For improving manufacturing efficiency and minimizing costs, design for additive manufacturing (AM) has been accordingly proposed. The existing design for AM methods are mainly surrogate model based. Due to the increasingly available data nowadays, machine learning (ML) has been applied to medical diagnosis, image processing, prediction, classification, learning association, etc. A variety of studies have also been carried out to use machine learning for optimizing the process parameters of AM with corresponding objectives. In this paper, a ML integrated design for AM framework is proposed, which takes advantage of ML that can learn the complex relationships between the design and performance spaces. Furthermore, the primary advantage of ML over other surrogate modelling methods is the capability to model input–output relationships in both directions. That is, a deep neural network can model property–structure relationships, given structure–property input–output data. A case study was carried out to demonstrate the effectiveness of using ML to design a customized ankle brace that has a tunable mechanical performance with tailored stiffness.

为了提高制造效率和最小化成本，已经提出了用于增材制造（AM）的设计。现有的AM方法设计主要基于替代模型。由于如今的数据越来越多，机器学习（ML）已被应用于医学诊断，图像处理，预测，分类，学习关联等。还进行了各种研究，以使用机器学习来优化过程参数。与相应的目标。本文提出了一种用于AM框架的ML集成设计，它利用ML可以学习设计和性能空间之间的复杂关系。此外，相对于其他替代建模方法，机器学习的主要优势在于可以对两个方向的输入-输出关系进行建模。也就是说，深度神经网络可以在给定结构-属性输入-输出数据的情况下对属性-结构关系进行建模。进行了一个案例研究，以证明使用ML设计定制的踝关节护具的有效性，该护具具有可调的机械性能和定制的刚度。