Artificial intelligence (AI) based deep learning of defects, surface roughness and their linkage to mechanical performance of additively manufactured (AM) aluminum alloys

该联盟项目的重点是将基于人工智能（AI）的机器学习（ML）方法应用于增材制造（AM）领域，以开发一个框架，用于桥接长度尺度，并在工艺参数，微观结构特征和增材制造零件的机械性能特征之间建立联系。AM构建部件中的主要问题之一是微观结构的变化，沿着的是工艺引起的缺陷，例如孔隙度和表面粗糙度。这些材料的机械性能如疲劳寿命在很大程度上取决于制造过程中所采用的工艺参数的选择。各种各样的协同因素，如激光工艺参数，表面粗糙度，残余应力和孔隙率可以显着影响AM部件的疲劳行为。对这些因素之间的联系缺乏了解是AM工艺优化及其广泛适用性的最关键决策差距。在这些方面，基于人工智能（AI）的机器学习工具是识别和量化这些基本材料和工艺参数或显着特征以提高AM零件机械性能的关键推动因素。该项目结合了人工智能和增材制造领域的最新进展，包括理论和实验，旨在通过激光粉末床熔融（LPBF）技术研究3D打印铝合金的微观结构，性能和性能关系。

This Alliance project focuses on application of artificial intelligence (AI) based machine learning (ML) approaches to the field of additive manufacturing (AM) to develop a framework for bridging length scales and establishing linkages between process parameters, microstructural features and the resulting mechanical performance characteristics of additively manufactured parts. One of the major concerns in AM build parts is the variation in the microstructure along with the process induced defects such as porosity and surface roughness. The mechanical properties such as fatigue life of these materials are strongly governed by the choice of process parameters employed during the fabrication process. A wide variety of synergistic factors such as laser process parameters, surface roughness, residual stresses and porosity can significantly influence the fatigue behavior of AM parts. A lack of understanding of the linkages between such factors is the most critical decision-making gap for AM process optimization and its wide scale applicability. In these regards, artificial intelligence (AI) based machine learning tools are key enablers for identification and quantification of such essential material and process parameters or salient features for improved mechanical performance of AM parts. This project combines the latest advancements in the field of artificial intelligence and additive manufacturing, both theoretically and experimentally with an aim to investigate microstructure, property and performance relationships for 3D printed aluminum alloys by means of laser powder bed fusion (LPBF) technology.