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The industrialization of the Additive Manufacturing (AM) processes is enabling the use of AM components as final product in several applications. These processes are particularly relevant for manufacturing components with optimized custom-tailored geometries. However, to fully exploit the potentiality of AM, the development of knowledge aimed to produce dedicated design methods is needed. Indeed, even if AM enables the manufacturing of new kinds of structures, e.g. 3D lattice structures, it introduces process-specific design input and limitations that needs design methods different to from the ones for subtractive manufacturing. Design for AM (DfAM) is a design methodology that aims to take advantage of new buildable geometries but taking into account also AM processed materials anisotropy and 3D printing constraints. Recent literature focused on the assembly of AM components and on the AM components joining to a main structure. The conclusion was that adhesive bonding is a promising joining process, especially considering its improved stress distribution compared to fastening, but at the time of writing a method that combines DfAM and adhesive bonding knowledge is not available. The work presented in this thesis focused on developing knowledge on design for AM and bonded joints. First step was evaluating testing methods for AM and producing data on materials properties. Secondly, the early works on tailoring approaches for AM joints, published recently in scientific literature, were analyzed. Then AM dedicated designs, modifications and testing methods were proposed both for the adherends (in the thickness and on the surfaces) and the joints. Specifically, an innovative joint design concept was introduced, i.e. using the 3D printing parameters as bonded joint design factors. Eventually, feasibility of performing joints using multi-material AM with conductive polymer to embed heating elements was assessed. The 3D printed through the thickness circuits is a cutting-edge approach to enable new solutions for joints structural monitoring and self-healing.