Automated, Quality Assured and High Volume Oriented Production of Fiber Metal Laminates (FML) for the Next Generation of Passenger Aircraft Fuselage Shells

2.1 Quality Assurance in Aluminum Foil Lay-Up Processes

In order to guarantee placement of the aluminum foils within tolerances and to increase confidence in the process, inline quality assurance (QA) measures are necessary. A laser triangulation sensor (LLS) has been selected to verify the correct foil lay-up. Information about the mold geometry and the expected contour of the plies are extracted from CAD (plybook) and used to generate paths for the LLS end-effector. Mounted on a robot, the scanner is guided along the cut-piece edges with a measurement range of ± 50 mm (see Figure 2, left). It generates a 3D unstructured point cloud with a resolution of 0.15 mm point to point distance on one scan line and 0.5 mm distance between individual lines (see Figure 2 top right). The LLS is monitored with a laser tracker. This system can determine the position of the end-effector with an accuracy of approximately ± 60 μm and thus exclude the tolerance of the robot movement from the error chain.

Figure 2

Edge detection with LLS in the virtual environment of the MFZ, © DLR

In order to detect the cut-piece edges, points along the edge are marked and connected to a closed line contour. For a significant target-actual comparison one approach is to discretize the contour between the edges of the cut-piece. Such comparison points are shown in Figure 2 (bottom right) as green dots for the target contour and as red dots for the actual contour. Local coordinate systems are placed at each target dot and the deviations can be plotted for each axis. Deviations that can be detected are: undulation in z-direction, elongation as well as compression in y-direction, and wrong position in x-direction. In principle, the detection of the edge points as well as the connection to form the actual contour can be done manually. Automation via algorithm is feasible and currently under implementation.

3.1 Quality Assurance in Glass Prepreg Lay-Up Processes

Currently, part inspection takes up to 68% of overall production time. The goal within this project is the enhancement of an existing inline inspection system. Inside the project AUTOGLARE, the approach includes concept design, development and implementation of feasible feature extraction algorithms for machine learning, as well as integration and validation during fiber layup. A cascadian analysis structure is developed which contains the rapid defect detection for data reduction, feature extraction in combination with classification by a support vector machine and defect measurement [9, 10].

The usage of the system during a fiber placement process is shown in Figure 4. The laser light section sensor scans the layup surface and uses depth and reflection information as input for defect detection and classification.

Figure 4

Laser light section sensor mounted to an automated fiber placement head, © DLR

The results of defect detection and classification are visualized in false colours in Figure 5. Two twist defects in a fiber layup are shown exemplary.

Figure 5

Two twist defects upon a fiber layup colorized in false colours, © DLR

The improved system is able to process data in the defect detection step within an average calculation time of 91.0 ms with standard deviation of σ = 5.6 ms. The average detection rate is 99.1% with a standard deviation of σ = 1,3%.

4.1 Integration of 6 Meter Long Stringers Using Cooperating Robots

In current aircraft production, stringers can size up to 12 meters. For such long stringers the deformation behavior needs to be taken into account. Furthermore, to grip a variety of stringer profiles, a system with two grippers was developed, each fitted to its own robot. One gripper fixes the stringer, and one integrates the stringer onto the skin field. For the handling, one robot follows the path of the other robot at a defined distance. The grippers automatically adapt to the length as well as the thickness of the stringer and therefore can grip stringers of differing geometries. The heating of the adhesive film under the stringer, the contact pressure, and the integration speed can be varied for each process. For quality assurance purposes all parameters are recorded. The grippers compensate unevenness in the stringers, thus ensuring integration within the force and temperature specifications. 6 meter long stringers can be integrated with an accuracy of ±2mm on double-curved surfaces.

5.1 One-Shot-Bonding and Optimized Autoclave Processes

In the DLR One-Shot-Bonding approach ^[3]the stringers are integrated onto the uncured part and bonded in the same autoclave cycle in which the actual curing of the skin laminate takes place. This eliminates the second autoclave cycle and enables a shorter production time. At the same time, the complexity of the vacuum bagging increases for the One-Shot-Bonding process, which calls for improved quality assurance.At DLR, flow measurement and infrared thermography are used to identify leakages in the bagging before and during the autoclave cycle.

To further optimize the autoclave cycle a curing simulation is used.With this simulation it is possible to identify cold and hot spots in the part (see lefthand side of Figure 7) and to find the best placement of the tooling inside the autoclave in order to achieve the best temperature distribution inside the part. The curing behavior of the material is analyzed and the resulting material model is implemented into the simulation, alongside the part and tool geometry. The first step is to simulate the standard curing process as given by the material supplier. As specific temperature tolerances need to be ensured, the curing cycle is extended and thus longer than the actual curing of the material requires. To optimize the process, a curing cycle depending on the degree of cure of the material is simulated. The target degree of cure is set to ≥ 95% in the coldest area of the part, which leads to a process time reduction of 27% from 199 min to 146 min. In the second step the heating rate is optimized to a steeper heating of the air (from 2 K/min to 2,8 K/min) which still ensures a heating rate of 2 K/min inside of the material, as suggested by the material supplier. This improvement leads to another process time reduction of 8min so that a total process time reduction of 30% is achieved in the simulation.

Figure 7

Simulated cold and hot spots areas (left), sensor data from the DEA (right), © DLR

The simulated curing cycle is tested on the demonstrator part. A comparison of the simulated and real process data shows a small deviation during the heating of the part. This deviation shows some room for improvement in the simulation, which needs to be considered when using the simulation for further process improvement. Altogether, the demonstrator showed that a process time improvement of 20% can be achieved.

5.2 Cure Monitoring by DC Dielectric Sensors

To detect the on-going degree of cure of FML laminates in situ, dielectric flexible sensors are introduced into the laminate. These sensors must be in touch with the resin during the entire cycle and can measure the resistivity and the temperature of the resin. The resistivity changes with the resin’s viscosity and can be correlated to the degree of cure and the glass transition temperature of the laminate. The most critical locations to place the sensors are revealed through simulation and four sensors are integrated into the laminate at different cold and hot spots as shown in Figure 7.

Preliminary trials showed the functionality of the sensors in the industrial environment. The final demonstrator confirms the potential of this sensor technology to save curing time, as the process targets is already achieved after 50% of the recommended cycle time, and the cycle thus terminated.

5.3 Strain Measurement by Fiber-Bragg-Gratings and Strain Gages

From an FML design point of view, the so-called stress-free temperature T_sf is of particular importance, as it directly relates to the material’s residual stress state after manufacturing, which determines the load bearing capacity of the final part. T_sf denotes the moment/temperature during the manufacturing process, where a permanent, sustainable load-bearing connection between the curing prepreg layers and the metal foils is established. Therefrom, an application of the classical laminate theory allows for calculation the manufacturing-related residual stress state in the material, based on the effective cooldown temperature of ᐃT = T_ambient − T_sf . Within the AUTOGLARE project, a bipath measurement concept has been pursued to monitor the evolving residual-stress state within the FML along the entire 125°C, 11bar curing process. Fiber-Bragg-gratings (FBG) and strain gages (SG) were used simultaneously to measure strains in the curing composite layers and the thin metal foils, respectively, at the same time (see Figure 8a). A study has been successfully conducted in the project, to validate the SG and FBG measurement techniques and to validate the specific temperature compensation factors,whichwere determined for the optical sensors in previous investigations [12].

Figure 8

In-situ strain measurement using FBG and strain gages during autoclave processing

A FML 3 3/2 laminate, with 0.3 mm thick anodized 2024 aluminum layers, manufactured on an aluminum tool, serves as reference specimen. It is instrumented with a SG on the top aluminum layer and an FBG aligned in fiber direction of the first glass-fiber-prepreg layer, in between the upper prepreg layers. The obtained strain signals are shown in Figure 8b. Both signals show a linear strain-temperature relation right from the beginning. At specimen temperatures of 55°C and 87°C, the FBG signal shows kinks, related to viscosity decrease and volumetric chemical shrinkage of the epoxy resin. The SG shows a linear thermo-elastic behavior of the aluminum layer. At temperature above 110°C both signals show identical linear slopes,which indicates that the material acts as cured composite material, which refers to the definition of the stress-free temperature T_sf . After validating the measurement infrastructure on laboratory scale, it is transferred to an industrial scale, by integrating FBGs into an automated-fiber placement process (AFP, see Figure 8c). Robust data acquisition is observed during the manufacturing of a full-scale FML demonstrator (see Figure 8d). The demonstrated ability to measure T_sf directly, on an industrial scale is considered the prerequisite for the development of smart-cure-cycles, which help to shorten manufacturing processes to the necessary length, which can lead to considerable cost savings in the future.