Single and multi-objective optimisation of processing parameters for fused deposition modelling in 3D printing technology

This paper presents practice and application of design of experiment techniques and genetic algorithm in single and multi-objective optimisation with low cost, robustness, and high effectiveness through 3D printing case studies. 3D printing brings many benefits for engineering design, product development, and production process. However, it faces many challenges related to parameters control. The wrong parameter setup can result in excessive time, high production cost, waste material, and low-quality printing. This study was conducted to optimise the parameter sets for 3D fused deposition modelling (FDM) products. The parameter sets are layer height, infill percentage, printing temperature, printing speed with different levels were experimented and analysed to build mathematic models. The objectives are to describe the relationship between the inputs (parameter values) and the outputs (printing quality in term of weight, printing time and tensile strength of products). Single-objective and multi-objective models, according to the user's desire, are constructed and studied to identify the optimal set, optimal trade-off set of parameters. The paper illustrates Taguchi parameter design that could yield accurate results with a minimal number of experiments to be performed compared with other design of experiment methods. This method is a simple and systematic methodology that is highly effective in optimising the process parameters with low cost. Besides, the paper proposed an approach which is a combination of the response surface methodology and genetic algorithm to solve the multi-objective optimisation problem. This method can fast identify overall Pareto-optimal solutions which define the best trade-off between competing objectives. 3D printer, testing machines, and quality tools were used for doing experiments, measurement and collecting data. Minitab and Matlab software aid for analysis and decision-making. Proposed solutions for handling multi-objective optimisation through 3D fused deposition modelling product printing case study are practical and can extend for other case studies. © The Authors 2020.