Werk 4.0

From the conventional production plant to the resilient competence center through Industry 4.0

Motivation

The automotive industry is changing. Electrification and digitalization are fundamentally changing it. As a result, Mercedes-Benz's global production network with all its vehicle and drive factories is also changing. Motivated by this, the project is looking at a future production system for the German luxury car manufacturer. The project is taking place in line with other companies undergoing transformation.

Aim and approach

The project will develop and implement a resilient production concept through the integration of innovative technologies. This will enable faster adaptations to unforeseeable changes in market and technology requirements and will enable the products of the future to be manufactured promptly. Building on various necessary technical innovations, this initially means a restructuring. The new structure is based on the DevOps concept developed in the software industry, meaning that development, planning, implementation and production are no longer carried out separately, sequentially or partially in parallel, but that these functions are designed and managed jointly from start to finish (E2E). Production will become more intelligent and will acquire the necessary competence to introduce and implement changes itself based on its current status and thus be able to respond better to external influences.


Innovation and perspectives

8 work packages are integrated in a real laboratory to implement a resilient competence plant. DevOps concept in production means:

  • Integration of digital twins to implement updates and adjustments in ongoing production

  • Establishment of technology-independent data formats and interfaces to enable digital twins in the respective context over the entire life cycle as a central data source and single source of truth (SSoT)

  • Development of human-machine interfaces for simple, intuitive operation of the digital twins and for the necessary qualification of the operating employees


Our contribution

Networked shop floor (AP1)

The aim is to develop a uniform software basis for distributed communication systems on the shop floor, with which industrial end users in automotive production can master application-driven complexity and adaptability and set up and operate future-proof automation systems. The aim is to achieve an approach that prevents vendor lock-ins and enables high-performance communication through open but secure standards, allows complex automation systems to be efficient through automated functions and keeps them changeable and expandable.

Mobile quality gates (AP3)

The aim is to develop a mobile, intuitive visual inspection system that can be easily operated by workers and trained within a few minutes and with just a few images. The so-called quality gate, consisting of hardware and networked software, should be able to be used locally and with cloud/edge support and connect to systems. The focus of the application is inline process control. In addition, the result of the sub-project should be an intuitively set-up quality gate (based on AI) that can be quickly adapted and trained to new components and environments. Test objects include, for example, monitoring the assembly or condition of parts and components in production. The focus here is particularly on training new components quickly and without programming knowledge or experience with AI.

Work 4.0: Presentation of the components in the project concept to clarify the holistic approach

Project consortium at kick-off ©WvSC

Our Partners

Werk 4.0

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Key facts

Framework for distributed communication

  • Partially automated setup, orchestration and monitoring of distributed, networked computing nodes for the shop floor

  • Support for embedded as well as (factory) edge and cloud computing

  • Provision of programming interfaces (APIs) for application development

IT / OT Convergence

  • Connection to industrial bus systems through the development of appropriate connecting elements / connectors

  • Integration and connection of various middleware/IoT standards

  • Integrative consideration of real-time communication via Time-Sensitive Networking (TSN)

Mobile quality assurance

  • Fast integration into workplace systems

  • Learning new tasks within minutes

  • Intuitive worker-centered user guidance

Efficient AI-based image recognition

  • Generation of synthetic image data

  • Use of few-shot learning

  • Development of generative AI methods