tire design and development application of hyperworks...

Simulate to Innovate 1

Tire Design and Development Application of HyperWorks Capabilities

Amol Bankar Dr.Jyoti Prakash Rath G. Unnikrishnan Tom Thomas

Assistant Manager, Head, Vice President, Executive Director, Advanced

Engineering(R&D) Advanced

Engineering(R&D) R&D Technology and

Projects CEAT Limited, Halol, CEAT Limited, Halol, CEAT Limited, Halol, CEAT Limited, Halol, Pin: 389350,INDIA Pin: 389350,INDIA Pin: 389350,INDIA Pin: 389350,INDIA

Abbreviations: Design of Experiments (DOE), passenger car radial (PCR), Finite Element Method

(FEM) Keywords: HyperMesh, HyperStudy, HyperMath, HyperXtrude, Optimization

Abstract

Tire is a very complex product from design point of view. The complexity arises because of complex design and

embedded components present in it. The performance demand from a tire is highly complex and conflicting. CAE is becoming popular day by day in tire industry to reduce the design cycle time and improve product performance. CAE applications can be used to predict performance parameters and to optimize tire structure and design through different simulations like; manufacturing/process, structural, acoustic analysis and also design optimization. Altair HyperWorks package can address CAE activity right from pre-processing to post-processing as well as optimization. The seamless interaction between all the HyperWorks applications makes life of a tire CAE analyst easier. On the other hand some solver neutral platforms like HyperStudy and HyperView increases the efficiency of the entire process. The present work covers the role of HyperWorks applications in tire design and development. The capability of HyperMesh in handling complex 3D tread geometry meshing is discussed. Use of Tcl scripts for process automation is also demonstrated. Design of Experiment methods and powerful optimization engines like genetic algorithm and ARSM are used to optimize performance parameters. HyperStudy and HyperXtrude are used for tire noise reduction and extrusion simulation respectively. Introduction Tires are the only contact between a vehicle and the road, transferring actions like steering, braking,, accelerating and tuning to the road surface [1]. Finite Element Method (FEM) has been used in tire design and development for many years. Tire is one of the most complex composites man has ever made. It has many different components with specific roles to play. Tires consist from the softest solid material to the toughest and the hardest materials like textile fibre and steel respectively. It is always difficult to meet different performance requirements from of a tire due to conflicting requirements. Prediction of tire performance becomes due to presence of highly non-linearity in structure, material and dynamic contact boundary conditions. Presence of all the kind of non-linearity increases the challenges in execution and convergence for a tire simulation. In order to reduce product development time, usage of Computer Aided Engineering (CAE) and simulation tools are already popular in tire industries. Simulation of vehicle and tire has become a very important aspect of a tire design and failure analysis to most tire companies. Simulation capability is getting developed in almost all important areas related to tire technology, for example; design, manufacturing, performance prediction and finally optimisation of design and structure as shown in figure 1. Altair HyperWorks package can address CAE activity right from pre-processing to post-processing as well as optimization. In the present work different case studies showing usage of Altair HyperWorks tools in different simulation activities for tire development have been discussed.


Figure1. Tire simulation

Case Studies:

1. Footprint and cornering simulation using HyperMesh, TCL script

175/70R13 radial passenger car tire is considered in the present study. HyperMesh and Abaqus are used for pre-processing and solving. Details of tire component are shown in Figure2. The meshed tread pattern, shown in Figure3, was tied over the revolved model (Figure 4).

Figure 2. 2-D mesh of tire (simplified pattern) Figure 3: 3D mesh of a tire tread

Figure 4: Modeling strategy for full tread pattern Figure 5: Tread mesh distortion (with and without TCL scrip

Mesh propagation


Tire free rolling and cornering simulation with a fully functional tread pattern was done by exploiting Tcl scripts along with inherent capabilities of HyperMesh. Simulation with tread pattern was tried by two approaches; without Tcl and with Tcl. The deformed meshes of both these approaches are shown in Figure5. Excessive distortion of elements was observed when Tcl script was not used and the analysis failed to converge. The problem was addressed by using Tcl script and the solution was converged. A comparison of foot print contact pressure, shown in Figure 6, was made between two simulation results (simplified pattern and with fully functional tread pattern) and also physical testing result. It was found that the footprint pressure distribution for fully functional tread pattern had a good correlation with the physical testing result. The footprint shape of the simplified pattern could match the actual testing result. However, the contact pressure distribution for simplified pattern differed from the actual testing. This pronounces the importance of doing simulation with fully functional tread pattern.

Figure 6: contact pressure distribution of a simplified pattern, tread tire and actual test tire

Hence, solution approach which includes Tcl script was favoured. Simulation was also performed for cornering of tire. Cornering simulation of tire was successfully converged up to 10 degree of slip angle. This shows the robustness of this model as cornering at 10 degree slip angle is very severe. Foot print contact pressure and force and moment diagram at 10 degree slip angle are shown in Figure 7a and Figure 7b respectively.

Figure 7a: cornering Footprint@100 Figure 7b: cornering properties of a tire


2. Design sensitivity analysis and optimization of tire design and construction parameters for handling using HyperStudy

Taguchi method of Design of experiments has been utilised to find out the significant parameters affecting cornering stiffness of a passenger car radial (PCR) tire. Tire performance depends upon many different factors. The significant factors affecting handling is not always very clear. Hence a screening DOE is run to find out the significant factors affecting handling. HyperStudy provides user defined method for DOE, which has been used for screening experiment.

It can be observed from figure 8 that tread compound stiffness, belt angle, belt construction, belt width, belt EPI are significant factors for handling. Tread plays a major role towards wear, rolling resistance etc. besides handling. Since only handling is considered for optimisation, tread is not considered for further studies. The ARSM optimization technique is used for optimizing belt design parameters of a tire like belt angle, belt wire construction & belt EPI for handling using HyperStudy & FEA solver.

Figure 8. Main Effect and interaction plot

The objective of optimization is to maximize the cornering stiffness. HyperStudy is used for

optimization. It helps to achieve reliable robust design using enhanced sequential optimization and reliability analysis. The optimization iteration history table for the study is shown in table I.

Table I. Optimization iteration history table.

It can be seen from table II that the iteration 7 shows the maximum cornering stiffness. The

corresponding settings of belt package for optimum cornering stiffness are belt angle- 700 i.e.200, belt construction (area)-0.2204 mm2 and belt EPI (spacing)-1.0580 mm.


3. Tire Pitch Sequence Optimization by using HyperMath and HyperStudy

Tire pattern and Pitch sequence plays an important role in the tire noise. In order to reduce the tire noise the pitch sequence has to be optimized across the circumference of a tire. Existing pitch sequence for tire size 155/70R13 was taken as the initial solution for optimization engine. Noise Index corresponding to optimized pitch sequence was calculated using the HyperMath code. The amplitude –harmonic number curve were plotted (Figure 9) for both the pitch sequences using HyperMath plot option. The optimized pitch sequence corresponds to a lower peak hence low Noise Index Value.

Figure 9: Noise index comparison between existing and optimized pitch sequence

4. Extrusion simulation of tire components using HyperXtrude

The tread, the most important component in a tire, is manufactured through extrusion. The

design of the die and prediction of die swell is important during extrusion of a tire tread. HyperXtrude has been used for extrusion simulation of a tread profile of a 145/80R12 tire. The model has been meshed in HyperMesh and then material properties, process and control parameters have been defined in HyperXtrude. Solving of the problem has been done using HyperXtrude solver and post processing has been done in HyperView.

Flow behavior of different rubber compounds has been analyzed through flow streamline analysis. Figures 10(a) and 10(b) show the streamlines of cap and base material flow respectively. The flow is found to be continuous and proper.

Figure 11(a) & 11(b) shows die swelling image of the extrudate using cross and Carreau model respectively. As shown in the figure below, as soon as the material comes out of the die, the material tries to get a shape of increased dimensions. It has been observed that while exiting from the die, more change in the gauge of the profile than that of the width takes place. The percentage die swell data is shown in Table II.

Figure 10(a). Downstream particle stresses of the cap material Figure 10(b). Downstream

particle stresses of the base material


Figure 11(a) Predicted swell using Modified Cross model Figure 11 (b) Predicted Swell using

Carreau Yasuda models

Table II: Comparison of die swelling with different viscosity models Gauge(mm) % Increment in Gauge Width(mm) % Increment in Width

Die 2.18 141.0 Modified Cross Model 2.24 2.75 142.0 1.13

Carreau Model 3.65 67.43 144.1 2.20 Experimental result showed gauges swell by around 60-70 % and the width around 10 %.

Carreau Yasuda model is found to be a suitable one for extrusion simulation of tire tread profiles. Benefits Summary

1. HyperMesh is a requisite for tread pattern modelling for tire simulation and Tcl scripting facilitates convergence and gives significance reduction in time.

2. HyperStudy is pretty efficient in for tire design sensitivity analysis. Using HyperStudy and simulation this optimization was achieved within 2 weeks which otherwise would have taken around 4 months.

3. Very rich and diverse syntax library of HyperMath helped to formulate the objective function for noise optimization. The optimized results were also promising.

4. HyperXtrude is efficient to predict die swell and can help in die designing for tire tread profiles. 5. HyperWorks provides many tools that are useful for tire design and development

Challenges

1. Converting an exponentially decaying function into an objective function was a difficult task. 2. To map the swelling phenomenon accurately from simulation is a difficult task.

Future Plans

Multi-Body dynamic (MBD) simulation using MotionSolve.

Conclusions

1. Simulation with fully functional tread pattern is close to reality. The results obtained have a better correlation with the actual application and such an analysis gives complete picture for better product understanding.

2. The product development time saving due to use of HyperStudy along with FEA solver is very much appreciated and gave cutting edge solution over trial and error methods. It saves the experimental effort and most importantly money.

3. Combined HyperMath and HyperStudy provide better pitch sequence of tire.


4. Flow behaviour of rubber during extrusion can be analyzed through HyperXtrude. HyperXtrude can help in die designing for rubber extrusion.

ACKNOWLEDGMENTS

The authors would like to thank the CEAT management for allowing publishing this work. We would also like to thank Altair HyperWorks and ATC2012 for providing us an opportunity to present this work

REFERENCES

[1] Okonieski, R. E., Moseley, D. J., Cai, K. Y., "Simplified Approach to Calculating Geometric Stiffness Properties of Tread Pattern Elements," Tire Science and Technology, TSTCA, Vol. 31, No. 3, July-September 2003, pp. 132-158. [2] O. A. Olatunbosun, O. Bolarinwa, “FE Simulation of the Effect of Tire Design Parameters on Lateral Forces and Moments”, Tire Science and Technology, TSTCA, Vol. 32, No. 3, July-September 2004, pp. 146-163. [3] Prakash, J., Bankar, A., Unnikrishnan, G., & Thomas, T. “Optimization of tire design and construction parameters for handling”. [4] Nakajima, Yukio; and Abe, Akihiko, “Application of Genetic Algorithms for Optimization of Tire Pitch Sequences”, Japan J. Indust. Appl. Math., 17, (2000) 404-426. [5] G. Zhao, Y. Mu, Finite-Element Simulation of Polymer Flow and Extrudate Swell through Hollow Profile Extrusion Die with the Multimode Differential Viscoelastic Model, Advances in Polymer Technology, Vol. 32, No. S1, E1–E19 (2013) [6] Rath, J.P., Kumar, U., Unnikrishnan, G., & Thomas, T., Facilitation of ‘symmetric result transfer’ and convergence of a ‘non-linear steady state’ simulation of tire through scripting.

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