A Multi-Objective Optimization Framework for Manufacturing Defect Reduction in Material Extrusion 3D Printing

Material extrusion 3D printing is one of the most versatile and accessible additive manufacturing techniques used by researchers and engineers across the globe. The technique is limited by manufacturing parameter-induced defects. These defects develop during the fabrication process, such as warping, poor layer adhesion, and dimensional inaccuracy. The defects can be controlled by optimizing process parameters, such as nozzle temperature, print speed, layer height, and cooling rate. Traditionally, parameter selection relies on iterative trial-and-error or single-objective optimization. These approaches often fail to capture the inherent trade-offs between conflicting quality characteristics. In this research, we used a systematic multi-objective optimization framework (MOO) to minimize manufacturing defects in the fabrication processing conditions.

We produced the specimens using the material extrusion 3D printing (ME3DP) method. We used polylactic acid (PLA) for fabricating the specimens in the ME3DP technique. PLA has good manufacturability and is a biodegradable material. We produced manufacturing defects by controlling the temperature and the basic components of material design used in the fabrication process, such as nozzle temperature, heated bed temperature, layer thickness, and infill density. We applied one of the mathematical and multi-attribute decision-making techniques for the MOO framework, used as ‘Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA)’. We used the (MOORA) technique to find the most significant configuration for evaluating manufacturing defect formation during the fabrication process. The MOORA technique is prevalently used in process engineering and optimization in industrial applications. The method simultaneously integrates both beneficial and non-beneficial attributes by using a ratio-based system. It then converts a decision matrix into a ranking system-based sample configuration. The ranking system determines the best parameters to control to reduce the manufacturing defects during the fabrication process. Our investigation shows that the MOORA method successfully determines the best manufacturing processing parameters for the reduction in defects.