The aim is to make the plant more efficient in a very competitive sector by applying methodologies that are applied in other, more digitized, sectors, from detecting anomalies in the different production processes, reducing waste (scrap, downtime, ...), to multiplying the generation of personalized quotes or preparing more precise and faster bids, to name a few examples that we will see shortly.
But, before we do, let’s take a look at how AI works. By studying the data stored in the databases and observing what the user does on a daily basis, the system extracts general and optimal behavior guidelines that it uses as a model that can optimally simulate what the user would do. This way, programs can be fully or partially automated using an AI model. For the moment, only the simplest ones, but in time this will also be applied to other, more complex, ones. For the time being, the aim is to teach the machines to anticipate solutions that help operators to be more efficient, streamlined and productive.
Therefore, both Artificial Intelligence and humans need each other. Technology so that people can contribute to improving these advanced tools; people, to make better decisions. Ultimately, in the automation process, the experience and skills of professionals will always be necessary, given that AI is not yet capable of surpassing people’s intelligence. Yes, it does surpass us when it comes to processing large amounts of data at breakneck speed and, indeed, with a lower error rate, but it will always require an operator to make the final decision or to train it.
Now that we understand how machine learning works, let’s look at different practical applications in our plants.
Specific uses of AI
Today, one of the biggest challenges facing manufacturers is the enormous amount of waste material that is generated when producing parts. The use of artificial intelligence in CAD/CAM software is proven to be decisive in reducing scrap. With automatic learning, we can optimize the placement and cutting of the pieces, reducing the generation of waste to a maximum. And this results in lower costs for the plant.
Another solution is the detection of anomalies. This is achieved by entering the data from all the processes into the program in such a way that if, for example, an electrical problem occurs and a cutting machine stops momentarily, the system is able to redirect production once the incident has been resolved. We can also find ourselves in situations where there’s a problem in one of the part manufacturing stages. The system is taught in such a way that, if it detects an anomaly, an alarm will sound in the program and the operator can identify the problem and resolve it quickly.
Predictive maintenance is essential to provide certainty to production and AI and, once again, opens up enormous possibilities, avoiding possible failures and defects in production by anticipating maintenance. In this sense, machine learning teaches the system to distribute the workload in a more optimized way when, for example, a machine has to be stopped because it requires a revision.
In terms of quotes, the system is also capable of outdoing humans and collecting data on raw material, energy and labor costs, among others, more quickly, drawing up more accurate and ad hoc quotes in record time.
The fact that Artificial Intelligence systems are able to detect consumption patterns and trends is also interesting. That’s why it’s essential for all the data generated in a plant to be collected through the sensorization of machines and processes. This way, the program can work to deal with spikes in demand more efficiently. In the not too distant future, we’ll even be able to anticipate possible orders.
It’s extremely useful when drawing up quotes. We know how complex they are in our sector. AI helps to reduce the time and resources used during the quote creation process. In this case, it’s especially important not to waste a lot of resources, because not all quotes are accepted, so the time spent on putting together those unaccepted offers is time wasted.
AI quoting software integrates with the CAD/CAM program, enabling quicker and more accurate assessments by gathering information on the geometry of the parts, hourly wages, placement time and material type, among other processes.
Value proposal
At Lantek, we’ve been using the mathematics and science of Artificial Intelligence for years, to apply it to sheet metal and metal cutting factories. We have the computing and data capacity to help our software users to do what they do. AI will make most of the decisions, but at specific times it will require user intervention, in these special cases the model will be readjusted to adapt its responses to the new conditions.
At the end of the day, it’s about guiding manufacturers who use design and cutting programs (CAD/CAM) and manufacturing management programs (MES) to make them more user-friendly and reduce the need for technical support. Subsequently, at Lantek, we’re developing Artificial Intelligence programs that help process data to guide the user through each manufacturing process, making the task simpler, as well as more efficient and productive.
Lantek’s software with AI operates on three pillars: production optimization, data visibility and analytical intelligence.
To make all of this possible, as we mentioned before, we need to have a photograph of our plant, to collect all the data to give us a comprehensive overview of the factory. From here, the data is processed and the algorithms learn to optimize production.
Are you ready to equip your factory with Artificial Intelligence and become one of the leaders in Industry 4.0 in the sheet metal and metal sector?
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