Microstructure Texture Prediction in Machining

Available online at www.sciencedirect.com

ScienceDirect Procedia CIRP 46 (2016) 595 – 598

7th HPC 2016 – CIRP Conference on High Performance Cutting

Microstructure Texture Prediction in Machining Processes Omar Fergania,b,*, Zhipeng Panb, Steven Y Liangb, Zoubir Atmanic Torgeir Weloa b c

a NTNU – Department of Engineering Design and Materials, Trondheim, Norway George W woodruff, School of Mechanical Engineering,Georgia Institute of Technology, Atlanta, USA

b University of Mohamed I, Equipe de Mécanique et Calcul Scientifique (EMCS), ENSAO, Oujda, Morocco

* Corresponding author. Tel.:+4773593884; fax:+4773593884.E-mail address: [email protected]

Abstract During machining and due to the thermo-mechanical load, the material properties of the component are expected to evolve because of the changes in the microstructure attributes. Crystallographic texture is important as it defines the anisotropy and influences directly the fatigue behavior. In this work, we propose a systematic approach to predict the texture evolution in machined workpiece using combined FEM and Visco Plastic Self Consistent (VPSC) methodologies. The prediction tool implementation is described in details and tested for bearing steel during hard turning. The output this model will help to develop meta-models capable of assisting the machining process planning to achieve desired properties and functionalities. © 2016 Published by Elsevier B.V This 2016The TheAuthors. Authors. Published by Elsevier B.V.is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the International Scientific Committee of 7th HPC 2016 in the person of the Conference Chair Prof. under responsibility of the International Scientific Committee of 7th HPC 2016 in the person of the Conference Chair Peer-review Matthias Putz. Prof. Matthias Putz Keywords:Machining; Machining; Crystallography; FEM; VPSC

1. Introduction In order to achieve superior components properties, the understanding of the interaction manufacturing processmaterials is critical. It is known that the thermo-mechanical load induced by most manufacturing processes and specifically machining is partly responsible of the final part properties. In fact, the induced loading drives microstructural changes like phase transformation and grain size change, etc. As a consequence, there is direct relation between the fine tuning of the manufacturing process and the final part functionality in terms of fatigue behavior, strength and corrosion resistance. Previous researchers showed interest in the understanding of the Process-Materials interactions. The concept of process signature was introduced for the first time to specifically identify the nature of the process in order to better study it influences on the material being transformed [1], the conclusion of this work is that any manufacturing process could be view as a special combined thermo-chemicomechanical signature. Other manufacturing process such as forming could be viewed from the functionality aspect as presented by [2]. More recently, an approach relating the

process to the properties through physics based understanding and microstructure kinetics is proposed by Fergani et al in [3]. The Materials-affected Manufacturing (MAM) analysis framework suggests also the effect of the properties changes on the process behavior itself. An iterative scheme was presented in order to capture the mutual influence. Based on this brief, summary, it is clear that in order to capture the interaction process-properties, the understanding of the materials behavior through the microstructure synthesis is a critical step. Based on previous studies, modeling tool can play a substantial role in the understanding of the relation between the materials microstructure attributes and the manufacturing process conditions. The computational flexibility provided using physics based modeling tool will help achieving the objective of this study. The machining process represents an ideal test bed realization to proof this concept. As mentioned earlier, the severe machining deformation affects different microstructure attributes. Microstructural attributes can vary based length scale. As an example, the average grain size will influence the strength of the materials as well as it hardness following the well-known Hall-Petch equation. Substantial amount of work proved this

2212-8271 © 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the International Scientific Committee of 7th HPC 2016 in the person of the Conference Chair Prof. Matthias Putz doi:10.1016/j.procir.2016.05.012

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Omar Fergani et al. / Procedia CIRP 46 (2016) 595 – 598

for the case of machining [4,5]. Crystallographic texture is another attribute that is affected by the machining process. Texture changes are affecting directly the materials properties in term of Young’s modulus as an example. The influence of texture on fatigue behavior is therefore established. Experimental investigation using nano-indentation associated with crystal plasticity modeling are performed to prove this claim [6]. Fergani et al in [7] presented a Visco-Plastic Self Consistent (VPSC) model is investigated for the first time to predict the surface texture after machining with different depth of cut. The model was validated with X-Ray measurement and showed very promising results. Basu et al in [8] Performed a series of in depth investigations of the texture evolution in machining focusing mainly on the experimental work through X-Ray measurements. The implementation of VPSC showed excellent results. Based on the previous studies, it appears that machining process planning could benefit from the benefit of associating the VPSC to FEM modeling. The optimization of the machined surface in terms of crystallographic texture could be achieved. In this study, the understanding of the deformation information necessary to capture texture evolution is presented. Later, the implementation of the VPSC Fortran based code to FEM analysis is explained. Finally, a short case study to predict crystallographic texture evolution is presented.

are selected as follow: t1 (Vc=90m/min, α=-20°), t2 (Vc=90m/min, α=5°), t3 (Vc=270m/min, α=-20°), and finally t4 (Vc=270m/min, α=5°). The Johnson-Cook materials model for AISI 52100 is implemented following the equation (1). The JC model captures simultaneously the effect of deformation parameters (strain, strain rate and temperature). ߪ ൌ ሺ͹͹Ͷ ൅ ͳ͵ͶǤ ߝ ଴Ǥଷ଻ଵ଴ ሻሺͳ ൅ ͲǤͲͳ͹͵ͳ݈݊ߝሶሻሺͳ െ ሺ

்ି்ೝ ଷǤଵ଻

்೘ ି்ೝ

(1 )

The related J-C damage model is obtained using the following parameters (d1=0.05, d2=3.44, d3=-2.12, d4=0.002, d5=0.61). ଻଼଼௞௚ The materials properties are defined as is ߩ ൌ య ǡ ߣ ൌ ସ଺௪ ௠௄

ǡ ܿ௣ ൌ Ͷ͹͹Ǥʹͻͷ

௃ ௞௚‫כ‬௄

௠

ǡ ‫ ܧ‬ൌ ʹͲͲ‫ܽܲܩ‬ǡ ߙ ൌ ͲǤʹͺ. Fig.2 show

the FEM results in term of strain and stresses. Machining temperature is also predicted but not presented in this work.

2. Simulation toolset The main interest of this study is the machined surface crystallographic texture. Few studies performed experimental investigation of the surface texture using expensive and time consuming tools such as X-Ray, EBSD or more recently Transmission Kikuchi Diffraction [9]. The implementation flowchart for this study is presented in Fig. 1. It shows step by step the task required to perform texture prediction for machining.

Fig.1.Texture prediction flowchart

First, an Arbitrary Lagrangian-Eulerian (ALE) is sued for the FEM setup. The investigated cutting conditions are the one recommended for the hard turning of AISI 52100. The depth of cut is fixed to 0.1mm with a feed of 0.05 mm/rev. Four tests

ሻ

Fig.2. Strain and stress contour for the simulated cases


In order to predict the deformation gradient, a FORTRAN subroutine is utilized. Based on the deformation increment from step n to n+1, the velocity gradient of the deformation on the machined surface is calculated at each point. The derivation implemented to predict the velocity gradient is presented giving the fact that we know‫ܨ‬ο௧ ǡ ‫ܨ‬ο௧ା௧ ǡ ο‫ݐ‬. ‫ܨ‬ሶ ൌ

ଵ ο௧

ሺ‫ܨ‬ο௧ା௧ െ ‫ܨ‬ο௧ ሻ

(2)

Using the basic rule of calculating‫ି ܨ‬ଵ , the velocity gradient is then calculated using ‫ ܮ‬ൌ ‫ܨ‬ሶ Ǥ ‫ି ܨ‬ଵ (3) The Visco-Elastic Self-Consistent model (VPSC) described by [10] is capable of predicting the crystallographic texture evolution during the deformation of each individual grain in a matrix or medium of the same homogeneous mean material response. The VPSC take as input the initial material texture. In this study the texture is considered as random for the considered population of grains, which is consistent with the post annealing material structure. Voce isotropic material model is utilized in order to capture the materials response. It predicts the evolution of the threshold stress as a function of shear strain accumulated inside each of the considered grains. ఏೞ

߬Ƹ ௦ ൌ ߬଴௦ ൅ ሺ߬ଵ௦ ൅ ߠ଴௦ ʒሻሺͳ െ ሺെʒ ቚ ೞబቚሻ ఛభ

597

3. Results and discussion The 100Cr6 steel (AISI 52100) is a hyper-eutectoid, low chromium. This steel is principally used for the manufacture of ring and ball bearings. Its microstructure consists of carbides dispersed in a ferritic matrix with a low carbon content The hardness is 272 HB. In the following, this material will be referred to as 100Cr6-FP. The basic crystallographic microstructure consists in a dominant martensitic phase with BCC structure, the remaining consist of a small portion of retained austenite with an FCC structure. Carbide inclusions are also present as shown in Fig.3. For the sake of the simulation, we consider a 70% martensitic and 30% austenite. The initial population is considered to be 500 grain.

(4)

where ʒ is the accumulated shear strain ins the grain, ߬଴௦ initial cross resolved shear stress, ߬ଵ௦ initial hardening rate, ߠ଴௦ asymptotic hardening rate, and the back extrapolated CRSS. Twinning deformation is not considered in this study. In order to get these data, a basic curve fitting is performed based on the curve obtained by as showed in Fig.3.

Fig.4. The relative slip activity simulated for the case 3, this figure show how strain is responsible of activating the BCC slip system

The Voce hardening parameters are implemented for each phase. The FCC crystal slip system is selected to be {111}, for the BCC the slip system is {110}. The results show that the relative slip activity is distributed between FCC and BCC crystals at two different stages as shown in Fig.4. It appears that very quickly after the cutting process, the deformation transforms from FCC to BCC slip dominant deformation. This is due to the CRSS of FCC that is almost half of BCC phase. Then the FCC phase start hardening and inducing a decrease of the FCC slip activity therefore an increase of the BCC phase. Fig.3. AISI 52100 microstructure SEM picture

In order to test the capacity and robustness of the model, a complex case of two phases material is proposed, the predicted texture is presented in the next section.

598


Conclusion In this study, the simulation tool used to predict crystallographic texture on the machined surface of a component is presented. The implementation details in terms of velocity gradient are provided. The FEM model details, the subroutine equation and finally the VPSC input and output are reviewed and implemented. A case study on AISI 52100 bearing steel with two crystallographic phases is performed. The results show that the model predicts shear texture for the dominant BCC phases, which is consistent with the machining deformation. Insights on the relative slip activity between the two phases are different strain levels is capture. Experimental validation will be proposed in order to enhance the model accuracy for this specific case. References

Fig.5. The predicted favorable texture in the cutting direction and the intensity (t1 to t4 represent case 1 to case 4)

The texture obtained for the BCC phase is shear texture. This is expected due to the severe shear stress experienced by the machined surface during the cutting process. In reality, the relatively high machining temperature will induce other microstructure changes such as phase transformation. This will generate random crystallographic texture. The crystallographic texture presented in this section is in the feed and the cutting direction. As the rake angle changes form the positive to negative values, the texture is increase in magnitude while keeping the same direction. This is due to the total strain experienced by the materials, while the clear new texture orientation in the directions and are the result of the velocity gradient experienced by the materials as shown in Fig.5.

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