approaches, which often prioritise programming machines to complete predefined tasks efficiently. By capturing information from machines in real time, engineers can better understand how their equipment operates and identify areas for improvement.
Beckhoff developed TwinCAT ® Analytics to help manufacturers analyse their machines’ operational data and uncover patterns and inefficiencies that may not be immediately obvious. By examining factors such as cycle times, energy consumption and machine behaviour, TwinCAT ® Analytics’ out-of-the-box algorithms can help identify bottlenecks, excessive idle time and inefficient operating stations, allowing manufacturers to make evidence-based decisions to improve processes and maximise equipment performance.
For example, engineers can use operational data to investigate why a machine is taking longer to complete a particular cycle than expected. By examining individual process steps, they can identify where time is being lost, whether through unnecessary movements, waiting for signals from other equipment or inconsistent operating conditions. This gives manufacturers a clearer understanding of where improvements will have the greatest impact, rather than relying on assumptions or making changes that simply move the problem elsewhere.
earlier, minimise unexpected disruptions and support more predictable production.
Making efficiency a continuous process
FourJaw Manufacturing Analytics’ findings offer encouraging insight into UK manufacturing productivity. However, the pressure to improve efficiency will only intensify, and the focus must extend beyond hardware upgrades. Combining intelligent automation with real-time data analysis can reduce energy consumption, improve productivity and extend equipment life. Ultimately, manufacturing efficiency will depend not just on the machines manufacturers invest in, but on how intelligently they make them work.
To find out more about TwinCAT ® Analytics and the process steps involved, from the collection of raw data to possible measures and optimisations, visit www. beckhoff. com / en-gb / products / automation / twincat-analytics /
Importantly, this data also supports condition monitoring strategies by highlighting changes in machine behaviour before they lead to unexpected issues. For example, by identifying changes in vibration, temperature or operating patterns, maintenance teams can address potential problems
www. beckhoff. com
44 MACHINERY, AUTOMATION, CONTROL & ENGINEERING