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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.

Numerical tools such as topology optimization (TO) have seen large development in both academic and industrial settings, enabling the optimization of structural objectives and/or attributes, subject to a wide range of constraints, pertinent to the engineering and design problems of automotive and aerospace industries. Classical TO methods assume the use of a single material (SMTO), however, a recent and important advancement in this field is that of multi-material topology optimization (MMTO), capable of simultaneous material existence and selection optimization. This is of heightened importance in the aforementioned industries, where many costly engineering materials can be used, but their selection is delegated to engineer experience. Consideration of modal characteristics (i.e., natural frequencies) in MMTO efforts have seen marginal development in recent years, yet is vital to both industries, who’s products are each subject to uncontrolled environments and vibratory motion.

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

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.

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).

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.

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.

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).

The presence of engineering plastics in the automotive, aerospace, and defense industries is rapidly increasing; the lightweight and cost-effective nature of these materials, coupled with improvements to their mechanical performance, is driving the replacement of more traditional materials. However, the stiffness of engineering plastics cannot rival that of their metal counterparts, making metal replacement challenging in cases where stiffness is paramount. Nanometal-polymer hybrids, which are engineering plastics reinforced by a thin high-strength metal coating, provide an innovative solution to this problem. However, implementing this hybrid material into innovative designs remains a challenge, as relatively little information about mechanical behaviour or appropriate modeling techniques for this complex material are available. In this article, an efficient and effective finite element modeling approach for the structural analysis of nanometal-polymer hybrids is presented.

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.