Thermal Simulation of the Automated Fiber Placement Process and its Validation CFK-Valley Stade Convention 2014 Session: Simulation 24.06.2014 Roland Lichtinger Content • • • • • • • Overview of Automated Fiber Placement (AFP) and its process parameters AFP at the LCC Offline Programming Experimental Setup Simulation model Experimental and simulation results Conclusion & Outlook 24.06.2014 CFK-Valley Stade Convention - Lichtinger, R. 2 Automated Fiber Placement AFP – Process: Advantages • Reduced • • • • • • labor cost material scrap manufacturing time Precise and repeatable process Complex geometry possible Steering along load path Fig 1: Fiber placement procedure AFP – Process: Procedure • • • Heating of the tool surface to increase tack of the prepreg and to reduce the viscosity of the resin Lay-up of one or more prepreg slit-tapes on a concave or convex tool surface Compaction of the prepreg slit-tapes with a roller 24.06.2014 CFK-Valley Stade Convention - Lichtinger, R. 3 Automated Fiber Placement Process Parameters AFP – process parameters: • Feed rate of the placement head • Heat output • Process kinematics • … Influencing • Fig. 1: AFP Robot at the LCC (by Coriolis Composites) • • Temperature distribution Material properties of the tapes Tack of the tapes Pressure distribution -> Tack of the tapes … Overall Goal: Laminate Quality within requirements Challenge: Complex interactions between process parameters and their influencing factors 24.06.2014 CFK-Valley Stade Convention - Lichtinger, R. 4 Automated Fiber Placement at the Institute for Carbon Composites Process 24.06.2014 Process simulation Lay-up effects CFK-Valley Stade Convention - Lichtinger, R. Material Testing 5 Offline-Programming: State-of-the-art Input: Structural design No material properties and process parameters included. Surface meshing Ply definition Process definition Hardware assignment Strategies Kinematic Simulation 24.06.2014 CFK-Valley Stade Convention - Lichtinger, R. 6 Importance of temperature prediction in AFP: The AFP process window is highly temperature dependent. • To optimize layup velocity and • Reduce iteration cycles for process testing a precise temperature prediction is imperative. 80 Temperature too high: • Smoke generation • Resin degradation 70 Temperature [°C] Fig. 1: Thermodynamic simulation of the AFP-Process 60 50 Reliable process window 40 30 Temperature too low: • Insufficient tack • Risk of process failure 20 10 0 0 20 40 60 Layup Speed [%] 24.06.2014 80 100 120 Fig. 2: AFP Manufacturing Process CFK-Valley Stade Convention - Lichtinger, R. 7 Goal of the research • Create a simulation tool for precise prediction of thermal history of the AFP process • Study influence of the radiation distribution of the IR lamp • Study surface temperature distribution of tool and layup • Study temperature increase in tool during layup Fig. 1: Temperature distribution during layup -150 -100 -50 0 50 Perpendicular to robot path [mm] 100 150 Fig. 2: Radiation distribution of IR lamp 24.06.2014 CFK-Valley Stade Convention - Lichtinger, R. 8 Experimental Setup: Placement of a plate component • • • • • Automated layup of a plate 450 x 450 mm Insulated flat aluminum tool Surface discretization: a rough grid of 4x5 Fixation of 20 Thermocouples 19 paths necessary for one 0° ply Fig. 2: TC Setup Fig. 3: Grid with underlying temperature simulation Fig. 1: Robot path 24.06.2014 CFK-Valley Stade Convention - Lichtinger, R. 9 Experimental Setup: Placement of a plate component TCi Ply 3 Ply 2 Ply 1 Fig. 2: Ply description Fig. 1: AFP Head with IR Camera Fig. 3: Ply with underlying TCs • Bulk temperature measurement between ply 1 and 2 • A thermal camera continuously measures the surface temperature Vid. 1: Placement of one path 24.06.2014 CFK-Valley Stade Convention - Lichtinger, R. 10 Experimental Results Path 1 Path 2 … Path 19 Fig. 1: Area grid Fig. 2: Temperature over time of TC 1 • Passing of the placement head is clearly visible • Local heat is dissipated quickly into the tooling • The width effect of the IR Lamp cannot be neglected Vid. 1: Temperature measurement 24.06.2014 CFK-Valley Stade Convention - Lichtinger, R. 11 3D Thermal FEM Model Visualized Placement Head Infrared Lamp Current ply – already deposited paths Already placed plies Tool Geometry 24.06.2014 Fig. 1: 3D FE model during placement of path 10 of ply 3 CFK-Valley Stade Convention - Lichtinger, R. 12 Simulation Boundary Conditions Infrared lamp: power uptake Deposition velocity Compaction Force 318 W Horizontal Offset Roller → Lamp 132 mm Vertical Offset Tool surface → Lamp 60.3 mm Angle of Lamp towards Nip-Point Fig. 2: Two areas in space 59.1 mm/s 250 N 𝜑12 = 1 𝜋𝐴1 𝐴2 𝐴1 𝑐𝑜𝑠𝛽1 ⋅ 𝑐𝑜𝑠𝛽2 𝑑𝐴1 𝑑𝐴2 𝑟2 20.0° Tab. 1: Process variables for the machine setup 𝑞𝑥3 = 𝜀𝜑12 𝜂𝐻 𝑑𝑃 𝑑𝐴 Fig. 3: View factor distribution in a 2D case 𝑞𝑥3 = ℎ ⋅ 𝑑𝐴 ⋅ 𝑑𝑇 𝑞𝑥3 = 𝜆3 𝑑𝑇 𝑑𝑥3 =0 𝑥3 =0 24.06.2014 Fig. 1: Sketch of the model CFK-Valley Stade Convention - Lichtinger, R. 13 FE Simulation Energy balance 𝑖 𝑖 𝑇𝑚,𝑛−1 − 𝑇𝑚,𝑛 Δ𝑥1 ∗ 𝜆3 Δ𝑥3 𝑖 𝑖 𝑇𝑚−1,𝑛 − 𝑇𝑚,𝑛 + Δ𝑥3 ∗ 𝜆1 Δ𝑥1 Conduction 𝑖 𝑖 𝑇𝑚+1,𝑛 − 𝑇𝑚,𝑛 + Δ𝑥3 ∗ 𝜆1 Δ𝑥1 𝑖 + Δ𝑥1 ∗ ℎ 𝑇𝑈 − 𝑇𝑚,𝑛 + 𝛼𝐴𝐵 𝜑12 𝜂𝐻 𝑃𝐻 𝑖+1 𝑖 𝑇𝑚,𝑛 − 𝑇𝑚,𝑛 = 𝜌𝑐𝑉𝐸𝑙𝑒𝑚𝑒𝑛𝑡 Δ𝑡 Convection & Thermal resistance Radiation Storage Fig. 1: Energy balance in a 2D Case 24.06.2014 CFK-Valley Stade Convention - Lichtinger, R. 14 FE Simulation • • Model is purely thermodynamic Change of the boundary conditions through • • • • Change of the view factor (Position dependent recalculation) (De -) Activation of thermal contacts Expansion of the simulation model with already placed slittapes during the simulation The placement head is visualized, but only the “shadow casting” areas are included in the model 24.06.2014 Fig. 1: Simulation results: Radiation Flux CFK-Valley Stade Convention - Lichtinger, R. 15 FE Simulation Vid. 1: Animated Simulation Results 24.06.2014 CFK-Valley Stade Convention - Lichtinger, R. 16 Bulk temperature Experimental and simulation results • Thermal contact resistance plays a major role in temperature prediction Ply 3 - TC 15 Temperature [°C] • Temperature and pressure dependent contact not included: • cooling phase underpredicted before passing • over-predicted after passing of the TC Ply 3 - TC 15 exp mean Ply 3 - TC 15 sim 3D FEM 45 40 35 30 25 20 0 100 200 300 400 500 Time [s] Fig. 1: Experimental and simulation results: bulk temperature 24.06.2014 CFK-Valley Stade Convention - Lichtinger, R. 17 Surface temperature Surface Temperature Perpendicular to Path Ply 3 [°C] Experimental and simulation results 60 Mean Temperature exp. Perpendicular Ply 3 Temperature sim. Perpendicular Ply 3 55 50 45 40 Fig. 2: Thermography image with temperature evaluation path 35 30 25 20 0 200 Distance Perpendicular [mm] 400 Fig. 1: Experimental and simulation results: surface temperature • Specular reflection of the IR lamp influences thermography measurements Fig. 3: Simulation result with temperature evaluation path 24.06.2014 CFK-Valley Stade Convention - Lichtinger, R. 18 Surface Temperature Nip Point Considerations 60 Surface Temperature [°C] Temperature [°C] 50 40 Fig. 2: Simulation results: surface temperature in path direction 30 20 20° • 10 0 -100 -50 0 50 100 150 200 Distance from Nip Point [mm] Maximum temp. rise Fig. 1: Surface temperature in front of nip point Nip point 24.06.2014 250 300 Entry in radiation cone 350 • • • Rising temperature with entry in radiation sphere Maximum temperature rise at point of maximum view factor, i.e. maximum heat input Maximum temperature at equilibration point Reduced temperature at nip point CFK-Valley Stade Convention - Lichtinger, R. 19 Radiation Distribution • Electrical Power Uptake of the IR lamp: 318 W • Efficiency: 0.9 • Radiation power: 286.2 W • Emissivity: • Tool: 0.3 • Tapes: 0.9 Radiation input on path: 44.5 W = 15.55 % Radiation input adjacent to path: 175.8 W = 61.44 % Radiation to ambient: 65.87 W 24.06.2014 = 23.01 % Fig. 1: Radiation distribution CFK-Valley Stade Convention - Lichtinger, R. 20 Time Integrated Heat Flux Possible consequences: • Possibility of variations in the material‘s shelf life • Especially for large or thick parts with prolonged exposure • Further work is needed to determine the effects of artificial ageing or partial curing of the component x y Path 1 Path 1 Fig. 1: Time integrated heat flux at a) Ply underneath and b) Current ply 24.06.2014 CFK-Valley Stade Convention - Lichtinger, R. 21 Conclusion & Outlook • A precise simulation tool has been developed • The results confirm the need for thermal prediction of the process • Further experiments are needed for the study • • • • Artificial ageing Partial Curing Different tool materials Industrial applications are implemented easily in the simulation model 24.06.2014 CFK-Valley Stade Convention - Lichtinger, R. 22 Thank you for your attention! Questions? Roland Lichtinger, Dipl.-Ing. (Univ.) Research Associate Technische Universität München Institute for Carbon Composites Boltzmannstraße 15 85748 Garching bei München Tel: + 49 89 289 10318 Fax: + 49 89 289 15097 [email protected] www.lcc.mw.tum.de 24.06.2014 CFK-Valley Stade Convention - Lichtinger, R. 23
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