Though many may consider terms like big data, analytics, and machine learning to be overused buzzwords, it’s important to remember that these concepts represent huge changes in much of the technology we encounter in our daily lives. And these developments span a wide range, from making our interaction with machines and information more natural and powerful to helping companies better understand consumers’ relationships, behaviors, locations, and motivations.
Artificial intelligence (AI) has taken center stage when it comes to recent innovations in the industrial revolution. It is already being used by companies to improve product quality, efficiency, and for reducing operating costs and has led to a working relationship between robots and humans. In smart factories, hyper-connected production processes rely on various machines that all communicate with one another, relying on AI automation platforms to collect and analyze all types of data, such as images, standardized code text, and categorized fixed field text.
And now, facing a new decade, it’s no surprise that we can likely expect even more innovations to arise from AI technology in manufacturing processes. From better quality control to more expedited, creative product development, the future is bright for the AI-powered Industry 4.0.
According to DataQuest, Industry 4.0 has the potential to be a powerful driver of economic growth, and is predicted to add between $500 billion- $1.5 trillion in value to the global economy by 2022. And while many organizations have started applying AI to increase their efficiency, there are still immense quantities of data that have not even been digitized or organized in a way that enables AI to use them.
For instance, manufacturing warehouses mainly rely on unstructured data, like handwritten paperwork and inventory checklists, as part of their day-to-day processes. Intelligent automation platforms that are built on fractal science will be instrumental in transforming modern manufacturing. As a result of this development, manufacturers will be able to cut down on production downtime, while also optimizing the overall operational efficiency of the manufacturing lines.
The Application of AI to Industry 4.0 Advantages
So, how exactly does AI support Industry 4.0? The application of AI in Industry 4.0 can take the following forms:
- Smart factories
Defined by their ability to harness large amounts of data, smart factories that are implementing Industry 4.0 are using AI in their production processes. Manufacturers who have successfully enacted a digital transformation and can both organize and utilize their data sets are applying AI and machine learning to improve quality control, standardization, and maintenance by producing predictive analyzes of equipment functionality and thoroughly streamlining factory lines. While the benefits of AI are widespread when it comes to production processes, it is important to remember that plants should have an AI development plan in place, as well as an idea of the type of automation platform to use.
- Predictive maintenance
In predictive maintenance scenarios, data is collected in real time to monitor the state of equipment. The goal is to find patterns that can help predict and ultimately prevent failures; increasingly, through learning algorithms, AI systems are being used to achieve this goal. When predictive maintenance is automated, plants can be more strategic when determining the condition of equipment and predicting when maintenance should be performed. Indeed, the implementation of ML-based solutions can lead to major cost savings, higher predictability, and the increased availability of the systems.
- Computer vision
One of the most compelling types of AI, computer vision is a field of computer science that focuses on enabling computers to identify and process objects in images and videos the same way that humans do. One of the driving factors behind the field’s growth is the amount of data generated today that is then used to train and make computer vision better. Thanks to advances in AI and innovations in deep learning and artificial neural networks, the field has been able to take great leaps in recent years and has been able to surpass humans in some tasks related to detecting and labeling objects.
- Cyber-physical systems
Cyber-physical systems embed software into the physical world and appear in a wide range of applications such as smart grids, robotics, and smart manufacturing. Rapid advances in Internet-based systems and applications have opened the possibility for industries to utilize the cyber workspace to conduct efficient and effective daily collaborations from any location worldwide to provide a fully distributed manufacturing environment.
- Industrial robots
An industrial robot is an automatically controlled, reprogrammable, multipurpose manipulator programmable in three or more axes. Typical applications include welding, painting, ironing, assembly, pick and place, palletizing, product inspection, and testing, which are all accomplished with high endurance, speed, and precision.
As we look for ways to optimize your production processes, we apply AI, machine learning, AR/VR, and cloud software. AI can advise on new processes, and digital twin technology can allow you to test and retest those processes before implementation. You can now program the factory line to adjust for customization quickly and easily.