Artificial intelligence (AI) is playing an increasingly significant role in the design of magnetics/electromagnetics and any complex system, revolutionizing the way engineers approach various aspects of the design process. Automated optimization of advanced problem sets is the largest contribution of AI currently to magnetic circuit design.
Magnets operate in a circuit, identical in almost every way to an electric circuit. We use many of the same design systems, tools, and optimization techniques to make these circuits as efficient and small as possible. The optimization provided through AI and machine learning are crucial to designing better on the first pass. AI algorithms can automate parts of the design process, allowing engineers to quickly generate and optimize. AI-driven design tools can explore a vast design space, finding solutions that human designers might not have considered. This can lead to more efficient and optimized designs, reducing development time and costs.
AI-driven tools can analyze data and simulate designs to find optimal configurations for signal and noise. Magnetic systems tend to have stray fields that can induce eddy currents (heat and unwanted power in nearby components) and cause noise in many analog signals and data transfer. Shielding and limiting these stray fields is an important component of magnetic design. Quickly simulating the large number of variables can be assisted using these AI tools.
Recent machine learning and intelligence breakthroughs assist in predicting failures and assessing the reliability of electronic components and systems. By analyzing historical data and real-time sensor inputs, AI models can identify potential points of failure and trigger maintenance actions before a complete breakdown occurs. This has a key role in our manufacturing, inspection, and reliability processes. Lower costs and increasing yield rates. A higher yield in magnet components with rare-earth materials is helping the environment in the long term. It is also used to optimize the magnetics supply chain by predicting demand, improving inventory management, and optimizing logistics, leading to cost reductions, lower carbon footprint and more efficient production processes.
As AI technologies continue to advance, we can expect even greater integration of AI into the design of magnetics and associated products, resulting in faster, more reliable, and energy-efficient industry.