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The automotive industry is facing significant challenges for next-generation vehicle design as fuel economy regulations and tailpipe emission standards continue to strive for greater efficiency. In order to ensure vehicle design reaches these sustainability targets, lightweighting through multi-material design and topology optimization (TO) has been suggested as the leading method to reduce weight from conventional component and small assembly structures. More effective tools, techniques, and methodologies are now required to advance the development of multi-phase optimization tools beyond current commercial capability, and help automotive designers achieve critical efficiency improvements without sacrificing performance. Presented here is a unique tool description and practical application of multi-material topology optimization (MMTO), a direct extension of the classical single-material problem statement (SMTO).

Automakers are under constant pressure to improve fuel economy and vehicle range to achieve a competitive advantage within the industry and meet government regulations. Reducing the overall weight of a vehicle contributes significantly to achieving this goal. Topology optimization (TO) has been identified within industry as a leading method to reduce weight on both a component and assembly level. With this tool, components can be redesigned to maintain structural performance requirements while also providing significant weight savings. On an assembly level, TO can be used to determine optimal loadpaths within large structures such as frames or bodies. These loadpaths can be interpreted to determine the locations of different components within the structure. To support the development of lightweight vehicle design, this paper presents a revised methodology and application of multi-joint topology optimization (MJTO).

The ever-expanding field of topology optimization (TO) includes the recent development of multi-material topology optimization (MMTO). In this work, the shortcomings of MMTO concerning concept complexity and impracticality with design interpretation are discussed. The current field is explored, for emerging manufacturing constraints which aim to reduce design complexity and promote practical usage of MMTO. The importance of the draw-direction constraint is established, with a methodology for their implementation into MMTO presented. The proposed MMTO approach is density-based and relies on solid isotropic material with penalization (SIMP) for material interpolation, and the method of moving asymptotes (MMA) for optimization. The aforementioned draw-direction constraints are implemented into MMTO with a design variable projection technique.

With the rising cost of fuels in addition to stricter emission standards, modern vehicles ought to be more fuel efficient. The best approach to increase fuel efficiency is to reduce the mass of vehicles. In order to produce light weight components for vehicles, topology optimization (TO) is now widely used by designers. However, the raw results obtained from TO cannot be manufactured directly and require significant reinterpretation to be able to be manufactured using traditional manufacturing processes. By considering the manufacturing process outside of TO, a sub-optimal design is obtained. The consideration of process specific manufacturing constraints within the TO ensures that a more optimal design will be produced. Previously the complex designs produced by TO have been a barrier to its implementation as the components cannot be produced without excessive costs. By coupling manufacturing constraints with TO more optimal designs can be obtained.

Rising gas prices and increasingly stringent vehicle emissions standards have pushed automakers to increase fuel economy. Mass reduction is the most practical method to increase fuel economy of a vehicle. New materials and CAE technology allow for lightweight automotive components to be designed and manufactured, which outperform traditional component designs. Topology optimization and other design optimization techniques are widely used by designers to create lightweight structural automotive parts. Other design optimization techniques include free-size, gauge, and size optimization. These optimization techniques are typically used in sequence or independently during the design process. Performing various types of design optimization simultaneously is only practical in certain cases, where different parts of the structure have different manufacturing constraints.

Increasing fuel prices and escalating emissions standards, are leading car manufacturers to develop vehicles with higher fuel efficiency. Reducing the mass of the vehicle is one technique to improve fuel efficiency. Shifting from metals to composite materials is a promising approach for great reductions to the vehicle mass. As more composite parts are introduced into vehicles, the approach to joining components is changing and requiring more investigation. Metallic chassis components are traditionally joined with mechanical fasteners, while composites are generally joined with adhesives. In a collaboration between Queen’s University and KCarbon, an automotive composite crossmember is being developed. A variety of lap joint geometries were modeled into a the crossmember assembly for composite-composite joints. Finite element-based optimization methods were applied to reduce mass of the crossmember. The optimized masses showed a 5% difference between the three joint geometries analyzed

There is an increasing need for lightweight structures in the transportation industry, and within these lightweight structures occupant safety is continually important to all stakeholders. Standard single and multi-material topology optimization (MMTO) techniques are effective for designing lightweight structures subjected to linear objectives and constraints but cannot consider crashworthiness. Crashworthiness must be evaluated using explicit dynamic simulation techniques, as a crash event contains geometric and material nonlinearities which cannot be captured by linear static finite element simulations. Explicit dynamic simulations prevent the calculation of sensitivity derivatives required for conventional gradient-based structural optimization strategies. This paper describes a design tool for multi-material topology optimization considering crashworthiness using the equivalent static load (ESL) method.

As conventional vehicle design is adjusted to suit the needs of all-electric, hybrid, and fuel-cell powered vehicles, designers are seeking new methods to improve system-level design and enhance structural efficiency; here, multi-material optimization is suggested as the leading method for developing these novel architectures. Currently, diverse materials such as composites, high strength steels, aluminum and magnesium are all considered candidates for advanced chassis and body structures. By utilizing various combinations and material arrangements, the application of multi-material design has helped designers achieve lightweighting targets while maintaining structural performance requirements. Unlike manual approaches, the multi-material topology optimization (MMTO) methodology and computational tool described in this paper demonstrates a practical approach to obtaining the optimum material selection and distribution of materials within a complex automotive structure.

Conventional vehicle design is continually being pushed by consumers and regulations to reach higher level of fuel efficiency and system performance. New methods such as use of alternative structural materials and structural optimization are being utilized heavily in the automotive industry. Currently, materials such as advanced composites, polymers, aluminum and magnesium are all being considered as candidates for alternatives to conventional steel parts to help meet lightweight performance targets. While topology optimization has proven to be a powerful in many case studies for automotive light weighting studies, it is currently constrained for use with one material in the optimization algorithm. Multi-material topology optimization (MMTO) methods presented in this paper demonstrate the tools capability to optimize material selection simultaneously alongside material layout for a given design space and desired weight target.

This paper describes the element interpolation scheme for multi-material topology optimization (MMTO), which generalizes the existing standard MMTO approach to overcome the inherent limitations of using only isotropic materials with a constant Poisson’s ratio. To address this limitation, the proposed method transforms the MMTO solution in a series of single material topology optimization (SMTO) by stacking multiple elements within the same design cell and assigning each weighted candidate material to an element. Solid Isotropic Material with Penalization (SIMP) equations are defined as weighting factors applied on each candidate material. As a result, anisotropic or isotropic materials with different Poisson’s ratios can be implemented in MMTO, allowing engineers to determine optimized designs that explore the full potential of isotropic and anisotropic materials properties.

Lightweighting of vehicles in the automotive industry is one of the most prevalent trends currently underway; influenced by government regulation and consumer demand. The reduction in vehicle mass of the next generation automobile offers increased dynamic performance, reduced fuel consumption, and potential component cost reduction. Development in composite materials, numerical methods, part consolidation, and advanced high strength metals represent a selection of the strategies being utilized for lightweighting. Additive manufacturing (AM) is a family of rapidly developing technology that is seeing use in the automotive industry both in the development and production stages. Fused deposition modelling (FDM) printed parts offer designers increased freedom, at a reduced weight, in comparison to conventionally fabricated parts as internal sections that are hollow, sparsely filled, or composed of a lattice structure can be realized instead of the traditional solid infill matrix.

As automakers continue to develop new lightweight vehicles, the application of multi-material parts, assemblies and systems is needed to enhance overall performance and safety of new and emerging architectures. To achieve these goals conventional material selection and design strategies may be employed, such as standard material performance indices or full-combinatorial substitution studies. While these detailed processes exist, they often succeed at only suggesting one material per component, and cannot consider a clean-slate design; here, multi-material topology optimization (MMTO) is suggested as an effective computational tool for performing large-scale combined multi-material selection and design. Unlike previous manual methods, MMTO provides an efficient method for simultaneously determining material existence and distribution within a predefined design domain from a library of material options.

A bumper system plays a significant role in absorbing impact energy and buffering the impact force. Important performance measures of an automotive bumper system include the maximum intrusions, the maximum absorbed energy, and the peak impact force. Finite element analysis (FEA) of crashworthiness involve geometry-nonlinearity, material-nonlinearity, and contact-nonlinearity. The computational cost would be prohibitively expensive if structural optimization directly perform on these highly nonlinear FE models. Solving crashworthiness optimization problems based on a surrogate model would be a cost-effective way. This paper presents a design optimization of an automotive rear bumper system based on the test scenarios from the Research Council for Automobile Repairs (RCAR) of Europe. Three different mainstream surrogate models, Response Surface Method (RSM), Kriging method, and Artificial Neural Network (ANN) method were compared.

The field of topology optimization (TO) has been evolving rapidly, notably due to the emergence of multi-material topology optimization (MMTO) algorithms. These developments follow the establishment of TO tools within industry, which has been accelerated and promoted through the introduction of various manufacturing constraints within algorithms. The integration of manufacturing constraints within MMTO is critical for promoting industry usage and adoption of these new software algorithms, as current usage of MMTO is dissuaded by the typically complex design solutions. The presented MMTO implementation is an extension of classical single-material topology optimization (SMTO). The TO problem is expanded to consider both material existence and selection, solid isotropic material with penalization (SIMP) is utilized for material interpolation.

Topology optimization (TO) represents an invaluable instrument for the structural design of components, with extensive use in numerous industries including automotive and aerospace. TO allows designers to generate lightweight, non-intuitive solutions that often improve overall system performance. Utilization of multiple materials within TO expands its range of applications, granting additional freedom and structural performance to designers. Often, use of multiple materials in TO results in material placement that may not have been previously identified as optimal, providing designers with the ability to produce novel high performance systems. As numerous modern engineering materials possess anisotropic properties, a logical extension of multi-material TO is to include provisions for anisotropic materials. Herein lies the focus of this work.

Evolving fuel efficiency and emissions standards, along with consumer demand for performance, are strong pressures for light-weighting of performance oriented motorcycles. The field of topology optimization (TO), with the extension of multi-material topology optimization (MMTO) provide manufacturers with advanced structural light-weighting methodology. TO methodology has been adopted in many industries, including automotive where light-weighting assists in meeting efficiency regulations. The development of process specific manufacturing constraints within MMTO is a critical step in increasing adoption within industries dealing with manufacturing cost restrictions. This capability can decrease design complexity, lowering manufacturing costs of optimization solutions. A conventional all-aluminum perimeter style motorcycle chassis is analyzed to develop baseline compliance (total strain energy) metrics.

As demand for fuel efficiency rises, an increasing number of automotive companies are replacing their existing metal designs with carbon-fiber-reinforced polymer (CFRP) redesigns. Due to the handling and manufacturing processes associated with CFRP materials, engineers have more design freedom to create complex, light-weight designs, which would be infeasible to manufacture using metal. Additionally, it is likely that by redesigning with CFRP, many steel assemblies can be consolidated to significantly fewer parts, simplifying or potentially eliminating the assembly process. When designing an automotive crossmember using CFRP materials, designers often aim for a two-piece design (top and bottom), while utilizing reinforcement material where needed. The joining of these two pieces is typically accomplished with many mechanical fasteners and adhesives, significantly increasing the part count and the manufacturing complexity.

With the recent push for electrification, automotive engineers are constantly striving to improve efficiency and performance of vehicle concepts. Although multiple vehicle attributes affect range, the overall mass of the vehicle plays a significant role. Computational tools such as topology optimization (TO) have long been utilized in industry to reduce mass while meeting structural design constraints. Over time, TO methods have been extended from traditional single material topology optimization (SMTO) to advanced methods such as multi-material topology optimization (MMTO). These advanced computational tools provide more design freedom in the conceptual design phase to develop superior load paths not possible with SMTO. However, MMTO is limited by the assumption of perfect joining between dissimilar materials, requiring manual re-interpretation to develop manufacturable designs.

Recently, topology optimization (TO) has seen increased usage in the automotive industry as a numerical tool, greatly enhancing the accessibility and production-readiness of optimal, lightweight solutions. By natural extension of classic single material TO (SMTO), a wealth of research has been completed in multi-material TO (MMTO), enabling simultaneous determination of material selection and existence. MMTO is effective for linear static analyses, making use of structural responses that are continuously differentiable, giving itself to efficient gradient-based optimization engines. A structural response that is inherently nonlinear and transient, thus providing difficulty to the mainstay MMTO process, is that of crashworthiness. This paper presents a multi-objective MMTO framework considering crashworthiness using the equivalent static load (ESL) method. The ESL method uses a series of linear static sub-models to approximate the transient crashworthiness model.

As additive manufacturing technology advances, it is becoming a more feasible option for fabricating highly complex, lightweight structures in the automotive industry. To take advantage of the improved design freedom and to reduce support structures for the selected printing orientation, components must be designed specifically for additive manufacturing. A new approach for accomplishing this process combines topology and build orientation optimization, which aims to simultaneously determine the ideal build direction and component design to maximize stiffness and reduce additive manufacturing costs. Current techniques in literature are formulated for specific categories of additive manufacturing: either methods that print on a support structure raft or print directly on the build plate. However, these two categories have very different relationships between part orientation and support structure, resulting in distinct optimal orientations for each additive manufacturing category.