These capabilities of AI make it possible for manufacturers and other users to improve operational efficiency and productivity.
Artificial intelligence (AI) can benefit some manufacturing processes. When deciding how to deploy AI technology on the factory floor, it's important to start with the end goal and then leverage the technology to help achieve it, such as factory line optimization, performance maintenance, anomaly detection, inventory management, and bottleneck prevention, to name a few.
Depending on the end goal, you can create an AI model lifecycle by collecting and organizing data, choosing the type of AI model you want to use, training the model, determining whether the model's performance is sufficient to achieve the end goal, and finally deploying it into production.
In addition, to extract value from AI, it is important to ensure that the model works properly and scales with the manufacturing speed we need. Through continuous learning and improvement over time, these programs can help us significantly improve the quality and efficiency of our work and help us make more informed, data-driven decisions.
AI can help improve efficiency, quality, and productivity, to name some of the use cases I listed earlier.
When it comes to maintenance, AI solutions can help collect, analyze, and detect machine issues on the shop floor before they occur. With the vast amount of upstream data provided by connected machines, AI models are able to make adverse events before they occur, allowing manufacturers to prevent potential failures and avoid downtime.
When it comes to anomaly detection, manufacturers can train AI models for quality control by detecting product defects and anomalies, reducing the need for manual inspection and improving product uniformity and quality. Anomaly detection can also be applied at the process level: AI models can leverage vast amounts of data from manufacturing execution systems (MES), machines, and operators to detect anomalies throughout the process and thus avoid any possible downtime.
In addition to operations, another effective use of AI in manufacturing is to review contracts. AI language models can review contracts, review "red lines," summarize and detect key points, dramatically reducing the overall processing time of contracts.
Overall, the use of AI can help people at all levels of the business make data-driven, informed decisions in real-time, resulting in significant cost savings and increased efficiency.
At the same time, AI can also help detect anomalies in the production process, ensuring quality while reducing the need for manual inspection.
On a particular production line, we have two capacitors that are almost identical. The only difference between the two elements is the valve. This subtle distinction is difficult for the human eye to distinguish the anomaly, and once the wrong capacitor is used, the product will not work properly.
Through the use of visual data and AI models, our advanced manufacturing technology can see if the operator has placed the component in the right place and provide feedback to resolve any issues that arise in real time. Not only does this improve performance and yield, but it also allows us to identify critical issues before parts are sent to the production line for further steps, reducing the chance of rejects.
Historical data for each test step was analyzed, and an AI ML (Machine Learning) classification model was used to develop a new plan for reordering priorities in the most efficient and reliable way. In doing so, the overall test time was reduced by 30 percent, and in the event of a failure, it was also reduced by 50 percent.
By providing these insights and optimizations, we deepen our relationships with our customers, who in turn will integrate product and test design optimizations into their next-generation products.
Combining AI with other Industry 40 technology combined, helping to create a more productive, efficient smart factory.