied Physics A doi: 10.1007/s00339-019-2782-7 contributor: fullname: J Ning – volume: 118 start-page: 159 year: 2019 ident: 4596_CR52 publication-title: Opt Laser Technol doi: 10.1016/j.optlastec.2019.05.010 contributor: fullname: M Mehrpouya – ident: 4596_CR13 doi: 10.1080/10426914.2017.1279290 – ident: 4596_CR1 doi: 10.1002/9781118426913 – volume: 83 start-page: 630 year: 2016 ident: 4596_CR2 publication-title: Prog Mater Sci doi: 10.1016/j.pmatsci.2016.08.001 contributor: fullname: M Elahinia – volume: 686 start-page: 1 year: 2017 ident: 4596_CR58 publication-title: Mater Sci Eng A doi: 10.1016/j.msea.2017.01.008 contributor: fullname: S Saedi – volume: 16 start-page: 1140 issue: 9 year: 2014 ident: 4596_CR26 publication-title: Adv Eng Mater doi: 10.1002/adem.201300558 contributor: fullname: S Dadbakhsh – volume: 12 start-page: 808 issue: 5 year: 2019 ident: 4596_CR34 publication-title: Materials doi: 10.3390/ma12050808 contributor: fullname: J Ning – volume: 269 start-page: 188 year: 2014 ident: 4596_CR49 publication-title: Inf Sci doi: 10.1016/j.ins.2014.01.038 contributor: fullname: S Mirjalili – volume: 44 start-page: 319 year: 2019 ident: 4596_CR32 publication-title: J Manuf Process doi: 10.1016/j.jmapro.2019.06.013 contributor: fullname: J Ning – volume: 564 start-page: 533 year: 2014 ident: 4596_CR11 publication-title: Appl Mech Mater doi: 10.4028/www.scientific.net/AMM.564.533 contributor: fullname: Hassan – ident: 4596_CR43 doi: 10.1201/9780203736166 – ident: 4596_CR10 doi: 10.1007/s40516-019-00084-0 – volume: 40 start-page: 731 issue: 8 year: 2009 ident: 4596_CR53 publication-title: Adv Eng Softw doi: 10.1016/j.advengsoft.2008.12.001 contributor: fullname: M Firat – volume: 144 start-page: 552 year: 2018 ident: 4596_CR21 publication-title: Acta Mater doi: 10.1016/j.actamat.2017.10.072 contributor: fullname: S Saedi – volume: 29 start-page: 40 issue: 4 year: 2018 ident: 4596_CR18 publication-title: J Mater Sci Mater Med doi: 10.1007/s10856-018-6044-6 contributor: fullname: S Saedi – volume: 23 start-page: 1917 issue: 6 year: 2014 ident: 4596_CR15 publication-title: J Mater Eng Perform doi: 10.1007/s11665-014-0958-z contributor: fullname: WE Frazier – ident: 4596_CR39 – volume: 39 start-page: 192 year: 2019 ident: 4596_CR41 publication-title: J Manuf Process doi: 10.1016/j.jmapro.2019.02.026 contributor: fullname: M Mehrpouya – ident: 4596_CR14 – volume: 41 start-page: 765 issue: 10 year: 2016 ident: 4596_CR20 publication-title: MRS Bull doi: 10.1557/mrs.2016.209 contributor: fullname: S Dadbakhsh – ident: 4596_CR44 doi: 10.1115/MSEC2016-8784 – volume: 650 start-page: 225 year: 2016 ident: 4596_CR57 publication-title: Mater Sci Eng A doi: 10.1016/j.msea.2015.10.032 contributor: fullname: S Dadbakhsh – volume: 6 start-page: 37 issue: 2 year: 2019 ident: 4596_CR5 publication-title: Bioengineering doi: 10.3390/bioengineering6020037 contributor: fullname: M Nematollahi – volume: 8 start-page: 79 issue: 2 year: 2016 ident: 4596_CR4 publication-title: Micro and Nanosystems doi: 10.2174/1876402908666161102151453 contributor: fullname: M Mehrpouya – volume: 83 start-page: 389 issue: 1-4 year: 2016 ident: 4596_CR36 publication-title: Int J Adv Manuf Technol doi: 10.1007/s00170-015-7576-2 contributor: fullname: H Bikas – volume: 92 start-page: 110 year: 2017 ident: 4596_CR42 publication-title: Opt Lasers Eng doi: 10.1016/j.optlaseng.2016.09.004 contributor: fullname: A Gisario – volume: 22 start-page: 527 issue: 3 year: 2016 ident: 4596_CR33 publication-title: Rapid Prototyp J doi: 10.1108/RPJ-11-2014-0149 contributor: fullname: H Chen – volume: 127 start-page: 11161 issue: 23 year: 2016 ident: 4596_CR54 publication-title: Optik doi: 10.1016/j.ijleo.2016.09.042 contributor: fullname: M Akbari – volume: 23 start-page: 104002 issue: 10 year: 2014 ident: 4596_CR25 publication-title: Smart Mater Struct doi: 10.1088/0964-1726/23/10/104002 contributor: fullname: C Haberland – volume: 39 start-page: 32 year: 2016 ident: 4596_CR46 publication-title: Robot Comput Integr Manuf doi: 10.1016/j.rcim.2015.12.004 contributor: fullname: D Ding – volume: 12 start-page: 90 year: 2016 ident: 4596_CR37 publication-title: Additive Manufacturing doi: 10.1016/j.addma.2016.07.001 contributor: fullname: Y Huang – volume: 2018 start-page: 162 issue: 31 year: 2018 ident: 4596_CR3 publication-title: J Manuf Process doi: 10.1016/j.jmapro.2017.11.011 contributor: fullname: M Mehrpouya – volume: 27 start-page: 2653 issue: 19 year: 2016 ident: 4596_CR27 publication-title: J Intell Mater Syst Struct doi: 10.1177/1045389X16635848 contributor: fullname: JM Walker – volume: 145 start-page: 90 year: 2018 ident: 4596_CR24 publication-title: Scr Mater doi: 10.1016/j.scriptamat.2017.10.016 contributor: fullname: M Elahinia – volume: 8 start-page: 12 year: 2015 ident: 4596_CR31 publication-title: Additive Manufacturing doi: 10.1016/j.addma.2015.07.002 contributor: fullname: N Shamsaei – volume: 40 start-page: 971 issue: 6 year: 2017 ident: 4596_CR38 publication-title: Fatigue Fract Eng Mater Struct doi: 10.1111/ffe.12560 contributor: fullname: O Fergani – volume: 140 start-page: 031009 issue: 3 year: 2018 ident: 4596_CR45 publication-title: J Manuf Sci Eng doi: 10.1115/1.4038293 contributor: fullname: S Chowdhury – volume: 378 start-page: 16 issue: 1-2 year: 2004 ident: 4596_CR6 publication-title: Mater Sci Eng A doi: 10.1016/j.msea.2003.10.326 contributor: fullname: N Morgan – volume: 5 start-page: 113 issue: 1 year: 2019 ident: 4596_CR23 publication-title: Shape Memory and Superelasticity doi: 10.1007/s40830-019-00214-9 contributor: fullname: M Nematollahi – ident: 4596_CR9 doi: 10.1117/12.2299817 – ident: 4596_CR8 – volume: 9 start-page: 3865 issue: 18 year: 2019 ident: 4596_CR16 publication-title: Appl Sci doi: 10.3390/app9183865 contributor: fullname: M Mehrpouya – volume: 73 start-page: 66 year: 2016 ident: 4596_CR47 publication-title: Comput Aided Des doi: 10.1016/j.cad.2015.12.003 contributor: fullname: D Ding
SSID	ssj0016168 ssib034539549 ssib019759004 ssib029851711
Score	2.5314698
Snippet	Shape memory alloys (SMAs) have been applied for various applications in the fields of aerospace, automotive, and medical. Nickel-titanium (NiTi) is the most...
SourceID	proquest crossref springer
SourceType	Aggregation Database Publisher
StartPage	4691
SubjectTerms	Additive manufacturing Alloys Artificial neural networks Austenite Automotive parts CAE) and Design Computer-Aided Engineering (CAD Engineering Industrial and Production Engineering Intermetallic compounds Lasers Martensite Martensitic transformations Mathematical models Mechanical Engineering Mechanical properties Media Management Nickel base alloys Nickel titanides Original Article Parameters Prediction models Prototyping Shape effects Shape memory alloys Strain Superelasticity Surgical implants Three dimensional printing Transformation temperature
Title	A prediction model for finding the optimal laser parameters in additive manufacturing of NiTi shape memory alloy
URI	https://link.springer.com/article/10.1007/s00170-019-04596-z https://www.proquest.com/docview/2320971063 https://www.proquest.com/docview/2490871144
Volume	105
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
link	http://sdu.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV07T8MwELZ4LDDwRpSXPLBBUOzYTjJW0FIQ6kKR2CI7caBCTas-hvLruXOTFlAZYMrgxFbuzneffS9CLgBkSK0j40kWKjigxMrTRjAP2G2slrmyPiYKt57C9kt028AyOcH86qJ4v648kk5Rz3PdXKUXOPniXb6EKT9WyTrYHgnCvV5vPLQeKzFicYitMOdixmPsP78Q40DIYObcKp0NirmMOTiNRLjfojK3Zvmq3-3XApT-8KM689Tc_teP7ZCtEo3S-kx8dsmKLfbI5pcahftkUKeDIXpzkIPUNc6hAHSpc3YXrxQAJO2D3unBPIDE7ZBiNfEeRtmMaLegGLCEKpX2dDHBNAqXF0n7OW13O106etMDGMN43ynFIIDpAXluNjo3La9s0-ClgPbGXmBikxtpDIuFb6WyUmdhaFNfxTLPfe2nKkuzHDSDDqNccMOUzTnLZKAsF2BCD8la0S_sEaGhyQzj8GlgIyFgYm6ylGnFU821H4c1clkRPxnMqnEk87rLjo4J0DFxdEw-auS04k9S7sxRAggSy2YBMls-jI5QEBYhauSq4tdi-PfFjv_2-gnZ4MhyFxhzStbGw4k9I6ujbHJeijM-7zt3zU8byez0
link.rule.ids	315,782,786,27933,27934,41073,42142,48344,48347,48357,49649,49652,49662,52153
linkProvider	Springer Nature
linkToHtml	http://sdu.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LU9swEN6B5EA5UB5lCKVUB27gGUuWZPuY4TFhCLk0zMDJI9lyySFOJiEH-PXsKnbSduihPcuWxrur1Sfv7rcAZwgylDGJDRSPNV5QUh0YK3mA6rbOqFK7kAqFez_iwWNydU00ObKphfHZ7k1I0nvqVbGbp3rBqy_9zFc459smtIntXLSg3X18erpq7IinMfXCXNmZSKkB_dqOI6miZXSrjjZo7kvm8DqS0IZL6uKaj1f9_QBbo9I_Aqn-fLr5_H9ftgs7NR5l3aUB7cGGq_Zh-xeWwgOYdtl0RvEc0iHzrXMYQl3mw93VT4YQkk3Q84xxHsTibsaIT3xMeTZzNqoYpSyRU2VjUy2okMJXRrJJyQaj4YjNn80Uxyjj95VRGsDrF3i4uR5e9oK6UUOQI957CSKb2tIqa3kqQ6e0U6aIY5eHOlVlGZow10VelOgbTJyUUliuXSl4oSLthMRD9BBa1aRyR8BiW1gu8NXIJVLixMIWOTda5EaYMI07cN5IP5su-TiyFfOyl2OGcsy8HLO3Dpw0CsrqvTnPEEMScRZis4-HKRSK1iJlBy4afa2H_77Y8b89_h22esP7fta_Hdx9hU-C1O_TZE6g9TJbuG-wOS8Wp7VtvwM6le-Y
linkToPdf	http://sdu.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1NT9wwEB3BIlXtoUA_1G2B-sCtjYgd20mOq8IWRLVCYiv1FtmJ3e5hs9F-HODXd8ab7LaIHlDPTmzFM7afM_PeAJwiyFDGZDZSPNV4Qcl1ZKzkEZrbOqO8djERhS9v09GP7PyCZHI2LP6Q7d6FJNecBlJpqpdnTeXPNsS3IPuC12D6sa-w__td2KPfYrIHe4Or8ddh51M8T6ku5sbnRE7F6Lc-nUiVrCNdbeRB80Cfw6tJRosva4k2j4_692G2RagPgqrhrBru__9XHsDLFqeywdqxDmHH1a_gxR_qha-hGbBmTnEesi0LJXUYQmAWwuD1T4bQks1wR5piP4jR3ZyRzviU8m8WbFIzSmWizZZNTb0igkVgTLKZZ6PJeMIWv0yDbZQJfMcoPeDuDXwfXoy_XEZtAYeoRBy4jBKbW2-VtTyXsVPaKVOlqStjnSvvYxOXuiorj3uGSTMvheXaecErlWgnJB6ub6FXz2r3DlhqK8sFvpq4TErsWNiq5EaL0ggT52kfPnWWKJq1TkexUWQO81jgPBZhHov7Phx1xiraNbsoEFuSoBZitsebKUSKniNlHz53tts2_3uw9097_CM8uzkfFt-uRtcf4Lkg64fsmSPoLecrdwy7i2p10rr5b7fb-C8
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+prediction+model+for+finding+the+optimal+laser+parameters+in+additive+manufacturing+of+NiTi+shape+memory+alloy&rft.jtitle=International+journal+of+advanced+manufacturing+technology&rft.au=Mehrpouya%2C+Mehrshad&rft.au=Gisario%2C+Annamaria&rft.au=Rahimzadeh%2C+Atabak&rft.au=Nematollahi%2C+Mohammadreza&rft.date=2019-12-01&rft.pub=Springer+London&rft.issn=0268-3768&rft.eissn=1433-3015&rft.volume=105&rft.issue=11&rft.spage=4691&rft.epage=4699&rft_id=info:doi/10.1007%2Fs00170-019-04596-z&rft.externalDocID=10_1007_s00170_019_04596_z
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0268-3768&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0268-3768&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0268-3768&client=summon