Short Introduction to Digital Twins
Digital twins are often defined as “Digital representation, sufficient to meet the requirements of a set of use cases” (Plattform Industrie 4.0). Integrating data about the asset (object which has a value to an organization and is therefore managed individually) from different life cycle phases, a digital machine twin can provide valuable structuring of data and insights; from asset development and configuration all the way to the asset’s end of life.
In the figure below, we can differentiate a type and an instance. All planning data creates the type of the asset. The type is used to implement the physical (real-world) instance of the asset. The planning activities are inclusive of all domains namely mechanical, electrical and software. A type becomes an instance when development and prototype production is completed, and the actual product is manufactured.
For example, the type of car includes CAD models, wiring diagrams, accessories which can be varied according to a desired use case. Thus, the type specifies which different car configuration are available in form of a 150% model. When the customer selects their individual configuration, for example in an online shop, a virtual 100% model is created. The 150% model as well as the 100% model are still part of the product type. As soon as the 100% model is manufactured, an individual instance is created. Thus, the customer gets a real-world touchable car with a unique serial number. Customers will use their car instances and create specific and individual lifecycles with respect to their usage.
When the product is delivered to the customer, this marks the beginning of a product’s lifetime or service time. During this, the product provides the service for which it was planned and manufactured. During service life, prescribed maintenance is suggested to avoid sudden breakdown of the product. Once the product’s service time ends or the product is no longer required, the product is decommissioned, and this marks as end of life. During the end of life, the product can be recycled, up cycled, or decomposed according to requirements.
The digital twin includes data from the type (analysis, specification, and design phases) as well as data from the instance (implementation, system test, operation, and maintenance phases). Also, end of life phases must be included. The collected data can be analyzed and a predictive model can be created. This way, objects which are, for example, subject to wear can be replaced at the right time.
Digital Twins of Operators
Assistance systems are important for humans in production systems to handle the increasing complexity. They can display relevant indicators, provide information about work routines, or notify their user in case of problems and errors. Combined with gamification approaches, they allow personalized feedback and targeted support during work. Using gamified level systems, habituation effects, and dependencies upon the assistance systems can be decreased.
However, the development and usage of gamified assistance systems require access to different data sources, which often remains a challenge.
In this course, we are accessing automatically generated work data from a manual work station and transfer it to useful indicators for user digital twins. This step is important as the raw data is not suitable for user digital twin applications.
The course is accessible here:
https://i40.fh-aachen.de/courses/dta/activities/dta_intro_oee_python.html
