Towards useroriented programming of skillbased Automation Systems using a domainspecific MetaModeling Approach Upcoming trends in manufacturing focus on flexible configuration of products up to lotsize one. This mass customization raises a need of flexible manufacturing and production systems that should react on fast changing market demands. As a result, it is getting more and more important to model all needed assets or skills in an useroriented and configurable manner. Assets can represent any type of data or skill information needed in a manufacturing domain. Data information could be for example shown to a human worker (e.g. work instructions, images, videos or animations) or could be required by robots (e.g. coordinates, velocities). Skill information represent any type of executable functionality provided by a device. This paper shows a generic approach to model needed context information of (collaborative) manufacturing tasks. This approach supports skillbased engineering enabling the usage of various components from different vendors. In addition, this work presents a domainspecific modeling approach of skills including the definition and validation of custom modeling rules.

Supporting the Data Model Integrator in an Engineering Network by Automating Data Integration Data exchange and integration in a heterogeneous engineering network are challenges that an engineering organization has to address. Most practitioners have realized that this challenge requires detailed consideration and involvement of the related engineering disciplines to specify and implement an appropriate data logistics. In this paper, we introduce a metamodel and method for the development of a datamapping infrastructure to support the work of the engineers responsible for data logistics. The metamodel is the foundation for data logistics with automated data transformation, integration, and exchange by automating the mapping of engineering data as envisioned in VDI Guideline 3695. We report on a first validation of the method with a proof of concept using AutomationML.