# An Optimization Workflow in Design for Additive Manufacturing
## 1 Introduction
All these considerations can be grouped within the Design for Additive Manufacturing (DfAM) concept. [PERSON] et al. [24] distinguished between opportunistic and restrictive DfAM; the former allows to take advantages of the unique capabilities of AM, such as cellular solids, part consolidation, and multi-material, whereas the latter focuses on the restrictions and limitations of the AM technologies, such as the minimum feature size and the need of support structures. [PERSON] [25] used a Process-Structure-Property-Behavior framework to describe and model a design and proposed a DfAM system organized in several modules dealing with the modeling, manu  
Figure 1: Additive Manufacturing needs. facturing simulation, and design behavior analysis phases. [PERSON] et al. [26] presented a DfAM methodology that takes into account the design requirements and the manufacturing specificities, adopting a redesign strategy based on the functional surface approach; they concentrated on metallic components produced by Additive Laser Manufacturing. [PERSON] et al. [27] applied a four-step designing methodology consisting of initial shape generation, set of geometrical parameter definition, shape optimization through the tuning of the parameters, and final validation of a metallic part produced by direct metal deposition and electron beam melting AM technologies. [PERSON] et al. [28] presented a four-step methodology to maximize the potential of generative design coupled with DfAM; the first phase deals with the translation of the problem to a suitable input for generative design, whereas the following three phases deal with an unconstrained iterative optimization, an iterative optimization driven by the AM guidelines, and a final iterative optimization refining the part including AM-enabled structures, such as lattices. [PERSON] et al. [29] presented a computational workflow for the design and the fabrication of multi-material and multi-scale structured objects; they focused on water-based heterogeneous materials based on polysaccharide hydrogels in 1% to 12% concentrations in _w/o_ of 1% acetic acid aqueous solutions and these gels were also mixed with cellulose microfiber to obtain volumetric composites. They created a model that considers the input data, like materials and geometry, and calculates all the instructions for the object fabrication via a pneumatic extruder mounted on a six-axes robotic arm. [PERSON] et al. [30] presented a digital design and manufacturing workflow able to design both the macroscopic topology and the microstructure of an object; the workflow is divided into three steps: a design automation process that optimizes the material distribution and its microstructure, a material compilation process that creates a material layout and generates the code for fabrication, and a digital fabrication step with multi-material photopolymer material jetting technology. [PERSON] et al. [31] proposed an evaluation framework to assess the design from the perspective of process planning for AM; two sets of indicators were used to check whether the part is suitable to be produced by AM manufacturing and to verify the design's utilization of the characteristics of an AM process. Similarly, [PERSON] et al. [32] proposed an approach to assess the compatibility and suitability of a product for the AM production through a set of reference questions and a compliance index; then, they validated the method with case studies found in the literature. [PERSON] and [PERSON] [33] reviewed the scientific literature to explore the relationship between AM processes and product design, concentrating on the conceptual design phase and the theory of inventive problem solving (TRIZ) [34]; some of the analyzed works use the TRIZ for the definition of the DfAM guidelines. Nonetheless, the literature highlights the lack of exhaustive frameworks that describe the design process and take full advantage of the possibilities offered by AM.