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Electrical characterization of ALD grown HfO2 memristive devices and their noise analysis
No full textNanoTR-18 18th Nanoscience And Nanotechnology Conference 26-28 August 2024, Koç University, İstanbul - Türkiye[No Abstract Available
Search for Pair-Production of Vector-Like Quarks in Lepton+jets Final States Containing at Least One B-Tagged Jet Using the Run 2 Data From the Atlas Experiment
No full textA search is presented for the pair-production of heavy vector-like quarks in the lepton+jets final state using 140 fb−1 of proton–proton collisions at s=13 TeV collected with the ATLAS detector. The search is optimised for vector-like top-quarks (T) that decay into a W boson and a b-quark, with one W boson decaying leptonically and the other hadronically. Other vector-like quark flavours and decay modes are also considered. Events are selected with one high transverse-momentum electron or muon, large missing transverse momentum, a large-radius jet identified as a W boson, and multiple small-radius jets, at least one of which is b-tagged. Vector-like T-quarks with 100% branching ratio to Wb are excluded at 95% CL for masses below 1700 GeV. These limits are also applied to vector-like Y-quarks, which decay exclusively into a W boson and a b-quark. Isospin singlets with B(T→Wb:Ht:Zt)=1/2:1/4:1/4 are excluded for masses below 1360 GeV. © 2024 The Author(s)Australian Research Council, ARC; Centre National pour la Recherche Scientifique et Technique, CNRST; Fundação para a Ciência e a Tecnologia, FCT; Cooperative Research Centres, Australian Government Department of Industry, CRCs; National Science Foundation, NSF; Science and Technology Facilities Council, STFC; H2020 Marie Skłodowska-Curie Actions, MSCA; HORIZON EUROPE Marie Sklodowska-Curie Actions, MSCA; Nederlandse Organisatie voor Wetenschappelijk Onderzoek, NWO; Ministry of Science and Technology, Taiwan, MOST; Israel Science Foundation, ISF; Leverhulme Trust; Baden-Württemberg Stiftung, BWS; Neubauer Family Foundation, NFF; Staatssekretariat für Bildung, Forschung und Innovation, SBFI; Javna Agencija za Raziskovalno Dejavnost RS, ARRS; Generalitat de Catalunya; Bundesministerium für Wissenschaft, Forschung und Wirtschaft, BMWFW; Austrian Science Fund, FWF; Agencia Nacional de Investigación y Desarrollo, ANID; Bundesministerium für Bildung und Forschung, BMBF; Canada Foundation for Innovation, CFI; Helmholtz-Gemeinschaft, HGF; Danmarks Grundforskningsfond, DNRF; Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq; Karlsruhe Institute of Technology, KIT; Canarie; Horizon 2020 Framework Programme, H2020; Göran Gustafssons Stiftelser; European Commission, EC; Ministarstvo Prosvete, Nauke i Tehnološkog Razvoja, MPNTR; U.S. Department of Energy, USDOE; European Cooperation in Science and Technology, COST; International Council of Shopping Centers, ICSC; Fundação de Amparo à Pesquisa do Estado de São Paulo, FAPESP; Institutul de Fizică Atomică, IFA; Natural Sciences and Engineering Research Council of Canada, NSERC; Nella and Leon Benoziyo Center for Neurological Diseases, Weizmann Institute of Science; Irish Rugby Football Union, IRFU; Chinese Academy of Sciences, CAS; Defence Science Institute, DSI; Agencia Nacional de Promoción Científica y Tecnológica, ANPCyT; Royal Society; Minerva Foundation; National Research Foundation, NRF; Royal Society of South Australia, RSSA; Generalitat Valenciana, GVA; CERN; Ministerstvo Školství, Mládeže a Tělovýchovy, MŠMT; National Research Council Canada, NRC; Brookhaven National Laboratory, BNL; Alexander von Humboldt-Stiftung, AvH; Multiple Sclerosis Scientific Research Foundation, MSSRF; Caring Futures Institute, Flinders University, CFI; British Columbia Knowledge Development Fund, BCKDF; Ministry of Education, Culture, Sports, Science and Technology, MEXT; 101033496; Narodowe Centrum Nauki, NCN: UMO-2020/37/B/ST2/01043, 2022/47/B/ST2/03059, UMO-2021/40/C/ST2/00187, UMO-2019/34/E/ST2/00393, 2021/42/E/ST2/00350; Narodowe Centrum Nauki, NCN; Japan Society for the Promotion of Science, JSPS: JP21H05085, 22KK0227, JP22H04944, JP22H01227; Japan Society for the Promotion of Science, JSPS; Knut och Alice Wallenbergs Stiftelse: KAW 2017.0100, KAW 2018.0157, KAW 2018.0458, KAW 2019.0447; Knut och Alice Wallenbergs Stiftelse; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, SNF: PCEFP2_194658, RPG-2020-004; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, SNF; LCF/BQ/PI20/11760025; Center for Advancing Research Impact in Society, ARIS: J1-3010; Center for Advancing Research Impact in Society, ARIS; CHIST-ERA-19-XAI-00; Instituto Nazionale di Fisica Nucleare, INFN: 754496; Instituto Nazionale di Fisica Nucleare, INFN; National Natural Science Foundation of China, NSFC: 12275265, 12175119, PRIMUS/21/SCI/017, NSFC-12075060; National Natural Science Foundation of China, NSFC; CC-IN2P3; Agence Nationale de la Recherche, ANR: ANR-20-CE31-0013, ANR-21-CE31-0013, ANR-11-LABX-0012, ANR-21-CE31-0022; Agence Nationale de la Recherche, ANR; Fondo Nacional de Desarrollo Científico y Tecnológico, FONDECYT: 1230987, 1210400, 1190886, 1230812; Fondo Nacional de Desarrollo Científico y Tecnológico, FONDECYT; 2014-2021; Deutsche Forschungsgemeinschaft, DFG: DFG - 469666862, DFG - CR 312/5-1; Deutsche Forschungsgemeinschaft, DFG; PE00000013; Narodowa Agencja Wymiany Akademickiej, NAWA: PPN/PPO/2020/1/00002/U/00001; Narodowa Agencja Wymiany Akademickiej, NAWA; European Research Council, ERC: 948254; European Research Council, ERC; European Regional Development Fund, ERDF: LCF/BQ/PI20/11760025, IDIFEDER/2018/048; European Regional Development Fund, ERDF; Fundación BBVA, FBBVA: LEO22-1-603; Fundación BBVA, FBBVA; Vetenskapsrådet, VR: VR 2022-03845, VR 2022-04683, VR 2018-00482, 2021-03651; Vetenskapsrådet, VR; Ministerio de Ciencia e Innovación, MCIN: PID2021-125273NB, RYC2022-038164-I, RYC2020-030254-I, PCI2022-135018-2, RYC2019-028510-I, RYC2021-031273-I; Ministerio de Ciencia e Innovación, MCIN; Norges Forskningsråd: RCN-314472; Norges Forskningsråd; 21/SCI/017; CIDEGENT/2019/027, CIDEGENT/2019/023; SCI/013; IN2P3-CNR
Cıvatalı Flanş Bağlantıları Olan Montajlı Rotor Modüllerindeçoklu Vekil Modeller Kullanılarak Yapısal Bütünlüğündoğrulanması
No full textBolted flange connection structures play a critical role in the assembly of aircraft engines. These structures bring together the fixed and moving parts of the engine, ensuring its safe and effective operation. In the initial stages of the design process, various design parameters such as the dimensions of the flanges, the number and size of the bolts, and their arrangement are considered. The selection of these parameters is made to ensure the structural integrity of the engine. To guarantee structural integrity and identify the most suitable design, comprehensive simulations are conducted using finite element analysis. During operation, the assembled rotor group is subjected to various thermal and mechanical loads. Each of these loads can affect the structural integrity of the rotor group. Therefore, it is essential to ensure that the rotor group meets these integrity criteria under every loading condition. These criteria include stresses, the preloading forces of the bolts, and the condition of contact points. The design process can be complex and time-consuming due to the large number of variables that must be considered. Therefore, various methods are applied to enhance the efficiency of the design process and develop new design approaches. In this study, as described in NASA's E3 High Pressure Turbine Test Hardware Detailed Design Report, a surrogate modeling method for the optimization of bolted flange connection design has been used. This method aims to accelerate the design process and achieve more accurate and effective results. The work was carried out on a rotor group similar to the E3 turbine module, and separate finite element models were developed for each flange. Design parameters were used as inputs for the surrogate models, which were created using Latin Hypercube Sampling (LHS) and Genetic Assembly Response Surface (GARS) methods. Multi-Objective Genetic Algorithm (MOGA) was employed for optimization to achieve optimum designs. The resulting designs were fed into the rotor module, and a comprehensive finite element model of the rotor module as a whole was established. The stress and reaction force values obtained for the flange areas of the rotor module are in agreement with the results from the optimization of the surrogate models, with a difference of only 0.1%. Although there was a mass increase of 3.27% in the optimized first flange area compared to the initial condition, the desired minimum flange reaction force for complete torque transfer could not be achieved in the fifth loading step of the initial flange area configuration. Improvements were observed in the stress levels of three holes in the first flange, with reductions of 5.10%, 3.03%, and 1.75%, respectively, compared to the initial condition. For the second flange area, there was a mass increase of 2.97% and a worsening of stresses by 1.94% in the middle hole area of the second flange. Similarly, the initial configuration of the second flange area could not provide the necessary flange reaction force. For the third flange area, due to the more flexible criteria defined in the design report, the optimization efforts resulted in a mass reduction of 13.66% and improvements in the stresses of the flange holes by 15.60%, 5.12%, and 4.89%, respectively. The results demonstrate that the use of multiple surrogate models in the design of flanged connections for assembled rotor groups enables precision analyses to be conducted in short periods without losing accuracy, and achieving optimum designs that meet structural integrity criteria is possible.Cıvatalı flanş bağlantı yapıları, uçak motorlarının montajında kritik bir rol oynar. Bu yapılar, motorun sabit ve hareketli parçalarını bir araya getirerek, motorun güvenli ve etkili bir şekilde çalışmasını sağlar. Tasarım sürecinin ilk aşamalarında, flanşların boyutları, cıvataların sayısı ve boyutları, ve bunların yerleşim düzeni gibi çeşitli tasarım parametreleri göz önünde bulundurulur. Bu parametrelerin seçimi, motorun yapısal bütünlüğünü sağlayacak şekilde yapılır. Yapısal bütünlüğü garantilemek ve en uygun tasarımı belirlemek amacıyla, sonlu elemanlar analizi kullanılarak kapsamlı simülasyonlar yapılır. Motorun çalışması sırasında, montajlı rotor grubu çeşitli ısıl ve mekanik yüklere maruz kalır. Bu yüklerin her biri, rotor grubunun yapısal bütünlüğünü etkileyebilir. Dolayısıyla, her bir yükleme durumunda rotor grubunun bu bütünlük kriterlerini karşıladığından emin olunması gerekir. Bu kriterler, gerilmeler, cıvataların ön yükleme kuvvetleri ve temas noktalarının durumu gibi ölçütleri içerir. Tasarım süreci, çok sayıda değişkenin göz önünde bulundurulması gerektiğinden, karmaşık ve zaman alıcı olabilir. Bu nedenle, tasarım sürecinin etkinliğini artırmak ve yeni tasarım yaklaşımları geliştirmek için çeşitli yöntemler uygulanmaktadır. Bu çalışmada, NASA'nın E3 Yüksek Basınçlı Türbin Test Donanımı için yapılan detay tasarım raporunda açıklanan gibi, cıvatalı flanş bağlantı tasarımının optimizasyonu için çoklu vekil modelleme yöntemi kullanılmıştır. Bu yöntem, tasarım sürecini hızlandırmanın yanı sıra, daha doğru ve etkili sonuçlar elde etmeyi amaçlar. Çalışma, E3 türbin modülü benzeri bir rotor grubu üzerinde yapılmış ve her bir flanş için ayrı sonlu elemanlar modelleri geliştirilmiştir. Tasarım parametreleri, vekil modeller için girdi olarak kullanılmış ve bu modeller, Latin Hiperküp Örnekleme (LHÖ) ve Genetik Birleştirme Yanıt Yüzeyi (GARS) metotları ile oluşturulmuştur. Optimizasyon için Çok Amaçlı Genetik Algoritma (MOGA) kullanılarak optimum tasarımlar elde edilmiştir. Elde edilen tasarımlar, rotor modülüne beslenmiş ve rotor modülünün bir bütün olarak sonlu elemanlar modeli kurulmuştur. Rotor modülünün flanş bölgeleri için elde edilen gerilme ve tepki kuvveti değerleri %0.1 mertebesinde bir fark ile, vekil modellerin optimizasyonundan alınan sonuçlar ile örtüşmektedir. Optimize edilen birinci flanş bölgesinde başlangıç durumuna göre kütle olarak %3.27 seviyesinde artış olsa da, başlangıç durumundaki flanş bölgesi konfigürasyonunda tork transferinin eksiksiz sağlanabilmesi için beşinci yükleme adımında istenen minimum flanş tepki kuvveti sağlanamamaktadır. Gerilmeler ise başlangıç durumuna göre birinci flanşta bulunan üç adet delikte sırasıyla %5.10, %3.03, ve %1.75 seviyelerinde iyileşmeler sağlanmıştır. İkinci flanş bölgesi için %2.97 seviyesinde kütle artışı ve ikinci flanşta ortada delik bölgesinde ise gerilmelerde %1.94 seviyesinde kötüleşme olmuştur. İkinci flanş bölgesinin başlangıç konfigürasyonu da benzer şekilde gereken flanş tepki kuvvetini verememektedir. Üçüncü flanş bölgesi için ise ilgili tasarım raporunda tanımlanan kriterler daha esnek olduğu için optimizasyon çalışmaları sonucunda kütlede %13.66 seviyesinde hafifleme, flanş deliklerinde gerilmelerde ise sırasıyla %15.60, %5.12, ve %4.89 seviyesinde iyileşmeler gözlemlenmiştir. Sonuçlar, montajlı rotor gruplarının flanşlı bağlantı tasarımlarında çoklu vekil modellerin kullanımı sayesinde doğruluk seviyesini kaybetmeden kısa sürelerde hassasiyet analizlerinin yapılabilmesinin ve yapısal bütünlük kriterlerini sağlayan optimum tasarımların elde edilmesinin mümkün olduğunu göstermektedir
From Black Boxes to Transparency: The Evolution of AI in Architectural Design
No full text[No Abstract Available
Kantorovich Version of Vector-Valued Shepard Operators
No full textIn the present work, in order to approximate integrable vector-valued functions, we study the Kantorovich version of vector-valued Shepard operators. We also display some applications supporting our results by using parametric plots of a surface and a space curve. Finally, we also investigate how nonnegative regular (matrix) summability methods affect the approximation
Prokinetics-Safety and Efficacy: the European Society of Neurogastroenterology and Motility/The American Neurogastroenterology and Motility Society Expert Review
No full textBackgroundProkinetics are a class of pharmacological drugs designed to improve gastrointestinal (GI) motility, either regionally or across the whole gut. Each drug has its merits and drawbacks, and based on current evidence as high-quality studies are limited, we have no clear recommendation on one class or other. However, there remains a large unmet need for both regionally selective and/or globally acting prokinetic drugs that work primarily intraluminally and are safe and without systemic side effects.PurposeHere, we describe the strengths and weaknesses of six classes of prokinetic drugs, including their pharmacokinetic properties, efficacy, safety and tolerability and potential indications
Search for Low-Mass Resonances Decaying Into Two Jets and Produced in Association With a Photon or a Jet at √i>s/I>=13 Tev With the Atlas Detector
No full textFernandez-Martinez, Pablo/0000-0002-7818-6971; Petersen, Troels/0000-0003-0221-3037; Aad, Georges/0000-0002-6665-4934; Stanislaus, Beojan/0000-0001-9007-7658; Kretzschmar, Jan/0000-0002-8515-1355; Roloff, Jennifer/0000-0001-6479-3079; Sala, Alessandro/0000-0003-0824-7326; Gwilliam, Carl/0000-0002-9401-5304; Tian, Yusong/0000-0001-8739-9250; Canbay, Ali Can/0000-0003-4602-473X; Mazzeo, Elena/0000-0002-8406-0195; Calafiura, Paolo/0000-0002-1692-1678; D'Auria, Saverio/0000-0003-3393-6318; Ventura, Andrea/0000-0002-3368-3413; Carbone, Antonio/0000-0002-4117-3800; Ragusa, Francesco/0000-0002-4064-0489; Rompotis, Nikolaos/0000-0003-2577-1875; Herde, Hannah/0000-0001-8926-6734; Camplani, Alessandra/0000-0002-6386-9788; Pezzullo, Gianantonio/0000-0002-6653-1555; Smirnova, Oxana/0000-0003-2517-531XA search is performed for localized excesses in the low-mass dijet invariant mass distribution, targeting a hypothetical new particle decaying into two jets and produced in association with either a high transverse momentum photon or a jet. The search uses the full Run 2 data sample from LHC proton-proton collisions collected by the ATLAS experiment at a center-of-mass energy of 13 TeV during 2015-2018. Two variants of the search are presented for each type of initial-state radiation: one that makes no jet flavor requirements and one that requires both of the jets to have been identified as containing b-hadrons. No excess is observed relative to the Standard Model prediction, and the data are used to set upper limits on the production cross section for a benchmark Z' model and, separately, for generic, beyond the Standard Model scenarios which might produce a Gaussian-shaped contribution to dijet invariant mass distributions. The results extend the current constraints on dijet resonances to the mass range between 200 and 650 GeV.CERN; NDGF (Denmark, Norway, Sweden); KIT/GridKA (Germany); INFN-CNAF (Italy); NL-T1 (Netherlands), PIC (Spain); BNL (USA) [105]; ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW; FWF, Austria; ANAS; CNPq; FAPESP, Brazil; NSERC; CFI, Canada; NSFC, China; MEYS CR, Czech Republic; DNRF; DNSRC, Denmark; IN2P3-CNRS; CEA-DRF/IRFU, France; BMBF; MPG, Germany; RGC and Hong Kong SAR, China; ISF; Benoziyo Center, Israel; INFN, Italy; MEXT; JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MEiN, Poland; FCT, Portugal; MNE/IFA, Romania; MESTD, Serbia; MSSR, Slovakia; ARRS; MIZS, Slovenia; MICINN, Spain; SRC; Wallenberg Foundation, Sweden; SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taipei; DOE; NSF; BCKDF; CANARIE; CRC; DRAC, Canada [PRIMUS 21/SCI/017, UNCE SCI/013]; Czech Republic; ERC; ERDF; Marie Sklodowska-Curie Actions, European Union; Investissements d'Avenir Labex, Investissements d'Avenir Idex; ANR, France; DFG; AvH Foundation, Germany - EU-ESF; Greek NSRF, Greece; BSF-NSF; NCN [UMO-2019/34/E/ST2/00393, UMO-2020/37/B/ST2/01043, UMO-2022/47/O/ST2/00148]; La Caixa Banking Foundation; CERCA Programme Generalitat de Catalunya; PROMETEO; Generalitat Valenciana, Spain; Goran Gustafssons Stiftelse, Sweden; Royal Society; Leverhulme Trust, United Kingdom; CERN: European Organization for Nuclear Research (CERN PJAS); Chile: Agencia Nacional de Investigacion y Desarrollo (FONDECYT) [1190886]; FONDECYT [1230987]; China: National Natural Science Foundation of China [NSFC-12175119, NSFC 12275265]; European Union: European Research Council [ERC-948254, ERC 101089007, MUCCA-CHIST-ERA-19-XAI-00]; Italian Center for High Performance Computing, Big Data and Quantum Computing (ICSC); France: Agence Nationale de la Recherche [ANR-20-CE31-0013, ANR-21-CE31-0022]; Investissements d'Avenir Labex; Germany: Baden-Wurttemberg Stiftung; Deutsche Forschungsgemeinschaft [DFG-469666862]; Japan: Japan Society for the Promotion of Science (JSPS KAKENHI) [22H01227, 22KK0227, JP21H05085, JP22H04944, NWO Veni 2020-VI]; Norway: Research Council of Norway [RCN-314472]; Polish National Agency for Academic Exchange [PPN/PPO/2020/1/00002/U/00001]; Polish National Science Centre (NCN) [2021/42/E/ST2/00350]; NCN OPUS [2022/47/B/ST2/03059]; Slovenian Research Agency [J1-3010]; BBVA Foundation [LEO22-1-603]; Generalitat Valenciana - European Union; FEDER Operative Programme of Comunitat Valenciana [IDIFEDER/2018/048]; Ministry of Science and Innovation [RYC2019-028510-I, RYC2020-030254-I]; GenT Programmes Generalitat Valenciana [CIDEGENT/2019/023, CIDEGENT/2019/027]; Swedish Research Council [VR 2022-03845]; Knut and Alice Wallenberg Foundation; Swiss National Science Foundation [SNSF-PCEFP2_194658]; United Kingdom: Leverhulme Trust (Leverhulme Trust) [RPG-2020-004]; USA: Neubauer Family FoundationWe thank CERN for the very successful operation of the LHC and its injectors, as well as the support staff at CERN and at our institutions worldwide without whom ATLAS could not be operated efficiently. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN, the ATLAS Tier-1 facilities at TRIUMF/SFU (Canada), NDGF (Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), RAL (UK) and BNL (USA), the Tier-2 facilities worldwide and large non-WLCG resource providers. Major contributors of computing resources are listed in Ref. [105]. We gratefully acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; ANID, Chile; CAS, MOST and NSFC, China; Minciencias, Colombia; MEYS CR, Czech Republic; DNRF and DNSRC, Denmark; IN2P3-CNRS and CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, HGF and MPG, Germany; GSRI, Greece; RGC and Hong Kong SAR, China; ISF and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MEiN, Poland; FCT, Portugal; MNE/IFA, Romania; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZS, Slovenia; DSI/NRF, South Africa; MICINN, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taipei; TENMAK, Turkiye; STFC, United Kingdom; DOE and NSF, USA. Individual groups and members have received support from BCKDF, CANARIE, CRC and DRAC, Canada; PRIMUS 21/SCI/017 and UNCE SCI/013, Czech Republic; COST, ERC, ERDF, Horizon 2020, ICSC-NextGenerationEU and Marie Sklodowska-Curie Actions, European Union; Investissements d'Avenir Labex, Investissements d'Avenir Idex and ANR, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF, Greece; BSF-NSF and MINERVA, Israel; Norwegian Financial Mechanism 2014-2021, Norway; NCN and NAWA, Poland; La Caixa Banking Foundation, CERCA Programme Generalitat de Catalunya and PROMETEO and GenT Programmes Generalitat Valenciana, Spain; Goran Gustafssons Stiftelse, Sweden; The Royal Society and Leverhulme Trust, United Kingdom. In addition, individual members wish to acknowledge support from CERN: European Organization for Nuclear Research (CERN PJAS); Chile: Agencia Nacional de Investigacion y Desarrollo (FONDECYT 1190886, FONDECYT 1210400, FONDECYT 1230987); China: National Natural Science Foundation of China (NSFC-12175119, NSFC 12275265); European Union: European Research Council (ERC-948254, ERC 101089007), Horizon 2020 Framework Programme (MUCCA-CHIST-ERA-19-XAI-00), Italian Center for High Performance Computing, Big Data and Quantum Computing (ICSC, NextGenerationEU); France: Agence Nationale de la Recherche (ANR-20-CE31-0013, ANR-21-CE31-0022), Investissements d'Avenir Labex (ANR-11-LABX-0012); Germany: Baden-Wurttemberg Stiftung (BW Stiftung-Postdoc Eliteprogramme), Deutsche Forschungsgemeinschaft (DFG-469666862, DFG-CR 312/5-2); Italy: Istituto Nazionale di Fisica Nucleare (ICSC, NextGenerationEU); Japan: Japan Society for the Promotion of Science (JSPS KAKENHI 22H01227, JSPS KAKENHI 22KK0227, JSPS KAKENHI JP21H05085, JSPS KAKENHI JP22H04944); Netherlands: Netherlands Organisation for Scientific Research (NWO Veni 2020-VI.Veni.202. 179); Norway: Research Council of Norway (RCN-314472); Poland: Polish National Agency for Academic Exchange (PPN/PPO/2020/1/00002/U/00001), Polish National Science Centre (NCN 2021/42/E/ST2/00350, NCN OPUS nr 2022/47/B/ST2/03059, NCN UMO-2019/34/E/ST2/00393, UMO-2020/37/B/ST2/01043, UMO-2022/47/O/ST2/00148); Slovenia: Slovenian Research Agency (ARIS Grant No. J1-3010); Spain: BBVA Foundation (LEO22-1-603), Generalitat Valenciana (Computer resources at Artemisa, co-funded by the European Union 2014-2020 FEDER Operative Programme of Comunitat Valenciana IDIFEDER/2018/048), Ministry of Science and Innovation (RYC2019-028510-I, RYC2020-030254-I), PROMETEO and GenT Programmes Generalitat Valenciana (CIDEGENT/2019/023, CIDEGENT/2019/027); Sweden: Swedish Research Council (VR 2022-03845), Knut and Alice Wallenberg Foundation (KAW 2022.0358); Switzerland: Swiss National Science Foundation (SNSF-PCEFP2_194658); United Kingdom: Leverhulme Trust (Leverhulme Trust RPG-2020-004); USA: Neubauer Family Foundation
Eklemeli İmalattan Gelen Belirsizlikler Altında Kendinden Destekli Latis Yapı Tasarımı için Bir Optimizasyon Yöntemi Geliştirilmesi
No full textLatis yapıların hafif ve yüksek dayanıma sahip olmaları ve kolay modellenebilir topolojik özellikleri sayesinde günümüzde giderek daha fazla tercih sebebi olmaktadır. Karmaşık geometrileri sebebiyle çubuk tabanlı latis yapıların üretiminde eklemeli imalat tercih edilmektedir. Mevcut eklemeli imalat tezgahlarında genellikle milimetre mertebesinde tasarlanan çubuk elemanlar üzerinde, katman katman üretim esnasında mikro ve milimetre seviyesinde değişkenlikler ve belirsizlikler oluşmaktadır. Bu değişimler tasarlanan ve üretilen yapı arasında mekanik özelliklerde fark çıkmasına sebep olmaktadır. Eklemeli imalat sayesinde latis yapılar üzerinde dayanımı arttıracak optimizasyon algoritmalarının uygulanması mümkündür ve optimizasyon sonuçlarında karmaşık geometriler oluşmaktadır. Optimize edilmiş tasarımlar, oluşan karmaşık topoloji sebebiyle destek yapılarının kullanımını gerektirebilir. Bu destek yapılarını var olan çubuk elemanları etkilemeden oluşturmak veya üretimden sonra karmaşık topolojiden çıkarmak, destek yapılarının söküm esnasında da yapıya zarar vermemek kolay değildir. Bu çalışmada, tasarlanan latis yapıların geometri ve malzeme özelliklerinde, üretim teknolojisinden dolayı oluşan belirsizlikler hesaba katılarak, elde edilen geometride destek yapısı gereksinimi olmadan kendinden destekli olarak üretilebilecek, iki adımlı bir latis optimizasyonu prosedürü önerilmiştir. Bu amaçla, latis yapıları oluşturan çubuk elemanları modellemek için kullanılacak çap ve açı değişkenleri ile bunların malzeme ekstrüzyonu ile eklemeli imalatından dolayı oluşan belirsizlikler altında daha önceki bir çalışmada belirlenen homojenize özellikler arasında yapay sinir ağları modelleri kullanarak yapı-özellik ilişkileri oluşturulmuştur. Geliştirilen yapay sinir ağı modeli, çubuk elemanlarla modellenerek latis optimizasyonu sürecine entegre edilmiştir. İlk olarak MATLAB üzerinde, çözüm ağıyla modellenmiş bir yapıyı latis hücrelerle modelleyen bir algoritma oluşturulmuştur. Burada oluşan topolojik bilgileri kullanarak iki adımlı optimizasyon algoritması başlatılır. İki adımlı optimizasyonun ilk adımı, çapları sıfıra yakın olan çubuk elemanlarının topolojiden çıkarıldığı klasik yerleşim optimizasyonudur. İkinci adımda gerçekleştirilen boyut optimizasyonu, belirlenen minimum üretim çapı kısıtlaması ile topolojideki çubuk elemanların optimize edilmiş çaplarını belirlemek için gerçekleştirilir. İki optimizasyon süreci arasında, topolojide destek yapısı gerektiren çubuk elemanlar tespit edilerek, yapıyı kendinden destekli bir şekilde üretebilecek destek yapı algoritması geliştirilmiştir. Elde edilen nihai optimize edilmiş geometriyi, üretilebilir bir STL model oluşturan bir yüzey oluşturma algoritması da çalışma kapsamında geliştirilmiştir. Optimizasyon ile tasarlanan örnek uygulamalar üretilerek test edilmiş ve metodolojinin etkinliği doğrulanmıştır.The lightweight and high strength characteristics of lattice structures, coupled with their easily modellable topological features, are increasingly becoming preferred choices in engineering applications. Due to their complex geometries, additive manufacturing is preferred for the fabrication of strut-based lattice structures. These structures are primarily composed of strut elements. Considering the current capabilities of additive manufacturing, variations and uncertainties at the micro and millimeter levels often arise during layer-by-layer production of strut elements typically designed at the millimeter scale. These changes cause a difference in mechanical properties between the designed and manufactured structure. The application of optimizations aimed at enhancing the strength of lattice structures is feasible through additive manufacturing, resulting in the formation of complex geometries in optimization outcomes. Optimized designs may necessitate the use of support structures due to the resulting topology optimization. However, generating these support structures without affecting existing strut elements or removing them from the complex topology post-production without causing damage to the structure is not straightforward. In this study, a two-step lattice optimization procedure is proposed as a design approach to account for uncertainties arising from the additive manufacturing of lattice structures, impacting their geometry and material properties. This approach allows to produce self-supporting optimization results without the need for additional support structures during the design process. For material extrusion, structure-property relationships were established using artificial neural networks between the parameters governing the modelling of strut elements, including diameter and angle variations, and the homogenized properties characterized in a previous study under uncertainties arising from the additive manufacturing by material extrusion. The artificial neural network model has been integrated into the lattice optimization process, which involves modelling with strut elements. Initially, an algorithm was created in MATLAB to convert a meshed model to lattice cells. Using the topological information obtained, a two-step optimization algorithm is initiated. The first step of the two-step optimization is the classical layout optimization where strut elements with diameters close to zero are removed from the topology. The second size optimization is performed to determine the optimized diameters of the strut elements in the topology with the specified minimum manufacturing constraint. Between the two optimization stages, a self-support structure algorithm has been developed to identify strut elements in the optimized topology requiring support. This algorithm adds supports by incorporating strut elements into the structure before topology optimization, enabling self-supporting structure fabrication. Additionally, within the scope of this study, a surface generation algorithm has been developed to create a manufacturable STL model representing the final optimized geometry. The proposed method's effectiveness is showed through benchmark examples in literature. Fabrication of the optimized designs was carried out using material extrusion technique, followed by testing to validate the efficacy of the proposed approach