How does AI help you master precision CNC machining?

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How does AI help you master precision CNC machining?

How does AI help you master precision CNC machining?

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Jun 28,23

Getting highly accurate CNC machining results allows a machine shop to build and maintain a reputation as a trustworthy industrial service provider. Artificial intelligence (AI) is rapidly emerging as a tool that can substantially improve CNC machining results, says Emily Newton.

How does AI help you master precision CNC machining?

Computer numerical control (CNC) machines are essential for various manufacturing processes. It makes sense that people are always interested in discovering new ways to improve CNC machining accuracy. Succeeding could reduce wasted time and materials, plus increase the percentage of satisfied customers.

Artificial intelligence (AI) is rapidly emerging as a tool that can substantially improve CNC machining results, provided people take the time to learn the best ways to use it. Here are some eye-opening ways that people can apply artificial intelligence to get better precision CNC machining results.

Achieving better CNC machining accuracy with ML

Artificial intelligence excels at processing large amounts of data much faster than humans could if working with that same information. Machine learning (ML) — a subset of AI — uses algorithms that function similarly to how the human brain gains new skills and knowledge. The algorithms gradually improve through increased data exposure and use.

Researchers at the Indian Institute of Science (IISc) wondered if ML algorithms could help them make better judgments about which input parameters would result in the best key performance indicators (KPI). The system can also detect deviations from inputted data and immediately alert operators. That feature supports improved CNC machining accuracy by enabling proactiveness.

The current approach is to use physics-based calculations to determine the ideal input parameters. However, those results are generic, so they typically don’t provide the necessary level of control for every project. The researchers also believe this is the first instance of using algorithms to predict a CNC machine’s KPIs.

This method removes any manual calculations or assumptions because the algorithms work without ongoing human input, tracking data as a CNC machine operates. Thus, successfully using AI this way could significantly change an operator’s workflow for the better.

Letting Cobots tackle lower-value tasks

People are better equipped to raise CNC machining accuracy when they have lower overall workloads that allow them to stay focused on the steps that will lead to the best outcomes.

That line of thought was likely partially responsible for why the decision-makers at ABACORP CNC Machined Parts chose Cobots (collaborative robots) to help the company innovate and stay competitive. The machine shop has a first-pass quality yield for finished products of more than 98 per cent, and Cobots have played a major role in helping the business get to that point. It installed the first two Cobots in 2018 and now has six.

Co-founder Rob Frankel disagrees with the belief that robots take humans’ jobs. He discussed how, before the company got its Cobots, operators earned minimum wage to stand at machines for their whole shifts. Once the company had its AI-powered machines, it retrained workers, allowing them to boost their earnings and capabilities. Instead of letting workers go, the company has expanded its workforce size since adding the Cobots.

The machines handle some machine-tending roles, but they also load and unload parts. Many machine shops must have various strategies for material handling. Some facilities have overhead cranes that can lift 50 tonnes, accommodating the largest pieces.

However, Cobots are often well-suited for smaller loads, especially since they excel at repetitive tasks and don’t get fatigued. Having machines assume the more labour-intensive tasks frees up humans for responsibilities that are more rewarding and require problem-solving the Cobots can’t do.

Deploying AI for predictive maintenance and optimisation

It’s not easy for even the most experienced machine tenders to know when CNC equipment needs a repair or replacement. For example, one of the harder-to-notice signs is that the machine will gradually take longer to do jobs. But the slowdown is often so subtle that people don’t notice right away — and maybe not until it’s too late to correct the problem without causing a substantial disruption.

Unaddressed problems can degrade CNC machining accuracy. However, some AI products can detect when equipment is operating unusually. It alerts human users, who can examine the cause sooner — saving time and money on a resolution.

One option uses a neural network to learn how a machine functions under ideal conditions. Then, instead of just alerting people to deviations, this solution tries to determine what’s wrong. Those behind this AI tool had to teach it to recognise 396 parameters, which required 2 million data points as training data. They used the AI on a three-axis CNC machine, and training took approximately 12 hours.

The resulting algorithm can pick up on problems such as mechanical and electrical failures or age-related issues. Then, after discovering those anomalies, the CNC machine can do everything from alerting the appropriate person to stopping production.

When optimisation and fault detection happen in the background like this, it’s easier for people to focus on other things within their control that cause better CNC machining accuracy. It then becomes more likely that machine shops will have consistently higher productivity rates.

CNC machining accuracy elevates customer happiness

Getting highly accurate CNC machining results allows a machine shop to build and maintain a reputation as a trustworthy industrial service provider. Artificial intelligence alone is insufficient for competitiveness in today’s marketplace. However, these examples illustrate that the technology can pay off in big ways for those who take the time to understand its capabilities and the best ways to apply it.

About the author:

Emily Newton is a tech and industrial journalist and the Editor-in-Chief of Revolutionized Magazine. Subscribe to the Revolutionized newsletter for more content from Emily.