How AI-Powered Machines Can Accelerate Industrial Automation in Manufacturing

October 18, 2021

Blog

Today artificial intelligence (AI) and machine learning (ML) are the key driving forces of change in the manufacturing industry. AI and ML are enabling manufacturing to become more automated, bringing speed to efficiency and driving down the costs of the goods that consumers use every day.

While factories have already deployed computer vision (CV) to optimize production lines, companies can combine the power of CV with AI to significantly improve manufacturing throughput and quality, two essential metrics for an efficient production line. As factories become more automated with interactive human-to-machine processes, AI is also being used to deliver a new level of workplace safety.

AI leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind, enabling systems to detect objects and make predictions with incredible accuracy and speed. Deployed with traditional CV, AI can accelerate anomaly detection to factories – for example, inspecting cereal boxes for defects on the production floor to car scratches on an assembly line – to improve manufacturing efficiency and lower production cost. 

However, deploying AI solutions for industrial automation has been a challenge. AI and ML techniques are still new in industrial automation when compared to traditional CV. Automation engineers in the manufacturing field do not yet have the expertise to develop effective AI algorithms. Several AI technology companies are removing these hurdles by providing a complete inference solution – high performance and low-power hardware in a small form factor, along with ready-to-deploy AI algorithms. We’ll see more investment being poured into this area as more factories want to take advantage of powerful AI processing solutions to improve efficiency and workplace safety.

Beyond anomaly detection on the production line, AI-powered robots are being deployed  to work side-by-side with humans. Examples are autonomous mobile robots (AMRs) that transport packages within warehouses and collaborative robots (cobots) which assemble products alongside humans on the manufacturing line. These factory robots combine the precision and efficiency of machines with skills and intelligence of human operators to offer the best of both worlds. AI-enabled robots increase productivity by performing repetitive and strenuous tasks, while also tracking the human operator’s position to maintain a safe operating environment.

Advancements in edge-AI processing have paved the way for today’s AI robots and will open up new possibilities for robots in the future. Intelligent robots will have to process an incredible amount of information and decisions will need to be made in real-time, so it is much more efficient for these machines to process information at the edge instead of sending it to the cloud and back.

Neural network inference processing is compute-intensive and power-hungry, and has traditionally required costly hardware, hundreds of watts of power, and bulky cooling solutions. New technologies – including analog compute-in-memory – have made it possible for high performance neural network processing to be extremely power-efficient and scalable for  deployment in endpoints to edge servers.

As the need for automation in factories continues to grow, factories will increasingly turn to AI-powered machines to improve the efficiency of day-to-day processes. This opens the door to introduce even smarter applications into today’s factories, from smart anomaly detection systems to autonomous robots and beyond.