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Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
TC 3.04: Thematic Symposium: TC 3 - Fracture and fatigue failure of engineering materials and structures 4
Time:
Wednesday, 28/Aug/2024:
11:00am - 12:45pm
Session Chair: Sabrina Vantadori Session Chair: Francesco Iacoviello
Location: HALL Voda Jug (Hall 1)
Presentations
11:00am - 11:15am ADVANCED CHARACTERIZATION OF BASALT FIBER REINFORCED COMPOSITES USING ENERGY METHODS
Martina Totaro , Giacomo Risitano, Pasqualino Corigliano, Guido Di Bella, Danilo D'Andrea
University of Messina, Italy
11:15am - 11:30am Development of a damage-dependent modified material model for enhanced description of transient material behavior
Tim Korschinsky 1 , Benjamin Möller1 , Marvin Kiel1 , Matthias Hecht2
1 Fraunhofer LBF, Institute for Structural Durability and System Reliability; 2 Technical University of Darmstadt, Research Group System Reliability, Adaptive Structures, and Machine Acoustics SAM
11:30am - 11:45am FLOW FORMING TO IMPROVE FATIGUE LIFE OF ORTHOPEDIC IMPLANTS
Mohamed Akram Mechter , Mina Gadour, Léa Romain, Myriam Brochu
Polytechnique montreal, Canada
11:45am - 12:00pm FATIGUE RESISTANCE PREDICTION MODEL APPLIED TO 410NiMo STAINLESS STEEL WELDS
Cédric Constantineau 1 , Pierre-Antony Deschênes2 , Myriam Brochu1
1 Polytechnique Montréal, 2500 Chem. De Polytechnique, Montréal, Canada; 2 Hydro-Quebec Research Institute (IREQ), 1800 Bd Lionel-Boulet, Varennes, Canada
12:00pm - 12:15pm NOVEL INDICATOR & INNOVATIVE METHOD FOR ONSET DAMAGE DETECTION IN VERY HIGH CYCLE FATIGUE TESTS (VHCF)
Julio C. Gomez-Mancilla
INSTITUTO POLITÉNICO NACIONAL, IPN, Mexico
12:15pm - 12:30pm EXPLORING THE IMPACT OF HETEROGENEOUS YIELDING ON DUCTILE FAILURE IN ALUMINIUM ALLOYS
David Morin , Lars Edvard Blystad Dæhli, Odd Sture Hopperstad
NTNU, Norway
12:30pm - 12:45pm EVALUATION OF CRACK FRONT GEOMETRY BY STRAIN FIELD CLASSIFICATION USING NEURAL NETWORKS
Giovanni Chianese 1 , Giovanni Pio Pucillo2 , Davide Leonetti3,4
1 Institute of Sciences and Technologies for Sustainable Energy and Mobility (STEMS),National Research Council of Italy, Italy; 2 Department of Industrial Engineering, University of Naples Federico II, Italy; 3 Eindhoven University of Technology, Department of the Built Environment, The Netherlands; 4 EAISI, Eindhoven, The Netherlands