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Because of high costs and effort PdM is only economically viable on machines and components with high revenue losses due to breakdown and where the failure is almost independent of uptime
Currently, the market is quite opaque, which makes it difficult to compare providers on the market and thus to compete. This thesis is written in cooperation with the Aachen-based startup developing the IIoT platform “United Manufacturing Hub”. Its objective is to set UMH apart from the existing red-ocean market with the development of a blue ocean strategy.
To select suitable hardware components, a five-stage decision logic is developed and implemented as a software application, which suggests suitable components to the user depending on the specified use case and prioritizes them according to list price. In a simulative evaluation, this achieves complexity reductions between 73 and 98% and cost savings between 46 and 93%. A decision between Deep Learning and conventional algorithms can be made based on the given development circumstances as well as the complexity of image features.
The central result is an overall process overview and a microservice architecture, with the help of which an industrial image processing system can be put into operation on the software side only by configuring the camera and entering the environment variables. Currently, cameras of the GenICam standard with GigE Vision interface and Cognex cameras are supported. The open architecture creates a basic platform for the development of further microservices and subsequent processes in the context of industrial image processing.
The results of this research provide an overview of the problems being faced regarding quality control during the manufacturing processes of technical textile in the automotive industry. In addition, information on the extent to which digital solutions for quality control are established in the industry is analyzed. Moreover, existing digital quality control solutions and measuring principles to tackle the identified problems in the industry are researched and identified.
This thesis is concerned with combining the subject areas Industry 4.0 and the implementation of manual time studies.