AI and Industrial Automation

The global industrial automation market will grow from $190.9 billion in 2023 and $256.7 billion in 2027 at a compound annual growth rate (CAGR) of a little more than 7%, according to The Business Research Co.'s Industrial Automation Global Market Report 2023.

AI In Industrial Automation

AI applications are already becoming prevalent in many industries. The fourth industrial revolution is driven by new ways of automating the industrial tasks with smart sensors, controllers, IO modules, PLCs, gateways, enterprise systems, etc. where humans and machines interact to create a stronger digital ecosystem. The implementation of artificial intelligence (AI) in industrial automation is a key trend that involves robots performing complex human tasks. Currently industrial automation requires significant programming time to accurately perform tasks. AI is changing this, allowing robots to take on more recurring activities, designing the production model, rising accuracy, building automation solutions, eradicating human error and delivering superior levels of quality assurance.

Machine Learning in Industrial Automation

ML is a subset of AI and empowers the computers to learn automatically from data inputs and applies that information without human intervention. ML aids in optimizing the production, supply chain efficiency, fraud detection, risk analysis and mitigation, GPS based predictions and targeted marketing to name a few. ML algorithms can be categorized as Supervised Machine Learning, Unsupervised Machine Learning and Semi-supervised Machine Learning.

Impact of AI on Industrial Automation

• Improved product and service quality through computer vision
• Valuable insights from Data
• Boost productivity and safety with Collaboration Robots (Cobots) and Digital Twins
• Decision making with reinforcement learning and big data analytics
• Analyse and predict future trends by deep learning platforms

Future of AI in Industrial Automation

• Expansion of Internet of Things (IIoT) with Predictive Analytics

Predictive maintenance programs are used to track equipment real time to enhance responsiveness and decrease unplanned outages, resulting in safer operations, lower expenses and higher customer satisfaction.

• Growth of Edge Computing

Edge technology shifts the information storage and processing from cloud services or data location where it is required as there could be significant rise in data from devices which operate 24/7 can cause bandwidth issues as well as slow processing times. It helps real time data for business decisions and process controls.?

• Smart Robot Usage

With 5G network technology covering remote areas, smart robotic applications in industries will expand quickly.

• Implementation of VR (Virtual Reality) and AR (Augmented Reality)

Augmented Reality (AR) and Virtual Reality (VR) tools offer interactive experiences specifically used for personnel training. With AR and VR tools the training will be more customised rather than generic and based on the skills of trainees. It will also enable the personnel to train in a safe and non disruptive environment, specially when the training is on rare operations. That may be difficult to understand and experience in real life.

As the need for automation in industries continues to grow industries will increasingly turn to AI powered machines for day-to-day operations AI and ML are key driving forces of change across industries. AI enables manufacturers to become more automated, bringing speed to efficiency and driving down costs with quality.