Changing a production process can be costly when the only way to test an idea is on the factory floor. Digital twins and simulation give manufacturers another way to examine how equipment and processes behave before making physical changes.
Connecting a model to production data
A digital twin is a virtual representation of a physical asset or system, such as a machine, production line, or factory. Data from the physical operation helps keep the model relevant and allows teams to compare expected performance with what is happening in production.
AI can be used alongside these models to identify patterns, estimate outcomes, and support decisions. Applications include identifying signs of equipment wear, investigating defects, and assessing energy use and throughput.
Testing changes through simulation
Simulation allows teams to compare operating conditions and test possible changes in a virtual setting. They can examine how a proposed adjustment might affect production, then use the results to guide further testing and implementation.
Combining simulation with operational data can make that work more useful. The quality of the results still depends on how well the model represents the process and the reliability of the data supplied to it.
A practical role in manufacturing
Digital twins offer a way to support maintenance planning, process improvement, and operational decisions. Their value comes from answering a specific production question and giving engineers information they can assess and act on.
For Connexo, these technologies are part of the wider discussion about connecting manufacturing systems and making production data more useful to the people responsible for quality and operations.
For inquiries, contact [email protected].