Introduction of Optimization Tools in BIW Design Himanshu Shekhar Deputy Manager, Maruti Suzuki India Ltd, Palam Gurgaon Road, Gurgaon. Vimal Kumar Deputy Manager, Maruti Suzuki India Ltd, Palam Gurgaon Road, Gurgaon. Rajdeep Khurana Sectional Manager, Maruti Suzuki India Ltd, Palam Gurgaon Road, Gurgaon. ABBREVIATIONS OptiStruct-FEA OptiStruct SBA BIW MDB CAE Analysis Run Optimization Run Seat Belt Anchorage Body in White Movable Deformable Barrier Abstract While the Indian automotive market is expanding at a very quick pace, intense competition has been instrumental for the OEMs to bring better and cheaper products from its predecessors as well as competition. The major challenge faced by OEMs today is to provide lighter car body structure with better fuel efficiency without compromising on the Ride and Handling performance. BIW optimization is a mathematical approach that optimizes material layout within a given design space, for a given set of loads and boundary conditions such that the resulting layout meets the prescribed set of performance targets. This enhances the design performance while reducing the overall cost and weight factors. This paper presents the application of a BIW Optimization tool OptiStruct in order to optimize the existing Design of a Low Weight Compact Car Body Structure and details of optimized design resulting in significant weight as well as cost reduction. One of the key challenges has been to maintain the body stiffness and torsional rigidity while reducing the weight. Introduction Structural optimization is a modern computational design approach that has found widespread use, particularly in the early design phase of products. Several commercial finite element programs, such as OptiStruct, now provide user-friendly interfaces to these powerful algorithms so that optimization may now be incorporated into early design stages. An optimization problem may be formulated as: Objective : Minimize Mass of the Structure Constraint : Structure compliance Optimization is a comprehensive solution aimed at guiding and simplifying the design of structures. Optimization is the best strategy in the hand of design engineers to choose the right selection of material, Simulate to Innovate 1
shape, orientations. Unless an optimized design is followed, the results often will be an overdesigned part with redundant material that adds cost and weight. Additionally the cost of an automotive component stems not just from the material itself but also from R&D, manufacturing and assembly of the components, which particularly for smaller productions may consume a larger portion of the final cost. CAE-based design helps easily identify opportunities for part consolidation, which is one of the tactical advantages of optimization with respect to traditional systems. Objective The objective of the study is to apply optimization tool to determine the best layout of the material that composes the structure of the car. Once a design space is defined using modeling software, a mesh body is constructed for finite element analysis and optimization. Existing structures such as the roll bars, suspension components and the engine are added to the model which is subjected to the loads that are to be considered as part of the design. This study focuses on computing the best material distribution under multiple and combined load conditions. A unique feature of this work is to effectively find ways to optimize the existing structure and thus achieve weight reduction. Altair OptiStruct is used at the concept level of the design process to arrive at a design proposal that is then further modified for performance and manufacturability. Thus reduces the design development time and overall cost while improving design performance. In some cases, proposals from an optimization may be optimal in design but it may be expensive. These challenges can be resolved through the use of manufacturing constraints in the optimization problem formulation. Using manufacturing constraints, the optimization tool will yield engineering designs that would satisfy practical manufacturing requirements also. Different Optimization Techniques Different optimization techniques that are adopted for optimization can be classified into three main categories as follows: 1. Load Path Optimization (Topology) Topology can be defined as a tool for optimal Load path identification i.e. material distribution should be only on places where it is required and hence elimination of surplus material. A bulk mass automobile structure can be converted into an optimized structure by placing the components in right places. 2. Bead Placement Optimization (Topography) Topography can be defined as optimization of bead patterns reinforcement to satisfy the input conditions like rigidity, panel deformation or natural frequency response of the part. For example natural frequency of the given part can be increased by changing the bead pattern of the part. 3. Gauge (Thickness) Optimization Thickness plays a very important role in optimizing the BIW weight. Region specific thickness requirements can be achieved by this tool as it gives output of thickness variations in given thickness fringe. For a given sheet this tool can define the thickness variation as shown in the Fig 1 below. This output helps designer to define the cut line for different thickness sheet boundaries. Simulate to Innovate 2
Fig 1: Gauge (Thickness) Optimization Example Methodology The methodology for carrying out optimization can be divided into following broad stages: Preprocessing Stage a) Meshing For model making first step is to convert the 3D CAD data into exact mesh model and mesh quality is ensured during this activity. b) Assigning material & property to components Material properties like Poisson s ratio, density, Young modulus and thickness property are assigned to each and every component of the model so as to represent the actual vehicle conditions. c) Making Connections A host of connectors available in the software are used to simulate Weld spots / Bolts / CO2welds / Bolt connections to replicate actual vehicle conditions. d) Assigning constraints / boundary conditions Loads are defined in terms of their numerical value, direction and assigned to the areas as per the regulation requirements. The constraint boundary conditions are provided on case basis. Simulate to Innovate 3
Processing Stage a) Running CAE solver "OptiStruct - FEA" (Target Setting) After the model is prepared with material properties and applicable load & constraint functions, analysis is carried out for model response for stress and displacement values. These values are taken as reference for optimization. Thus, the OptiStruct-FEA CAE solver is used to identify performance targets. b) Solid Model Preparation (Topology) The whole model is converted into solid blocks with tetrahedral meshing for solid elements. Solid blocks are used for Load path identification i.e. Topology optimization c) Assigning objectives Optimization of mass, compliance, rigidity can be targeted as objective problem while maintaining the same loading conditions and constraint parameters of original model. d) Running optimization solver "OptiStruct" Optimization solver "OptiStruct" is run to achieve the desired objectives with the given load & constraint conditions while maintaining target performance. 3. Result Interpretation a) Result interpretation. The OptiStruct output is post-processed. Based on OptiStruct output, the 3D CAD data is then modified. b) Verification of the new data. The modified 3D CAD data thus obtained is subjected again for performance validation, maintaining identified loads & constraints. This is an iterative process. The various optimization techniques i.e. Topology / Topography / Size optimization follow the above methodology in general. The differences are there in setting up of constraints, objectives, data type (Solid/Shell) on case basis. Case Studies Using Optistruct OptiStruct tool is applied on current production Model to find scope for achieving further optimized structure. Study was carried out in two major BIW areas with different OptiStruct tools. These areas are: a) C Pillar b) B Pillar Total BIW model is converted into finite element mesh and analysis is carried out. Simulate to Innovate 4
Case Study 1: C Pillar Optimization (By Topology) C Pillar is the rear most BIW Area of a car as shown in Fig 2. It is subjected to severe Suspension and body twist loads. In addition to this, Seat Belt Anchorage (SBA) Loads comes to this area while deceleration. Fig 2: Structure of Model For C Pillar optimizations following loads are taken into consideration 1. BIW Twist Load. 2. RR Seat Belt Anchorage as per ECE R 14. 3. RR Vertical Durability Test for RR Suspension. With the above mentioned load cases RADIOSS CAE reference values are obtained. These values are taken as performance parameter benchmark for optimization iterations. Since Topology is a material layout Optimization technique, solid 3D CAD data is used for the same. Fig 3 shows the structure of C pillar for which Solid 3D Data is made. Simulate to Innovate 5
Fig 3: Structure of C Pillar Preparation Of Solid Model With Boundary Conditions Fig 4 shows that the entire C Pillar area is converted to solid block and attached with BIW by the help of rigid connectors to make the whole body act as one system. Fig 4: Material block of C Pillar Simulate to Innovate 6
After application of loads, OptiStruct generates the material density distribution. That means with zero density elements no load is absorbed or transmitted so those element regions can be eliminated as these are surplus material region. Also, a high density region highlights essential material requirement for the set loads and constraints. A typical material layout obtained is as follows. The following inferences can be drawn as per Fig 5: Fig 5: Topological material layout of C Pillar 1. Structure indicates that Suspension load carrying member should be merged gradually in Back Door Area. 2. QTR Upper Area & RR Door Ring has very high element density. Based on this inference after several iterations consisting of topology as well as Gauze optimization, Derived data structure (as compare to Current data Structure as per Fig6) is explained in Fig 7. Simulate to Innovate 7
Fig 6: Current C Pillar Structure Fig 7: Proposed C Pillar Simulate to Innovate 8
Case Study Conclusion With the proposed optimal design total weight saving of 900gm per vehicle (RH & LH) was achieved in C Pillar area. Case Study 2: B Pillar Optimization (By Free-Size Optimization) B Pillar is the structural area between Front & Rear Door. It consists of two major weight contributing components which are shown in Fig 8. a). Reinf. RR Door Hinge b). Panel CTR Pillar Inner. Fig 8: Structure of Ctr Pillar Simulate to Innovate 9
This area is subjected to following major loads. 1. BIW Twist load. 2. Front Seat Belt Anchorage as per ECE R 14. 3. Side MDB crash as per AIS 099. Solid block is created in B Pillar Area. Load conditions as identified are applied and topology optimization on solid body is carried out. Fig 9: B Pillar Topology Results The following inferences can be drawn from the topology result as shown in Fig 9: 1. The material layout suggested by software shows box kind of structure, connecting from Roof Rail area to Side Sill structure. 2. More material tends towards inner & outer side of structure. 3. More material density is shown in upper area compared to lower area. 4. There is possibility of material reduction in lower B Pillar region. Results from topology optimization can be interpreted as Requirement of shell structure in B Pillar area. Thus shell structure was created & Gauge Optimization tool was used to further optimize the shell structure. Simulate to Innovate 10
Fig 10: Reinf Hinge pillar thickness optimization Fig 10 illustrates that local thickness requirements in Reinf. Hinge Pillar varies Hence proposal is generated with 1.4 mm in upper region & 1.2mm in lower region. from 1.2mm to 1.6mm. Similarly Panel, RR Pillar Inner optimization is done with 1mm & 0.8 mm thickness as shown in Fig 11. Fig 11: Panel, Ctr Pillar Inner thickness Optimization Simulate to Innovate 11
Case Study Conclusion: With the proposed Design total weight saving of 1380 gm per vehicle (RH & LH) was achieved in B Pillar Area. Conclusion The optimization tool OptiStruct is quite helpful in optimizing the design space. The case studies converged to a total weight saving of 2.33 Kg per vehicle. With the above reference it is quite evident that OptiStruct tool is very much relevant for early stage Designer conceptualization application. It is helpful in placing the components tactfully while doing design considerations, also reduces the dependence on the designer judgment. These case studies include the relevancy of Topology for Optimal Load Path, which means that extra material can be avoided without compromising the performance of the vehicle. Topography can be used for bead pattern generation without adding any extra component or weight. This also optimizes the part s compliance performance. Gauze (Size) optimization is instrumental in deciding final sheet thickness. ACKNOWLEDGMENT The authors would like to sincerely thank the team of their seniors Mr. DN Dave, Mr. Alok Jaitley and Mr. Parveen Kr Sharma for their continuous support and encouragement throughout the development of this project. Also authors would like to extend their thanks to CAE Team for understanding the load case input and M/s Altair for their support in CAE Model Making without which the analysis was not possible. REFERENCES 1. Seat Belt Anchorage as per Seat Belt Anchorage regulation (ECE R 14) 2. Side MDB crash as per AIS 099 Approval of Vehicles With Regards To the Protection of the Occupants In The Event Of a Lateral Collision 3. http://www.altairhyperworks.com/tutorials 4. http://altairenlighten.com/knowledge-center/ Simulate to Innovate 12