Optimization of the Body-In-White (BIW) for Multi Domain (NVH & Strength) Functionality

Transcription

1 Optimization of the Body-In-White (BIW) for Multi Domain (NVH & Strength) Functionality Nilesh Dehariya, Deputy Manager, Maruti Suzuki India Ltd. Hemant Sharma, Deputy Manager, Maruti Suzuki India Ltd. Introduction: Lightweight automobile has become focus area of automobile technologies in 21st century. As the BIW is the main bearing structure for the body and occupies a large proportion of the body weight, reducing the weight of the BIW contributes to the overall mass reduction. While designing the BIW we need to consider multiple disciplines, e.g. structural strength and stiffness, NVH and safety performance. In the design process of an automotive BIW structural optimization, the influence of multiple disciplines on performances of the BIW must be considered. In this paper, the MDO (Multi-Disciplinary Optimization) of the BIW is done through MMO (Multi Model Optimization) approach considering the stiffness, modal analysis and vibration aspects. Multidisciplinary Design Optimization (MDO): The mathematical model for optimization can be defined by the following relations: The objective function f(x) and the functions g(x) in the constraint function are structural responses obtained from a finite element analysis. A constraint is considered active if it is satisfied exactly (g = 0); it is considered inactive if g < 0; it is considered violated if g > 0. Optimal solution is achieved when all the constraints are satisfied and along with the minimization or maximization of the objective function. The selection of the vector of design variables x depends on the type of optimization being performed. In size optimization (including free-size), the design variables are properties of structural elements. In case of multi domain optimization design responses are basically from different domain like NVH and strength etc.

2 Optimization Process: In the present paper, size optimization is performed using the recently released MMO (Multi Model Optimization) approach in Optistruct software. Optimization is on BIW level. Following Load cases are considered. 1. Static stiffness which includes Torsional and Lateral Stiffness 2. FRF (Frequency Response Function Analysis) at the hard points like engine mounts and suspension attachment locations on BIW. The MMO (Multi Model Optimization) approach ensures that we can have different models (BIW or BIB) with different modeling practices (e.g. spot weld modeling) for stiffness and FRF loadcases. The panels which are selected for the optimization across various models have same property collector and component collector name. 51 panels of the BIW (refer Fig.1) were selected for size optimization. These panels were decided after having discussion with the designers regarding the feasibility of the die change. In order to ensure that the panels having symmetry should have same thickness, both the panels (left and right side) were assigned in the same design variable. This will ensure the same thickness for the symmetric panels and hence more feasible design. There are total 35 design variables in the optimization. Fig.1: Panels selected for optimization Structural Analysis of BIW: Modeling of BIW: Pre-processing is the main task for constructing the FE model of the BIW. The FE model quality has great influence on the results and computational efficiency of FE analysis. Modeling of the BIW is done using Hypermesh software.

3 Stiffness Analysis of BIW: The static torsional and lateral stiffness have great effect on vehicle life. If the body has insufficient stiffness, it could cause large deformation in the openings of door and window beams, which results in the door locking, the glass breaking and the sealing property being bad. Such issues could directly or indirectly cause a considerable reduction in the ride comfort, control stability, fatigue, durability, NVH etc. Therefore, it is necessary to incorporate the stiffness performance in optimization analysis. Torsion Stiffness Analysis The load and boundary conditions for evaluation of the torsional stiffness are mentioned in Fig. 2. In order to perform the torsional stiffness optimization, the translational displacements are calculated at 8 nodes along front and rear end of the vehicle on both sides. These displacements (refer fig. 2) are used for the calculation of the torsional stiffness. Since the targets were to optimise the torsional stiffness with minimum panel thickness, it was decided to use these resultant displacement values as the design constraint. Fig. 2: load & boundary condition for torsional stiffness Lateral Stiffness Analysis The load and boundary conditions for calculating lateral stiffness are mentioned in Fig no.3. High value of Lateral stiffness ensures the better handling of vehicle. Fig.3: boundary condition for lateral stiffness

4 Vibration (FRF) Analysis of BIW: FRF analysis is performed to evaluate the dynamic stiffness at the hard point locations like engine mount, suspension atttachment location etc.these hard points are path of vibration transfer to the BIW. The FRF evaluation ensures that proper vibration isolation at these attachment locations. Fig. 4 showed below mention the FRF locations where single point mobility analysis is performed. Fig. 4: locations for FRF analysis Optimization Strategy: The optimization is done using MMO approach. There are 2 models with different modeling practises used for MMO. In case of torsional and lateral stiffness, displacements in all translational directions are used as the responses which in turn are used as constrain through DCONSTRAINT. Property Based Design Variables are used using DVPREL. Also DDVAL card is used in order to ensure that only discrete and feasible panel thicknesses are used for optimization. Mass response is used finally to minimize the summation of all the panel thicknesses and thus the mass of the BIW. Same approach is used for the lateral stiffness. In case of FRF optimization FRF velocity is used as the response which in turn is used as constraint. Also the same panels are used as property based design variables. Mass response is used for the optimization. After setting the optimization for FRF and stiffness load cases in two different models, these are clubbed together through MMO. Mass response is used as the objective function in MMO. Thus MMO will ensure that Panel thicknesses are optimized only after satisfying the constraints of both the models.

5 In this analysis we have 2 models, so as per the guidelines 3 CPU were used for the computation. Optimization Results: Total weight saving of 2.68 Kg is achieved. Out of 35 design variables, 27 design variables have shown the change in thicknesses. 9 Panels have shown the thickness increase whereas 18 panels have shown the reduction in the panel thickness. Table 1 shows the compiled data for the optimized thickness. Table.1 BIW optimized panel thickness details

6 Result Validation: After performing the optimization on 35 Panels validation of final results is done. Validation is for torsional stiffness, Lateral stiffness and FRF results. Along with this validation sound pressure level (SPL) analysis and BIW modal analysis is also done to compare the results with the base design. Torsional & Lateral Stiffness Results As mentioned in Table 2, torsional stiffness of the optimized model has increased by 3.53% more than the Base design. In case of Lateral stiffness, there is improvement of Lateral stiffness in front strut location by 1.76%, Front Lower Arm location by 0.74% and Torsion Beam Location by 1.87% respectively. Table 2: Torsional and Lateral Stiffness result comparison FRF Analysis Optimized model is verified for the FRF load cases also. Fig.5 shows the change w.r.t to the Base Design.

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8 Fig. 5: FRF Results As clear from the above curves, there is either no or minimal increase in the amplitude of the mobility curves at various frequencies. Thus the optimized design ensures that there is no decrease in the vehicle performance while optimizing the vehicle weight. Result Validation in other Load Cases: Modal Analysis: Apart from static and NVH optimized BIW need to be ensured for other load cases too. BIW fundamental frequencies are important design criteria and need to be evaluated w.r.t to the Base Design. As shown in the Table 3, the Rear torsion mode of the BIW has decreased by 0.03 Hz. The maximum fall in the modal values is observed in Roof Front end along with windshield bending which is in acceptable limit. Table 3 BIW modal frequency and mode shape comparison Sound Pressure Level Analysis: The BIW is also evaluated in the sound pressure level analysis. As seen in the fig. 6, the optimized model SPL performance is quite comparable with the base design.

9 Fig. 6: SPL Results Summary: In this paper, BIW level optimization was performed using MMO approach for size optimization. Weight saving of 2.68 Kg was achieved. Also the BIW performance was found satisfactory in modal analysis and sound pressure level analysis. The material and tooling cost for BIW panels for the design steel grades is 110 rupees/kg approx. The mass reduction of 2.68kg will results in saving of 295 rupees/vehicle. The annual saving will be around 17.7 MRs considering the annual volume of units. There is time saving of 6 weeks as compare to the many manual iterations required for finding the optimal panels thickness. Apart from this, weight saving will also lead to the Fuel Economy improvement of approx kmpl. Future Plan: The validations of the optimized model in BIW twist strength and crash load cases are in progress. It the future scope, authors will try to incorporate the BIW Twist analysis and sound Pressure analysis with in the optimization domain. Also the performance of optimized model in the crash load cases will be evaluated. Acknowledgement: Authors would like to acknowledge the support and guidance of Senior Management of Maruti Suzuki India Ltd. The authors would also like to acknowledge the continual help and support of Altair support team for this case study. References: 1. Altair Optistruct Manual