A smooth operation is the goal of any manufacturer – meeting deadlines, avoiding downtime and maintaining an overall cost-effective process. Yet there are so many unknowns that can influence the operation, especially when it comes to complex machinery and systems. Machines are composed of so many parts, every single one breaking or malfunctioning can harm the manufacturing equilibrium and the entire operation, every minute of downtime can be costly. What if you knew ahead of time of a problem before it even happens? That’s Predictive Maintenance – a system designed to answer a manufacturing pain point and help manufacturers minimize or even eliminate downtime. But how can Additive Manufacturing (AM) and Predictive Maintenance (PdM) work together? Let’s take a look at the possibilities.
Bridging the Time Gap
Predictive Maintenance relies on IIoT (Industrial Internet of Things), using sensors and other connected devices which collect and analyze data such as temperature and vibrations, there are even a couple of startups analyzing the sounds on the production floor. The data is used to gain insights on equipment productivity, down to a single machine component. Usually, when it comes to maintenance manufacturers operate in two ways, the first is routine scheduled maintenance, the second is reacting when an issue comes up. Calendar-based maintenance isn’t an ideal solution, it means time and money spent on occasions that might not require it. And reacting is no longer enough. Reacting means it’s already too late, even if you react fast any downtime comes at great costs, in the automotive industry, for example, a minute of downtime can cost $22,000. So it only makes sense manufacturers aim to switch from reactive to preemptive action. It’s not clear how much time in advance a predictive system can alert in case of a part needing to be replaced, for certain parts ordering and receiving a new part can be a matter of weeks if not months. If the system alerts days in advance, that probably still won’t be enough to prevent downtime. But an emergency spare part can be additively manufactured in days. When downtime is calculated by thousands of dollars lost per minute, time is of the essence is an understatement. (below part from an Aalto University and VTT Finland Research project – a digitized network of 3D printed spare parts, up top 3D printed metal gears by Sculpteo)
The Bigger Picture – Connectivity
Manufacturers are already collecting data – as automation and digitization are part of many production lines – but, it’s not just about obtaining the data it’s about how the data is used, asking the right questions and creating a whole picture. More information allows for better and more accurate predictions yet at the same time, it makes decision making even more complex. The goal is to eliminate downtime but the way to do so has to take into account data from all levels of the company as well as outside of it. What if even an additively manufactured emergency spare part can’t get there in time? The answer can be a combination of changing machine settings, ordering both a permanent replacement part and an additively manufactured emergency part. For example, if there is a notification of a failure that might occur due to high temperatures a possible solution can be slowing down the machine just enough to lower the temperature and extend the time until the replacement part arrives. This may lengthen production time but in some cases, a manufacturer will prefer slowing down rather than reaching the point of standstill. In other cases, the same manufacturer will prefer keeping production running based on an estimation that a certain order can be fulfilled before failure occurs. This requires looking not only at production data but also management and supply chain data in order to evaluate, consider all aspects and recommend a solution. Such an approach is beyond prediction and is called Prescriptive Maintenance. “It just isn’t enough to know what can fail or when it might fail. It requires having enough information to understand the options for maintenance as well as the financial implications of each option,” says Dan Miklovic, principal analyst at LNS Research. This is another point where AM and PdM are aligned, both work best integrated into a virtual supply chain, both exemplify the need for connectivity across the company.
A Combo Solution
Another interesting PdM and AM combo can answer a dilemma manufacturers face when implementing predictive maintenance – is predictive maintenance worth scrapping older machines and acquiring new ones? Predictive maintenance isn’t limited to newer connected machines, sensors can be implemented in older existing machines, yet in this case, as well, there are many factors to take into consideration. Perhaps the initial investment of generating data from older machines isn’t cost effective – if a part breaks down, even if you are alerted in advance, it might be a discontinued part, therefore, hard and costly to replace. But again, here a combination with AM can provide the solution. By identifying discontinued parts that might be prone to failure, a company can use scanning and reverse engineering to create in advance a CAD library of parts ready for 3D printing, keeping files of replacement parts and ordering only when necessary. The same logic can be applied for increased efficiency. If the system recognizes a part that can work better, with AM the design can be easily adapted and improved, taking advantage of the AM benefits of quick production, and iterations. (above Siemens additively manufactured parts for gas turbines, below gear parts additively manufactured by Graphite-AM)
While there might not be a direct line between the Additive Manufacturing and Predictive Maintenance, the two can complement one another in the goal of reaching a smoother and more efficient manufacturing operation. What are your thoughts on AM and PdM? Tell us in the comments below. For more insights and information follow us on LinkedIn or subscribe to our newsletter for weekly updates.