Three Ways AI Is Revolutionizing Manufacturing

Among many industries that are being disrupted and revolutionized through the adoption of artificial intelligence and machine learning, the manufacturing sector holds the most promise.


Automation, robotics, and advanced analytics have already been a crucial tool in the manufacturer’s toolbox for years. They know how valuable data can be to streamline operations, increase efficiency, and reduce waste.

The manufacturing industry in the US alone generates around $2.4 trillion in output yearly, and this is why companies are ready to pour investment in smart manufacturing, industrial IoT and industrial business intelligence, to pick up a few percentage points.

Artificial intelligence in manufacturing is not causing disruption. Quite the opposite, the industry-wide application of AI and machine learning is pushing the sector to adapt — use more accurate and powerful sensors, faster migrate their data and analytics to the cloud, improve the connectivity of all systems and processes, and more.


In this article, we will look at a few examples and use cases of artificial intelligence in the manufacturing industry and explore how it can help businesses resolve the most common challenges in the manufacturing process.

Smart Maintenance of Production Line Machinery

In manufacturing, when any piece of equipment in the production line is damaged, repairing or replacing it is always only part of the cost. Production interruptions, downtime, workers' compensation and litigation (if anyone gets injured) should all be factored in to assess direct costs of a mismanaged equipment maintenance program.

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According to research, on average unplanned downtime costs manufacturers $250K/hour, which equates to ~50B annually. 42% of that cost is afflicted by equipment failure. 


For this reason alone, manufacturers are moving away from regular, preventive maintenance to predictive maintenance, enabled by AI and industrial IoT. Instead of running maintenance at regular intervals, which leads to planned downtime and slows production cycles, they implement predictive analytics solutions that predict the next failure or machine malfunction.


Solutions as such collect various data points from IoT devices, high-resolution cameras, and sensors to process it using machine learning and deep neural networks to detect malfunction patterns, which can be used to predict failures and administer inspections and fixes.


Because technicians are notified about failures ahead of time, they can act proactively while choosing in advance the best tools and methods to resolve the problem. This enables surgically accurate maintenance work, which leads to longer Remaining Useful Life (RUL) of the equipment and reduced downtime.

Defect Detection & Quality Inspection

Manufacturers today need to comply with complex quality regulations and standards while meeting strict product delivery deadlines and product quality expectations of their clients. As the products they manufacture are getting increasingly sophisticated, the production gets more complicated and nuanced.


This combination of factors pushes them to invest more resources in developing highly accurate and efficient quality control systems capable of detecting defects at the earlier stages of the production cycle, thus increasing overall quality, reducing the costs of post-production quality checks, and smoothing up production cycles. 


And this they have been doing for years, pouring billions in automated optical inspection (AOI) systems. According to MarketsandMarkets report, the automated optical inspection system market is expected to reach USD 1.6B by 2024.


AOI systems have proven to be effective. They ensure high product quality, enable early error detection, help reduce scrap and costs associated with waste, repair, and rework.

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However, AOI systems do have some flaws. They are slow, expensive, and relatively inaccurate when it comes to detecting defects in similar parts in changing conditions (e.g. different color or curvature, different lighting conditions, etc.). AOI systems can also struggle to assess defect complexity — be it a cosmetic or functional defect.

To address these challenges, manufacturers are moving away from AOI systems towards a computer vision-based visual inspection process. Powered by machine learning or deep learning algorithms, computer vision solutions not just capture and compare the images to pre-existing product templates, but self-learn on visual data to classify features of inspected products based on their characteristics. Through that, they can efficiently identify a wide range of existing and new defects in varying conditions. For instance, solutions as such accurately detect steel defects that are not noticeable to human eye.

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Real-Time Analytics & Predictive Analytics

In manufacturing, more data is generated than traditional systems and users can digest. With the advance of big data, cloud technologies and industrial IoT, manufacturers can now take advantage of this data to gain insight into how their equipment is used and how various operations and processes are managed.


As reported by Forbes, “Manufacturers are achieving only 40% of their potential because they are spending too much valuable time manually updating inventory control, production reporting, and pricing reports, when their competitors using real-time data are busy winning deals and planning next-generation real-time factories.”


Over-reliance on manual processes and traditional systems means more downtime, greater capital expenditure and asset maintenance costs, lagging product quality and worker safety.


To address these challenges, manufacturers seek to get their industrial business intelligence to the next level by implementing AI-powered real-time/predictive manufacturing analytics systems.

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Systems as such collect data from industrial control systems, sensors, wearables, and high-resolution cameras in real time, process it using machine or deep learning algorithms, and generate insights into the health of manufacturing equipment and processes. For instance, they can troubleshoot the root cause of equipment failure or process quality problems, benchmark worker productivity levels and machinery utilization rates, predict machinery repairs, and more.

Industry 4.0 Revolution Is Coming

The manufacturing sector has traditionally faced many challenges, from short time-to-market deadlines and product quality issues to inefficiencies in equipment maintenance, material movement, inventory management, and worker safety. The application of artificial intelligence (specifically, machine learning and deep learning) will resolve most of these.


Manufacturers can significantly cut unplanned downtime, design and produce higher quality products, reduce labor costs, detect and eliminate product defect earlier at the production cycle, increase production speed, and improve transition times.


All of these competitive advantages can be achieved through AI/ML-powered production monitoring, defect detection, real-time and predictive analytics coupled with industrial IoT and sophisticated industrial business intelligence.


And even through the industry 4.0 revolution is still in its infancy, manufacturers are already driving significant benefits. For example, Siemens has applied AI to reduce the turbine’s nitrous oxide emissions by 10-15%, thus optimizing combustion in engines. GE has used AI-powered process tracking and monitoring to increase productivity at its factories by 5%.


In the future AI will continue to transform the manufacturing sector. It will be used to augment factory robotics, improve human-robot collaboration on the factory floor, drive mass customization and mass production, and more.

Thank you for reading! 

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