By Jose Paez, solution strategist for Pricefx
Here are three ways artificial intelligence is being used to transform manufacturing companies and how technologies like ChatGPT can elevate the benefits of AI.
1. Improving quality checks on manufacturing lines
Quality checks are an essential aspect of the manufacturing process, as they ensure that products meet the required specifications and standards. Traditionally, quality checks were performed by human operators, which could be time-consuming and prone to error. However, AI can help improve the quality-check process by automating it and reducing errors. By training algorithms on images of products with known defects, the system can learn to identify similar defects in future products, which can significantly reduce the need for human intervention and speed up the quality-check process.
AI can also empower workers in the production line to continuously train the models for defects that could be missed by the algorithm. This technology can be used in various stages of the manufacturing process, from raw-material inspection to final-product inspection.
Traditionally these AI models are not easy to interact with, as they require maintenance from data experts and a very high level of fine-tuning that can be time- and resource-intensive. ChatGPT as a language model can bridge this gap as it provides a natural language-based way to train the model to recognize new defects. Additionally, it can help the organization to identify hard-to-see issues in the production line that are the cause of defects.
2. Digital twins
Digital twins are virtual replicas of physical products, processes or systems used to simulate and test products before they are built, enabling manufacturers to identify and correct any issues before the physical production process begins.
AI can be used to improve the accuracy and reliability of digital twins by analyzing large amounts of data collected from sensors, cameras and other sources, which can create more accurate and detailed digital twins. AI-powered digital twins can be used to predict how a product will behave under different conditions, such as changes in temperature, humidity or pressure. By simulating these conditions, manufacturers can identify potential issues before they occur and make any necessary adjustments.
Here, once again, we can find that creating these simulations is a job that requires a lot of planning and many iterations, as well as a heavy load in data interpretation. Models such as ChatGPT can synthesize and interpret large volumes of data from these simulations and provide insights to the team, enabling manufacturers to set the criteria for these simulations and ask a ChatGPT-based model to create all the setup data while introducing variations that these simulations are intended to test.
Digital twins supported with models like ChatGPT can also be used to create employee-training programs by creating interactive modules that simulate real-life scenarios for employees to practice and learn from, all without the need for additional expense in setting up real-life practice environments.
3. Maintenance predictions and forecasting
AI can use historical data to predict when assets might require repairs and enable users to plan for that demand accordingly. But now it can also analyze data collected from sensors that record metrics such as vibration levels, temperature and humidity to identify patterns and anomalies that indicate when equipment is likely to fail.
ChatGPT can streamline this process by interpreting large volumes of data coming from a variety of sensors and metrics to get to the key findings that enable teams to pinpoint where a failure is likely to show up and act before shutdowns are needed.
The benefits of AI in the manufacturing industry are undeniable. The implementation of AI-powered systems can help manufacturing companies stay competitive in a rapidly changing market. As the industry continues to evolve, the use of AI will become increasingly important in maintaining a competitive edge, and models like ChatGPT will enable smart manufacturers to bring the AI to the production line itself.
