Periodic Reporting for period 1 - ZAero (Zero-defect manufacturing of composite parts in the aerospace industry)

In the aerospace industry very high quality standards have to be met. For the manufacturing of carbon fibre parts this is currently solved through extended end-of-line inspection in combination with re-work processes to deal with defective parts. Also, in-situ visual...\n\nIn the aerospace industry very high quality standards have to be met. For the manufacturing of carbon fibre parts this is currently solved through extended end-of-line inspection in combination with re-work processes to deal with defective parts. Also, in-situ visual inspection is used for quality control. This is currently causing huge productivity losses during lay-up and leads to a real bottleneck in carbon fibre parts manufacturing.

The ZAero project aims at the improvement of production of large carbon fiber parts. Advanced sensor technology for in-line quality control is developed in order to acquire detailed data about the manufactured parts. In combination with a distributed data gathering system and advanced simulation models, more insight into production processes is made available. After completion of the first half of the project, all major elements of the project were successfully demonstrated: in-line sensors for carbon fiber lay-up, sensors for monitoring of curing processes, and simulation models for mechanical and logistic simulation. Sensor data and simulation results are displayed in order to support decision making in the complex production processes.

Technologies developed within the ZAero project provide important information about production processes. This makes large carbon fiber part production more efficient, helps to avoid waste, and gives the human operator the chance to focus on the important decisions. With a pioneering role in the aerospace domain, the developed technologies from the ZAero project are expected to give a boost also to other sectors.\n\nCurrent and future carbon fiber part production processes were analyzed in the first year of the project. Based on this, a plant layout was created and a model for logistic simulation was implemented. Results from logistic simulations indicated that technologies which are developed within the ZAero project lead to significant improvements of different production performance indicators.

Three optical sensors for in-line quality control of lay-up processes were realized. It was necessary to take into account different requirements for the two main lay-up technologies addressed within the project: ADMP and DFP. A specific challenge for Automated Dry Fiber Placement (ADMP) was the large width of material that is placed with this method. In order to cope with this, wide versions of a laser triangulation sensor and a fiber orientation sensor were designed and built by Profactor and calibrated by Ideko. First experiments with these wide sensors were successfully conducted with the ADMP machinery at Danobat. For Dry Fiber Placement (DFP), the main challenge was the tight integration into the lay-up machinery from MTorres. First tests of the respective in-line sensor conducted at the facility of FIDAMC showed good results. First prototypes of in-line sensors for curing monitoring were built and tested by Airbus.

Lay-up and curing sensors were connected to a distributed data gathering system designed by Dassault. Different processing units access collected data via that system and use it to run mechanical and logistic simulations. Results from simulations are fed back to the data gathering system. In this way, information about production processes is constantly updated and enhanced. A first version of a decision support tool provides a comprehensive view on all relevant information to the human operator.\n\n"The ZAero project aims at going beyond the current state of the art concerning intelligent production of large carbon fiber parts. Three major topics are involved: smart inline quality control, closing the feedback loop from individual parts ""as produced"" to mechanical simulation, and implementation of smart decision support for human operators.

Lay-up sensor technology developed within the ZAero project covers large areas of carbon fiber material at a high resolution. The large amount of data that is acquired in this way puts high demands on data processing. The ZAero project aims at the development of systems that can handle large and complex data sets. Cutting-edge machine learning methods are being deployed to extract the most relevant information from the data. The use of such methods is expected to lead to a shift of responsibilities for the human operator. The focus for the human will shift from manual quality inspection of individual processes towards supervision of sensor systems and data analysis.

Mechanical simulation nowadays is mainly focused on the ""ideal"" manufacturing process of a part. In real production processes, however, deviations of the real part from its design occur. The assessment of such deviations is in many cases very difficult to perform. Subsequently, a very restrictive handling of defects is often implemented nowadays. Because of strict rules, deviations observed for real parts in production often lead to unnecessary scrapping or re-work. By implementation of real-time mechanical simulation based on the real part as manufactured, the ZAero project enables quick assessment of individual deviations during production. The boundaries of design and production phases are therefore becoming more and more blurred. Although such advanced feedback-loops are currently limited to specific production domains (i.e. carbon fiber parts in the aerospace industry), future production environments are expected to make much more use of the quick interaction of design and manufacturing. This will enable more iterative production processes in different domains. The ZAero project is contributing to the implementation of such flexible production systems.

As a huge amount of data is collected in future production environments, it will be necessary to equip the human operator with the right tools to control and run production systems. As top-level decisions will always be in the hands of a human operator, all relevant data must be prepared and displayed to that operator. Within the ZAero project, a decision support tool is developed. A first implementation was demonstrated after the first half of the project. The tool collects information from the manufacturing processes (sensor data), mechanical simulations for individual parts, and logistic simulation of the complete production environment. This global view enables the human operator to meet optimal decisions at the level of the produced part as well as on the level of the whole production chain."
More information
Zero-defect manufacturing of composite parts in the aerospace industry
Not specified (see website if available) or see associated project
  • Not specified