### Literature Review
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Authors** & **Focus Area** & **Key Contributions** \\ \hline [PERSON] et al. [11] & Physics of 3D food printing. & Provided a physics perspective on the material behaviour and process parameters for 3 DFP. \\ [PERSON] et al. [13] & Printability and texture in 3D printed food. & Investigated the printability and texture of 3D printed potato with added nutrients. \\ [PERSON] et al. [14] & Food-ink formulations by studying material behaviour during printing. & Designed hydrocolloid-based food-ink for 3D printing. \\ [PERSON] et al. [15] & Printability in food printing. & Reviewed progress in extrusion-based food printing for enhanced printability and efficiency. \\ [PERSON] et al. [16] & Textural properties of 3D printed food. & Studied how product and process parameters affect the microstructure and texture of 3D printed codes. \\ [PERSON] et al. [17] & Consumer assessment of 3D printed food. & Evaluated consumer responses to 3D printed food’s shape, taste, and fidelity. \\ [PERSON] et al. [19] & Sustainable food supply chains. & Modeled implementation barriers for 3 DFP in sustainable food supply chains. \\ [PERSON] et al. [20] & Design framework for additive manufacturing. & Developed a design framework that integrates AM capabilities in the design process. \\ [PERSON] et al. [23] & Methodology for integrating generative design tools with DIAM. & Developed a G-DMAM framework and validated its effectiveness through an automobile case study. \\ [PERSON] et al. [24] & Topology optimization for AM. & Focused on topology optimization to maximize material placement and structural efficiency. \\ [PERSON] et al. [25] & Topology optimization for AM considering the anisotropy of AM parts. & Proposed a topology optimization approach for AM that considers material anisotropy and strength constraints to minimize the structural weight or volume fraction while maintaining stiffness. \\ [PERSON] et al. [26] & Topology optimization for AM. & Demonstrated a case for using topology optimization in AM to redesign a jet engine bracket for reduced weight while maintaining functionality. \\ [PERSON] et al. [27] & Machine learning in AM design. & Discussed a hybrid machine learning approach for recommending AM design features. \\ [PERSON] et al. [28] & Machine learning in additive manufacturing. & Reviewed machine learning techniques in AM for design, processes, and production control. \\ [PERSON] et al. [29] & Generative design (GD) tools for shape variations. & Proposed a GD technique to demonstrate the importance of design exploration in achieving optimal solutions. \\ [PERSON] et al. [30] & Design evaluation tool for AM. & Created and assessed a novel design evaluation tool for improving AM design decisions. \\ [PERSON] et al. [31] & Evaluation of the printability of concrete materials. & Developed test methodologies to evaluate the printability of concrete materials for AM. \\ [PERSON] et al. [32] & Evaluation of the printability of laser powder bed fusion (LPBR) parts. & Developed a framework using machine learning and experimental data to predict the printability of LPBR parts. \\ [PERSON] et al. [33] & Virtual prototyping tool for extrusion-based AM. & Developed a virtual prototyping tool that evaluates part geometry for compliance with DIAM guidelines to improve part quality in extrusion-based AM. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Key contributions for enhancing the printability through design in AM extracted from the literature.