FE-Simulation of Metal Cutting Processes
Doctoral thesis, 2023
Machining a new component or a new material requires the selection of the cutting conditions, the tool material and the tool geometry. The selections should be also optimized for the existing components and materials to improve the quality of the produced components and reduce the cost of production. Selecting the most suitable and optimum conditions for the metal cutting processes can be done by performing finite element (FE) simulations which provide more in-depth and detailed information about the cutting processes and also reduce the experimental effort compared to trial-and-error approach.
In this thesis, the challenges and complexities that are needed to be considered in FE simulations of cutting processes are addressed. Firstly, the type of FE simulation should be selected according to the purpose of performing the simulation. Different types of FE simulations of metal cutting such as chip forming, heat transfer and material flow simulations are discussed while explaining their purpose and advantages. These simulations are also combined with semi-analytical methods and machine learning approaches to improve the performance of the simulations in terms of both accuracy and time consumption. Secondly, the selection of the suitable material model for the workpiece and the identification process of the material model parameters are crucial to obtain realistic results from FE simulations. In this aspect, an efficient and robust method of inverse identification of the material model parameters is presented in the scope of the thesis to improve the results of the metal cutting simulations. This identification approach is also implemented to identify the parameters of different material models to find the best-suited model to represent the behavior of the presented carbon steel workpiece material under different cutting conditions. In addition, different effects such as elastic, plastic, viscous and damage behaviors in the material modeling are also discussed throughout the thesis while touching upon their indicators in metal cutting.
There are many more effects and parameters that can be implemented in FE simulations which make the simulations more in-depth and accurate in exchange for computational time. That is why finding the optimum point between the accuracy and time consumption for metal cutting simulations is of interest to many researchers and engineers. The aim of this thesis is to accomplish this while assessing the different aspects of FE simulations of metal cutting processes and discussing the mentioned challenges and complexities in more detail.
In this thesis, the challenges and complexities that are needed to be considered in FE simulations of cutting processes are addressed. Firstly, the type of FE simulation should be selected according to the purpose of performing the simulation. Different types of FE simulations of metal cutting such as chip forming, heat transfer and material flow simulations are discussed while explaining their purpose and advantages. These simulations are also combined with semi-analytical methods and machine learning approaches to improve the performance of the simulations in terms of both accuracy and time consumption. Secondly, the selection of the suitable material model for the workpiece and the identification process of the material model parameters are crucial to obtain realistic results from FE simulations. In this aspect, an efficient and robust method of inverse identification of the material model parameters is presented in the scope of the thesis to improve the results of the metal cutting simulations. This identification approach is also implemented to identify the parameters of different material models to find the best-suited model to represent the behavior of the presented carbon steel workpiece material under different cutting conditions. In addition, different effects such as elastic, plastic, viscous and damage behaviors in the material modeling are also discussed throughout the thesis while touching upon their indicators in metal cutting.
There are many more effects and parameters that can be implemented in FE simulations which make the simulations more in-depth and accurate in exchange for computational time. That is why finding the optimum point between the accuracy and time consumption for metal cutting simulations is of interest to many researchers and engineers. The aim of this thesis is to accomplish this while assessing the different aspects of FE simulations of metal cutting processes and discussing the mentioned challenges and complexities in more detail.
Inverse identification
Machine learning
Metal cutting
Finite element method
Heat transfer
Machining
Virtual Development Laboratory, Tvärgata 4C, 412 96 Göteborg
Opponent: Professor Lars-Erik Lindgren, Department of Engineering Sciences and Mathematics, Faculty of Civil Engineering and Geosciences, Luleå University of Technology, Sweden
Author
Ahmet Semih Ertürk
Chalmers, Industrial and Materials Science, Material and Computational Mechanics
A thermomechanically motivated approach for identification of flow stress properties in metal cutting
International Journal of Advanced Manufacturing Technology,;Vol. 111(2020)p. 1055-1068
Journal article
Gradient-enhanced damage growth modelling of ductile fracture
International Journal for Numerical Methods in Engineering,;Vol. 122(2021)p. 5676-5691
Journal article
A. S. Erturk, A. Malakizadi, and R. Larsson. Evaluation of different flow stress models for machining simulations.
An ML-based approach for inverse identification of heat flux in machining
Procedia CIRP,;Vol. 115(2022)p. 208-213
Paper in proceeding
Towards an accurate estimation of heat flux distribution in metal cutting by machine learning
Procedia CIRP,;Vol. 117(2023)p. 359-364
Paper in proceeding
A. S. Erturk and R. Larsson. Subscale modeling of material flow in the primary shear zone in orthogonal metal cutting.
Take a deep breath and look around. How many metal objects are you seeing? Are they small or big? Are they moving or standing still? Did you know how they are shaped or produced? Did someone bend it into that shape or cut it maybe?
If you are seeing a metal object, there is a high chance it is produced by a metal cutting process. It is quite easy to guess what the process does, eh? With this process, we can cut metals into the shapes we want. Some of them are small like screws and nuts, and others are bigger like car engines. But regardless of the size, they all go through this process. It is one of those things that is easy to say but hard to do. Because metals are hard, get it? I am sorry, it was a bad pun.
A part of the difficulty of the metal cutting process is related to the process parameters. What should be the parameters of the process so we can produce the metal part faster with good quality and low cost? If we can do that, we would waste less energy and the price of the products would be lower for people to buy. So, selecting the process parameters is very important.
Some of these parameters are the answers to the questions like “How fast should we cut this metal?”, “How deep should we cut it?” and “What kind of tool should we use to cut it?”. That’s exactly where I come in and help people to find the answers to these questions. But there are lots of people searching for the answers. Some of these people do trial-and-error. They do some experiments; if the product is not good, then they change the process parameters and try again. But this can cause a lot of waste of material and energy. Instead, I do virtual experiments a.k.a. metal cutting simulations. I simulate the same process in a computer environment and try to find the optimum process parameters. By doing so, I am hoping to help and guide people regarding metal cutting and reduce the waste of material and energy. It is certainly not an easy task, because metal cutting is a very complex process where you see very fast deformation and high temperatures. So, you should definitely watch a video of it and see it for yourself. It is a very cool and fascinating process.
If you are seeing a metal object, there is a high chance it is produced by a metal cutting process. It is quite easy to guess what the process does, eh? With this process, we can cut metals into the shapes we want. Some of them are small like screws and nuts, and others are bigger like car engines. But regardless of the size, they all go through this process. It is one of those things that is easy to say but hard to do. Because metals are hard, get it? I am sorry, it was a bad pun.
A part of the difficulty of the metal cutting process is related to the process parameters. What should be the parameters of the process so we can produce the metal part faster with good quality and low cost? If we can do that, we would waste less energy and the price of the products would be lower for people to buy. So, selecting the process parameters is very important.
Some of these parameters are the answers to the questions like “How fast should we cut this metal?”, “How deep should we cut it?” and “What kind of tool should we use to cut it?”. That’s exactly where I come in and help people to find the answers to these questions. But there are lots of people searching for the answers. Some of these people do trial-and-error. They do some experiments; if the product is not good, then they change the process parameters and try again. But this can cause a lot of waste of material and energy. Instead, I do virtual experiments a.k.a. metal cutting simulations. I simulate the same process in a computer environment and try to find the optimum process parameters. By doing so, I am hoping to help and guide people regarding metal cutting and reduce the waste of material and energy. It is certainly not an easy task, because metal cutting is a very complex process where you see very fast deformation and high temperatures. So, you should definitely watch a video of it and see it for yourself. It is a very cool and fascinating process.
A simulation based guide to machinability assessment
VINNOVA (2016-05397), 2017-09-01 -- 2020-11-27.
Flerskalig modellering av arbetsmaterialet vid skärandebearbetning
Swedish Research Council (VR) (2021-05583), 2022-01-01 -- 2025-12-31.
Subject Categories
Applied Mechanics
Manufacturing, Surface and Joining Technology
Other Materials Engineering
Areas of Advance
Production
Infrastructure
C3SE (Chalmers Centre for Computational Science and Engineering)
ISBN
978-91-7905-913-2
Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 5379
Publisher
Chalmers
Virtual Development Laboratory, Tvärgata 4C, 412 96 Göteborg
Opponent: Professor Lars-Erik Lindgren, Department of Engineering Sciences and Mathematics, Faculty of Civil Engineering and Geosciences, Luleå University of Technology, Sweden