Integration of the mechanical and the electronics worlds. Integration of the different motion control and power-train control functions so that important synergies can be exploited. Combining entertainment, communication and navigation subsystems. Coupling the world of electronics where the life time of a product is around 2 years and shrinking, with the automotive world, where the product life time is 10 years and growing. Developing new services based on electronics technology. Mechanical Capabilities. Expertise in Design for Manufacturing.
Engineers with rounded experience with hands on engineering experience. An integrated Smart Engineering Ecosystem which is time tested and proven. Availability of industry experts to consult during the project. Wu and colleagues Wu et al. Additionally, Xiao et al. Optimized results were subjected to a modal analysis in order to validate topology optimization effectiveness through structural stiffness evaluation.
Similar studies examined isolated bicycles parts, such as the upper rocker of a mountain bike Zeleny and Cadek, In the same way isolated motorcycle parts like motorcycle swing arm Powar et al. In respect of chassis optimization, a study of a motorcycle frame structure was presented by Wang et al. The frame consisted of nine main components which were linked together, analyzed and optimized, the cost of motorcycle frame was reduced and weight was lightened about 6.
Nevertheless, this study had a multiple part approach and not a unified frame one. The most recent example which combines a study and a manufacturing process of a unified motorcycle chassis from scratch is the Airbus APWorks Light Rider APWorks, This electric motorcycle has a total weight of 35 kg, with the frame alone weighting only 6 kg. Despite the minimal weight, the design meets all stiffness and natural frequency requirements.
Light Rider combined topology optimization, a new high-performance aluminum alloy and metal 3D printing in order to produce an optimum lightweight design. The combination of these factors not only led to weight and cost savings, but also reduced assembly time significantly and helped integrate new additional functions in parts and components.
Optimal material distribution is being sought under specific loads, boundary conditions and constraints or alternatively topology optimization give answers to the fundamental engineering question: how to place material within a prescribed design domain in order to obtain the best structural performance Sigmund and Maute, ? Topology belongs to the broader category of constraint optimization problems.
The most popular topology optimization constraints are mass, volume, displacement and stress.
Moreover, stress-based constraints could also unlock a new fatigue constraint category as this described by Collet et al. Application of this methodology is growing fast in the engineering community due to its key ability to produce innovative shapes during the conceptual design. Topology optimization provides a valuable perspective at the concept level of a design process and at preliminary design phases where multiple design ideas are being implemented in search of accepted performance. The method's range of applications extends from the automotive, aerospace, civil and naval engineering to bio-engineering, heat transfer, fluid flow, acoustics, materials design and other multi-physics disciplines Lagaros et al.
Despite the wide range of applications, the method frequently produces geometries which cannot be manufactured with classical methods.
The Automotive Chassis
This gap between topology result and design for manufacturability DFM is still measurable due to the fact that topology optimization tends to lead to a non-smooth and complex structural geometry Tang and Chang, However, the rapid growth of additive manufacturing technology is constantly closing this gap allowing for complex optimized parts to be made. In the present study, the topology optimization part of the problem formulation is dealt with the SIMP approach solid isotropic material with penalization.
This density-based approach, is one of the most popular and widely used ones for structural topology optimization. The role of penalty parameter p is to make intermediate densities unfavorable in the optimized solution. For the SIMP approach the penalization is achieved by the following power law formulation:. Furthermore, hard-kill methods, including Evolutionary Structural Optimization ESO , boundary variation methods level set and phase field , and a new biologically inspired method based on cellular division rules seem to be also applied in topology optimization approach during the two last decades Deaton and Grandhi, The nested methodology followed in this work is explained in Figure 1.
The method was implemented using Hyperstudy software, a design exploration tool with multiple optimization algorithms provided by Altair Engineering. During the first phase of the design optimization, shape coordinates are updated and inserted into the process. This shape was then used to reform the initial design domain. In the second phase and before domain reformation, the algorithm resets the design domain in its initial state, so each new reformation initiates from the same design.
Finally, in the third phase, the inserted shape coordinates are used to form a new shape and exclude the enclosed elements from the design domain. This new generated domain is then used for the topology optimization. Once the topology ends, results objective function and constraints shape are evaluated and the process continues until convergence is achieved. The topology optimization was executed using OptiStruct's SIMP approach which penalizes intermediate densities and force the final design to be represented by densities of 0 or 1 for each element.
Additional manufacturing constraints see section Load Cases and Displacement Constraints were introduced in order for the design concept to be manufacturable. Gradient based methods develop a strong link to the original design, which enhances the possibility of locking the solution locally Bartz-Beielstein et al. Within the first iteration, a Design of Experiment or DOE is constructed internally to provide the data to construct an initial response surface. The initial number of designs in the present study equals to 7 in order to ensure a good balance between local and global search.
All iterations beyond the initial step are similar. A new DOE is constructed using the optimal points from the previous iteration Pajot, This DOE is executed and the adaptive response surface is updated to absorb the new design points. The optimization problem is solved again on the newly constructed response surface, with the optimal design feeding forward to the next iteration until termination.
The GRSM algorithm belongs in the category of exploratory optimization, meaning that the method does not show the typical numerical convergence characteristics observed in other algorithms, such as gradient based systems. Consequently, the termination state is determined by reaching the maximum number of iterations designs evaluation defined by the user. The process of nested optimization and especially the external optimization process is based primarily on two new in-house code files developed Tcl and Batch making use of a HyperMesh fem which contains model geometry and can be updated in each external iteration and output files created from OptiStruct after the completion of the topology optimization.
The process is explained in eight steps which demonstrate the main functionality of the algorithm, the connections between the different files and their overall coordination. Step 1: GRSM algorithm calls the batch file which coordinates the process. File executed: Batch. Step 2: Batch file retrieves data from the model, including information about the design space and non-design space, the computational grid, the boundary conditions and the data for the topology optimizer objective function, responses, constraints, etc.
File used: Fem. Step 3: Batch file calls a Tcl code responsible to create a new model.
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Files executed: Batch, Tcl. Step 4: New model is created, in which elements have been removed according to the new shape. File created: Fem new. Step 5: The process returns to the Batch code. Step 6: Internal optimization process starts using OptiStruct. File used: Fem new. Step 7: Two new files created that contain information about the displacements, the final mass and the optimum design shape.
Volume 2: System Design
Files created: Out, Des. Step 8: Results are fed back to the beginning and the GRSM algorithm that evaluates the design and decides on the continuation or termination of the process. Files used: Out, Des. Inputs and outputs of Tcl and Batch files are presented in Table 1 :. The above eight steps as well as the connection and dependence of the different code files are depicted in the following scheme Figure 3. Two integrated optimization algorithms were enclosed in the presented nested structure.
The minimization of the overall frame mass together with additional manufacturing constraints is defined as the internal optimization process whilst the minimization of the total displacements the sum of the displacements of each load case described subsequently as the external optimization process.
Where d 1 is the torsional displacement, d 2 is the lateral displacement, d 3 is the longitudinal displacement and d 4 refers to the seat deflection. The present application of the nested method is described in the following scheme Figure 4. The initial design domain now represents the space from which the chassis will be formed while the battery pack represents the shape, which reforms the aforementioned domain in each iteration.
A cad file for the two-wheel chassis was created and exported in iges format, a geometric clean up procedure followed Figure 5 including redesign of complex surfaces graphic design and areas considered non-structural to avoid discretization abnormalities. The updated geometry was imported in HyperMesh followed by model preparation including meshing, required load and boundary conditions, manufacturing constraints, responses, objective function, etc.
During this process, 4 node tetrahedron elements were used for the design domain while rod and rigid body elements were used for applying boundary conditions and loads see Figure 6. The frame was subjected to four different load cases see Table 3 including a torsional, a lateral, a longitudinal and a case representing the driver's weight or seat deflection Figure 6. For each of the load cases a maximum displacement constraint was set in order to limit the optimizer to solutions with the required structural frame stiffnesses.
A crucial aspect taken into consideration in the present study was the level of manufacturability of the resulting design. As such, additional manufacturing constraints were applied to limit the solution range of the topology optimization problem and accelerate the design-to-prototype process. A scheme illustrating the relationship between manufacturing constraints and manufacturing techniques was presented by Vatanabe et al.
The scheme shows the necessary constraints in order to generate compatible design for each technique. The figure emphasizes the important role of two constraints symmetry constraint and minimum member size in almost every manufacturing technique. Therefore, symmetry was used in order to reach a simplified and weight balanced frame design as well as minimum member size to prevent decreasing members formation due to numerical instabilities mesh dependency.
Finally, checkboard control was also used to overcome abnormal material distribution, especially as first order finite elements were used Zhou et al. During vehicle development specific goals of maximum speed and total range were set. The battery pack voltage and capacity Table 4 determine the range of an electric vehicle's maximum speed and range. For computational purposes the predefined volume must be contained within adjacent bounds. This double inequality constitutes the first constrain of the external process.
The formula below presents the calculation of the volume from the 6 design variables which constitute the coordinates of two peaks. Being among the most expensive parts of an electric vehicle, batteries must be protected during a possible collision or stability loss. It is therefore essential for the battery pack to be fully surrounded from the chassis. Furthermore, the battery pack must be symmetrical with regard to the longitudinal axis for stability reasons. For the purposes of the present study, the battery pack was represented with a parallelepiped rectangular shape see Figure 7 determined by nodes n i and n j.
These two peaks had to remain inside the design domain to fulfill the second battery pack positioning constrain. The results can be classified in two major categories. The first concerns the battery pack design optimization while design domain maintained unaffected. This allows a better understanding of battery pack adjustment through iterations. Figure 8 demonstrates the shape and positioning evolution from the initial to the optimal design formation. It is highlighted that battery pack design white color borders , are entirely enclosed by the design domain. The second category demonstrates the combination of design and topology optimization.
In the diagram below Figure 9 , the four displacement values corresponding to the load conditions torsional, lateral, longitudinal and seat deflection for each unique design are presented. It is noted that the displacement due to the load under seat deflection exhibits a steady behavior in comparison with the rest, which are being decreased. This behavior is connected with the magnitude of seat deflection force in comparison with others and also in the connection method subframe insertion with the main chassis.
The two diagrams below Figure 10 presents the behavior of the objective function sum of the four displacements-black color , which gradually decreases. It is observed that the objective function reaches a reverse plateau at iteration 36 blue arrow after 30 h. In addition, due to the selection of the respective DOE, the GRSM optimization algorithm is not expected to produce an objective function curve similar to those of a gradient-based method. Figure Nested optimization results: objective function and polynomial interpolation for 83 iterations.
Table 2 shows the results for the initial and optimum design until iteration This result indicates that for a slight mass increase the value of the objective function simultaneously reduces significantly. Moreover, it is equally important to compare the change of the objective function in relation to the change of the polynomial interpolation curve.
This method provides the ability to compare not only the extreme designs worst vs. It is observed that the change rate is reduced to half, from 9. As indicated from Figures 11 , 12 the initial and optimum chassis design blue color including the battery pack darker gray-transparent color covers a limited percentage of the initial design domain gray-transparent color. More specifically, the initial mass including the non-design area mass green color of kg was reduced to In this section topology optimization results for mass minimization are compared with nested optimization results in order to get clear view of chassis development from the initial stage until optimum result.
In the first stage of the present study, a simple mass minimization topology optimization took place, manufacturing constraints were added with succeed at the second stage and finally during stage three the nested optimization process was executed. Divided the study in the abovementioned stages provided the ability to evaluate each one in depth before final nested method development. Table 6 presents the results of chassis development for the three stages.
In stage two it is observed that the manufacturing constraints reduce the total mass by 2 kg, however the solution was more computationally expensive and each run was longer in comparison with stage one. In addition to Table 6 results, Figures 13 , 14 below depict difference in structural members width and material discontinuities between stage one and two. These differences are connected with manufacturing constraints import which added manufacturability to the final design, especially through checkboard control, minimum member size and symmetry constraints.
These constraints should be taken into consideration by engineering team when the chassis is going to be inserted in a production line and tight time schedule must be kept. A nested optimization process for developing a two-wheeled vehicle chassis is presented. The objective was to present an alternative process which combined structural, manufacturing and battery pack data in the design process. The main advantage of the method is located in electric vehicle chassis development acceleration by gathering all relevant data from different engineering subteams into one process.
The methodology demonstrates the reformation of the design space before topology optimization to achieve optimum battery pack fitment while maintaining mass and stiffness in acceptable levels. However, due to large input data each new nested process must be constructed step by step in order to avoid constraints overlap or a possible conflict.
The process was implemented using a 3. Nevertheless, total time could be easily decreased to lower levels by using more computational power. Design engineers should also show special care to initial design domain formation, which must be free of complex geometric shapes and surfaces. A smooth design accelerates domain discretization, finite element analysis and therefore topology optimization. It is highly noted that, initial design domain has a direct impact in topology optimization and can almost predefined the result through in advance material removal from specific areas.
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Regarding the constraints of the external optimization process, these are linked to the geometric characteristics of the battery pack and neglect dynamic characteristics of the entire vehicle. An interesting direction for future research is the incorporation and usage of constraints resulting from vehicle dynamic analysis and driving behavior.
These constraints might be the Center of gravity CoG or aerodynamic factors drag coefficient , which are directly related to the driving behavior and vehicle performance.
Of particular interest would also be the application of the method to the optimal positioning and topology of other critical components and mechanisms of the vehicle. One such example is the swing arm. The mounting, design and weight of the arm significantly affect suspension kinematics squat and dive and so overall vehicle behavior. Therefore, the suggested method is not limited only to chassis implementation and can be potentially extended to various applications.