Project description
New models enhance the design of robust printed composites
Additive manufacturing and automated fibre placement processes gave rise to a new class of fibre-reinforced materials – the variable angle tow (VAT) composites. VAT placement allows fibre orientations to change with position over the entire ply plane, resulting in varying stiffness properties. This allows the creation of tailored composite designs in terms of stiffness and buckling resistance. However, preserving fibre integrity requires the right combination of temperature, velocity, curvature radii and pressure during the printing process. To address this, the EU-funded PRE-ECO project will develop new models to describe VAT composites from a fibre-matrix to a component scale, and new machine learning models for defect identification. PRE-ECO’s new methods will significantly help reduce composite structural failures during the printing process.
Objective
Additive manufacturing and Automated Fibre Placement (AFP) processes brought to the emergence of a new class of fibre-reinforced materials; namely, the Variable Angle Tow (VAT) composites. AFP machines allow the fibres to be relaxed along curvilinear paths within the lamina, thus implying a point-wise variation of the material properties. In theory, the designer can conceive VAT structures with unexplored capabilities and tailor materials with optimized stiffness-to-weight ratios. In practise, steering brittle fibres, generally made of glass or carbon, is not trivial. Printing must be performed at the right combination of temperature, velocity, curvature radii and pressure to preserve the integrity of fibres. The lack of information on how the effect of these parameters propagates through the scales, from fibres to the final structure, represents the missing piece in the puzzle of VAT composites, which today are either costly or difficult to design because affected by unpredictable failure mechanisms and unwanted defects (gaps, overlaps, and fibre kinking).
This proposal is for an exploratory study into a radical new approach to the problem of design, manufacturing and analysis of tow-steered printed composite materials. The program will act as a pre-echo, a precursor, to: 1) implement global/local models for the simulation and analysis of VATs with unprecedented accuracy from fibre-matrix to component scales; 2) develop a (hybrid) metamodeling platform based on machine learning for defect sensitivity and optimization; and 3) set new rules and best-practices to design for manufacturing. A 5-year, highly inter-disciplinary programme is planned, encompassing structural mechanics, numerical calculus, 3D printing and AFP, measurements and testing of advanced composites, data science and artificial intelligence, and constrained optimization problems to finally fill the gap between the design and the digital manufacturing chain of advanced printed materials.
Fields of science
- natural sciencescomputer and information sciencesdata science
- engineering and technologymaterials engineeringcomposites
- engineering and technologymechanical engineeringmanufacturing engineeringadditive manufacturing
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Programme(s)
Topic(s)
Funding Scheme
ERC-STG - Starting GrantHost institution
10129 Torino
Italy