A clever tool once used by automakers for limited use cases in vehicle design, high performance computer (HPC) simulation using computer aided engineering (CAE) applications has come a long way to now improving and optimising all aspects of the product development, including the manufacturing process. The most efficient and cost effective manufacturing processes are now determined by the same CAE simulation tools used in the vehicle design process.
One of the major ways in which HPC simulation has been put into wider use is in sheet metal stamping simulations, removing inefficiencies from the eventual manufacturing process by preventing potential defects such as metal wrinkles.
Simulation has become especially valuable as new materials such as aluminium and plastics are introduced and enables manufacturers to avoid the expensive inefficiencies of the traditional trial and error approach to establishing the production process of a new vehicle.
HPC simulation is essential in enabling manufacturers to understand what can be achieved with new materials and can predict the final shape of a new component through the digital replication of material properties.
As new materials are implemented for the advancement of vehicle design, manufacturers can no longer depend on the experience and expertise that they traditionally relied upon to understand the extents to which these new materials can be manipulated.
Supercharging the data flow
Throughout the automotive industry there is growing demand for data gathered during manufacturing simulation that can be utilised in vehicle design processes, improving the finalised design of a vehicle based on the learnings from the manufacturing simulation.
To achieve a higher fidelity in results, it is no longer adequate to use nominal or average material properties for individual components. The structural properties and tolerances of the material will change as the material is manipulated throughout the vehicle’s production, therefore it is more accurate to use the final material properties of the component after the manufacturing process.
For example, the simulation results of a piece of sheet metal being bent into a certain shape, such as a body panel, can provide the material’s stress and strain state without a physical piece of metal having to be bent.
With high fidelity crash simulation, the slight difference in material properties before and after stamping will affect the crash response and hence including this effect will deliver a more accurate simulation.
Through the realisation of what can be achieved through these results, there is now a growing requirement for increased communication between teams working on different stages of the manufacturing process and the flow of data from the manufacturing process back into the vehicle CAE analysis process.
Applying the stochastic approach
Accounting for the variation in the real-world structure after manufacturing, has led to the use of a “stochastic approach” in CAE simulation. In other words, a “robust design” approach that ensures the final product is highly resilient against slight variations that result from the manufacturing process.
A good example of this is the location of spot welds in the vehicle’s construction, commonly used to connect large components in the vehicle’s structure, such as the floor pan and the body shell.
It has been shown that even the slightest variation, including variations within manufacturing tolerances, – in the location of a spot weld can have a major impact on the safety performance of the finished vehicle.
To understand the sensitivity of the design to manufacturing variations, a stochastic simulation process runs hundreds of simulations which vary the parameter of interest such as spot weld location.
Comparing the results allows the manufacturer to determine the sensitivity of the design to the spot weld location or other parameters. Understanding this sensitivity enables a more robust vehicle design which reduces the risk of failing a safety test due to the slight variations in manufacturing.
This robust design does require an investment in HPC compute power. The stochastic investigation of a single component in a vehicle’s design requires hundreds of simulations and for each of these simulations to run simultaneously, a greater amount of computing is required.
The computing requirements have traditionally been a limiting factor in adopting the stochastic approach to simulation but the increased availability in HPC power now makes this practical for automobile design and production process to run more efficiently.
Shaping the future
Further development lays in the synergy between manufacturing technology and CAE simulations – the results of which advance additive manufacturing technology and the use of topology (i.e. shape) optimisation tools.
For example, topological optimisation might generate a more organically shaped vehicle component that could be shaped similarly to a human bone to add more strength, resilience and weight reduction than a traditional structural component such as an I-beam.
Topology optimisation has been available for many years, yet this approach requires a huge amount of computing power and often the final “optimised” design from this process would have a very complex shape that manufacturers would deem ultimately impractical to manufacture.
Additive manufacturing processes now make it reasonable to produce these very complex parts and the substantial increase in available computing power makes the amount of simulations required far easier to achieve.
The combination of these technology advancements creates an entirely new set of design possibilities and will play a key role in industry initiatives such as vehicle weight reduction.
Safety in numbers
Not only are we seeing a huge increase in the amount of data available but also in the requirement to share this data throughout the organisation. Simulation is producing high fidelity results for a wide range of design options while the manufacturing process and tools are also producing highly valuable data. In some cases, data is also streaming back to the manufacturer via the Internet of Things (IoT) once the vehicle is on the road.
Incorporating various sensors in the vehicle design that can relay information back to the manufacturer contributes to the wealth of information on the past, current, and potentially the future performance of the vehicle, or similar models and conceptual designs based on the same vehicle platform.
Clearly there is a tremendous amount of value in this data and the ability to analyse this, finding the patterns and gaining actionable information is an aim every automaker wishes to fulfil.
The challenge lays in recognising what to look for and how to find this value in the enormity and variety of data available.
Just like supercomputing power has enabled the simulation of the vehicle design and manufacturing, that same power can be applied to the data analytics challenge and is opening up a new field of “high performance data analytics” (HPDA).
Although still a relatively new field, automotive companies are making huge investments in data analytics. There is no shortage of potential use cases, from spotting warranty or recall issues earlier in vehicle life cycles, to optimising the manufacturing processes, developing better understanding of industry trends and learning about evolutions in consumer requirements.
These are not necessarily new concepts but the technology available is evolving rapidly. Implementation in production infrastructure remains a work in progress, however, the uptake throughout the automotive industry is likely to gather pace sooner rather than later.
Much like the increased adoption of vehicle electronics, autonomous driving and other industry trends, HPDA will redefine the technology and the associated skills required to compete in the automotive industry.
Sourced by Greg Clifford, manufacturing segment leader at CRAY
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