W-7
Citation:
W-7. WorldAutoSteel, “FutureSteelVehicle – Final engineering report“, 2011.
Steel Structures for Autonomous Vehicles
The WorldAutoSteel Steel E-Motive program has been moving along now for nearly a year, and we’d like to share an update with you, our engineering colleagues, on some of the design decisions we’re facing. If you recall, the Steel E-Motive program is designing vehicle concepts for Mobility as a Service (MaaS), characterized by autonomous, electric, ride sharing vehicles.
Some Background
We partnered with Ricardo headquartered in the UK to conduct the design and engineering of the vehicles. Ricardo was selected for their well-known reputation for innovation, their demonstrated knowledge of vehicle powertrains and electrification and their commitment to sustainable transportation. Our steel members subject matter experts work with Ricardo via various teams and working groups to push the envelope of steel applications. And given our pandemic, all of this currently occurs via virtual meetings.
Targeting technologies available for deployment in 2030+, we are considering the impacts to vehicle manufacturers, fleet operators and the ride hailing customer, as MaaS inevitably leads to an increase in demand for vehicle sharing, rental models and ride-hailing services over the next decade. We can represent these requirements as shown in Figure 1. On the left you’ll see broad needs for the critical stakeholders including space efficiency, flexibility and total cost of ownership. Those requirements translate into 12 key considerations for the mobility service provider, shown on the right, aimed at delivering value to customers and a sustainable and profitable business model. These considerations then require innovative design, engineering and materials applications.
Figure 1: MaaS key attributes and functions.
There are four main phases to Steel E-Motive (Figure 2). Phase 0 was a 3-month pre-study, beginning 30 June 2020, to review and confirm vehicle targets, essentially defining the foundations, goals and approach for the project. On 1 October 2020, we entered the Phase 1 concept engineering, exploring the challenges and steel solutions for Level 5 autonomous vehicles. Essentially, we are designing the body structure in this Phase, utilizing CAE tools to guide us. Phase 2 focusses on further refinement and optimization of the selected body concept, and ensuring the design is fully validated as there will be no working prototypes or hardware produced in the project. Phase 3 will be the roll out and dissemination activities, although you will see from the Steel E-Motive website and blogs that we are continually releasing material throughout the project.
Figure 2: Project timing and key activities.
We’ll be disclosing detailed targets and specifications later in the program, but Figure 3 provides overall dimensions. Battery electric will be the primary propulsion with competitive range. There are two variants: urban for inner city and shorter journeys, and an extra-urban variant for longer city-to-city (or city-to-airport) journeys. With Level 5 autonomy, there are no direct driver interfaces such as the steering column and pedals. You can see from the Figure that the vehicles are fairly compact. The urban variant sits between a European B and C segment in size. The extra-urban vehicle has a stretched wheelbase and can accommodate up to six passengers and a greater luggage capacity.
Figure 3: SEM vehicle technical specification and dimensions – base vehicle geometry.
The vehicles will be engineered and purposed for global application; therefore, we are considering the major global crash and safety standards and load cases. High volume production is targeted, greater than 250,000 units per annum, and a hypothetical production date of 2030, which influences the steel grades and fabrication processes considered. Third Generation AHSS (3rd Gen AHSS) and press-hardened steels continue to evolve with higher strength and improved formability. Between these innovative product capabilities, we are addressing the challenges associated with Mobility as a Service and tackling geometries that otherwise would have been difficult to produce.
To further assist in the design and manufacture of efficient vehicle structures, there are many new manufacturing processes, such as roll forming and hot stamping, that help fabricate these stronger materials effectively, while often doubling material use efficiency. Figure 4 provides a list of technologies that will be considered for Steel E-Motive.
Figure 4: Steel technologies included in SEM’s portfolio.
With the portfolio of steel product and manufacturing processes already available and the addition of those forecasted for future commercial availability, we are expecting innovations that will be a roadmap for future mobility vehicle manufacturers.
Our end goal is to demonstrate multi-purpose opportunities for the vehicles via a modular architecture enabled by the application of innovative steel solutions. These solutions will help Steel E-Motive achieve a low environment footprint measured over the vehicle Life Cycle, and meet global crash standards while delivering the lowest Total Cost of Ownership (TCO).
To reach our goal of demonstrating steel innovation in this program, we are using a theoretical frameworkH-2 as a guide, shown in Figure 5, considering innovation at an architectural level. That is, using body structure load paths shown in the vertical axis, and modular innovation for the major body components such as battery enclosure, side/crash rails, shown in the horizontal axis. Combining innovation levels and types of the two axes should enable us to demonstrate radical innovation in Steel E-Motive.
Figure 5: Steel E-Motive explores and demonstrates steel innovation. Exploring “modular” and “architectural” innovations for 2030 production.
Design Challenges
Figure 6 reveals an early or basic Steel E-Motive architecture. You can see that Level 5 autonomy creates both design freedoms that allow new occupant seating positions, while also creating challenges for short front and rear overhangs. We have an open pod-type structure with large door apertures for enhanced occupant ergonomics.
Figure 6: Challenges and opportunities of Level 5 autonomous MaaS battery electric vehicle.
Passenger comfort is key for MaaS vehicles. The open pod structure may give challenges with the air cavity mode coupling with structural modes. With occupants in different positions, we have different NVH source-path-receiver paths to consider. The larger door aperture gives us an inherent deficiency in overall body structure stiffness, for which we need to compensate. As with any BEV, the mass of the battery suspended on the lower structure may reduce body modes to frequencies that interact with other vehicle systems such as suspension modes. With a lot of emphasis on lower structure crash zones and battery protection, we may encounter some lower frequency upper body modes (such as lozenging), especially as we are targeting low overall body mass. These NVH risks and challenges are being addressed by taking a modal mapping approach, utilizing steel’s inherent high structural stiffness properties and undertaking thorough NVH simulation throughout the engineering phases.
Level 5 autonomy removes the requirements for driver vision and obscuration, but we do need to acknowledge passenger comfort issues, such as motion sickness. Consequently the designs consider a good level of outward visibility. However, we now have the freedom to place structure where we could not previously. We are using 3D topology FEA tools to help determine the optimum placement of structure in the body, and we have allowed the tool the freedom to place structure in the front and rear glazed areas. In the Figure 7 example, the software is recommending some structural members across the front windscreen, and further analysis shows that this has the potential to give us an overall Body-In-White weight saving as the load paths are more evenly distributed.
Figure 7: Level 5 autonomy removes the driver vision and obscuration requirements—an opportunity for new solutions.
To summarize the Steel E-Motive engineering activities, we are currently exploring the numerous challenges and opportunities that Level 5 autonomous BEVs provide us. We are in the concept phase, investigating both the overall body structure layout and load paths, as well as developing components and modules utilizing the unique properties of steel.
We expect to complete the program with full virtual concepts by December 2022. Our plans are to unveil more and more of the design concepts over the months to come, and we’ll be using virtual reality and other tools to communicate the concepts’ engineering. We invite you to subscribe at the website to receive all the news that will be coming out of the program, including more technical details as they become available. You can do that at www.steelemotive.world. We’re excited to share Steel E-Motive innovations in the future!
LME Simulation During RSW
Modern car bodies today are made of increasing volumes of Advanced High-Strength Steels (AHSS), the superb performance of which facilitates lightweighting concepts (see Figure 1). To join the different parts of a car body and create the crash structure, the components are usually welded to achieve a reliable connection. The most prominent welding process in automotive production is resistance spot welding. It is known for its great robustness, and easily applicable in fully automated production lines.
Figure 1: AHSS Content in Modern Car Body.W-7
There are, however, challenges to be met to guarantee a high-quality joint when the boundary conditions change, for example, when new material grades are introduced. Interaction of a liquefied zinc coating and a steel substrate can lead to small surface cracks during resistance spot welding of current AHSS, as shown in Figure 2. This so-called liquid metal embrittlement (LME) cracking is mainly governed by grain boundary penetration with zinc, and tensile stresses. The latter may be induced by various sources during the manufacturing process, especially under ‘rough’ industrial conditions. But currently, there is a lack of knowledge, regarding what is ‘rough’, and what conditions may still be tolerable.
Figure 2: Top View of LME-Afflicted Spot Weld.
The material-specific amount of tensile stresses necessary for LME enforcement can be determined by the experimental procedure ‘welding under external load’. The idea of this method, which is commonly used for comparing cracking susceptibilities of different materials to each other, is to apply increasing levels of tensile stresses to a sample during the welding process and monitor the reaction. Figure 3 shows the corresponding experimental setup.
Figure 3: Welding under external load setup.L-51
However, the known externally applied stresses are not exclusively responsible for LME, but also the welding process itself, which puts both thermally and mechanically induced stresses/strains on the sample. Here, the conventional measuring techniques fail. A numerical reproduction of the experiment grants access to the temperature, stress and strain fields present during the procedure, providing insights on the formation of LME. The electro-thermomechanical simulation model is described in detail in Modelling RSW of AHSS. It is used to simulate the welding under external load procedure (see Figure 4).
Figure 4: Simulation Model of Welding Under External Load.
The videos that can be found in the link above show the corresponding temperature and plastic strain fields. As heat dissipates quickly through the water-cooled electrode, a temperature gradient towards the adjacent areas and a local temperature maximum on the surface forms. The plastic strains accumulate mainly at the electrode indentation area. The simulated strain field shows a local maximum of plastic deformation at the left edge of the electrode indentation, amplified by the externally applied stresses and the boundary conditions implied by the procedure. This area correlates with experimentally observed LME cracking sites and paths as shown in Figure 5.
The simulation shows that significant plastic strains are present during welding. When external stresses (in reality e.g. due to poor part fit-up or distorted parts) contribute to the already high load, LME cracking becomes more likely. The numerical simulation model facilitates the determination of material-specific safety limits regarding LME cracking. Parameter variations and their effects on the LME susceptibility can easily be investigated by use of the model, enabling the user to develop strict processing protocols to reduce the likelihood of LME. Finally, these experimental procedures can be adapted to other high-strength materials, to aid their application understanding and industrial set-up conditions.
Figure 5: LME Cracks in Cross Section View at Highly Strained Locations.
For more information on this topic, see the paper, co-authored by Fraunhofer and LWF Paderborn, documented in Citation F-23. You may also download the full report documenting the WorldAutoSteel LME project for which this work was conducted.
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Dr.-Ing Max Biegler, Group Lead, Joining & Coating Technology Fraunhofer Institute for Production Systems and Design Technology IPK |
Modelling RSW of AHSS
Modelling resistance spot welding can help to understand the process and drive innovation by asking the right questions and giving new viewpoints outside of limited experimental trials. The models can calculate industrial-scale automotive assemblies and allow visualization of the highly dynamic interplay between mechanical forces, electrical currents and thermal flow during welding. Applications of such models allow efficient weldability tests necessary for new material-thickness combinations, thus well-suited for applications involving Advanced High -Strength Steels (AHSS).
Virtual resistance spot weld tests can narrow down the parameter space and reduce the amount of experiments, material consumed as well as personnel- and machine- time. They can also highlight necessary process modifications, for example the greater electrode force required by AHSS, or the impact of hold times and nugget geometry. Other applications are the evaluation of whole-part distortion to ensure good part-clearance and the investigation of stress, strain and temperature as they occur during welding. This more research-focused application is useful to study phenomena arising around the weld such as the formation of unwanted phases or cracks.
Modern Finite-Element resistance spot welding models account for electric heating, mechanical forces and heat flow into the surrounding part and the electrodes. The video shows the simulated temperature in a cross-section for two 1.5 mm DP1000 sheets:
First, the electrodes close and then heat starts to form due to the electric current flow and agglomerates over time. The dark-red area around the sheet-sheet interface represents the molten zone, where the nugget forms after cooling. While the simulated temperature field looks plausible at first glance, the question is how to make sure that the model calculates the physically correct results. To ensure that the simulation is reliable, the user needs to understand how it works and needs to validate the simulation results against experimental tests. In this text, we will discuss which inputs and tests are needed for a basic resistance spot welding model.
At the base of the simulation stands an electro-thermomechanical resistance spot welding model. Today, there are several Finite Element software producers offering pre-made models that facilitate the input and interpretation of the data. First tests in a new software should be conducted with as many known variables as possible, i.e., a commonly used material, a weld with a lot of experimental data available etc.
As first input, a reliable material data set is required for all involved sheets. The data set must include thermal conductivity and capacity, mechanical properties like Young’s modulus, tensile strength, plastic flow behavior and the thermal expansion coefficient, as well as the electrical conductivity. As the material properties change drastically with temperature, temperature dependent data is necessary at least until 800°C. For more commonly used steels, high quality data sets are usually available in the literature or in software databases. For special materials, values for a different material of the same class can be scaled to the respective strength levels. In any case, a few tests should be conducted to make sure that the given material matches the data set. The next Figure shows an exemplary material data set for a DP1000. Most of the values were measured for a DP600 and scaled, but the changes for the thermal and electrical properties within a material class are usually small.
Figure 1: Material Data set for a DP1000.S-73
Next, meaningful boundary conditions must be chosen and validated against experiments. These include both the electrode cooling and the electrical contact resistance. To set up the thermal flow into the electrode, temperature measurements on the surface are common. In the following picture, a measurement with thermocouples during welding and the corresponding result is shown. By adjusting the thermal boundary in the model, the simulated temperatures are adjusted until a good match between simulation and experiment is visible. This calibration needs to be conducted only once when the model is established because the thermal boundary remains relatively constant for different materials and coatings.
Figure 2: Temperature measurement with thermocouples during welding and the results. The simulated temperature development is compared to the experimental curve and can be adjusted via the boundary conditions.F-23
The second boundary condition is the electrical contact resistance and it is strongly dependent on the coating, the surface quality and the electrode force. It needs to be determined experimentally for every new coating and for as many material thickness combinations as possible. In the measuring protocol, a reference test eliminates the bulk material resistance and allows for the determination of the contact resistances using a µOhm-capable digital multimeter.
Finally, a metallographic cross-section shows whether the nugget size and -shape matches the experiment. The graphic shows a comparison between an actual and simulated cross section with a very small deviation of 0.5 mm in the diameter. As with the temperature measurements, a small deviation is not cause for concern. The experimental measurements also exhibit scatter, and there are a couple of simplifications in the model that will reduce the accuracy but still allow for fast calculation and good evaluation of trends.
Figure 3: Comparison of experimental and virtual cross-sections.F-23
After validation, consider conducting weldability investigations with the model. Try creating virtual force / current maps and the resulting nugget diameter to generate first guesses for experimental trials. We can also gain a feeling how the quality of each weld is affected by changes in coatings or by heated electrodes when we vary the boundary conditions for contact resistance and electrode cooling. The investigation of large spot-welded assemblies is possible for part fit-up and secondary effects such as shunting. Finally, the in-depth data on temperature flow and mechanical stresses is available for research-oriented investigations, cracking and joint strength impacts.
Note: The work represented in this article is a part a study of Liquid Metal Embrittlement (LME), commissioned by WorldAutoSteel. You can download the free report on the results of the LME study, including how this modelling was used to verify physical tests, from the WorldAutoSteel website.
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Dr.-Ing Max Biegler, Group Lead, Joining & Coating Technology Fraunhofer Institute for Production Systems and Design Technology IPK |
RSW Failure Prediction
This article summarizes the findings of a paper entitled, “Prediction of Spot Weld Failure in Automotive Steels,”L-48 authored by J. H. Lim and J.W. Ha, POSCO, as presented at the 12th European LS-DYNA Conference, Koblenz, 2019.
To better predict car crashworthiness it is important to have an accurate prediction of spot weld failure. A new approach for prediction of resistance spot weld failure was proposed by POSCO researchers. This model considers the interaction of normal and bending components and calculating the stress by dividing the load by the area of plug fracture.
Background
Lee, et al.L-49 developed a model to predict spot welding failure under combined loading conditions using the following equation, based upon experimental results .
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Equation 1 |
Where FS and FN are shear and normal failure load, respectively, and n is a shape parameter.
Later, Wung and coworkersW-38 developed a model to predict the failure mode based upon the normal load, shear load, bending and torsion as shown in Equation 2.
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Equation 2 |
Here, FS, FN ,Mb and Mt are normal failure load, shear failure load, failure moment and failure torsion of spot weld, respectively. α, β, γ and μ are shape parameters.
Seeger et al.S-106 proposed a model for failure criterion that describes a 3D polynomial failure surface. Spot weld failure occurs if the sum of the components of the normal, bending and shear stresses are above the surface, as shown in the Figure 1.
Figure 1: Spot weld failure model proposed by Seeger et al.S-106
The failure criterion can be expressed via Equation 3.
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Equation 3 |
Here, σN , σB , and τ are normal, bending and shear stress of the spot weld, respectively. And nN, nB and nc are the shape parameters. Toyota Motor CorporationL-50 has developed the stress-based failure model as shown in Equation 4.
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Equation 4 |
Hybrid Method to Determine Coefficients for Failure Models
This work used a unique hybrid method to determine the failure coefficients for modeling. The hybrid procedure steps are as follows:
- Failure tests are performed with respect to loading conditions.
- Finite element simulations are performed for each experiment.
- Based on the failure loads obtained in each test, the instant of onset of spot weld failure is determined. Failure loads are extracted comparing experiments with simulations.
- Post processing of those simulations gives the failure load components acting on spot welds such as normal, shear and bending loads.
These failure load components are plotted on the plane consisting of normal, shear and bending axes.
The hybrid method described above is shown in Figure 2.L-48
Figure 2: Hybrid method to obtain the failure load with respect to test conditions.L-48
New Spot Weld Failure Model
The new proposed spot weld failure model in this paper considers only plug fracture mode as a normal spot weld failure. Secondly normal and bending components considered to be dependent upon each other. Stress generated by normal and bending components is shear, and shear component generates normal stress. Lastly authors have used πdt to calculate the area of stress instead of πd2/4. The final expression is shown in the Equation 5.
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Equation 5 |
Here τn is the shear stress by normal load components, σS is the normal stress due to shear load component. And 
, 
, c, α and β are coefficients.
This work included verification experiments of 42 kinds of homogenous steel stack-ups and 23 heterogeneous stack-ups. The strength levels of the steels used was between 270 MPa and 1500 MPa, and thickness between 0.55 mm and 2.3 mm. These experiments were used to evaluate the model and compare the results to the Wung model.
Conclusions
Overall, this new model considers interaction between normal and bending components as they have the same loading direction and plane. The current developed model was compared with the Wung model described above and has shown better results with a desirable error, especially for asymmetric material and thickness.