4. Application of Multi-attribute Decision Making Methods for Fused Deposition Modelling

Fused Deposition Modelling (FDM) is accountably more used 3D printing process because this process has more flexibility to build complex parts. FDM is layered manufacturing process and is highly affected by a number of working variables. Many research based on the optimum combination of working variables for 3D printing process by aid of conventional and recent optimization techniques. The parametric optimization methods are effectively used to understand conflicting nature of different attributes to increase features of part quality and dimensional accuracy. The main branch of optimization methods is Multi-Criteria Decision Making (MCDM), which was further divided into Multi-Attribute Decision Making (MADM) and Multi-Objective Decision Making (MODM). MADM methods are easy to understand and apply, which includes Simple Additive Weighting (SAW), Weighted Product Method (WPM), Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE), Analytic Hierarchy Process (AHP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Grey Relational Analysis (GRA), etc. This study mainly includes parametric optimization of FDM working variables with the help of MCDM methods. Multi-attribute methods are applied to experimental data of FDM process parameters. The problem for optimization taken on the I-optimality criteria applied to FDM process. The mathematical model showing nonlinear relation of working variables and geometric precision is considered while applying MADM methods. The input parameters taken for optimization are layer thickness, air gap, raster angle, build orientation, road width and number of contours, including response variables as percentage change in length, width and thickness. After application of MADM methods to the selected alternatives and attributes, the methods under consideration have shown different rankings. The ranking is determined on the basis of percentage error between best results shown by earlier researcher and the result shown by each selected alternative. Final ranks for these results are determined by the combination of ranking given by percentage error method and applied MADM method and the concluded best rank can be utilized for further applications. Best ranking gives the productive concatenation of working variables which are responsible for dimensional accuracy of FDM of 3D printed part. Also, the comparative evaluation of MADM methods under consideration is carried out and it is found that PROMETHEE method shows the best and more accurate results for this 3D printing process.