From there, a technician must determine the value of this data. At first glance, the data can serve as an alert function when something is amiss or when a technician is attempting to solve an issue with the mold, part, press or people. For example, the data indicates when a preventive maintenance (PM) run is due or overdue, how many cycles are run during a specific timeframe, if a mold cycle time is in or out of an accepted range, when the mold starts or stops, how long a machine is idle or down and when process conditions exceed a set range.
Once this data is implemented and tested, and technicians have determined a verified range, they can maintain a more consistent process, which helps them troubleshoot mold function and part quality issues and more accurately forecast tooling life. Without process consistency, it can be difficult—if not impossible—for technicians to determine a root cause, leaving them with few options, like simply cleaning the mold or sticking in new tooling and hoping the issue magically disappears.
It takes human interaction to fill in the blanks that these live data signals create. Before a toolroom tackles an issue, the technicians need to know exactly which issue that they should examine. It takes skilled technicians to prioritize the issues that they must investigate. Some larger companies have thousands of documented mold and part issues in their databases. Imagine trying to chase all the “out-of-range” issues that live electronic signals provide.
For example, what if during only one run, a host system was flagged indicating that a mold experienced a clamp or an injection pressure increase along with a cycle time and mold temperature change? What if the result of these changes were negligible and the mold still produced good parts during the run? What if the mold had a couple of cavities blocked for non-fill, flash or a burn during a run? The answer is unclear. The technician either sets up a DOE (Design of Experiment) to chase these process-related issues or hopes that they go away during the next production run. Perhaps the data is so confusing that the best answer is to stick in new tooling, give it a good cleaning and hope for the best.
Where and when a runner or part got hung up.
The occurrence of the tooling or the component galling or locking up.
The corrective action that was taken, when it was performed, the technician who was responsible or the length of time that the action took.
Which operator was on break, leaving the door open.
The frequency and maintenance costs of the issues.
Typically, when a mold lands on a bench for PM and repair, a work order spells out the current issues that the repair technician must address. A continuous improvement culture requires technicians to identify what they need to fix now and the high-frequency or high-cost issues that have plagued the mold during past runs and the typical corrective actions. Technicians should also perform defect -osition analysis to identify defect patterns and trends quickly. This analysis is at the heart of a continuous improvement maintenance strategy, arming repair technicians with the right data to make more informed decisions.
Electronic data and the range of connectivity surely will improve a toolroom’s ability to produce quality parts on-time and efficiently, but in the world of maintenance, entries that are accurate and manual will always be required to give technicians the necessary information to attack real issues and measure real improvement.