PILOT : SMART DIGITAL ENGINEERING

The expansion of competitiveness and sustainability are essential goals of companies. To achieve this, every business needs to deploy digital technologies in a variety of areas, including customer relationships and services, productivity, business model, IT security, and privacy. Digitization and networking are playing an increasing important role, as the digital data volume will increase significantly.

An important success factor of smart digitization is the transformation of these big data sets (big data) into valuable information, but this is not a trivial task. The growth speed of the data volume, the diversity of the data and the various data sources pose many challenges, such as collection of sensor data and the mapping of model data underlying the machine, their integration and interpretation into a structured database system.

Production machines deliver many data, which are usually not connected to each other and cannot be fully processed. Often there is no connection between machine configuration, ERP data environment (e.g. job data) and data streams (e.g sensor data). In addition, data streams are stored – if at all – in different systems. Therefore, it is often not possible to derive valuable information from the existing data.

Within the FILL trial it is possible to record the data of their machines standardized by OPC UA and subsequently to use them for analyses and optimizations. Using standardized communication technology, the existing specific solution Machine-Work-Flow-Framework can be generalized and used for further customer requests. In doing so, FILL takes a big step forward in the digitization of its machines with the expanded machine state model. FILL pursues the following goals:

• Cost reduction expected by reducing the time spent on future development and customer projects.
• Development of data-driven business models in service and support
• Identification of optimization potentials in the engineering process for a long-term reduction of the development times of machines.

The FILL trial primarily serves the engineering process of the machine builder. It allows for a better understanding of machinery by detecting cause-and-effect relationships due to anomalies and patterns. In addition, maintenance intervals and cycles can be optimized and, as result, quality improvements of the production and the product can be achieved.

The objective of the FILL trial is a general engineering approach for highly flexible machines manufacturing optimization.

• Model-based and big data-driven engineering process, analyse engineering data and operation data for quick identification of unattained requirements and design faults, completion of design studies and new machine designs.
• Machine and Process Models Optimisation Engine for production plants reconfiguration and reduced design efforts with big data analytics over distributed data (dynamic machine data, part information, engineering data).
• Machine Big Data Logger and Exchange Platform. OPC UA and TSN based open hybrid fog node & cloud computing infrastructure for data exchange between different machines within and across different factories.

Connected 3D Production Simulation. Digital twin manufacturing configuration virtual validation/visualisation and productivity optimisation using pre-existing and real time data from different factory levels (small cell to entire factory).

Benefits in the engineering process for machine producer:

1. Quality improvement
   1. Better achievement of customer requirements
   2. Higher level of maturity after engineering phase
   3. Less difference between the planned (as engineered) and the built (as built or as manufactured) production system
   4. Possibility to check if the production system is used according to the specification
   5. Less design failures
2. Time (mainly throughput)
   1. Due to the frontloading time saving effects are expected in the later life cycle phases (e.g. commissioning Phase)
   2. High and fast data availability and correct linkage between engineering data and machine/production data leads to quicker findings during clarification and specification phase
   3. Simplification of the reusability of engineering data
3. Cost effects due to
   1. shorter throughput time – reducing time to market
   2. Increased delivery reliability
   3. More office work, less on-site work (suboptimal working conditions)

The main KPIs of the pilot are:

• Reducing time to market: lot-size-1 engineering lead time -15%
• Increasing quality / reducing failure costs: service costs -15%
• Increasing efficiency: unplanned downtimes -20%