Applications of Machine learning in the manufacturing industry opens up a wide range of opportunities for optimizing the manufacturing processes. Technology has drastically changed how organizations go about their manufacturing operations. But no innovation has provided more incentives than machine learning (ML).

In fact, Zion Market’s report found that the global machine learning market is expected to reach $20 billion by 2024.

This implies that more organizations are willing to adopt the technology in the near future. But more than any industry, it’s manufacturing that’s showing the most interest.

Advantages of using machine learning in manufacturing industry

Here are some of the reasons why we should start integrating hardware’s that support machine learning in manufacturing industry.

Faster engineering of circuits

Printed circuit boards (PCBs) are a critical part of every electronic device, as they hold all of the important components like its microprocessor and diodes. Due to the highly technical nature of PCBs, it currently takes a bit longer to produce them. Fortunately, machine learning algorithms can help speed up the process. Protel PCB software has smart algorithms that help designers find the most optimal placement for PCB components. It even has an intelligent routing feature that’ll let them skip out on the need to wire the entire circuit manually. Machine learning integrated hardware can also analyze previous tape-outs to identify pre-existing bugs and complexities, allowing PCB designers to avoid making the same mistakes again.

Prevent equipment failures

Manufacturers are avid users of internet-of-things (IoT) endpoints to control the production process from one location. However, the most valuable aspect of IoT endpoints is how they can be programmed with the machine learning code. For example, this code can notify the engineers if something isn’t working normally. AI consultant Alexandre Gonfalonieri explains that machine learning estimates the Remaining Useful Life (RUL) of a machine. Aspects like temperature, processing speed, and failure windows can be tracked. If the system senses that its RUL score is below average, it can call maintenance to have it fixed. If it’s at critical levels, then it will make the recommendation to replace it. This way, factories will always be at optimal efficiency.

More efficient logistics processes

Manufacturing is more than the creation of products and intricate devices. There’s also a lot of logistics involved in the process, which machine learning can help with. For example, every pallet of raw material can be tracked with Real-Time Location System (RTLS), so the inventory can always be accounted for. Similar to machine learning integrated hardware, this system’s algorithm can also remember past mismanagement incidences and inform companies on ways to avoid it. This ensures that truck paths and inventory handling will be done in the most optimal way possible, saving companies time and money.

Regulates information access

With such a connected IT infrastructure, access to that data, and control over the system must be heavily limited. In 2017, the world experienced a devastating breach called NotPeya, which wreaked havoc on industrial systems and cost the industry more than $10 billion in damage. But with a well-placed machine learning algorithm in place, most of these breaches can be prevented. Machine learning has the potential to use a framework called “Zero Trust Security.” This means that every user, even those affiliated with the company, undergo security validation before they’re granted access. The machine learning analyzes how these users access the data and reports any suspicious behavior.

The use of machine learning in manufacturing industry has brought further advancements and more widespread adoption of the technology. With the adoption of machine learning integrated hardware, manufacturing operations have become much safer and more efficient.