飞机制造中的一个关键要求是实现精确公差，这是普渡大学和南加州大学的研究人员进行加性制造的担忧，已经开发了自动化机器学习技术，以改善添加剂制造。
The technology addresses a current significant challenge withinadditivemanufacturing: individual parts that are produced need to have a high degree of precision and reproducibility. The technology allows a user to run the software component locally within their current network, exposing an API, or programming interface. The software uses machine learning to analyze the product data and create plans to manufacture the needed pieces with greater accuracy.
“We’re really taking a giant leap and working on the future of manufacturing,” says Arman Sabbaghi, an assistant professor of statistics in Purdue’s College of Science, who led the research team at Purdue with support from the National Science Foundation. “We have developed automated machine learning技术to help improveadditive manufacturing。这种创新正在前往基本上允许任何人成为制造商的道路。“
Sabbaghi补充道。“这具有许多行业的应用，例如航空航天，确切的几何尺寸至关重要，以确保可靠性和安全性。这是我第一次看到我的统计工作真的有所作为，这是世界上最令人难以置信的感觉。“
研究人员已经开发了一种新的模型 - 建筑算法和计算机应用，用于添加制造系统中的几何精度控制。这将进一步使用户能够受益于添加剂制造，其竞争优势包括形状复杂性，减少浪费和潜在的昂贵的制造，与传统的减色制造相比。
“We use machine learning technology to quickly correct computer-aided design models and produce parts with improved geometric accuracy,” Sabbaghi says. The improved accuracy ensures that the produced parts are within the needed tolerances and that every part produced is consistent and will perform that same way, whether it was created on a different machine or 12 months later.
Filed Under:3D printing • additive manufacturing • stereolithography,Product design
