Nonlinear unloading plays an important role in predicting springback during plastic forming process. To improve the accuracy of springback prediction which could provide a guide for precision forming, uniaxial tensile tests and uniaxial loading-unloading-loading tensile tests on SUS304 stainless steel were carried out. The flow stress mathematical model and chord modulus mathematical model were calibrated according to the test results. A constant elastic modulus three-point bending finite element model (E0FEMB) and a constant elastic modulus roll forming finite element model (E0FEMR) were established in MSC.MARC. The chord modulus was output by the PLOTV subroutine to determine the mean modulus of different regions, and the mean modulus three-point bending finite element model (E¯cFEMB) and the mean modulus roll forming finite element model (E¯cFEMR) were defined. The constant modulus finite element model (E0FEM) simulation results and the mean modulus finite element model (E¯cFEM) simulation results were compared with the three-point bending tests and roll forming tests test results. The difference between the simulation results and the test results was small, indicating that the mean modulus was feasible to predict the springback, which verified the suitability of the E¯cFEM.
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http://dx.doi.org/10.3390/ma16072571 | DOI Listing |
Front Robot AI
January 2025
School of Metallurgy and Materials, University of Birmingham, Birmingham, United Kingdom.
Introduction: The transition to electric vehicles (EVs) has highlighted the need for efficient diagnostic methods to assess the state of health (SoH) of lithium-ion batteries (LIBs) at the end of their life cycle. Electrochemical Impedance Spectroscopy (EIS) offers a non-invasive technique for determining battery degradation. However, automating this process in industrial settings remains a challenge.
View Article and Find Full Text PDFPak J Med Sci
January 2025
Sasankoti Mohan Ravi Prakash, DMD, MDS, BDS Dentist and Independent Researcher, Hope Health Inc, 360 N Irby St. Florence, South Carolina, USA 29501.
Background & Objective: Currently, there are many implants in clinical use, making it hard to choose the right one for the patient. The success rate of an implant depends on its diameter, length, and direction of insertion in bone. In implant dentistry, Finite Element Analysis (FEA) simulates intraoral conditions in vitro and analyzes the effects of implant material, diameter, size, and other components related to oral structure on the implant and peri-implant tissues.
View Article and Find Full Text PDFProg Addit Manuf
July 2024
Empa Swiss Federal Laboratories for Materials Science and Technology, Überlandstrasse 129, 8600 Dübendorf, Switzerland.
Fast and accurate representation of heat transfer in laser powder-bed fusion of metals (PBF-LB/M) is essential for thermo-mechanical analyses. As an example, it benefits the detection of thermal hotspots at the design stage. While traditional physics-based numerical approaches such as the finite element (FE) method are applicable to a wide variety of problems, they are computationally too expensive for PBF-LB/M due to the space- and time-discretization requirements.
View Article and Find Full Text PDFInt J Clin Pediatr Dent
December 2024
Department of Orthodontics, Yenepoya Dental College, Yenepoya (Deemed to be University), Mangaluru, Karnataka, India.
Introduction: This study describes a novel device known as "SAVE" to effectively protract the deficient maxilla in class III malocclusion by quantifying and evaluating the changes in the maxilla through a finite element analysis (FEA).
Materials And Methods: The patented novel SAVE device was three-dimensionally modeled using Autodesk Fusion 360. An existing computed tomography (CT) scan of a patient exhibiting class III malocclusion was used to generate a finite element (FE) model.
Wood Sci Technol
January 2025
TU Wien, Institute for Mechanics of Materials and Structures, Karlsplatz 13, Vienna, 1040 Austria.
Unlabelled: Accurate prediction of moisture distributions in wood is among the most critical challenges in timber engineering. Achieving this requires a well-coordinated comparison of experimental methods and simulation tools. While significant progress has been made in developing simulation tools in recent years, a lack of experience with and trust in these tools continues to hinder broader implementation, especially when it comes to free water and its absorption.
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