Periodic Reporting for period 2 - PRE-ECO (A new paradigm to re-engineering printed composites)
Période du rapport: 2021-08-01 au 2023-01-31
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 project 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.
This project 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.
Advanced multi-scale models for the static and dynamic, linear and non-linear mechanical responses of variable stiffness composite (VSC) beam/plate/shells structures have been implemented. These models have, thus, been utilized for the deterministic and non-deterministic manufacture-induced defects analysis of VSC parts. Among the others, Continuous Tow Shearing (CTS)-induced thickness variation, and gaps and overlaps, arising from Automated Fibre Placement (AFP), have been studied. Furthermore, to study non-deterministic manufacture-induced defects of VSC, a stochastic-field-based method has been coupled with the in-house high-order finite element code. Such stochastic fields have been employed alongside process simulations to model uncertainty defects that occur at different scale levels, namely the micro- and meso-scale.
An heuristic optimisation code based on Genetic Algorithm (GA) has been developed for structural optimisation purposes. This tool, which is ideal when the objective function is the mass minimisation, will be further developed in the future to deal with both continuous and discrete design variable values, taking into account the effect of defects concurrently.
Other activities include: investigating the possibility of reducing the strain/stress concentrations in an open-hole plate using localized 3D printed carbon fibre reinforcements; using non-local elasticity models to conduct crack propagation analysis; solving inverse problems (e.g. detection of damage or printing issues) via algorithms based on artificial intelligence; extending developed methods to thin-ply composites for deployable structures, elastomers/soft matter and other applications of practical relevance.
An heuristic optimisation code based on Genetic Algorithm (GA) has been developed for structural optimisation purposes. This tool, which is ideal when the objective function is the mass minimisation, will be further developed in the future to deal with both continuous and discrete design variable values, taking into account the effect of defects concurrently.
Other activities include: investigating the possibility of reducing the strain/stress concentrations in an open-hole plate using localized 3D printed carbon fibre reinforcements; using non-local elasticity models to conduct crack propagation analysis; solving inverse problems (e.g. detection of damage or printing issues) via algorithms based on artificial intelligence; extending developed methods to thin-ply composites for deployable structures, elastomers/soft matter and other applications of practical relevance.
Our vision beyond the state of the art is to develop a robust, flexible and hybrid simulation technology for the analysis and design of variable angle tow composite structures, consisting of (i) a high-fidelity, multi-scale model for micro-, meso- and macro-mechanics characterization, (ii) completed by existing and innovative optimization algorithms which take into account current manufacturing limitations, and (iii) embedded into a data intelligence, hybrid platform encompassing virtual experiments and tests.