ForZDM - Integrated Zero Defect Manufacturing Solution for High Value Adding Multi-Stage Manufacturing Systems

Background

The scope of the ForZDM project is the development and integration of strategies and methods to reduce rejects in multi-stage manufacturing systems. The use of additional sensor systems and sophisticated control strategies will decrease the occurrence of defects and avoid defect propagation. With different strategies, for example downstream compensation and enhanced process control, „Zero Defect Manufacturing“ (ZDM) can be achieved. This research project is based on the project results of MuProD (http://www.muprod.eu/), which was funded by the European Union (FP7). Within the scope of the research and innovation program horizon 2020, ForZDM is to decrease the gap between the research and the market. The use cases in ForZDM are focused on the aerospace sector, traffic technology and medical devices.



Problem

The adaptability of different production systems is subject to constantly growing requirements, for example, to reduce waste in production systems and to minimize time-consuming quality controls. Defective components identified in the production system often have to be disposed or recycled. ForZDM focuses on this issue based on three specific applications. Moreover, the task is to increase the degree of adaptation within the production systems in order to avoid waste at the end of the manufacturing process. Strategies have to be derived according to the resulting outcomes, which can be used in other fields of application.



Knowledge gain

The objective of ForZDM is to develop waste reduction methods for multi-stage production systems. The systems will be implemented in existing machines at an advanced stage of the project. In order to minimize the reject rates, trends and deviations within the production can be detected at an early stage with an adapted process control and other strategies.

One of the first steps will be the implementation of additional sensors to get more information out of the processes. Collecting more and more parameters results in the challenge to record and analyze the resulting huge amount of data. The use of data fusion and methods of artificial intelligence supports the detection of relevant information and correlations between different parameters.

The generated information is used to decrease the occurrence of defects and to avoid the defect propagation. The main focus will be, on the one hand, on the in-line repair in the same process step and, on the other hand, on the downstream compensation in later processes. Preliminary results of the rotor assembly in electric vehicles have already been achieved in MuProD. These results are to be further developed and generalized within ForZDM. Models of multi-stage production lines have to be produced (markov-chains) to quantify the impact of the corrective measures on the production system (cycle time, throughput, etc.).

Afterwards, the developed strategies can be implemented in control systems of existing production lines. For this purpose, a new control architecture has to be developed, which allows to integrate reflows from optimization strategies. An experimental validation of the strategies will be performed in three demo systems covering sectors of aerospace, medical technology and railway. The results will be used for new applications, in order to spread them in other branches of industry.



Further Information

 

Project-Coordinator:       Dr. Davide Caputo, GKN Aerospace Norway

Project-Website:             www.forzdmproject.eu         

Cluster-Website:             www.4zdm.eu 

 

"This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 723698"

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