To address these challenges, Optimal Industrial Automation and Mitsubishi Electric collaborated to develop a state-of-the-art, fully automated leak detection system. The key components of this solution include:
- Two Mitsubishi Electric FR-Series SCARA robots that precisely pick and position MDI canisters into moving pucks.
- MELSEC iQ-Platform PLC for seamless synchronisation between robots, conveyors, and testing equipment.
- MAPS SCADA software for real-time monitoring, data logging, and process optimisation.
- An 86-cavity puck conveyor system, designed to transport canisters through a sealed testing tunnel.
- Advanced laser gas analysers, which detect even the smallest gas leaks with unmatched sensitivity.
The SCARA robots are especially important, because they ensure that canisters are accurately positioned into the moving pucks for continuous, in-line testing. This eliminates the need to stop the conveyor, allowing for uninterrupted production and higher throughput.
"Synchronisation on this line is critical because the canisters are unstable and may topple unless the motion of the robots and conveyors matches perfectly," explains Martin Gadsby, Director at Optimal Industrial Automation.
Critical to meeting the demanding timing and reporting requirements of the new line was a control solution offering ultra-responsive command processing and seamless data capture. In his own words, Steve Kirby of Mitsubishi Electric explains how their platform rises to this challenge:
"The iQ series PLC is ideal to meet the levels of speed and accuracy required by the system. The controller executes command processes in a matter of nanoseconds. It also offers a high-speed data logger module, where the sampling function is synchronised with the sequence scan," adds Steve Kirby, Key Account Manager at Mitsubishi Electric.
Operators can monitor the process in real time via Mitsubishi Electric’s SCADA platform, which collects and logs analytical data for each canister. This not only enhances traceability but also enables data-driven decision-making to optimise production processes further.