In recent years, fused deposition modelling (FDM) is gaining more popularity due to its distinct advantages in terms of cost-effectiveness, lower build times, and flexibility. Compared to other 3-D printing processes such as SLS, this process does not use any kind of high-intensity laser power to build functional parts out of CAD models and, hence, makes the process much simpler, cheaper, and adaptable. Past studies reveal that productivity of the FDM process can be further increased by effectively controlling its process parameters such as layer thickness, part orientation, extrusion temperature, and so on. In this regard, many authors have investigated the optimal parameter settings for improving part strength, surface finish, wear, and fatigue properties of FDM made prototypes. However, warping performance behavior has got very recent attention due to complex heat transfer mechanism involved during this process. Experimental investigations are necessary to understand the deformation behavior of prototypes. In addition, the quantification and optimization of warp deformation along with dimensional error poses a challenging multi-objective optimization problem. Therefore, this work proposes an evolutionary system identification (SI) approach to explicitly quantify the warp deformation and dimensional error based on the four inputs such as line width compensation, extrusion velocity, filling velocity, and layer thickness of FDM prototypes. The two models' performance analysis comprising of error metrics evaluation, cross-validation, and hypothesis tests is performed to validate its robustness. The analysis concluded that the layer thickness and extrusion velocity influence the warp deformation and, while filling velocity and line width compensation, influences the dimensional error the most.
