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Applications of Machine Learning in Electronics

Key Takeaways

  • Machine learning enables machines to read and interpret data.

  • Predictive maintenance takes the communicative advantages of machine learning and applies them to everyday maintenance.

  • Machine learning is not some pie-in-the-sky sales pitch - it’s seeing current adoption in high-tech industries today.

Computer connected to a robot head

Machine learning uses user-defined criteria and datasets to drive automation

Technology and electronics are two fields that walk hand-in-hand, and perhaps no current development has as much likelihood to revolutionize the use of automation in businesses. The application of machine learning in electronics is limitless, yet design refinements have already shown profound results in current productions. Machine learning has the potential to push design flow past its current limitations and increase safety and efficiency.

The Basics of Machine Learning 

Perhaps it may at first seem like an overwrought method of data interpretation, but machine learning provides an avenue to unlocking automation in system processes that may otherwise be overlooked. At its core, machine learning is given some algorithm that outlines the data it finds noteworthy as well as how to parse this information from the surrounding noise. Well-trained machine learning algorithms can be used for many different kinds of data and quality assurances, such as visual inspection of component placement on a PCB or monitoring the sound waveforms of equipment to determine if there are any abnormalities compared to normal operation.

The Benefits of Automation

Before continuing, the reader would be well-served to understand why machine learning is such a tantalizing subject. Automation is able to perform tasks with a high level of accuracy and confidence, provided the framework of the goals is well-defined. This has been the case in jobs for centuries, but only in recent years has machine learning gained enough sophistication to become present at the design level. The synergy of a user-defined algorithm and computing power provides an excellent potential tool to transfer some of the workload and oversight to automation while still realizing many benefits.

Predictive Maintenance: Save Time and Money

White board with text describing predictive maintenance benefits

Predictive maintenance provides these benefits and more

No matter the industry, system downtime threatens bottom-line efficiency. Depending on the particular machinery or process, this can run the gamut from mere nuisance to a complete operations shutdown. The foolhardy approach to maintenance is handling repairs as they manifest - essentially this is throwing care to the wind and allowing the machine wear to dictate to operators when unannounced downtime is to occur. Maintenance can also take a preventative form where repairs are done on some schedule that is likely (though not necessarily guaranteed) to occur before the minimum time or cycles to failure.

There is another way. Predictive maintenance uses data generated from equipment to gauge when wear begins to result in an appreciable difference in function. The advantage of this is it does not have to rely on sensor data (though that can be a component of diagnostics), but instead can take information directly from the component, device, or machine in question. Take an example of fans providing air circulation and cooling; without predictive maintenance, mechanical failure could result in significant damage to anything the fan is cooling. Predictive data could incorporate a tachometer to track fan speed or an anemometer to measure the speed and direction of airflow, but may also look at more fundamental aspects of wear such as vibrations or sound. Tracking these basic manifestations provides a deeper level of insight into when maintenance should take place.

One final benefit of predictive maintenance is that it reduces the need for constant human oversight. Electronics that have the capability to read their own sensors and react proactively by ensuring that human attention is called only when the algorithm believes it is absolutely necessary. This also provides some quantification of how slow or abnormal a device may be running, yielding insight to the operator as to how pressing the maintenance may be. Ultimately, adding additional informational resources only serves to bolster the operational knowledge of any business.

Other Applications of Machine Learning in Electronics 

Inherently, machine learning is exciting because, given the correct dataset and algorithm, it has the ability to transform any section of design and industry that can generate a usable dataset to fuel automation. With machine learning being something of a lightweight heuristic model, it also endears itself to widespread adoption, especially among platforms that may otherwise struggle to run more computationally intensive models. A relative simplicity, in these cases, proves more important than raw computational ability. 

Some current and future applications of machine learning in electronics include:

  • Voice commands provide a way to interact with systems at a distance. This can prevent safety issues by distancing operators from moving equipment, temperature extremes, and other occupational hazards. In addition to avoiding hazards, there can also be ergonomic gains for operators who, instead of having to stoop, bend over, or adopt other taxing postures, can indirectly access the requisite functionality.
  • Computer vision is the standard data format mentioned when discussing machine learning. However, the ability to visually process many small features or design elements in rapid succession, such as in a high-volume production run, would accelerate QA lead times without sacrificing accuracy.
  • Chip design is a field where power, performance, and the area must all be optimized to reach the goals of future processing power in increasingly dense packages. In addition to improvements in key efficiency metrics, machine learning algorithms free up engineers from the mentally grueling task of chasing efficiency so they may better focus their efforts elsewhere.

Square package with traces in/out highlighted

Chip design is just one discipline of engineering that stands to make great gains from machine learning

For your future machine learning design needs, look no further than the integrated PCB design and analysis software from Cadence. Allegro PCB Editor has all the tools you require to optimize designs for the application of machine learning in electronics in a powerful yet easy-to-use package.

Leading electronics providers rely on Cadence products to optimize power, space, and energy needs for a wide variety of market applications. If you’re looking to learn more about our innovative solutions, talk to our team of experts or subscribe to our YouTube channel.