Automated Process Planning for Five-Axis Additive Manufacturing

Abstract

Five-axis additive manufacturing provides a higher surface quality and needs less support than conventional three-axis machines. Automating the planning process for five-axis additive manufacturing could revolutionize manufacturing processes across industries. However, the increase in complexity creates challenges in automating the process planning. Thus, investigating new approaches to automate process planning could increase its adaptation as an innovative method for creating intricate 3D parts with high surface quality and minimal support. This thesis presents three methods to automate the process planning by analyzing the printing parts. The first method optimized the build orientation based on three objectives: support volume, surface quality, and print stability. The Pareto frontier solves the multi-objective optimization, finding the non-dominated orientations. The second proposed method is developing a new slicing technique using quadric surface fitting. The method is designed to fit sample points, solving the least squares problem with the Levenberg-Marquardt algorithm. Additionally, the surface orientation of a printing part is considered in the optimization. The optimization starts from a flat plane and changes as the printing layers progress to follow a part’s curvature. The result of this optimization is a list oflayers that are derived from the fitted quadric surfaces. In the last method, we developed an approach to optimize the placement of printing parts in a multi-part process. The presented method uses a part’s convex hull of a contacting layer with the build print to generate search candidates. We solve a multi-objective optimization, which maximizes the number of parts and surface quality and minimizes support. The outcomes of the third research are the non-dominated candidates. The proposed methods are applied to different 3D parts that vary in complexity. The proposed methods automate the process planning for five-axis additive manufacturing.