Design Automation for Additive Manufacturing : A Multi-Disciplinary Optimization Approach

Abstract: Additive manufacturing (AM) is a group of manufacturing methods which have attracted rapidly increasing interest in academia and industry during the last years. AM's main benefits are manufacturing of complex shapes and small-scale manufacturing, without the additional cost of traditional manufacturing methods. Creating complex geometries that fully leverage the potential of AM requires time, knowledge, and design skills. Design for additive manufacturing (DfAM) is a vast area that includes methods and tools that aim to overcome the challenges of AM and support the development of new components and products.Design automation and optimization are two terms often mentioned as potential methods to support the DfAM process. In a broad definition, design automation (DA) refers to reusable computer tools developed to aid the design engineering process. The general idea with DA is to create flexible design processes where different solutions can be explored without an increase in manual work. Together with methods for design optimization, DA has shown the potential to support the DfAM process.This work focuses on how DA technologies can support the development of components manufactured by AM. By analyzing the current state of the art, today's DfAM process is mapped, and the potential for automation is explored. The work contributes to the field by presenting a holistic DA framework that bridges function, design, AM setup, and post-processing. A master model is used to span the different phases of the design process and utilize combined optimization of geometry and manufacturing setup. The proposed method is refined in an iterative process where details are solved, and computer tools supporting the process are developed. Application cases from the aerospace sector and the fluid power industry are used to evaluate and demonstrate the developed methods and computer support.

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