scholarly journals Modeling of Microstructure Evolution of Ti6Al4V for Additive Manufacturing

Metals ◽  
2018 ◽  
Vol 8 (8) ◽  
pp. 633 ◽  
Author(s):  
Emilio Salsi ◽  
Michele Chiumenti ◽  
Miguel Cervera
Keyword(s):  
Microstructure Evolution ◽  
Manufacturing Processes ◽  
Material Parameters ◽  
Proposed Model ◽  
Engineering Problems ◽  
Microstructural Model ◽  
Initial Content ◽  
Fast Cooling ◽  
Simulation Results ◽  
Am Processes

AM processes are characterized by complex thermal cycles that have a deep influence on the microstructural transformations of the deposited alloy. In this work, a general model for the prediction of microstructure evolution during solid state transformations of Ti6Al4V is presented. Several formulations have been developed and employed for modeling phase transformations in other manufacturing processes and, particularly, in casting. The proposed model is mainly based on the combination and modification of some of these existing formulations, leading to a new overall model specifically dedicated to AM. The accuracy and suitability of the integrated model is enhanced, introducing new dedicated features. In fact the model is designed to deal with fast cooling and re-heating cycles typical of AM processes because: (a) it is able to consider incomplete transformations and varying initial content of phases and (b) it can take into account simultaneous transformations.The model is implemented in COMET, an in-house Finite Element (FE)-based framework for the solution of thermo-mechanical engineering problems. The validation of the microstructural model is performed by comparing the simulation results with the data available in the literature. The sensitivity of the model to the variation of material parameters is also discussed.

scholarly journals A novel physics-based and data-supported microstructure model for part-scale simulation of laser powder bed fusion of Ti-6Al-4V

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Jonas Nitzler ◽  
Christoph Meier ◽  
Kei W. Müller ◽  
Wolfgang A. Wall ◽  
N. E. Hodge
Keyword(s):  
Selective Laser Melting ◽  
Microstructure Evolution ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Laser Melting ◽  
Cooling Rates ◽  
Powder Bed ◽  
Proposed Model ◽  
Microstructure Model ◽  
Transformation Diagrams

AbstractThe elasto-plastic material behavior, material strength and failure modes of metals fabricated by additive manufacturing technologies are significantly determined by the underlying process-specific microstructure evolution. In this work a novel physics-based and data-supported phenomenological microstructure model for Ti-6Al-4V is proposed that is suitable for the part-scale simulation of laser powder bed fusion processes. The model predicts spatially homogenized phase fractions of the most relevant microstructural species, namely the stable $$\beta $$ β -phase, the stable $$\alpha _{\text {s}}$$ α s -phase as well as the metastable Martensite $$\alpha _{\text {m}}$$ α m -phase, in a physically consistent manner. In particular, the modeled microstructure evolution, in form of diffusion-based and non-diffusional transformations, is a pure consequence of energy and mobility competitions among the different species, without the need for heuristic transformation criteria as often applied in existing models. The mathematically consistent formulation of the evolution equations in rate form renders the model suitable for the practically relevant scenario of temperature- or time-dependent diffusion coefficients, arbitrary temperature profiles, and multiple coexisting phases. Due to its physically motivated foundation, the proposed model requires only a minimal number of free parameters, which are determined in an inverse identification process considering a broad experimental data basis in form of time-temperature transformation diagrams. Subsequently, the predictive ability of the model is demonstrated by means of continuous cooling transformation diagrams, showing that experimentally observed characteristics such as critical cooling rates emerge naturally from the proposed microstructure model, instead of being enforced as heuristic transformation criteria. Eventually, the proposed model is exploited to predict the microstructure evolution for a realistic selective laser melting application scenario and for the cooling/quenching process of a Ti-6Al-4V cube of practically relevant size. Numerical results confirm experimental observations that Martensite is the dominating microstructure species in regimes of high cooling rates, e.g., due to highly localized heat sources or in near-surface domains, while a proper manipulation of the temperature field, e.g., by preheating the base-plate in selective laser melting, can suppress the formation of this metastable phase.

scholarly journals Prediction of Dead Oil Viscosity: Machine Learning vs. Classical Correlations

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 930
Author(s):  
Fahimeh Hadavimoghaddam ◽  
Mehdi Ostadhassan ◽  
Ehsan Heidaryan ◽  
Mohammad Ali Sadri ◽  
Inna Chapanova ◽  
...  
Keyword(s):  
Machine Learning ◽  
Viscosity Data ◽  
Oil Viscosity ◽  
Pvt Data ◽  
Proposed Model ◽  
Wide Range ◽  
Engineering Problems ◽  
Functional Forms ◽  
Insight Into

Dead oil viscosity is a critical parameter to solve numerous reservoir engineering problems and one of the most unreliable properties to predict with classical black oil correlations. Determination of dead oil viscosity by experiments is expensive and time-consuming, which means developing an accurate and quick prediction model is required. This paper implements six machine learning models: random forest (RF), lightgbm, XGBoost, multilayer perceptron (MLP) neural network, stochastic real-valued (SRV) and SuperLearner to predict dead oil viscosity. More than 2000 pressure–volume–temperature (PVT) data were used for developing and testing these models. A huge range of viscosity data were used, from light intermediate to heavy oil. In this study, we give insight into the performance of different functional forms that have been used in the literature to formulate dead oil viscosity. The results show that the functional form f(γAPI,T), has the best performance, and additional correlating parameters might be unnecessary. Furthermore, SuperLearner outperformed other machine learning (ML) algorithms as well as common correlations that are based on the metric analysis. The SuperLearner model can potentially replace the empirical models for viscosity predictions on a wide range of viscosities (any oil type). Ultimately, the proposed model is capable of simulating the true physical trend of the dead oil viscosity with variations of oil API gravity, temperature and shear rate.

Light-Induced Metastable Defects in a-Si:H: Towards an Understanding

1985 ◽  
Vol 49 ◽  
Author(s):  
Martin Stutzmann ◽  
Warren B. Jackson ◽  
Chuang Chuang Tsai
Keyword(s):  
Amorphous Silicon ◽  
Mechanical Stress ◽  
Hydrogenated Amorphous Silicon ◽  
Material Parameters ◽  
Kinetic Behaviour ◽  
Proposed Model ◽  
Special Cases ◽  
The Stability ◽  
Hydrogenated Amorphous

AbstractThe dependence of the creation and the annealing of metastable dangling bonds in hydrogenated amorphous silicon on various material parameters will be discussed in the context of a recently proposed model. After a brief review of the kinetic behaviour governing defect creation and annealing in undoped a- Si:H, a number of special cases will be analyzed: the influence of alloying with O, N, C, and Ge, changes introduced by doping and compensation, and the role of mechanical stress. Finally, possibilities to increase the stability of a-Si:H based devices will be examined.

Wear of a Tool in Double-Disk Lapping of Silicon Wafers

2010 ◽  
Author(s):  
Adam Barylski ◽  
Mariusz Deja
Keyword(s):  
Experimental Data ◽  
Integrated Circuits ◽  
Tool Wear ◽  
Silicon Wafers ◽  
Silicon Ingot ◽  
Proposed Model ◽  
Tool Wear Prediction ◽  
Simulation Results ◽  
High Degree ◽  
Kinematical Parameters

Silicon wafers are the most widely used substrates for fabricating integrated circuits. A sequence of processes is needed to turn a silicon ingot into silicon wafers. One of the processes is flattening by lapping or by grinding to achieve a high degree of flatness and parallelism of the wafer [1, 2, 3]. Lapping can effectively remove or reduce the waviness induced by preceding operations [2, 4]. The main aim of this paper is to compare the simulation results with lapping experimental data obtained from the Polish producer of silicon wafers, the company Cemat Silicon from Warsaw (www.cematsil.com). Proposed model is going to be implemented by this company for the tool wear prediction. Proposed model can be applied for lapping or grinding with single or double-disc lapping kinematics [5, 6, 7]. Geometrical and kinematical relations with the simulations are presented in the work. Generated results for given workpiece diameter and for different kinematical parameters are studied using models programmed in the Matlab environment.

Methods of Mathematical Modeling of the Leaching Process of Cobalt-Containing Solutions

2021 ◽  
Vol 316 ◽  
pp. 661-666
Author(s):  
Nataliya V. Mokrova
Keyword(s):  
Mathematical Modeling ◽  
Mathematical Model ◽  
Chemical Kinetics ◽  
Group Method ◽  
Data Handling ◽  
Multistage Processes ◽  
Leaching Process ◽  
Proposed Model ◽  
Simulation Results ◽  
The Mathematical Model

Current cobalt processing practices are described. This article discusses the advantages of the group argument accounting method for mathematical modeling of the leaching process of cobalt solutions. Identification of the mathematical model of the cascade of reactors of cobalt-producing is presented. Group method of data handling is allowing: to eliminate the need to calculate quantities of chemical kinetics; to get the opportunity to take into account the results of mixed experiments; to exclude the influence of random interference on the simulation results. The proposed model confirms the capabilities of the group method of data handling for describing multistage processes.

ACCURATE ANALYSIS OF GLOBAL INTERCONNECTS IN NANO-FPGAs

NANO ◽  
2009 ◽  
Vol 04 (03) ◽  
pp. 171-176 ◽  
Author(s):  
DAVOOD FATHI ◽  
BEHJAT FOROUZANDEH
Keyword(s):  
Propagation Delay ◽  
Modeling Method ◽  
Accurate Analysis ◽  
Proposed Model ◽  
Simulation Results ◽  
The Difference ◽  
A New Technique ◽  
Global Interconnects ◽  
Technology Nodes ◽  
Model Technique

This paper introduces a new technique for analyzing the behavior of global interconnects in FPGAs, for nanoscale technologies. Using this new enhanced modeling method, new enhanced accurate expressions for calculating the propagation delay of global interconnects in nano-FPGAs have been derived. In order to verify the proposed model, we have performed the delay simulations in 45 nm, 65 nm, 90 nm, and 130 nm technology nodes, with our modeling method and the conventional Pi-model technique. Then, the results obtained from these two methods have been compared with HSPICE simulation results. The obtained results show a better match in the propagation delay computations for global interconnects between our proposed model and HSPICE simulations, with respect to the conventional techniques such as Pi-model. According to the obtained results, the difference between our model and HSPICE simulations in the mentioned technology nodes is (0.29–22.92)%, whereas this difference is (11.13–38.29)% for another model.

Modeling and Simulation of Microstructure Evolution in Extruded Aluminum Profiles

2009 ◽  
Vol 424 ◽  
pp. 43-50
Author(s):  
Farhad Parvizian ◽  
T. Kayser ◽  
Bob Svendsen
Keyword(s):  
Plastic Deformation ◽  
Aluminum Alloys ◽  
Modeling And Simulation ◽  
Microstructure Evolution ◽  
Internal State ◽  
Extrusion Process ◽  
State Variables ◽  
Cooling Process ◽  
Microstructure Parameters ◽  
Simulation Results

The purpose of this work is to predict the microstructure evolution of aluminum alloys during hot metal forming processes using the Finite Element Method (FEM). Here, the focus will be on the extrusion process of aluminum alloys. Several micromechanical mechanisms such as diffusion, recovery, recrystallization and grain growth are involved in various subsequent stages of the extrusion and the cooling process afterward. The evolution of microstructure parameters is motivated by plastic deformation and temperature. A number of thermomechanical aspects such as plastic deformation, heat transfer between the material and the container, heat generated by friction, and cooling process after the extrusion are involved in the extrusion process and result in changes in temperature and microstructure parameters subsequently. Therefore a thermomechanically coupled modeling and simulation which includes all of these aspects is required for an accurate prediction of the microstructure evolution. A brief explanation of the isotropic thermoelastic viscoplastic material model including some of the simulation results of this model, which is implemented as a user material (UMAT) in the FEM software ABAQUS, will be given. The microstructure variables are thereby modeled as internal state variables. The simulation results are finally compared with some experimental results.

Dynamic Quality Prediction of Manufacturing Process Based on Extreme Learning Machine

2014 ◽  
Vol 889-890 ◽  
pp. 1231-1235
Author(s):  
Jun Guo ◽  
Yi Bing Li ◽  
Bai Gang Du
Keyword(s):  
Extreme Learning Machine ◽  
Manufacturing Process ◽  
Manufacturing Processes ◽  
Manufacturing Cost ◽  
Quality Prediction ◽  
Proposed Model ◽  
Machine Learning Approach ◽  
Learning Machine ◽  
Dynamic Quality ◽  
Quality Categories

In many manufacturing processes, the abnormal changes of some key process parameters could result in various categories of faulty products. In this paper, a machine learning approach is developed for dynamic quality prediction of the manufacturing processes. In the proposed model, an extreme learning machine is developed for monitoring the manufacturing process and recognizing faulty quality categories of the products being produced. The proposed model is successfully applied to a japanning-line, which improves the product quality and saves manufacturing cost.

scholarly journals Timetable Design for Urban Rail Line with Capacity Constraints

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Yu-Ting Zhu ◽  
Bao-Hua Mao ◽  
Lu Liu ◽  
Ming-Gao Li
Keyword(s):  
Solution Method ◽  
Operating Cost ◽  
Capacity Constraints ◽  
Scheduling Model ◽  
Urban Rail ◽  
Proposed Model ◽  
Rail Line ◽  
First Come First Serve ◽  
Waiting Cost ◽  
Simulation Results

To design an efficient and economical timetable for a heavily congested urban rail corridor, a scheduling model is proposed in this paper. The objective of the proposed model is to find the departure time of trains at the start terminal to minimize the system cost, which includes passenger waiting cost and operating cost. To evaluate the performance of the timetable, a simulation model is developed to simulate the detailed movements of passengers and trains with strict constraints of station and train capacities. It assumes that passengers who arrive early will have more chances to access a station and board a train. The accessing and boarding processes of passengers are all based on a first-come-first-serve basis. When a station is full, passengers unable to access must wait outside until the number of waiting passengers at platform falls below a given value. When a train is full, passengers unable to board must wait at the platform for the next train to arrive. Then, based on the simulation results, a two-stage genetic algorithm is introduced to find the best timetable. Finally, a numerical example is given to demonstrate the effectiveness of the proposed model and solution method.

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