A Systematic Survey of FDM Process Parameter Optimization and Their Influence on Part Characteristics <s> 207 <s> Abstract Fused deposition modelling (FDM) is gaining distinct advantage in manufacturing industries because of its ability to manufacture parts with complex shapes without any tooling requirement and human interface. The properties of FDM built parts exhibit high dependence on process parameters and can be improved by setting parameters at suitable levels. Anisotropic and brittle nature of build part makes it important to study the effect of process parameters to the resistance to compressive loading for enhancing service life of functional parts. Hence, the present work focuses on extensive study to understand the effect of five important parameters such as layer thickness, part build orientation, raster angle, raster width and air gap on the compressive stress of test specimen. The study not only provides insight into complex dependency of compressive stress on process parameters but also develops a statistically validated predictive equation. The equation is used to find optimal parameter setting through quantum-behaved particle swarm optimization (QPSO). As FDM process is a highly complex one and process parameters influence the responses in a non linear manner, compressive stress is predicted using artificial neural network (ANN) and is compared with predictive equation. <s> BIB001 </s> A Systematic Survey of FDM Process Parameter Optimization and Their Influence on Part Characteristics <s> 207 <s> This paper presents the research done to determine the functional relationship between process parameters and tensile strength for the fused deposition modelling (FDM) process using the group method for data modelling for prediction purposes. An initial test was carried out to determine whether part orientation and raster angle variations affect the tensile strength. It was found that both process parameters affect tensile strength response. Further experimentations were carried out in which the process parameters considered were part orientation, raster angle, raster width and air gap. The process parameters and the experimental results were submitted to the group method of data handling (GMDH), resulting in predicted output, in which the predicted output values were found to correlate very closely with the measured values. Using differential evolution (DE), optimal process parameters have been found to achieve good strength simultaneously for the response. The mathematical model of the response of the tensile strength with respect to the process parameters comprising part orientation, raster angle, raster width and air gap has been developed based on GMDH, and it has been found that the functionality of the additive manufacturing part produced is improved by optimizing the process parameters. The results obtained are very promising, and hence, the approach presented in this paper has practical application for the design and manufacture of parts using additive manufacturing technologies. <s> BIB002
Rayegani et al. BIB002 worked on optimizing mathematical models of part characteristics to determine 208 optimal levels of process parameters. There were also approaches that looked into characteristics of 209 more than one part that were optimized simultaneously. For this, various heuristic optimization 210 algorithms, for example, quantum-behaved particle swarm optimization (QPSO) BIB001 , non-dominated 211 sorting genetic algorithm II (NSGA-II)  , differential evolution (DE) BIB002 , and genetic algorithm