Composites 4.0 is one small galaxy in the universe of Industry 4.0, which is the digital transformation in how goods and services are designed, produced, delivered, operated, maintained and decommissioned. For composites manufacturing, the goal is to use automation, sensors and data, 5G communications, software and other continuously evolving digital technologies to make products and processes more efficient, intelligent and adaptive.
Composites manufacturers are proceeding through this digital transformation along a spectrum. Initial steps include inline inspection and optimized processes that reduce waste and cost while increasing part quality and yield. More advanced solutions work toward intelligent, autonomous production that is not just agile, but responds to and even anticipates changing markets and customer demands.
“Composites 4.0 is not an end, but a tool,” explains Dr. Michael Emonts, managing director of the AZL Aachen Center for Integrative Lightweight Production at RWTH Aachen University (Aachen, Germany), whose iComposite 4.0 project demonstrated an adaptive process chain with potential to reduce an automotive floor pan cost by 50-64%.
“There is a difference between just making things digital and digital transformation that actually changes the processes behind your business and opens new opportunities and business models,” contends Christian Koppenberg, managing director for composite parts producer Dynexa (Laudenbach, Germany).
“Composites 4.0 is not just using robots,” asserts Dr. Michael Kupke, head of the German Aerospace Center’s (DLR) Center for Lightweight Production Technology (ZLP, Augsburg), which has developed an artificial intelligence [AI]-equipped work cell where collaborative robots can switch from producing composite rear pressure bulkheads to fuselage panels without reprogramming or retraining. “It is the technology that ensures you don’t have to teach the robots, because there is no business case for that. Composites 4.0 is more than just increasing efficiency and cutting cost. It is a change in how companies think about and approach production that will determine which companies survive and which do not.”
Adaptive preforming, RTM
“The idea of the iComposite 4.0 project was to create preforms from cost-effective rovings and tows by combining dry, long glass fiber (25-30 millimeters) sprayed and subsequently reinforced with a grid of unidirectional (UD) carbon fibers via automated fiber placement (AFP),” explains Emonts. “The chosen demonstrator, a rear under vehicle floor pan, was previously made with more costly textiles which also produced more than 60% waste.”
The Composites 4.0 transformation required integration of the fiber spraying, fiber deposition and subsequent resin transfer molding (RTM) processes so that they reacted to each other and adapted based on the part quality measured between steps (Fig. 1). “We used a machine vision system from Apodius GmbH [Aachen, Germany] with an optical laser sensor and a camera module to characterize the surface topology of the sprayed preform,” says Emonts. “Apodius adapted the software to analyze the percentage of fibers in each direction. The iComposite 4.0 line compared this to the digital design and decided if it met the mechanical requirements. If yes, it applied the standard UD grid for reinforcement. If no, it decided where to place additional UD fiber layers.”
However, these additional UD layers could cause part thickness and geometry to exceed tolerances. “Therefore,” he explains, “we combined the preforming line with an adaptive RTM process which, if necessary, adjusted the part thickness by increasing pressure on certain parts of the press.” This too was automated, with the aim to replace intervention from the line operator, but it did require simulation of part performance using measurement data and standard FEA software.
“Currently, simulation of the part mechanical properties is performed offline,” says Emonts. “We generated a database of process and part variations, created algorithms to react to each variation and validated these via FEA. Thus, based on the variations measured by the line, the algorithms directed it to perform an appropriate mitigation. To make the line adaptive in-situ, the next step would be to add machine learning.” Meanwhile, AZL is pursuing numerous Composite 4.0 projects including self-optimized production of hybrid thermoplastic composites and injection molded parts with integrated stiffening of tape-based tailored blanks.
Zero-defect CFRP wingskins
The ZAero project (see “Zero-defect manufacturing of composite parts”) is another key Composites 4.0 project, that started in 2016. It aimed to increase productivity for large carbon fiber-reinforced plastic (CFRP) structures such as wingskins. Defects would be reduced by using automated inline inspection with either prepreg AFP or Danobat’s (Elgoibar, Spain) automated dry material placement (ADMP, see “Proving viability of dry fabrics, infusion for large aerostructures”). Process monitoring during resin infusion or prepreg cure would predict state of cure and shorten cycle time. Collected process and defect data were used with FEA to predict part performance. This was then input into a decision support tool for how to address identified defects. A part flow simulation for CFRP wingskins was developed that, when fed into this tool, helped to optimize a rework strategy (Fig. 2). Today, many such parts are reworked during manufacturing, but only after NDI. Earlier rework and improved process control were indeed goals of the ZAero project, as well as enablers for its targeted 15% increase in production rate, 15-20% reduction in production cost and 50% less waste.
By the September 2019 final review, the prepreg AFP sensor developed by project leader Profactor (Steyr, Austria) not only achieved automated inline inspection, but could also be used to correct parts in-situ. “This sensor can detect the standard defects such as gaps, overlaps, FOD, fuzzballs and twisted tows, as well as early and late cut of each tow,” says Dr. Christian Eitzinger, head of machine vision for Profactor. A missing tow can be corrected automatically with the placement of an additional tow precisely where it was omitted. The machine must be stopped, however, to remove fuzzballs or a twisted tow. “A database built using Dassault Systèmes’ (Paris, France) 3D Experience for CATIA allows us to calculate the effects on the part’s performance based on the size, shape and type of defect. Processing all defects in a ply takes only seconds. The machine operator then decides what defects can be left and what must be reworked.”
For infusion process monitoring and control, Airbus (Toulouse, France) worked through subsidiary InFactory Solutions (Taufkirchen, Germany) to develop three sensors that measure temperature, state of cure and resin flow front (see “Sensors for monitoring resin infusion flow front”.) “We have integrated these with CATIA 3D Experience and shown that the data can be reliably acquired and added to each part’s digital thread,” says Eitzinger. (See online sidebar “Composites 4.0: Digital thread vs. digital twin”.)
The final of three part demonstrators was an upper wing cover subsection with three stringers (see online sidebar “ZAero project update”). For this part, Profactor’s decision support tool was demonstrated live at partner FIDAMC (Madrid, Spain), connected to the part flow simulation — based on Siemens PLM (Plano, Texas, U.S.) Tecnomatix Plant Simulation software — running on Profactor’s server in Austria. In addition to building a defect database, ZAero conducted experiments with machine learning. Manually designed, generative computer models combined with deep neural networks detected and classified defects, achieving a rate of 95% correct classification of different regions (gap, overlap, tow, fuzzball) in real ADMP monitoring data, even when artificially created defect data was used for the deep network training (analogous to how ultrasonic testing systems are calibrated on a range of deliberate defects).
“We will definitely pursue some kind of next phase,” says Eitzinger. Meanwhile, Profactor is commercializing modular sensors for fiber orientation and defects during automated layup. InFactory Solutions is also offering its AFP and resin infusion sensors, and fiber placement partners Danobat and MTorres (Torres de Elorz, Navarra, Spain) are now selling their equipment with integrated inline inspection.