Implementing AI for process control is not easy. It requires careful design, testing, and deployment. It also requires ongoing evaluation. Here are some tips to help get started quickly.
With the adoption of artificial intelligence (AI) in applications such as production, decision making, and operational efficiency, manufacturing is likely to undergo a major transformation. The expansion of AI has the potential to dramatically improve the way we conceive, create, and build, leading to innovative and efficient developments in these fields. As AI technology matures, becomes more accessible and widely available, its impact will only grow.
Traditional approaches to controlling processes often rely on predetermined models and rules, which are often based on historical data and human expertise. However, these approaches also have their limitations, especially when dealing with the increasing complexity, variability and unpredictability of modern manufacturing processes and the environments in which they operate. In addition, these methods often fail to take full advantage of the large amounts of predictable data, generated in real time by modern sensors and systems. This is where artificial intelligence comes into play.
Artificial intelligence is broadly defined as the ability of machines to do things that we normally think require human intelligence. Artificial intelligence comes down to mathematical relationships between variables, which is why we often describe AI programs or algorithms as "AI models." Over the past decade, machine learning models that learn to improve performance through data rather than explicit programming have become prominent.
More recently, this has been further enhanced by the rise of foundational models and generative AI. Base models are large models trained on very large data sets that can be adapted to a variety of tasks. Propelling AI into the mainstream like never before.
The potential benefits of artificial intelligence
In general, AI can provide better measurements, solutions, and communications.
Increase productivity: Often, manufacturers have more data than they know what to do with, and data is the fuel for AI. By analyzing large amounts of data, hidden patterns, anomalies, and insights can be discovered. These relationships can then be used to better understand the process and make more informed decisions. This in turn optimizes process performance and reduces downtime.
Improve quality, resilience and sustainability: AI can help manufacturers monitor and maintain the quality of raw materials, products and equipment by detecting and responding to defects, anomalies and errors in real time. As data increases and complexity increases, this allows us to use new materials more quickly in the event of supply disruptions and efficiently use natural or recycled materials with variable properties.
Increase innovation and competitiveness: Businesses can apply AI to innovate faster and increase the speed of product development. This can include better product designs, such as those driven by fast and efficient data-driven simulations, and faster reconfigurations driven by supply and demand. In the future, AI could even help break down siloed knowledge in design, manufacturing, and supply chains, enabling step changes in capabilities.
Reduce waste: AI technology can reduce waste related to materials, energy, time and space. This can take many forms, including combining process monitoring with anomaly detection to fix defects or stop work on faulty parts, and using artificial intelligence to operate processes in a way that minimizes energy or material use.
Empowering humans: AI systems can enhance workers' knowledge by easily accessing the most relevant expert insights. Ai can also help with communication and coordination with customers and suppliers, for example using chatbots.
How to successfully use artificial intelligence for process control
Implementing AI for process control is not easy. It requires careful design, testing, and deployment. It also requires ongoing evaluation. Here are some tips to help get started quickly.
1. Define goals and metrics: Before you start using AI for process control, you need to have a clear vision of what you want to achieve and how success will be measured, and you should define specific goals and key performance indicators that align with business goals and customer expectations.
2. Understand the data: Data is the foundation of AI models. Without good data, you can't build good AI systems. It is important to understand the available sources and the type, format, quality, deviation, and availability that these sources may provide. In general, more data is better, but in general, a small, clean data set is more useful than a large, noisy, and biased one. Finally, ensure that the data source is protected. Ai models are often vulnerable to adversarial attacks that degrade or subvert decisions.
3. Choose the right AI technology: Many different AI technologies are available for process control, and new ones are constantly being invented. The right AI technology must be selected that fits the data characteristics, process requirements, and goals. There are also trade-offs to consider between the complexity, accuracy, speed, scalability, and interpretability of AI technologies. It is usually best to use the simplest techniques to achieve acceptable results.
4. Verify the AI system: Before deploying process control, you need to make sure the system is working as expected. Validation is done by quantifying how well it meets the previously defined metrics. In general, the point of an AI system is its ability to make useful predictions in situations that have never been seen before. Therefore, it is important to evaluate the system based on data that the system has not seen before. In addition, AI-based systems can sometimes operate in unpredictable ways, so it is best to establish safeguards to control such risks.
5. Monitoring: After deployment, the framework needs to be regularly monitored and improved. To monitor results, a good dashboard with reports, alerts, and logs will be a big help.
