A hybrid multi-objective optimization of 3D printing process parameters using genetic algorithm

The role of 3D printing in the industry 4.0 has become an essential segment and most of the industries are focusing toward the additive manufacturing methodology. In this context, PLA materials have been commonly used in the 3D printing manufacturing methodology. As the result, various studies have been carrying out by the researchers in the PLA and its properties. Despite of much work, further studies also been needed for identification of the suitable machining performances. As machining output responses also plays a crucial role over the properties of the material after manufacturing. So in this research, wood PLA have been considered and the input machining parameters for the 3D printer such as layer height, infill percentage, and infill pattern have been optimized to yield better tensile strength, tensile modulus and energy absorption rate. As part of this research, set of 27 different experiments had also been conducted to study the vital logic exist between the parameters and the responses. The effects of the printing process parameters and responses have been used to formulate the multi objective function. This multi objective function has been used as the fitness function for the optimization algorithm. Genetic algorithm has been used to optimize the 3D printer process parameters. The aim of the manuscript is to analyze the effects of input and output responses of 3D printer by experimentation, formulation of relational equation and optimization of the control parameters using genetic algorithm. Further, the developed genetic algorithm has been validated by conducting the test experiments with the optimized values. The obtained test results are comparable with the simulation results and thus concluded that the developed algorithm module can be used for the optimization of the 3D printer process parameters.