Why CNC Machining and AI Make a Perfect Match

In the world of manufacturing, CNC machining is coming out on top. CNC machining is used everywhere from the automotive and medical industries to aerospace, gas and oil, and warehouse operations. Anywhere you see a machine, a molded part, or a product in use, CNC machining probably has a part in its construction.

Given the rise in uses for CNC machining, it’s no wonder industries are catching onto the high-accuracy, quick-production times that CNC operations provide. And, with the manufacturing world increasingly looking to incorporate IoT into their productions, the future of CNC machining is looking incredibly bright.

What’s next for the world of CNC operators? An increasing move to incorporate artificial intelligence, or AI, into operations. Over the next several years, CNC machining could see something of a revolution that includes machines that respond to Alexa-like voice commands, predictive learning and machine scheduling for optimized performance and down-time, and better data analysis, programming, and testing.

Sound like a stretch? It’s probably coming sooner than you think. AI is poised to be the next big revolution that will enhance production capacity, boost machine productivity, and optimize an industry whose goal is to function with lights-out capability. Here are just a few ways AI and CNC machining make such a perfect match, and why these changes are rapidly approaching.

 

Deep Learning Drives Machining Changes

Human operators aren’t the only ones that drive machining changes. Real-time data, analytics, and deep learning can also drive the way a machine learns, responds, and optimizes production. Data sets are key to helping operators gain insight on how a machine functions and, ultimately, how an entire floor of machines work in sync with each other.

Data is critical to driving the way CNC machines and even 3D printing processes are used. Data sets can determine how downtime is scheduled and discover ways productivity can be increased. Metrics including utilization rates, prescriptive and predictive data, and diagnostic data all combine to form a picture of how each machine is performing in contrast to production goals. Where AI comes in is in taking the numbers, relaying them directly to machine operators and the machines themselves, and automatically suggesting performance changes, timing changes, and production changes to ultimately increase total throughput. 

When deep learning is introduced to machines on the shop floor, the potential to make production-boosting changes is exponential. Deep learning means that machines don’t simply respond to one data set. AI is always dynamic, meaning that machines learn as they are fed instructions from operators and data sets. This flux creates an environment in which machines are continually learning how to process an order. As deep learning takes place, overall efficiency, productivity, and production value increase.

Artificial intelligence software and programming that drives deep learning provides more than a short-term benefit. Deep learning is a long-term investment that will benefit CNC machining by boosting operational capabilities, eliminating unnecessary downtime, and ultimately benefiting the bottom line of every CNC machining company.

 

Productivity and Efficiency Increases

Two categories where artificial intelligence benefits CNC manufacturing the most are in productivity and efficiency. It could be argued that every single benefit of AI and deep machine learning will directly or indirectly impact the core values of shop productivity and efficiency.

Machines that produce and analyze production data, providing results in real-time to human operators, are prime candidates for boosting productivity. As data is analyzed and suggested changes reviewed by operators, shop owners can instantly change up the way a machine performs, resulting in entire percentage points’ worth of through-put and greater resiliency for the company.

Predictive and diagnostic data with deep learning will produce machines that automatically alert an operator when a machine will require servicing, part changes, and downtime. Diagnostic data can also help machinists and maintenance crews to make changes for better efficiency and functioning in real-time thanks to a never-ending data loop.

Imagine receiving automatic alerts when a machine should be serviced, a part changed, or a function repaired before the machine goes down and at just the right time in your workflow to allow for maximum shop flow while your machine is offline. That is what artificial intelligence can conceivably accomplish when combined with CNC machines.

Ultimately, more knowledge and better decision making result in less lost time for the shop floor. Scheduling maintenance, organizing jobs, and facilitating increased production all become realities thanks to machine learning and the artificial intelligence software that produces it.

 

Cost Savings are Driven Upward

One of the most significant issues with running a machine shop is in knowing when CNC machines require calibration, tune-ups, part adjustments, and servicing. No matter when these needs pop up, the process will always cost time and, as any shop owner knows, time equals money.

What if you could predict the optimal time for servicing, jump on calibration before it’s needed, swap out parts before they start misfiring on a production run, and ruin hours of work? That is exactly what artificial intelligence can provide. When a machine is driven by data and operators receive real-time streams of data feedback, predictive measures can be taken.

Optimizing the shop floor is critical in saving money and ultimately boosting total profit per production run. The big problems in shop optimization occurs when machine failure causes production to cease. Lost time is always lost money and unexpected or unaccounted for problems can create a significant dent in the expected profit from a production run. Some failures can cause so much lost time that profit from a single production run vanishes altogether. Knowing your machines is vital to keep the shop floor humming along.

Artificial intelligence can predict when machines need to be serviced and gauge the optimal time to do so. By working with a set of data that is connected to your production runs, run times, machine productivity, and tool life, AI can predict optimal times for servicing and machine tune-ups. Figuring out optimal servicing and downtime slots is crucial to keep shops running at peak performance. AI data will also provide information on how long a machine can run before it needs to be serviced. Predictive data such as this means less tool failure, prolonged tool life, less downtime, and saved time and money.

 

Industry-Wide Transformation

Artificial intelligence is set to transform the entire machining industry from the ground up and it’s easy to see why. With AI-based software introduced into CNC machine shops, productivity level is expected to rise, shop owners should see their bottom-line increase, and the industry will be one step closer to a lights-out setup. After the supply line disruption and painful reality of where manufacturing shortfalls in 2020, AI is looking to swoop in and save the day.

Progress certainly won’t be immediate, and many shops will need time to incorporate changes to their manufacturing practices, but those that do will reap the benefits. Eventually, everything is shifting toward IoT and AI incorporation. For CNC machine shop owners, it’s no longer a matter of “if” but “when” these technologies will get adopted. Many early adopters are already seeing positive results. Unfortunately, with the rapid transition we’re seeing, if owners wait to incorporate this technology for too long, they might just get left behind.

 

Christine Evans is the Director of Product Marketing & Content Strategy at Fictiv, an on-demand manufacturing company. Over the past six years, Christine has grown Fictiv’s popular Hardware Guide and Digital Manufacturing Resource Center, with over 2,000 teardowns, DFM guides, and mechanical design articles to help democratize access to manufacturing and hardware design knowledge.

 

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