Thermal Simulation of the Automated Fiber Placement Process and its Validation

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1 Thermal Simulation of the Automated Fiber Placement Process and its Validation R. Lichtinger A Comprehensive Approach to Carbon Composites Technology Symposium on the occasion of the 5 th anniversary of the Institute for Carbon Composites Research Campus Garching, September 11 th - 12 th 2014 Institute for Carbon Composites donated by

2 Agenda 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 and Outlook 2

3 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 3

1: AFP Robot at the LCC (by Coriolis Composites) Temperature distribution Material properties of the tapes Tack of

4 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 4

5 Automated Fiber Placement at the Institute for Carbon Composites Process Process simulation Lay-up effects Material Testing 5

6 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 6

7 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. Fig. 1: Thermodynamic simulation of the AFP-Process Temperature [ C] Reliable process window Layup Speed [%] Temperature too high: Smoke generation Resin degradation Temperature too low: Insufficient tack Risk of process failure Fig. 2: AFP Manufacturing Process 7

8 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 Fig. 1: Temperature distribution during layup Study surface temperature distribution of tool and layup Study temperature increase in tool during layup Perpendicular to robot path [mm] Fig. 2: Radiation distribution of IR lamp 8

9 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. 1: Robot path Fig. 3: Grid with underlying temperature simulation 9

measurement between ply 1 and 2 A thermal camera

10 Experimental Setup: Placement of a plate component Ply 3 Ply 2 TC Ply 1 i 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 10

2: Temperature over time of TC 1 Passing of the placement head is

11 Experimental Results Path 1 Path 2 Fig. 1: Area grid Vid. 1: Temperature measurement Path 19 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 11

12 3D Thermal FEM Model Visualized Placement Head Infrared Lamp Current ply already deposited paths Already placed plies Tool Geometry Fig. 1: 3D FE model during placement of path 10 of ply 3 12

2: Two areas in space φ 12 = 1 cccβ 1 cccβ 2 πa 1 r 2 da 1 da 2 A 2 A 1 Tab.

13 Simulation Boundary Conditions Infrared lamp: power uptake Deposition velocity Compaction Force Horizontal Offset Roller Lamp Vertical Offset Tool surface Lamp 318 W 59.1 mm/s 250 N 132 mm 60.3 mm Angle of Lamp towards Nip-Point 20.0 Fig. 2: Two areas in space φ 12 = 1 cccβ 1 cccβ 2 πa 1 r 2 da 1 da 2 A 2 A 1 Tab. 1: Process variables for the machine setup q x 3 = εφ 12 η H dd dd Fig. 3: View factor distribution in a 2D case q x 3 = h dd dd q x 3 = λ 3 dd dx 3 x 3 =0 = 0 Fig. 1: Sketch of the model 13

14 FE Simulation Energy balance Δx 1 λ T i m,n Δx 3 λ T i m 1,n 1 + Δx 3 λ T i m+1,n 1 Δx 3 Δx 1 Δx 1 i + Δx 1 h T U T m,n i T m,n i T m,n i T m,n Conduction Convection & Thermal resistance + α AA φ 12 η H P H Radiation Fig. 1: Energy balance in a 2D Case T i+1 i m,n T m,n = ρρv EEEEEEE Δt Storage 14

15 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 Fig. 1: Simulation results: Radiation Flux 15

16 FE Simulation Vid. 1: Animated Simulation Results 16

Bulk temperature Experimental and simulation results Temperature and pressure dependent contact not included: cooling phase underpredicted before passing over-predicted after passing of the TC

17 Bulk temperature Experimental and simulation results Temperature and pressure dependent contact not included: cooling phase underpredicted before passing over-predicted after passing of the TC Thermal contact resistance plays a major role in temperature prediction Ply 3 - TC 15 Temperature [ C] Ply 3 - TC 15 exp mean Ply 3 - TC 15 sim 3D FEM Time [s] Fig. 1: Experimental and simulation results: bulk temperature 17

Surface Temperature Perpendicular to Path Ply 3 [ C] Surface Temperature Experimental and simulation results 60 55 50 45 40 35

Perpendicular Ply 3 0 200 400 Distance Perpendicular [mm] Fig. 1: Experimental and simulation results: surface temperature Fig.

18 Surface Temperature Perpendicular to Path Ply 3 [ C] Surface Temperature Experimental and simulation results Mean Temperature exp. Perpendicular Ply 3 Temperature sim. Perpendicular Ply Distance Perpendicular [mm] Fig. 1: Experimental and simulation results: surface temperature Fig. 2: Thermography image with temperature evaluation path Specular reflection of the IR lamp influences thermography measurements Fig. 3: Simulation result with temperature evaluation path 18

Surface Temperature Nip Point Considerations Surface Temperature [ C] 60 50 40 30 20 10 Temperature [ C] 0-100 -50 0 50 100 150 200 250 300 350 Distance from Nip Point [mm] Maximum Nip point temp.

19 Surface Temperature Nip Point Considerations Surface Temperature [ C] Temperature [ C] Distance from Nip Point [mm] Maximum Nip point temp. rise Fig. 1: Surface temperature in front of nip point 20 Entry in radiation cone Fig. 2: Simulation results: surface temperature in path direction 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 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.

20 Radiation Distribution Electrical Power Uptake of the IR lamp: 318 W Efficiency: 0.9 Radiation power: W Emissivity: Tool: 0.3 Tapes: 0.9 Radiation input on path: 44.5 W = % Radiation input adjacent to path: W = % Radiation to ambient: W = % Fig. 1: Radiation distribution 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

21 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 y x Path 1 Path 1 Fig. 1: Time integrated heat flux at a) Ply underneath & b) Current ply 21

22 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 22

Contact Dipl.-Ing. Roland Lichtinger Room Tel Fax Email 8102.03.103 +49 89 / 289-10318 +49 89 / 289-15097 lichtinger@lcc.mw.

thank GE Global Research for supplying the material for the experimental testing Address Technische Universität München

23 Contact Dipl.-Ing. Roland Lichtinger Room Tel Fax / / lichtinger@lcc.mw.tum.de Acknowledgements: This work has been partly funded by AIRBUS HELICOPTERS DEUTSCHLAND GmbH The authors would like to thank GE Global Research for supplying the material for the experimental testing Address Technische Universität München Institute for Carbon Composites Boltzmannstraße Garching Institute for Carbon Composites donated by 23

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