The artificial intelligence technologies behind smart manufacturing

Increased connectivity, low-cost sensors and high-tech analytics are enabling many industries to increase revenues while decreasing costs.

Today, manufacturers face the challenge of ensuring global supply chains and delivering global orders on time. Disruptions due to bad weather, strikes or other unforeseen issues, could result in delays or cancellations of component parts. Additionally, machines break down.

Automated manufacturing plant
AI technologies make smart manufacturing possible

The fourth industrial revolution has brought artificial intelligence (AI) technologies, such as machine learning and data analytics. These enable smart manufacturing and the ability to check machines, orders, processes and external factors in real time. The information gathered arms manufacturers with insights they can use to plan for diverse contingencies. For example, knowing the exact status of machines, allows for the deployment of predictive maintenance and can reduce equipment failure, increase reliability and improve asset performance.

Automotive manufacturers benefit greatly from the digital twin car. Diverse scenarios can be tested on this virtual model using real-world data, from design to production, to find issues, failures and solutions before a new model is built.

On a broader scale, more industries are incorporating AI into their products and services. These include advances in transport, such as airline autopilot and safety systems, and data analytics for healthcare, which help doctors choose the best treatments for patients. Increasingly, humans are putting their trust in machines.

Despite all the benefits, there are concerns around innovative technologies, which need to be addressed. In production plants, programmed robotic arms and humans work side by side, so AI systems must be trustworthy for the safety of workers. Additionally, ethical and societal issues have been raised around the ability of AI systems to learn and make decisions and the potential for inadvertent bias. Thus, there is a growing need to understand how algorithms work, so that if something goes wrong, a solution can be found, in order to avoid the problem occurring again.

IEC carries out diverse work for the development of AI standards. It is a founder member of the Open Community for Ethics in Autonomous and Intelligent Systems (OCEANIS), which brings together standardization organizations from around the world with the aim of enhancing awareness about the role of standards in facilitating innovation and addressing issues related to ethics and values.

Additionally, IEC works with ISO to develop international standards for information technologies, including AI. In this issue, we look at how these standards, aim to achieve AI systems which are more transparent.

We also hear from an expert on digital twins, who explains why we will need standards, which will cover the terminology, reference architecture and semantic interoperability of digital twins, in order to provide a foundational understanding for different stakeholders in diverse application areas.