SMEs can also make good use of a Digital Twin
Digital twins seem well suited for use in the manufacturing industry and could even bring about a revolution there in the form of more efficient production and better maintenance. But how do you deploy this technology as an SME? The knowledge and insights needed to invest in twins are often lacking. Teade Punter, however, sees plenty of possibilities and opportunities for the digital twin in SMEs.
Making a digital twin
A digital twin is created by collecting data about the asset. This data comes from the sensors of the asset and is visualized with the twin. Therefore, twin development starts with the data structure of the asset. In Industry 4.0, methods such as RAMI4.0 and Asset Administration Shell (AAS) are propagated for this purpose. Some companies are already picking this up, others are more cautious. In addition to the data structure, it is often also important to look at the behavior of the asset. This might include "agents" that perform specific tasks of the asset, such as keeping track of inventory. Behavior of the asset requires modeling, a description of the states, with the conditions under which it occurs.
For the development and application of digital twins, three phases are recognized: data logging, asset mapping, and asset-model integration.
Data logging forms the basis of every twin development: the data is collected via (IoT) sensors. For many companies, this is often already a big step, which should therefore not be underestimated.
Smart Industry Twin
Motivations for SMEs
A digital twin can be developed for a variety of reasons. For SMEs, the following are possible motivations for starting a twin.
Because the twin is a copy of the real system, it can serve as a remote monitoring tool for companies that have machines at customers' premises. Thanks to the digital twin, a service operator can immediately see if there are any problems at one of its customers. In this way, maintenance can be carried out more efficiently and possibly also better informed.
Twin can also be developed to collect data on machine parts or machine usage to recognize patterns. In this way, for example, the wear of parts can be predicted and their replacement scheduled without having to interrupt the manufacturing process at undesirable times: so-called predictive maintenance.
The twin can also be used to collect data. The data logging phase is then done from the digital twin. Data is collected about (simulated) situations in which you don't want to, or can't, bring the real machine in because it would break down or pose a danger to employees.
Yet another motivation is to allow multiple people to work on the machine simultaneously by having them work with a twin and thus train the real machine.
Finally, a motivation can be to align the assets of different companies. Companies can thus form a chain of assets, where machine information is exchanged and machines can hook up to each other, creating new forms of cooperation.
With digital twinning, the manufacturing industry is digitizing. Besides the opportunities, as described above, and risks such as security and IT dependence, twinning should be seen as an investment. Of course it is important to determine beforehand why the twin is being developed. A cost-benefit analysis can help here. However, the benefits of digital twins cannot be fully predicted before the actual twin implementation, because twinning also changes the view on the possibilities with the assets, machines and processes. Digital twinning is thus primarily a decision for companies with an eye for and confidence in a digital future.
Author: Teade Punter is a lecturer in High Tech Embedded Software (HTES) and a leading lecturer in Fontys Centre of Expertise High Tech Systems and Materials (HTSM).