AI in Manufacturing
- Introduction
- Industrial Internet of Things
- Preventative vs Predictive Maintenance
- Using real-time data
- Generative Design
- Robotics
- Summary
Introduction
Advanced equipment like the Internet of Things (IoT) and the Industrial Internet of Things (IIoT) are generating more data than we’ve ever had before. The value of data is undeniable and becoming an essential part of manufacturing operations to remain competitive. AI platforms to capitalise on this data are slowly being adopted within the industry. Whilst such systems can be costly, factories are realising the major benefits they can see in their processes, productivity and efficiency if they begin to invest. Cloud computing and the access to real-time analytics are bringing more significant advantages to manufacturing and are being accelerated through innovative technology such as Blockchain and 5G. This article looks at the manufacturing industries and where the opportunities lie for those in the sector.
When we talk about the Internet of Things (IoT), it refers to any device or service that is connected to the Internet. This could be our Smartphone, Amazon Echo or even streaming services like Netflix and Spotify. Whilst IIoT is also associated with connectivity, it relates to the large number of industrial devices that are filled with sensors, connected via wireless networks to gather and share data with each other.
Preventative vs Predictive Maintenance
Traditionally, machine optimisation and repairs within an industrial environment would be conducted in a scheduled manner. In performing regular maintenance, the chance of the equipment failing is minimised. It’s the same reason you might take a car for an annual check-up. It is based on assumptions that a machine or its components will degrade over time. Companies might use some data like looking at the average time between previous failures, but it is quite limited. The problem here is that carrying out maintenance just because it is scheduled may not be necessary and costs a business money. A predictive maintenance strategy is determined by the condition of equipment rather than assumptions on its degradation. It will try to predict failure before it even happens. Predictive maintenance systems use sensors to monitor and analyse industrial data like machinery and return information about productivity levels, consumption or status. The data can be used to make decisions on whether maintenance is required without necessarily completing such tasks on a preventative schedule. Predictive maintenance is founded on an application of artificial intelligence known as machine learning. This takes large sets of historical data, often referred to as training data to run different scenarios and predict what is likely to go wrong and when the events will happen. As the machine algorithms learn, they will recognise potential problems without the need for any human intervention. For example, if a temperature of 50 degrees always causes a machine to break, it could automatically switch off without the need for human analysis.
According to Fero Labs, typical manufacturing companies discard 98% of the data they can collect because they simply can’t integrate it into their operations.
Hitachi is one company that have been trying to tap into their unused data. This has unlocked data that they were not aware of in the past, tracking many more variables with AI sensors.
Real time monitoring of these variables will allow immediate intervention before an issue arises. Let’s say that you have a sensor in place monitoring the vibrations of a machine. Increased vibrations can be a sign that components are failing but having just one data point in isolation when the numbers hit a specified alert won’t be enough to prevent problems.
Other common use cases are in infrared thermography, motor condition analysis, precision balancing and laser alignment. Realistically, anything that uses data has its own case for a predictive maintenance strategy.
Companies like Autodesk are using AI for what is known as generative design. Creating a new product or part can take companies weeks, months or even years. Using AI, experiments and recommendations could in theory be generated in seconds that greatly speed up the process. In generative design, a developer or engine would start by inputting all of their variables like performance and spatial requirements, materials, manufacturing methods and cost constraints. Software, like Autodesk will provide all the different permutations for a solution, quickly showing any potential alternatives. Beyond just making suggestions, the software will learn from the iterations and optimise the experiment as it tries to find the perfect solution. A computer will generate thousands of designs in the time a human can create one. Some of them will be things that weren’t even thought to be a possibility. In doing this and resolving any constraints, designers and engineers can focus their time on innovating and developing better process strategies.
A report from the International Federation of Robotics predicted that by the end of 2018, there would be more than 1.3 million industrial robots at work in factories all over the world. It is unknown as to whether this came into fruition but deployment of such technology has certainly accelerated in the last 12 to 24 months. The objective, in theory, is that robots can carry out the repetitive jobs done by humans allowing workers to be trained for more complex roles in design or programming. The key to success in robotics is a collaborative environment. We are not in a position where robots can operate completely without humans and there is a strong case supporting that we would not want that to happen. However, robots will become more cognitive over time and start making autonomous decisions against real-time data. The speed and efficiency benefits as well as improved health and safety as robots can complete dangerous jobs, will ensure robotics are a major part of the future factory.
Summary
Investors are pouring billions of dollars into IIoT startups hoping to develop more revolutionary solutions for sensors that utilise new technology such as edge equipment and Blockchain (both of these emerging technologies would need their own write-ups). What we must remember is that the technology is still in its infancy. It will take time for algorithms to hold enough data so that they can self-learn and become truly efficient. There is also a huge reliance on networks of connected devices creating security risks which companies will have to mitigate. The challenges are not insurmountable and IIoT will be part of the factory of the future for a faster and more efficient manufacturing industry.