Industrial Use Cases of FaaS: The basics you need to know

The SmartFactoryKL e.V. technology initiative was established in 2005 in Kaiserslautern and now consists of more than 50 organizations from different fields of research. The initiative aims to bring together industrial and research partners within a common network in order to implement joint Industry 4.0 projects for the factories. The following demonstrator was chosen to integrate PHYSICS related components and increase its usability. With lot-size-one production, it assembles user-customized USB pen drives using different autonomous and interoperable modules, each dedicated for a single step of the production. Modules are self-contained and independent of others. Their capabilities are abstracted by skills, which are then orchestrated by a higher-level software component (Production Flow Control, PFC). The software architecture of the overall infrastructure maintains how the product is produced, by generating a recipe for manufacturing, scheduling the products by their priorities, production time, etc. The software architecture is designed with a service-oriented approach, which enables decoupling and an easier conversion into Function as a Service (FaaS) approach in PHYSICS project.

Island “Java” at SmartFactory-KL

This demonstrator produces USB sticks customized for user needs. A user can choose the color and the data to be inserted into the stick.

Product Customization Window

After an order is placed, the transport rail moves the product tray between the modules to complete the production.

Different Production Steps

After the production is complete, a quality check (QC) using artificial intelligence (AI) is performed in the QC module (Figure 3, bottom right). Current implementation gives possibilities for two improvements:

  1. Currently, if a software failure at QC module occurs, no automatic QC is performed. In that case, the operator must check the integrity/quality of the product, manually. Maintenance user should also fix the software problem to enable automatic QC for later.
  2. If the output of the AI QC’s certainty level is below the threshold, the QC must be done manually by the operator to cover for edge cases.

Both issues decrease the production rate. Therefore, DFKI introduced two use cases dealing with these two issues. With PHYSICS project, DFKI aims to improve the production rate in case of software failures and/or low QC certainty levels.

For the first issue, a failover scenario is designed. A local Edge infrastructure with PHYSICS components was set up and connected to the central PHYSICS platform available in Amazon Web Services (AWS). This in combination with FaaS enables QC to be performed regardless of its physical location. In case of an issue, the maintenance user will be informed about the problem, and they can continue working on it without time pressure. It is expected that there will be no downtime, as long as an active Internet connection is available.

For the second issue, if a QC certainty level is below a specific threshold, the PHYSICS platform will invoke an additional QC service at AWS to perform more complex computations using more computing resources. It is expected that certainty level improves such that a manual inspection is rarely required. Due to the pay-per-use nature of FaaS, this additional QC is very economical.

Although as a test pilot plant, SmartFactoryKL does not have a requirement on production rates, these use cases enable us to test the FaaS approaches and transfer the knowledge into the industry to solve similar problems in the real factory environments.

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