applications
occurrences such as porosity and key-holing during laser scanning to cracking and microstructural control during component fabrication. Tis range of issues affects the simulations, as Crompton said: ‘It depends what level of detail is require to look at the phenomena of interest. Te more detail that is required, the longer the analysis times and at some point it becomes quicker to run the test than do the analysis.’ ‘Te Holy Grail is to produce thermal
histories that can create the required microstructure/properties and overall structure. At the moment simulation is not at a point where it can do this.’
Additive application areas AM is popular in the aerospace and biomedical sectors, with the automotive and the sporting goods industries also showing interest. Weight reduction, part customisation and structural integrity are the main drivers for these sectors, but quality is also a key concern, as Mustafa Megahed, manager of CFD and Multiphysics Centre of Excellence at the ESI Group, said: ‘If you are producing a component that is going to be placed in an aircraſt, for example, you have to be able to guarantee the quality of that component. Tese industries need performance guarantees.’ Topology optimisation is a mathematical
approach used to simulate AM processes and meet such stringent requirements. It optimises the material layout within a given design space, for a specific set of boundary conditions and loads, so that the resulting layout meets predefined performance targets. Tis allows engineers to find the best concept design to meet these prescribed requirements. It also replaces costly and time-consuming design iterations. Research at the University of Pittsburgh
focuses on the development of efficient topology optimisation algorithms for
lightweight design of additive manufactured components by incorporating cellular structures. Albert To, associate professor at the University of Pittsburgh, said: ‘Te main challenge to the industry is exploiting the enormous design space provided by 3D printing and AM while addressing various manufacturability requirements. My group and others have developed numerical algorithms and soſtware to tackle this challenge, but we are still not there yet.’ Robert Yancey, vice president of additive
manufacturing at Altair, said: ‘Without topology optimisation, 3D printing is just a new manufacturing method. With topology optimisation, 3D printing is a technology that allows us to design and build structures that are similar to what we see in nature – elegant, structurally efficient, aesthetically pleasing, and tuned to meet precisely the intended purpose in the environment where it exists. Tat is very exciting.’
THERE ARE MANY DIFFERENT AM PROCESSES, WHICH VARY IN THEIR METHOD OF LAYER MANUFACTURING
Altair’s topology optimisation soſtware,
OptiStruct, helps engineers take full advantage of 3D printing by generating elegant organic designs that are structurally efficient and lightweight. Yancey said: ‘OptiStruct coupled with metal AM methods is showing weight reductions as high as 80 per cent over traditionally machined parts while, at the same time, guaranteeing that components withstand the required loads.’ Another benefit of AM is to reduce the
number of single parts in a system, leading to further weight and cost reductions. For
example, the biomedical sector could use AM to create individually customised products such as prosthetic limbs or surgical aids. Tat’s not all, as Yancey added: ‘Customisation can also come into play when looking at individual solutions for bikes or other sporting goods.’
On your (robot) bike Te Robot Bike Co. R160 mountain bike frame, unveiled earlier this year, allows each frame to be tailored to a customer’s individual measurements or specifications. Altair’s suite of simulation products
helped to optimise the bike’s connectors, as Ed Haythornthwaite, co-founder of Robot Bike Co, explained: ‘Aſter developing the suspension kinematic, construction design methodology and aesthetic design, we utilised SolidTinking Inspire, in conjunction with Altair ProductDesign team, to enable the detailed design through stress-based topological optimisation. Tis gave us the confidence that we had a robust yet lightweight design based on the load cases acting on the bike.’ SolidTinking Inspire allowed the team to
take the existing designs and apply a variety of loads to which the bike frame would be subjected during real-world rides. Tis data was then used to generate a geometry layout that removed material where it was not required to meet performance targets. Haythornthwaite added: ‘Te team took this geometry and used OptiStruct to provide further refinement to material thicknesses. Troughout this process, the designs had to be optimised for the AM process, which included determining the ideal print angle and placement of the supporting structure to avoid the component collapsing during the manufacturing process.’ Haythornthwaite said: ‘SolidTinking Inspire
allows us to benefit from the flexibility that AM offers, all without expensive tooling. It means that we can offer this unique customisable bike frame, which would have otherwise been too ➤
Left: Screenshot Inspire – defining the ‘design space’ for the lug; Right: Screenshot Inspire – topology result showing where material can be removed
www.scientific-computing.com l @scwmagazine AUGUST/SEPTEMBER 2016 25
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