Efficient production control is a key industrial technology. The notion of building up two parallel factories instead of one sounds like a doubling of effort. But what if one of the factories existed only in virtual form? This is the basic idea behind the digital twin. The real factory is fully modeled at a digital level, creating a virtual twin that not only visualizes the production system with all its machines, but also reproduces the dynamic processes and the behavior of system components during production in real time. In the virtual twin, it is possible to observe the manufacturing process in detail. Sensors continuously feed the operating status of the individual workstations to the system and opens up new possibilities for production control. Production planners can analyze the manufacturing process in the virtual simulation and then optimize or reorganize individual physical steps as required. The merging of real and digital production creates an overall system that monitors, controls and corrects itself while production is running. Whenever required, machines and software communicate with each other autonomously and keep production moving.
The main objective of all processes used to manufacture high-tech products is compliance with the specified ranges of permissible variation. For this purpose, all data must be recorded that might provide some evidence of status changes anywhere along the process chain. Sensors in machinery and equipment can provide valuable clues as to whether or not the actual values will fall into the tolerance range.
All data from the sensors and the production system are stored individually for each product, creating a digital twin that retains a full production history including project data and order specifications. Identification systems allow this twin to be assigned to the individual component, making it available for every downstream process step. The extended product data models provide relevant, context-specific data from the manufacturing history for further analyses, accelerating process development and process optimization in the production of prototypes as well as large series.
A specific time and place must be recorded for each set of sensor data about default process parameters – temperature, machine vibrations etc. This still provides technological challenges, but the causes of certain defects can be revealed only if the process is fully traceable.
Benefit of the digital twin in production
- Detailed recording and storage of all relevant process data from the manufacturing chain
- More immediate use of information about manufacturing errors and component defects to identify the critical manufacturing steps
- Customized and adapted repair processes based on the knowledge of entire product histories throughout the product life cycle
- Higher levels of machine availability, lower down-times and quicker response times following breakdowns through predictive maintenance of machine tools
Role of Big Data
Complex manufacturing processes, particularly with high quality requirements, can benefit enormously from a detailed knowledge of all existing data and from the resulting insights into influencing factors and variables. Analyses of large data volumes can serve to convert information into predictive models, which then allow the configuration of processes within a range of optimum parameters. These ranges are customarily defined to lie safely within the specification limits while, at the same time, allowing high yields.
Intelligent analytical methods can be highly useful in classical discrete manufacturing environments where large and highly heterogeneous data volumes must be processed. Under such circumstances, predictive models can serve to establish and eliminate potential error sources at an early stage.
Benefit of big data analytics for production
- Transformation of Big Data into Smart Data through contextualization
- Accelerated product release through comprehensive, integrated process simulations
- Strict compliance with ideal process requirements through automatic establishment of key reference variables and continuous target-actual comparisons
- Faster and more precise adjustments to changing manufacturing conditions
- Improved utilization of production line capacities even for small batch sizes
Digital assistance systems and technology apps in networked adaptive production operations help the engineers to monitor the data, to control process conditions and to apply their personal skills and competences with maximum efficiency. Visualization tools such as smartphones, tablets and smart glasses can provide immediate information, allowing flexible adjustments and quick changes of production plans or processes.
Data from model-based simulations optimize the decision-making about possible improvements of products and processes. The software detects critical situations during the manufacturing process and accounts for frequent changes of product specifications in the production of prototypes or small series, allowing the process planning to reach high levels of efficiency even before the first component leaves the production line. Constant checks and comparisons of real data and matching simulation data ensure that the models are continuously improved, with direct benefits for the quality and performance of the end product.
Benefit of Digital Assistants and Production Simulations
- Predictive planning of concrete process chains and processing sequences in view of specific result targets
- Better, more profound understanding of the processes involved through more accurate simulations of the individual process steps using recent real data
- Higher levels of component quality and shorter manufacturing times by adjusting downstream process steps
- Earlier recognition and automated correction of errors through online adaption in ongoing production processes
Edge Computing and Cloud Technologies – Smart Manufacturing Networks
The “Smart Manufacturing Network” provides an environment in which machines, production systems, databases and simulation systems can communicate with each other and share their data and services in a jointly used cloud. Users use mobile devices to access the process and can interact with all subsystems, control these systems or request specific data.
Such decentralized and modular systems allow engineers to plan, implement, monitor and configure individual manufacturing processes as well as entire process chains quickly and cost-effectively. Efficient networks allow the provision of flexible and easily adjustable systems for all stages of the customized production cycle, from the drawing board to the recycling facility.
The Smart Manufacturing Network stores a digital twin for every individual component, making these twins available for all systems.
For more information: www.vernetzte-adaptive-produktion.de