The Plug-and-Produce paradigm is central to the goal of “digital factories”, i.e. maximum efficiency through flexible and agile production lines capable of responding to fast changing environments. Building “modular” production lines made of interchangeable and inter-connected assets is the key-success factor, but it also poses a key-challenge to the entire ecosystem: addressing the physical safety and cyber security of a multitude of real-time interactions and changes. This what we call “adaptive safety & security”. Even though the complexity can be encapsulated and controlled through modularization, new dynamic safety and security issues may arise due to: i) changes in modules, ii) connectivity within a production module, iii) connectivity with other production modules. This brings about the need for an automatic, adaptive safety and security check. If we consider ‘adaptive safety’, they mainly consist of:
- Safety profiles: A collection of parameters/factors that define the safety of an asset based on relevant international safety standards, along with safety measures for relevant interactions
- Decision trees: A comprehensive rule-based algorithm for automated risk assessments based on safety profiles.
The data required for validation comes from several sources and are stored in the ‘safety profiles’ of the digital twin/asset administration shell of the production module. This includes relevant safety and security data along with parameter range definition by the manufacturers. Correct definition (from the development phase) of safety & security measures into profiles at module interfaces is key to dynamic assessment and validation, so that all possible risks are defined and integrated into the decision tree. This in turn requires that risks and safety profiles be semantically defined. Manufacturer-independent semantics need to be established to refer to the nodes of the decision trees. eCl@ss, with its electronically exchangeable, properties-based semantics in the model of a quality-controlled knowledge architecture, provides the necessary foundation for the realization of such semantics. its standardized descriptions and unique identifiers of properties/ process variables/ parameters are ideal for defining the semantics of the ‘safety profiles’ and facilitating adaptive safety and security validations.
TÜV SÜD Korea is the regional eCl@ss office in South Korea and intends to support in fostering smart manufacturing with eCl@ss in Korea. Join us In our booth in the Smart Factory Expo Korea on 4th to 6th March where you can take a deep dive on the mentioned use-case, the usability of eCl@ss and identify how you can make the smart manufacturing journey safe, secure and efficient. Please contact us for more information at south-korea( at )eclass-office.com.