Multi-Phase FE Model for Machining Inconel 718

2010 ◽  
Author(s):  
Yu Long ◽  
Changsheng Guo ◽  
Santosh Ranganath ◽  
Ronald A. Talarico
Keyword(s):  
Finite Element ◽  
Titanium Carbide ◽  
Process Parameters ◽  
Inconel 718 ◽  
Cutting Tool ◽  
Fe Model ◽  
Carbide Phases ◽  
Edge Geometry ◽  
Multi Phase ◽  
Machined Surfaces

Inconel 718 presents significant challenges in machining attributable to the secondary hard niobium and titanium carbide phases. Machined surfaces typically show a tendency of those brittle carbides to crack, drag and smear under the cutting tool. This paper presents a finite element modeling approach to predict the carbide cracking and distortion phenomenon. The influence of cutting process parameters and cutting edge geometry on carbide damages will be investigated with the model. The model results will be qualitatively compared with SEM observations of the machined surfaces.

Finite Element Simulation of Vacuum Hot Bulge Forming of Titanium Alloy Cylindrical Workpiece

2011 ◽  
Vol 675-677 ◽  
pp. 921-924 ◽  
Author(s):  
Ming Wei Wang ◽  
Chun Yan Wang ◽  
Li Wen Zhang
Keyword(s):  
Numerical Simulation ◽  
Finite Element ◽  
Titanium Alloy ◽  
Finite Element Simulation ◽  
Process Parameters ◽  
Fe Model ◽  
Analysis Software ◽  
Forming Process ◽  
Element Simulation ◽  
Cylindrical Workpiece

Vacuum hot bulge forming (VHBF) is becoming an increasingly important manufacturing process for titanium alloy cylindrical workpiece in the aerospace industries. Finite element simulation is an essential tool for the specification of process parameters. In this paper, a two-dimensional nonlinear thermo-mechanical couple FE model was established. Numerical simulation of vacuum hot bulge forming of titanium alloy cylindrical workpiece was carried out using FE analysis software MSC.Marc. The effects of process parameter on vacuum hot bulge forming of BT20 titanium alloy cylindrical workpiece was analyzed by numerical simulation. The proposed an optimized vacuum hot bulge forming process parameters and die size. And the corresponding experiments were carried out. The simulated results agreed well with the experimental results.

Taguchi Grey Relational Approach for Optimizing Process Parameters of Laser Peening on Titanium Alloy to Induce Enhanced Compressive Stress based on Finite Element Simulation

2020 ◽  
Vol 19 (02) ◽  
pp. 365-387
Author(s):  
G. Ranjith Kumar ◽  
G. Rajyalakshmi
Keyword(s):  
Finite Element ◽  
Residual Stresses ◽  
Compressive Stress ◽  
Process Parameters ◽  
Surface Deformation ◽  
Material Behavior ◽  
Laser Spot ◽  
Fatigue Properties ◽  
Fe Model ◽  
Grey Relational

Laser Shock Peening (LSP) turned out to be the most efficient surface engineering process for advanced materials to induce beneficial deep compressive residual stress which helps in improving mechanical, fatigue properties and surface damage resistance. But, analyzing the nonuniform distribution of residual stresses in the treated sample with X-ray diffraction (XRD) is much time taking and a costly process. This problem can be resolved with LSP finite element numerical simulation model which is feasible with the realistic experimental process. The FE model allows the user to control the laser parameters in order to achieve the optimal level of all controllable parameters. This study is intended to analyze and optimize the influence of laser processing parameters that assists in inducing the residual compressive stress with minimal surface deformation. A Ti6Al4V material model with Johnson–Cook’s visco-elastic–plastic material behavior law is prepared for LSP simulation. Gaussian pressure profile is utilized for uniform loading of the targeted zone for the proposed model. Taguchi Grey Relational Analysis (TGRA) with L27 orthogonal array is applied to LSP simulation, and the results were analyzed with consideration of multiple response measures. It is noted that surface deformation is increased with the rise in a number of laser shots and pressure pulse duration. Maximum compressive residual stresses are falling for higher levels of laser spot diameter, laser spot overlap and laser power density. The correlation is observed between the FE simulation and the published results. The optimal set of process parameters are obtained for improving the LSP on Ti alloys.

scholarly journals Evaluation of Self-Propelled Rotary Tool in the Machining of Hardened Steel Using Finite Element Models

Materials ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 5092
Author(s):  
Usama Umer ◽  
Hossam Kishawy ◽  
Mustufa Haider Abidi ◽  
Syed Hammad Mian ◽  
Khaja Moiduddin
Keyword(s):  
Finite Element ◽  
Cutting Tool ◽  
Hardened Steel ◽  
Heat Transfer Analysis ◽  
Fe Model ◽  
Rotary Tool ◽  
Chip Flow ◽  
Heat Partitioning ◽  
Rotary Cutting ◽  
Good Agreement

This paper presents a model for assessing the performance of self-propelled rotary tool during the processing of hardened steel. A finite element (FE) model has been proposed in this analysis to study the hard turning of AISI 51200 hardened steel using a self-propelled rotary cutting tool. The model is developed by utilizing the explicit coupled temperature displacement analysis in the presence of realistic boundary conditions. This model does not take into account any assumptions regarding the heat partitioning and the tool-workpiece contact area. The model can predict the cutting forces, chip flow, induced stresses, and the generated temperature on the cutting tool and the workpiece. The nodal temperatures and heat flux data from the chip formation analysis are used to achieve steady-state temperatures on the cutting tool in the heat transfer analysis. The model outcomes are compared with reported experimental data and a good agreement has been found.

Development of Evolutionary Method for Optimizing a Roll Forming Process of Aluminum Parts

2012 ◽  
Vol 134 (2) ◽  
Author(s):  
Hong Seok Park ◽  
Tran Viet Anh
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Process Parameters ◽  
Roll Forming ◽  
Fe Model ◽  
Forming Process ◽  
Element Analysis ◽  
Modeling And Optimization ◽  
Aluminum Part ◽  
Back Angle

This paper presents the development of the knowledge-based neural network (KBNN) and genetic algorithm (GA) in modeling and optimization of the roll forming (RF) process of aluminum parts. The idea of a KBNN using multifidelity finite element (FE) models was developed to model the mechanical behaviors of the aluminum sheet. Initially, the less costly but less accurate FE model was used to build the response surface functions for the knowledge path of the KBNN. After that, a small number of the more accurate but expensive finite element analysis (FEA) of the high fidelity FE model were utilized in a multilayer perceptron (MLP) neural network with the prior knowledge to produce the KBNN prediction results. Two powerful optimization algorithms, the Levenberg–Marquadrt (LM) and GA, were applied to train the KBNN. The trained KBNN was used to perform the parametric study for investigating the effects of process parameters on the part quality. After that, the optimization of the process parameters was carried out by employing the combination of the GA and KBNN. The optimization objective was minimizing the overall damage in the aluminum part while keeping the longitudinal strain and spring back angle less than allowable limits to prevent the existence of defects. The modeling and optimization results by using the KBNN and GA were compared with the results from other methods to prove the advantages of the developed one against others.

A Finite Element Model for Temperature Prediction in Laser-Assisted Milling of AerMet100 Steel

2020 ◽  
Vol 977 ◽  
pp. 130-138
Author(s):  
Hao Hao Zeng ◽  
Rong Yan ◽  
Wei Wang ◽  
Peng Le Du ◽  
Tian Tian Hu ◽  
...  
Keyword(s):  
Finite Element ◽  
Surface Quality ◽  
Process Parameters ◽  
Incident Angle ◽  
Element Model ◽  
Fe Model ◽  
Temperature Prediction ◽  
Machined Surface ◽  
Workpiece Temperature ◽  
Machined Surface Quality

Laser-assisted milling (LAM) represents an innovative process to enhance productivity in comparison with conventional milling. The workpiece temperature in LAM not only affects the cutting performance of materials, but also the machined surface quality of the part. This paper presents a 3D transient finite element (FE) model for workpiece temperature prediction in LAM. A moving Gaussian laser heat source model is implemented as a user-defined subroutine and linked to ABAQUS. The thermal model is validated by machining AerMet100 steel under different process parameters (laser power, spindle speed and feed per tooth). Good agreement between predicted and measured workpiece temperatures indicates that the FE model is feasible. In addition, the effects of laser spot size and incident angle on workpiece temperature are analyzed based on the proposed model. This work can be further applied to optimize process parameters for controlling the machined surface quality in LAM.

Laser Transmission Joining of PC and POM Process: A Finite Element Simulation

2013 ◽  
Vol 579-580 ◽  
pp. 856-861
Author(s):  
Hao Chen ◽  
Yang Yang Gao ◽  
Xiao Wang ◽  
Pin Li ◽  
Chuang Huang ◽  
...  
Keyword(s):  
Finite Element ◽  
Process Parameters ◽  
Parametric Design ◽  
Fe Simulation ◽  
Scanning Speed ◽  
Fe Model ◽  
Element Simulation ◽  
Joint Width ◽  
Ansys Parametric Design Language ◽  
Good Agreement

In this paper, the process of laser transmission joining (LTJ) of polycarbonate (PC) and polyformaldehyde (POM) which are thermoplastic plastics is investigated through a finite element (FE) simulation. Firstly, a 3D thermal model is developed with a moving Super-Gaussian heat source based on the ANSYS parametric design language APDL and the distribution of the temperature field is obtained. Then the effect of process parameters namely laser power, scanning speed and spot diameter on the joint width is analyzed. At the same time, the calculated joint width is achieved. Finally, the curves of calculated results are compared with the curves of experimental results. The comparison shows a good agreement between them which shows that the FE model is reliable. This lays the foundation for reducing experimental times, designing of experiments based on FE simulation and optimizing process parameters.

Optimization of Local Laser Heat Treatment Process Using a Simple FE-Model

2013 ◽  
Vol 762 ◽  
pp. 360-367
Author(s):  
Antti Järvenpää ◽  
T. Kiuru ◽  
Antti Määttä ◽  
Matias Jaskari ◽  
Kari Mäntyjärvi
Keyword(s):  
Heat Treatment ◽  
Finite Element ◽  
Process Parameters ◽  
Treatment Process ◽  
Maximum Temperature ◽  
Fe Model ◽  
Heat Treatment Process ◽  
Laser Heat Treatment ◽  
Laser Heat ◽  
Heat Treated

Local laser heat treatment is an efficient method to manufacture tailored heat-treated steel strips. It can be applied to soften narrow zones of the strip in order to improve its formability on desired areas. However, the properties achieved are dependent on several process parameters. An objective is to develop a predictive model to optimize the heat treatment parameters instead of using experimental trials. In the present study, a finite element model was applied to predict the maximum temperature and heating and cooling rates, as well as the heat distribution along the heat treated area. To develop the model and to test its feasibility, experiments were performed, in which process parameters were varied to study their effects on temperature distribution in a 6 mm thick abrasion resistant steel grade. Scanning of a laser beam was used to optimize the width and depth of the heat-affected zone.In practice, local laser heat treatment process parameters have to be optimized with care for successful results. The most important task is to minimize the temperature gradient between the surfaces and to keep the peak temperatures close to the austenitizing temperature. The results indicate that a simple model can be used to predict the outcome of the heat treatment, so that finite element modeling can be adopted as a suitable tool for design of local heat treatments, allowing more advanced treatments and applications with complex geometries.

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