With the advent of more advanced manufacturing technologies concurrent with the ever-increasing and complex clinical needs, the biomedical devices sector is wit­nessing a paradigm shift. Currently, different classes of materials, namely metallic, ceramics, polymeric and composites, are typically used for fabricating medical devices. In fact, recent times have enabled bio fabrication, i.e., the manufacturing of biological systems integrated with soft polymeric materials, to be an integral part of manufacturing. Another development in the biomedical manufacturing space is the emergence of additive manufacturing technologies that aid in rapidly prototyping customized medical devices. This is truly the need of the hour, as it overcomes the limitations of conventional manufacturing processes by providing enormous exi­bility in design and material candidates, expanding the scope of medical devices. Different categories of additive manufacturing technologies are available depending on the class of material used, its physical form, nal desired product requirement, and properties. Each technique will have its advantages and disadvantages, and hence, an optimal trade-off is often needed to achieve the desired biomedical device with the desired properties. Among the multitude of biomedical applications of additive manufacturing, the most prominent include the creation of anatomical models and phantoms for surgical training, patient-specic prosthetics and implants, 3D tissue mimetic models for drug testing, biopharmaceuticals for drug delivery, etc. [1]. The increased adoption of additive manufacturing in the medical eld is due to its key benets such as customization of medical implants suiting exact patients’ needs, relatively low cost with minimal wastage of material, reduced production time, and design freedom [2]. Despite these advantages, additive manufacturing has still not matured enough for large-scale production owing to its relatively low speed compared to conventional manufacturing processes [3]. However, this can be a blessing in disguise, especially in the biomedical sector, where high accuracy com­bined with customization is required in small numbers. This is because of the unique demands of medicine and personalized therapy that change a lot with patients. The working principle of most additive manufacturing processes, except the form and processing of feedstock, remains the same, and it begins with capturing the clinical data through either Computed Tomography (CT) or Magnetic Resonance Imaging (MRI) modalities. Afterward, these scanned images are modied into a computer­aided design (CAD) model using Digital Imaging and Communications in Medicine (DICOM) software [4]. The prepared 3D CAD models are then processed through MIMICS or similar 3D software to assess the optimal tment and generate the required Standard Triangulation Language (STL) format of the desired implants to be fed to the printer [5]. Post generation of STL le, the input parameters such as unit layer thickness, print tool and bed/platform temperature, pressure/ow rate, print orientation/hatch spacing, raster angle, type of input current, types of lasers (if any) and its parameters (such as power density), etc., need to be optimized accord­ing to the desired nal part accuracy, type of material and printer being used. Figure14.1 shows the schematic of the entire workow of generating printed parts