Metal Additive manufacturing (AM) is a complex operation, which requires the fine-tuning of hundreds of processes parameters to obtain repeatability and a good quality design at dimensional, geometric, structural levels. Therefore, to be used as final product, metal AM parts must go through ad-vanced quality control processes. This implies large capital equipment in-vestment in measurement systems (i.e., tomography and lengthy inspection operations that adversely impact costs and lead times). A large amount of data can be collected in metal AM processes, as most industrial AM sys-tems are equipped with sensors providing log signals, images and videos. This paper develops and proposes an innovative quality-oriented decision support framework, composed by a Model-based Design tool providing De-sign for Additive Manufacturing features, and a Cyber-Physical System cre-ated by integrating an AM asset with a real-time smart monitoring software application. Such framework caters to process engineers and quality manag-ers needs to improve a set of quality and economic KPIs.

This paper explores the relationship between technical implementation and busi-ness strategy decisions of smart services in the manufacturing industry. The study employed a research methodology that involved examining real-world use cases through expert interviews with companies offering smart services. The business models were assessed by using the business model patterns proposed by Gassmann et al. while the technical implementation aspect employed generic smart service patterns based on the conceptual model outlined in ISO/IEC 30141:2018. The data analysis resulted in the identification of 11 distinct generic smart service patterns with varying properties. Furthermore, the distribution of the business model patterns among the generic smart service patterns was exam-ined to determine potential relationships and influences on the technical imple-mentation of the smart service. This study's findings indicate several dependen-cies and connections between the technical implementation of smart services and their corresponding business models. The identification of interdependencies can serve as a foundation for informed decision-making in the planning and devel-opment phases of smart service implementation for organizations.

The digital twin (DT) as the core for digitalization activities becomes ever more prevalent in both research and industry. One emerging interpretation of the DT concept is the Asset Administration Shell (AAS), which describes – on a se-mantic and machine-readable level – each asset with structured data set for its characteristics, lifecycle status, capabilities etc.

In complex products, such as production systems that consist of many sub-systems, there are consequently also many AAS, each with individual submod-el structures. Because the AAS hierarchy does not necessarily align with the physical structure of a system, assistance in accessing information and services from the digital twin is required for different processes, e.g., maintenance and service tasks.

In this contribution, the concept for a web-based visualization application is presented, which uses the association of 3D geometry and AAS to create an in-teractive 3D view. This allows access to AAS hierarchies, submodels and asso-ciated information on different levels and granularity. The goal is to utilize standardized submodels to enable easy navigation through the many-faceted AAS and fast data retrieval. In this way, the AAS and respectively the DT can intuitively be queried, and its contents can be displayed to the user without pri-or knowledge of the structure. The first working prototype is implemented for a production system, where the focus is on accessing information about its con-trol cabinet with certain terminals.