Transport Research International Documentation (TRID) https://trid.trb.org/ en-us Copyright © 2024. National Academy of Sciences. All rights reserved. http://blogs.law.harvard.edu/tech/rss tris-trb@nas.edu (Bill McLeod) tris-trb@nas.edu (Bill McLeod) Transport Research International Documentation (TRID) https://trid.trb.org/Images/PageHeader-wTitle.jpg https://trid.trb.org/ Research of Different Processes for Forming Fiber Metal Laminates https://trid.trb.org/View/1665547 Fri, 20 Dec 2019 16:26:20 GMT https://trid.trb.org/View/1665547 Enhanced Constitutive Model for Aeronautic Aluminium Alloy (AA2024-T351) under High Strain Rates and Elevated Temperatures https://trid.trb.org/View/1665546 10³ s⁻¹) compared to low and medium strain rates. Meanwhile, plastic deformation of any ductile material under high strain rate conditions results in heat generation due to plastic work. Hence, a reliable constitutive model should be able to predict the accurate thermo-mechanical response of the material over a wide range of strain rate loading conditions. In the present work, an enhanced constitutive model for high strain rate and elevated temperature is proposed. For calibration purpose, the stress-strain response of AA2024-T351 is studied under quasi-static and dynamic loading conditions using uniaxial compression and split Hopkinson compressive pressure bar (SHPB) respectively at various temperatures. A threshold strain rate value is identified and used to improve the prediction capabilities of the present model. Later, the proposed model is compared with Johnson-Cook (JC) and Khan-Huang-Liang (KHL) models using the different statistical parameters. This analysis revealed the improved stress-strain prediction capability of the proposed model compared to the others.]]> Fri, 20 Dec 2019 16:26:20 GMT https://trid.trb.org/View/1665546 Warm Forming Behavior of Magnesium Alloy Sheet in Manufacturing of Window Regulator Rail https://trid.trb.org/View/1665545 Fri, 20 Dec 2019 16:26:20 GMT https://trid.trb.org/View/1665545 Minimization of Surface Deflection in Rectangular Embossing Using Automatic Training of Artificial Neural Network and Genetic Algorithm https://trid.trb.org/View/1665544 Fri, 20 Dec 2019 16:26:20 GMT https://trid.trb.org/View/1665544 Optimal Process Design in Hot Forging in Terms of Grain Flow Quality https://trid.trb.org/View/1665543 Fri, 20 Dec 2019 16:26:20 GMT https://trid.trb.org/View/1665543 Recrystallization Texture of Electrodeposited Zinc https://trid.trb.org/View/1665542 //normal direction (ND) texture of the zinc electrodeposit can be obtained from zinc oxide, sodium hydroxide, potassium chloride, and sodium cyanide solution baths. The <11.0>//ND texture of the zinc electrodeposit did not change even after recrystallization. This phenomenon of zinc electrodeposits could be described by the strain-energy release maximization model.]]> Fri, 20 Dec 2019 16:26:20 GMT https://trid.trb.org/View/1665542 On the Constitutive Models for Ultra-High Strain Rate Deformation of Metals https://trid.trb.org/View/1665541 10⁴ s⁻¹) is common in high speed manufacturing and impact engineering. However, a general constitutive model suitable for describing the material deformation at ultra-high strain rates is still unavailable. The purpose of this study is of two-folds. The first is to systematically evaluate the performances of four typical constitutive models, Johnson-Cook (J-C), Khan-Huang-Liang (KHL), Zerilli-Armstrong (Z-A), and Gao-Zhang (G-Z), in predicting the dynamic behaviors of materials. The second is to obtain an improved constitutive model to better describe the deformation of materials under ultra-high strain rates. To this end, high strain rate tests were carried out on different crystalline structures, i.e., BCC, FCC, and HCP over a wide range of strain rate from 10² s⁻¹ to 1.5 × 10⁴ s⁻¹. It was found that before the critical strain rate, around 10⁴ s⁻¹, all of the previous models can predict the flow stresses. When the strain rate passes a critical point, however, these models fail to predict the sudden upsurge of the flow stresses. The improved model developed in this paper, by considering the dislocation drag mechanism, can successfully characterize the dynamic behaviours of materials over the whole range of strain rates.]]> Fri, 20 Dec 2019 16:26:20 GMT https://trid.trb.org/View/1665541 Constitutive Modeling of Asymmetric Hardening Behavior of Transformation-Induced Plasticity Steels https://trid.trb.org/View/1665540 Fri, 20 Dec 2019 16:26:20 GMT https://trid.trb.org/View/1665540 Development of a Machine Learning Based Fast Running Model to Determine Rapidly the Process Conditions in Drawing Process https://trid.trb.org/View/1665539 Fri, 20 Dec 2019 16:26:20 GMT https://trid.trb.org/View/1665539 Application of a Graphical Method on Estimating Forming Limit Curve of Automotive Sheet Metals https://trid.trb.org/View/1665538 Fri, 20 Dec 2019 16:26:20 GMT https://trid.trb.org/View/1665538 H∞ Control for Battery/Supercapacitor Hybrid Energy Storage System Used in Electric Vehicles https://trid.trb.org/View/1652523 Mon, 18 Nov 2019 17:15:48 GMT https://trid.trb.org/View/1652523 Vehicle-Level Electromagnetic Compatibility Prediction Based on Multi-Port Network Theory https://trid.trb.org/View/1652522 Mon, 18 Nov 2019 17:15:48 GMT https://trid.trb.org/View/1652522 Learning To Recognize Driving Patterns For Collectively Characterizing Electric Vehicle Driving Behaviors https://trid.trb.org/View/1652521 Mon, 18 Nov 2019 17:15:48 GMT https://trid.trb.org/View/1652521 Improvement of Transient Operation Controllability in Engine Test Bench for Heavy-Duty Vehicles https://trid.trb.org/View/1652520 Mon, 18 Nov 2019 17:15:48 GMT https://trid.trb.org/View/1652520 Drift Compensation of Mono-Visual Odometry and Vehicle Localization Using Public Road Sign Database https://trid.trb.org/View/1652519 Mon, 18 Nov 2019 17:15:48 GMT https://trid.trb.org/View/1652519