During sheet metal fabrication, a host of equipment is used to cut, slice and shape your raw materials according to specific dimensions. Whether you're developing a prototype or you simply need a specific component fabricated for downstream processes, you'll need a fabrication partner who keeps their machines in good shape. Indeed, random failure in such equipment may result in costly downtime and project delays.
Before selecting a company to work with, it may be worthwhile to ensure that they implement some form of predictive maintenance on their machines. Predictive maintenance is the use of data-driven insights to ensure that all equipment is working under optimal conditions. This is a more effective approach than preventative maintenance (where machines are only serviced according to manufacturer recommendations).
If your fabricating partner isn't using this new technique, you can recommend useful steps that they can follow towards this end. Implementing predictive maintenance can be achieved by the following "step-by-step" procedures.
Identifying where the problem lies
Your metal fabrication company can begin predictive maintenance by identifying where common equipment issues occur. For example, a laser cutter may commonly experience overheating after being used for certain periods. Similarly, a gutter machine may obtain worn out bearings or loose moving parts when handling large-capacity projects. It's up to your metal fabrication company to identify these problematic variables and determine how they can be addressed.
In most cases, metrics may need to be used to measure, analyse and address problematic areas. You should also request an analysis of how these problems may affect your project timelines and costs whenever the issue arises.
Incorporating company-wide processes
Quantifying equipment issues is only the first step in implementing predictive maintenance. Next, your fabricating partner should identify how the larger company can respond to equipment issues in a manner that causes minimal impact on your project. In other words, existing workflows and assets should be optimised for data gathering using sensors.
Real-time data sharing is the best way of detecting operational issues and addressing them before they become worse. There should also be clear communication channels to inform you of how your raw materials are doing whenever a problem is detected.
Structuring and analysing data
Placing sensors on equipment will provide a stream of real-time data. But how can this data be used to limit machine failure? The key is to structure and analyse data as soon as it's collected. Your fabricating partner should have an ERP or data mining platform where information regarding laser cutting components, robotic arms and CNCs can be integrated for analysis.
By detecting fabricating equipment issues early, maintenance can be carried out even before the machine starts to operate at a sub-optimal level.