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Experimental Investigation on Mode I/II/III Fracture Behaviour of Additively Manufactured PLA Materials: Effects of Build Orientation
No full textPolylactic acid (PLA) is a bio-based material that can potentially replace environmentally harmful petroleum-based polymers in engineering applications due to its high strength. The fused filament fabrication (FFF) additive manufacturing (AM) technique has recently been widely used to manufacture PLA-based components. However, the build orientation in the FFF technique influences the fracture properties in different fracture modes due to the stacking of the material layers. Although there are studies done on the mode I and II fracture behaviour of the PLA manufactured by FFF, no study is available to investigate the effect of build orientation on all three fracture modes, including Modes I, II and III. This study aims to experimentally investigate the effect of build orientation on fracture behaviour (e.g., fracture toughness and crack propagation) of PLA fabricated by FFF for three pure fracture modes (Modes I, II and III). Mode I and II fracture toughness values were extracted using the symmetric and asymmetric four-point bending tests, respectively. A Mode III testing procedure is developed to apply appropriate loading in a tensile testing machine. Specimens were fabricated by FFF in three build orientations and tested. Fracture toughness and corresponding fracture energy values were calculated for different build orientations. Crack propagation in the samples after fracture was investigated through the images taken with a camera and scanning electron microscope. Based on fracture surfaces, it was observed that the failure could be of interlayer, intralayer and combined types, considering the build orientations. The differences in the fracture surfaces govern the fracture behaviour of PLA materials fabricated in different build orientations. Build orientation was observed to be a dominant factor in all three fracture modes of FFF-printed PLA. The testing procedures and the fracture properties provided in this study can be used to develop FFF-printed PLA-based engineering applications.Scientific and Technological Research Council of Turkiye [122M823]This research was funded by a grant from the Scientific and Technological Research Council of Turkiye, project number 122M823
Kentsel ve Kırsal Yoksullukta Yaşam Koşulları, Ebeveynlik ve Çocukların Günlük Aktiviteleri
No full textPoverty is a risk factor for children's development in several domains. However, since urban and rural poverty differ in terms of living conditions, they are related to different risk factors. The current study aims to compare the living conditions of urban and rural poverty, parenting behaviors, and children's daily activities. The participants were 275 mothers living in disadvantaged urban and rural regions. The data were collected via scales and openended questions. The results indicated that income-to-needs ratio, fathers' education level, food insecurity, home stimulation (educational materials and activities), physical resources (i.e., market, kindergarten, hospital) in the neighborhood, neighborhood stress (i.e., fighting, noise), and mothers' expectations for their children's educational attainment were higher in urban poverty, whereas, social relations and physical qualities (i.e., security, cleanliness) of the neighborhood and support from neighbors were higher in rural poverty. Additionally, mothers from rural regions reported the importance of neighborhood quality in child development more frequently. When the content of the conversations with children was examined, mothers from urban poverty talked to their children about advice and stimulating activities more frequently, whereas mothers from rural poverty talked about future and household chores more frequently. Moreover, children living in urban poverty were playing with tablets, whereas children living in rural poverty were spending time on farms and interacting with animals. The findings are important in terms of indicatingthe similarities and differences in child-rearing conditions and parenting in urban and rural poverty. The results are expected to guide future studies regardingthe role of urban and rural poverty in children's development
Mqtt Ağına Gerçekleştirilen Saldırıların Makine Öğrenmesi Modelleri ile Tespiti
No full textThe rapid development of the Internet of Things (IoT) has enabled a large number of devices to share data over networks, paving the way for significant advancements across many industries. Various communication protocols are used to ensure the efficient exchange of data among IoT devices. Among these protocols, MQTT (Message Queuing Telemetry Transport) stands out as an ideal communication mechanism for IoT systems due to its low bandwidth requirements and lightweight structure. However, the widespread adoption of the MQTT protocol has also exposed these networks to various security vulnerabilities, making them susceptible to cyberattacks. This study examines the use of machine learning techniques to detect attacks specific to MQTT networks and aims to identify potential threats. The study focuses particularly on the SlowITe attack and the SlowTT attack, which was integrated into the dataset later. The dataset used, called MQTTset, did not originally include data related to SlowTT attacks, so this data was added during the course of the study. To ensure a balanced dataset, %50 of the dataset consists of normal traffic data, while the remaining %50 is equally divided among SlowITe, SlowTT, Brute Force, Malformed, DoS, and Flood attacks. The use of a balanced dataset eliminates the problem of models overfitting to the dominant class in imbalanced datasets, ensuring fair evaluation of all classes. As a result, the performance metrics of the algorithms become more reliable. Although some models exhibited lower performance, using a balanced dataset allowed for more accurate class distribution and realistic outcomes. In this study, comprehensive experiments were conducted using various machine learning algorithms such as Random Forest, XGBoost, LightGBM, DNN (Deep Neural Network), and CatBoost on a balanced dataset. In the initial phase, Random Forest demonstrated the highest success rates in tests performed on the extended MQTTset dataset. While other models also exhibited acceptable performance, the DNN model showed relatively lower success rates. In the second phase of the study, new features (TCP window size and frame time delta) were added to the dataset, and the same models were tested again using this new dataset. With the addition of these features, a general improvement in model performance was observed, and Random Forest, XGBoost, LightGBM, and CatBoost achieved high success rates. Additionally, LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations) methods were used to further analyze the results of the models and determine which features contributed the most to model performance. SHAP analysis enhanced the interpretability of the machine learning models used in attack detection and clearly identified the features that contributed most significantly to the detection process. In conclusion, this study demonstrates the effectiveness of machine learning-based approaches in detecting attacks on MQTT networks. It was observed that the addition of new features significantly improved detection performance and that the SHAP method provided clearer insights into which features were most critical in the detection process. The methods used in this study offer significant contributions to securing IoT networks and provide effective solutions for the early detection of potential attacks.Nesnelerin İnterneti (IoT)'nin hızlı gelişimi, çok sayıda cihazın ağlar üzerinden veri paylaşımını mümkün kılmış ve birçok sektörde kayda değer ilerlemelere zemin hazırlamıstır. IoT cihazları arasındaki bu veri alışverişini verimli kılmak amacıyla çeşitli iletişim protokolleri kullanılmaktadır. Bu protokoller arasında, MQTT (Message Queuing Telemetry Transport), düşük bant genişliği gereksinimi ve hafif yapısı ile IoT sistemleri için ideal bir iletişim mekanizması olarak öne çıkmaktadır. Ancak, MQTT protokolünün yaygın benimsenmesi, bu ağlara yönelik siber saldırılar karşısında çeşitli güvenlik açıklarının ortaya çıkmasına da neden olmuştur. Bu çalışma, MQTT ağlarına özgü saldırıların tespitine yönelik olarak makine öğrenmesi tekniklerinin kullanımını incelemekte ve olası tehditlerin belirlenmesini amaçlamaktadır. Çalışmada, özellikle SlowITe ve veri setine sonradan entegre edilen SlowTT saldırı türleri üzerine odaklanılmıştır. Kullanılan veri seti MQTTset, başlangıçta SlowTT saldırılarına ilişkin verileri içermediği için bu saldırılara ait veriler çalışma kapsamında sonradan eklenmiştir. Dengeli bir veri yapısı sağlamak amacıyla, veri setinin %50'si normal trafik verilerinden, geri kalan %50'si ise eşit sayıda SlowITe, SlowTT, Brute Force, Malformed, DoS (Denial of Service) ve Flood saldırılarından oluşturulmuştur. Dengeli veri seti kullanımı, dengesiz veri setlerinin yaygın sınıfı daha iyi tahmin etme eğilimi sorununu ortadan kaldırmakta ve bu sayede tüm sınıfların adil bir şekilde değerlendirilmesini sağlamaktadır. Böylece, algoritmaların performans ölçümleri daha güvenilir hale gelmiştir. Yapılan testlerde, bazı modeller daha düşük performans gösterse de dengeli veri kullanımı sayesinde sınıflar arasında daha doğru bir dağılım ve gerçekçi sonuçlar elde edilmektedir. Çalışmada, dengeli veri seti üzerinde Random Forest, XGBoost, LightGBM, DNN (Deep Neural Network) ve CatBoost gibi farklı makine öğrenmesi algoritmaları ile kapsamlı deneyler gerçekleştirilmistir. İlk aşamada, yeni MQTTset veri seti üzerinde yapılan testlerde, Random Forest modeli en yüksek başarı oranlarını göstermiştir. Diğer modeller de kabul edilebilir performans sergilemiş olmakla birlikte, DNN modeli görece daha düşük bir başarı sergilemiştir. Çalışmanın ikinci aşamasında, veri setine yeni özellikler (TCP window size ve frame time delta) eklenmiş ve bu genişletilmiş veri seti ile aynı modeller tekrar test edilmiştir. Eklenen bu özelliklerle modellerin performansında genel bir artış gözlemlenmiş ve Random Forest, XGBoost, LightGBM ve CatBoost modelleri yüksek başarı oranlarına ulaşmıştır. Ayrıca, kullanılan modellerin sonuçlarını daha derinlemesine incelemek ve hangi özelliklerin model performansına en fazla katkıyı sağladığını belirlemek amacıyla LIME (Local Interpretable Model-agnostic Explanations) ve SHAP (SHapley Additive exPlanations) yöntemleri kullanılmıştır. SHAP analizi, saldırı tespitinde kullanılan makine öğrenmesi modellerinin anlaşılabilirliğini artırmış ve hangi özelliklerin tespit sürecine en fazla katkıyı sağladığını açıkça ortaya koymuştur. Sonuç olarak, bu çalışma, MQTT ağlarına yönelik saldırıların tespitinde makine öğrenmesi tabanlı yaklaşımların etkinliğini göstermektedir. Veriye eklenen yeni özelliklerin saldırı tespit performansını önemli ölçüde artırdığı ve SHAP yöntemi ile hangi özelliklerin tespit sürecinde kritik bir rol oynadığının daha net bir şekilde anlaşıldığı görülmüştür. Bu çalışmada kullanılan yöntemler, IoT ağlarının güvenliğini sağlamada önemli katkılar sunmakta ve potansiyel saldırıların erken tespiti için etkili çözümler sağlamaktadır
Understanding Rowhammer Under Reduced Refresh Latency: Experimental Analysis of Real Dram Chips and Implications on Future Solutions
No full textErgin, Oguz/0000-0003-2701-3787Read disturbance in modern DRAM chips is a widespread weakness that is used for breaking memory isolation, one of the fundamental building blocks of system security and privacy. RowHammer is a prime example of read disturbance in DRAM where repeatedly accessing (hammering) a row of DRAM cells (DRAM row) induces bitflips in physically nearby DRAM rows (victim rows). Unfortunately, shrinking technology node size exacerbates RowHammer and as such, significantly fewer accesses can induce bitflips in newer DRAM chip generations. To ensure robust DRAM operation, state-of-the-art mitigation mechanisms restore the charge in potential victim rows (i.e., they perform preventive refresh or charge restoration). With newer DRAM chip generations, these mechanisms perform preventive refresh more aggressively and cause larger performance, energy, or area overheads. Therefore, it is essential to develop a better understanding and in-depth insights into the preventive refresh to secure real DRAM chips at low cost. In this paper, our goal is to mitigate RowHammer at low cost by understanding the preventive refresh latency and the impact of reduced refresh latency on RowHammer. To this end, we present the first rigorous experimental study on the interactions between refresh latency and RowHammer characteristics in real DRAM chips. Our experimental characterization using 388 real DDR4 DRAM chips from three major manufacturers demonstrates that a preventive refresh latency can be significantly reduced (by 64%) at the expense of requiring slightly more (by 0.54%) preventive refreshes. To investigate the impact of reduced preventive refresh latency on system performance and energy efficiency, we reduce the preventive refresh latency and adjust the aggressiveness of existing RowHammer solutions by developing a new mechanism, Partial Charge Restoration for Aggressive Mitigation (PaCRAM). Our results show that by reducing the preventive refresh latency, PaCRAM reduces the performance and energy overheads induced by five state-of-the-art RowHammer mitigation mechanisms with small additional area overhead. Thus, PaCRAM introduces a novel perspective into addressing RowHammer vulnerability at low cost by leveraging our experimental observations. To aid future research, we open-source our PaCRAM implementation at https://github.com/CMU- SAFARI/PaCRAM.Google Security and Privacy Research Award; Microsoft Swiss Joint Research Center; ETH Future Computing Laboratory; Google; Huawei; Intel; Microsoft; VMwareWe thank the anonymous reviewers of HPCA 2025 (both main submission and artifact evaluation), MICRO 2024, and ISCA 2024 for the encouraging feedback. We thank the SAFARI Research Group members for valuable feedback and the stimulating scientific and intellectual environment. We acknowledge the generous gift funding provided by our industrial partners (especially Google, Huawei, Intel, Microsoft, VMware), which has been instrumental in enabling the research we have been conducting on read disturbance in DRAM since 2011 [3]. This work was also in part supported by the Google Security and Privacy Research Award, the Microsoft Swiss Joint Research Center, and the ETH Future Computing Laboratory (EFCL)
Faster Approximation To Multivariate Functions by Combined Bernstein-Taylor Operators
No full textIn this article, we incorporate multivariate Taylor polynomials into the definition of the Bernstein operators to get a faster approximation to multivariate functions by these combined operators. We also give various numerical simulations including graphical illustrations and error estimations. Our results improve not only the linear approximation by classical Bernstein polynomials but also the nonlinear approximation obtained by max-product operations. © 2025 the author(s), published by De Gruyter
Silver Sulfadiazine and Boric Acid Are Effective in Protecting the Stasis Zone from Secondary Ischemia
No full textA burn wound is characterized by hyperemia on the outer layer, stasis in the middle zone, and coagulation zones in the innermost region due to thermal damage to the skin. It is crucial to provide prompt and adequate treatment to prevent further damage. The depth of the burn increases as ischemic indicators become more distinct in the stasis zone when the burn is not adequately treated, despite the absence of ischemic signs in the stasis zone at the initial stages of the wound. This study aims to assess the impact of silver sulfadiazine, boric acid, low-molecular-weight heparin, and glyceryl trinitrate on wound healing in the stasis zone. The study involved 4 intervention groups, each consisting of 6 rats, and a sham group. After 7 days of daily topical application of the active substances, the animals were sacrificed, and wound healing in the stasis zones was evaluated through macroscopic, histological, and immunohistochemical analysis. These findings demonstrate the effectiveness of these treatments in promoting wound healing. The results demonstrated that the boric acid and silver sulfadiazine groups exhibited the highest levels of wound healing, both macroscopically and histologically. Immunohistochemistry revealed significant differences, with the silver sulfadiazine group demonstrating superior results in MMP9 staining and the boric acid group in VEGF staining (P .05). These findings suggest that boric acid and silver sulfadiazine effectively prevent ischemia in the stasis zone. Boric acid, in particular, appears to have significant potential as a wound-healing agent due to its anti-inflammatory properties. This record is sourced from MEDLINE/PubMed, a database of the U.S. National Library of Medicin
Differential Cross-Section Measurements of Higgs Boson Production in the H → τ+τ− Decay Channel in pp Collisions at S = 13 TeV With the ATLAS Detector
No full textDifferential measurements of Higgs boson production in the τ-lepton-pair decay channel are presented in the gluon fusion, vector-boson fusion (VBF), VH and tt¯H associated production modes, with particular focus on the VBF production mode. The data used to perform the measurements correspond to 140 fb−1 of proton-proton collisions collected by the ATLAS experiment at the LHC. Two methods are used to perform the measurements: the Simplified Template Cross-Section (STXS) approach and an Unfolded Fiducial Differential measurement considering only the VBF phase space. For the STXS measurement, events are categorized by their production mode and kinematic properties such as the Higgs boson’s transverse momentum (pTH), the number of jets produced in association with the Higgs boson, or the invariant mass of the two leading jets (mjj). For the VBF production mode, the ratio of the measured cross-section to the Standard Model prediction for mjj > 1.5 TeV and pTH > 200 GeV (pTH 200 GeV) is 1.29−0.34+0.39 (0.12−0.33+0.34). This is the first VBF measurement for the higher-pTH criteria, and the most precise for the lower-pTH criteria. The fiducial cross-section measurements, which only consider the kinematic properties of the event, are performed as functions of variables characterizing the VBF topology, such as the signed ∆ϕjj between the two leading jets. The measurements have a precision of 30%–50% and agree well with the Standard Model predictions. These results are interpreted in the SMEFT framework, and place the strongest constraints to date on the CP-odd Wilson coefficient cHW~. © The Author(s) 2025.Ministerio de Ciencia, Innovación y Universidades, MCIU; Agencia Nacional de Investigación y Desarrollo; BSF-NSF; Australian Research Council, ARC; DRAC; La Caixa Banking Foundation; Centre National pour la Recherche Scientifique et Technique, CNRST; NAWA; Center for African Studies, CAS; Fundação para a Ciência e a Tecnologia, FCT; European Union, Future Artificial Intelligence Research; European Organization for Nuclear Research; Polish National Science Centre; Georgia Health Initiative, HGF; National Science Foundation, NSF; Baden-Württemberg Stiftung; Science and Technology Facilities Council, STFC; Horizon 2020, ICSC-NextGenerationEU; Nederlandse Organisatie voor Wetenschappelijk Onderzoek, NWO; Ministry of Science and Innovation; Istituto Nazionale di Fisica Nucleare; Ministry of Science and Higher Education; Japan Society for the Promotion of Science; MVZI; PROMETEO; Spine Education and Research Institute, SERI; IDUB AGH; Ministry of Education Youth and Sports; Neubauer Family Foundation; Bundesministerium für Wissenschaft, Forschung und Wirtschaft, BMWFW; Austrian Science Fund, FFWF; BCKDF; Yerevan Physics Institute; ERDF; Agencia Nacional de Investigación y Desarrollo, ANID; Bundesministerium für Bildung und Forschung, BMBF; Slovenian Research Agency; Canada Foundation for Innovation, FCI; Danmarks Grundforskningsfond, DNRF; Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq; Göran Gustafssons Stiftelse; Generalitat de Catalunya; U.S. Department of Energy, USDOE; EU-ESF; COST; CRC; Generalitat Valenciana; RGC; Duchenne Research Fund, DRF; Netherlands Organisation for Scientific Research; Fundação de Amparo à Pesquisa do Estado de São Paulo, FAPESP; PRIMUS; Agencia Estatal de Investigación, AEI; Islamic Scholarship Fund, ISF; ICSC; ANR; Institutul de Fizică Atomică, IFA; Ministry of Science and Technology of the People's Republic of China, MOST; Natural Sciences and Engineering Research Council of Canada, NSERC; Nella and Leon Benoziyo Center for Neurological Diseases, Weizmann Institute of Science; GenT Programmes Generalitat Valenciana, Spain; Marie Skłodowska-Curie Actions; National Science and Technology Council, NSTC; EU; MINERVA, Israel; Irish Rugby Football Union, IRFU; Cantons of Bern and Geneva; Defence Science Institute, DSI; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, SNF; MSTDI; Horizon 2020 Framework Programme; MNE; Agencia Nacional de Promoción Científica y Tecnológica, ANPCyT; Marcus och Amalia Wallenbergs minnesfond, MMW; CERN-CZ; National Research Foundation, NRF; Ministerstwo Edukacji i Nauki, MNiSW; FAPERJ; European Research Council; CERN, CERN; Ministerstvo Školství, Mládeže a Tělovýchovy, MEYS; European Union; National Research Council Canada, NRC; Multiple Sclerosis Scientific Research Foundation, MSSRF; DFG; AvH Foundation; Istituto Nazionale di Fisica Nucleare, INFN; CANARIE; Ministry of Education, Culture, Sports, Science and Technology, MEXT; UK Research and Innovation, UKRI; ERC, (948254, 101089007); DNSRC, (IN2P3-CNRS); Investissements d’Avenir Labex, (ANR-11-LABX-0012); FEDER, (IDIFEDER/2018/048); Japan Society for the Promotion of Science, JSPS, (JP22KK0227, JP22H04944, JP23KK0245, JP22H01227); Japan Society for the Promotion of Science, JSPS; Czech Science Foundation, (GACR — 24-11373S); MUCCA, (CHIST-ERA-19-XAI-00); Chinese Ministry of Science and Technology, (MOST-2023YFA1609300, MOST-2023YFA1605700); Agence Nationale de la Recherche, (ANR-20-CE31-0013, ANR-21-CE31-0013, ANR-22-EDIR-0002, ANR-21-CE31-0022); H2020 European Research Council, (ERC — 101002463); Knut and Alice Wallenberg Foundation, (KAW 2018.0157, KAW 2018.0458, KAW 2019.0447, KAW 2022.0358); Research Council of Norway, (RCN-314472); Royal Society, (NIF-R1-231091); NCN, (UMO-2022/47/O/ST2/00148, UMO-2023/51/B/ST2/00920, UMO-2020/37/B/ST2/01043, UMO-2023/49/B/ST2/04085, H2020 MSCA 945339, UMO-2021/40/C/ST2/00187, UMO-2019/34/E/ST2/00393, 2022/47/B/ST2/03059, 2021/42/E/ST2/00350); Leverhulme Trust, (RPG-2020-004); MCIN, (PID2021-125273NB, PCI2022-135018-2, RYC2021-031273-I, RYC2019-028510-I, RYC2020-030254-I, RYC2022-038164-I); National Natural Science Foundation of China, (NSFC — 12175119); FONDECYT, (1240864, 1230987, 1230812); Polish National Agency for Academic Exchange, (PPN/PPO/2020/1/00002/U/00001); Center for Advancing Research Impact in Society, ARIS, (J1-3010); Center for Advancing Research Impact in Society, ARIS; Carl Trygger Foundation, (CTS 22:2312); Deutsche Forschungsgemeinschaft, (DFG — CR 312/5-2, DFG — 469666862); FAIR-NextGenerationEU, (PE00000013); GenT Programmes Generalitat Valenciana, (CIDEGENT/2019/027); Swedish Research Council, (2021-03651, 2023-04654, VR 2022-04683, VR 2023-03403, VR 2022-03845, VR 2018-00482); U.S. Department of Energy, (ECA DE-AC02-76SF00515); FORTE, (CZ.02.01.01/00/22_008/0004632, PRIMUS/21/SCI/017); Swiss National Science Foundation, (SNSF — PCEFP2_194658); National Natural Science Foundation of China, NSFC, (12275265, NSFC-12075060); National Natural Science Foundation of China, NSFC; Ministero dell’Università e della Ricerca, (PRIN — 20223N7F8K — PNRR M4.C2.1.1
A Polyphenol-Based Hydrogel for Enabling Enhanced Metal Ion Sorption, Antimicrobial Activity, and Water Remediation
No full textGolder, Animes Kumar/0000-0001-8144-5316Naturally derived, highly functional, and widely accessible materials represent enabling technologies for sustainable development. However, current bio-derived materials often present a trade-off between sustainability potential and functional performance. Sorbents that can remove potentially toxic elements (PTEs) and inhibit bacterial growth to enable water remediation exemplify this dilemma. Conventional plant-based biopolymer materials are attractive for their low cost and environmental compatibility, but many require additional specialized synthetic components to impart the requisite performance. We now report an approach for preparing majority plant polyphenol hydrogels composed of the widely available tannic acid (TA) at an unprecedented 75% content. A minority seaweed alginate (Alg) matrix is used to bind TA into conveniently handled beads. Convenient application is also demonstrated by conducting all experiments with dried beads rehydrated directly during use. Multifold enhancements in water swelling, sorption of a suite of PTEs, and antimicrobial activity are found with increasing TA content. Moreover, we report a novel additional enhancement of antimicrobial activity based on TA-induced iron incorporation, as characterized by XPS, SEM, TGA, and EDX. Further enhancement of sorption for a PTE in this Alg-TA-Fe matrix is also demonstrated. Our hydrogels can be produced at room temperature in low resource settings and exhibit performance generally superior to other biopolymer sorbents and on par with those combining synthetic functionalities. A qualitative evaluation of our polyphenol hydrogels' sustainability potential is performed based on their novel functionalities, greenhouse gas emissions, environmental compatibility, material abundance, and potential for localized production.A.J. acknowledges support of a Commonwealth Split-site PhD scholarship (INCN-2019-204). K.H.A.L., A.K.G., and L.M.P. thank the U.K. India Education Research Initiative and the Department of Science and Technology, India, for a Research Partnership Grant (DST-UKIERI 2017 18-009). C.M.D. and K.H.A.L. acknowledge the University of Strathclyde for AR's Global Research Scholarship. The authors also thank Drs. J.R. Bame and G.J. Anderson for assistance with ICP-MS at Strathclyde's Pure and Applied Chemistry Mass Spectrometry Facility.University of Strathclyde [INCN-2019-204]; Commonwealth Split-site PhD scholarship; U.K. India Education Research Initiative [DST-UKIERI 2017 18-009]; Department of Science and Technology, India; University of Strathclyd
Effects of Build Orientation on Mode Iii Fracture Load of Additively Manufactured Polylactic Acid Material
No full textThis study investigates the mode III fracture behavior of polylactic acid (PLA) fabricated by additive manufacturing (AM) through both experimental testing and numerical simulation. While AM is widely used in structural applications, research on mode III fracture behavior remains limited, particularly regarding the influence of build orientation on shear-driven failure. In this study, transverse shear cracked plate specimens were fabricated using fused filament fabrication (FFF) in three build orientations: horizontal, lateral, and vertical. These specimens were tested under mode III loading using a newly designed tensile testing fixture to determine the fracture toughness of PLA for each orientation. Young's modulus values, obtained from tensile tests on dog-bone specimens, were used to compute the mode III fracture energy. Numerical simulations were performed using the extended finite element method (XFEM) to predict fracture load and crack propagation. The results showed close agreement with experiments, with deviations of 1.1%, 8.5%, and 0.4% for horizontal, lateral, and vertical orientations, respectively. The closest agreement was observed for vertically printed specimens, attributed to the intrinsic nature of mode III loading. These findings highlight the potential of XFEM as a reliable tool for predicting shear-driven fracture behavior in FFF-printed PLA across different build orientations, supporting improved structural evaluation and design decisions.This research was funded by a grant from The Scientific and Technological Research Council of Turkiye, project number 122M823.Scientific and Technological Research Council of Turkiye [122M823