Using convolution techniques that minimize distortion of shape features! Classification is possible even when viewing the overall shape from above.
"Shape classification" is the task of classifying three-dimensional shapes into predefined classes. It uses convolution techniques that minimize the loss of shape features, allowing for classification whether focusing on a part of the shape or viewing the overall shape from a broader perspective. It can be utilized not only for simple class categorization but also for other tasks. Additionally, when calculating cutter paths for NC lathes for injection molding and forging molds, it automatically identifies bolt hole shapes from the mold model and removes them from the overall shape. [Features] - Uses convolution techniques that minimize the loss of shape features - Allows classification whether focusing on a part of the shape or viewing the overall shape - Can be utilized not only for simple class categorization but also for other tasks *For more details, please refer to the PDF document or feel free to contact us.
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In the manufacturing industry, the use of three-dimensional data such as 3D CAD data is becoming widespread, but in AI, the use of two-dimensional data is mainstream, and the use of three-dimensional data is still not in sight. Our company is considering how to make better use of 3D CAD data in the manufacturing industry and contribute to improving QCD, and we are conducting research and development on three-dimensional shape recognition technology using AI and deep learning. Based on the latest AI research results, our company has successfully developed a three-dimensional AI model that can recognize three-dimensional shape data. With this groundbreaking three-dimensional AI technology, we will spread three-dimensional AI throughout the engineering chain centered on CAD, CAM, and CAE, contributing to the improvement of QCD. Moreover, three-dimensional AI is a new technology, and its potential is limitless. We will promote research and development of three-dimensional AI and spread this pioneering three-dimensional AI technology not only in the manufacturing industry but also in many other industries, contributing to the improvement of QCD.
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