Artificial intelligence (AI) is a technology which promises to improve productivity across every industry where it is applied. And indeed this is not just a pipe dream, but rather a reality for a growing number of sectors where machine learning algorithms are being implemented to overcome long standing barriers to efficiency.
Manufacturing is one such industry which has a lot to gain from AI, and CNC equipment in particular can be made more productive and less prone to errors with the right software solutions onboard.
With all that in mind, here is a look at the specific advantages that AI offers in a CNC machinery context, and why this is relevant to manufacturing firms of all sizes.
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AI & machine learning explained
Broadly speaking, AI is an expansive term that takes in tech which is capable of emulating the thinking processes and problem-solving skills of humans, with a combination of software and hardware making this possible.
Machine learning is a subcategory of this, and as the name suggests it describes the use of algorithms which are able to get better at the tasks they are charged with over time.
This means that they don’t need as much intervention from flesh and blood programmers and operators to deliver performance improvements.
You can apply machine learning to everything from data analytics for digital marketing purposes, to CNC equipment operations and beyond.
Making an impact on manufacturing
While we’re going to focus on CNC machinery in a moment, it’s worth talking about the broader context of AI on the manufacturing floor.
In essence, the right machine learning algorithms can interpret the deluge of data delivered by all sorts of sensors throughout the production line, monitoring mission-critical systems and delivering insights in real time.
As such, AI makes it possible for entire infrastructures to be monitored, managed and made more efficient as a whole, without neglecting the individual parts of a given ecosystem.
The more sensors are rolled out and the more points at which data can be collected, parsed and interpreted, the greater the role that AI will have to play in this industry.
Integrating with legacy equipment
It might sound like AI is only intended to have an effect on cutting edge manufacturing environments, but the reality is that it can improve the performance and productivity of an old lathe machine just as much as it can the latest and greatest CNC laser cutters and additive manufacturing gear.
So long as there is data to be fed into the software, machine learning can be implemented with legacy equipment and new hardware alike.
And because CNC tech is so well established, while still relying on a standardized set of instructions and parameters, it is a natural fit for integration with AI solutions that are being designed at the moment.
Tackling the troubles of downtime
When machinery is taken out of action unexpectedly, entire production lines will grind to a halt and the costs can quickly spiral into the millions for manufacturers that are hit by such disasters.
It is not enough to be aware that maintenance is needed at some point to prevent unplanned outages; businesses need to be able to predict when this will occur to ensure productivity can be kept at a peak while maintenance costs are minimized.
AI tools are capable of taking performance data generated by CNC equipment, comparing this to the base level readings that are expected in normal operation and detecting any anomalies so that operators can plan to replace parts that are succumbing to wear and tear and thus performing suboptimally.
The first advantage is that this can be done automatically, so alerts of anomalies don’t need to be detected through the painstaking efforts of operators manually monitoring machinery.
The second advantage is that the data used for determining whether equipment is working as intended, or on its way to a period of disastrous downtime, doesn’t just have to come from one business.
Instead the algorithms can compare moment to moment metrics against those accumulated from across a huge number of manufacturing setups, enabling undoubted accuracy.
And to top it all off, the machine learning element means that with each data point that flows in, the software gets better at looking for nascent faults in CNC hardware.
So while in the past productivity would take a nosedive with even the smallest amount of downtime, businesses can plan maintenance efficiently and schedule it at the right moments to keep the entire line working optimally.
Optimization through virtualization
Talking of optimizations, it should also be noted that AI is helping to improve productivity in manufacturing even before CNC equipment has been brought in to process materials and create new components.
This is thanks to the advanced modeling and virtual testing tools that once again rely on data drawn from real machines to create intangible equivalents in software.
These digital replicas of CNC gear can then be trialed by designers and operators so that their operational parameters are well optimized prior to a real production run kicking into gear.
This saves a lot of time that would normally be sent on tweaking machine settings and adjusting other aspects of the CNC coding. The result is lower costs as well as less waste.
Last of all, it goes without saying that automated, AI-enhanced CNC machinery and the associated mechanical systems which modern manufacturers have at their disposal can be far safer for human workers than older technologies.
Whether by taking flesh and blood operators out of hazardous environments, or reducing accidents by fulfilling potentially taxing duties with heavy equipment and tooling, AI is protecting people in the workplace like never before.
The further good news is that all of this automation and productivity is not rendering human employees redundant.
Instead it is empowering them, and giving them the opportunities they need to be more productive as well. Once again it is an example of people working in harmony with machinery.