Generative AI

Artificial intelligence (AI) has appeared recently as a disruptive technology that can or could be applied in all sectors and for different purposes. AI has been widely used to create predictions based on historical data, such as data related to machine executions or people’s habits, among others. These algorithms, called machine learning (one of the aspects of AI), aim to learn from historical data and make predictions. 

If we look at the industrial environment, we can see applications of these algorithms to predict possible defects in products taking into account machine parameterizations or the Remaining Useful Life (RUL) of a machine, based on its use, among many other cases. 

However, in recent months there has been talking mainly of Generative AI, in which, unlike the previous examples, this aspect of AI aims to create new content (i.e., data). This data can be in images, videos, text, and sounds, among others. Although Generative AI is a branch of AI that has been explored and studied in recent years, it has been commented on recently due to the chats that allow the system to create a conversation with users. It seems that the user is talking to a person with advanced knowledge in the topic, in this case, in any topic, as the chats available use Deep Learning networks to create the answer to any question asked in the chat. 

Although these approaches are impressive, Generative AI can be used for many different purposes, one of which is to improve industrial processes. 

In industrial processes, Generative AI can assist in designing new products or creating the same products but with different characteristics. Tools with Generative AI are starting to be used to propose different designs for the same product, automatically evaluating the performance of each of the proposals. Another case where Generative AI can be used is the use of this type of solution to optimize production processes, whether these optimizations are related to the best use of factory resources, through the generation of parameterizations, different combinations of use of those same resources, or even the suggestion of new shopfloor layouts, using AGVs, conveyor belts and suggesting the best positions for the various workstations. 

Generative AI can also be used to mitigate the limitations of AI solutions that have been applied to date. As is known, the most “traditional” AI solutions require data for machine learning models to be trained, and that can later be used to make predictions. With Generative AI, it is also possible to create synthetic data that is used together with real data to increase the performance of created machine learning models. This way, Generative AI can also be used to help develop other AI models. In this way, with Generative AI, it will be possible to increase the number of solutions, such as predictive maintenance and quality inspection, which previously could not be developed due to the lack of real data. 

In general, it is possible to verify the potential of these approaches for industrial environments. In the case of Manufacturing Execution Systems (MES), they can benefit a lot from this technology when using it to develop new advanced functionalities.


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