A Systematic Survey of FDM Process Parameter Optimization and Their Influence on Part Characteristics <s> Process Parameter Optimization <s> In rapid prototyping processes, the deposition orientation of the part is very important as it affects part surface quality, production time and the requirement for support structure and hence cost. Depositing the part with thinner slices results in a larger build time. At the same time, if a large slice thickness is chosen, the surface finish is very poor due to stair-stepping. These are two contradicting issues and are tackled by using adaptive slicing. In adaptive slicing, slice thickness is calculated based on local geometry of the computer-aided design model and rapid proto-typing machine specifications. Even though adaptive slicing controls part surface quality by compromising on build time for a deposition orientation, an optimum orientation can further reduce build time and enhance part surface quality. In the present work, an attempt has been made to determine the optimal part deposition orientations by considering two objective functions at a time, namely average part surface roughness (average ... <s> BIB001 </s> A Systematic Survey of FDM Process Parameter Optimization and Their Influence on Part Characteristics <s> Process Parameter Optimization <s> Abstract This paper presents experimental investigations on influence of important process parameters viz., layer thickness, part orientation, raster angle, air gap and raster width along with their interactions on dimensional accuracy of Fused Deposition Modelling (FDM) processed ABSP400 (acrylonitrile-butadine-styrene) part. It is observed that shrinkage is dominant along length and width direction of built part. But, positive deviation from the required value is observed in the thickness direction. Optimum parameters setting to minimize percentage change in length, width and thickness of standard test specimen have been found out using Taguchi’s parameter design. Experimental results indicate that optimal factor settings for each performance characteristic are different. Therefore, all the three responses are expressed in a single response called grey relational grade. Finally, grey Taguchi method is adopted to obtain optimum level of process parameters to minimize percentage change in length, width and thickness simultaneously. The FDM process is highly complex one and hardly any theoretical model exist for the prediction purpose. The process parameters influence the responses in a highly non-linear manner. Therefore, prediction of overall dimensional accuracy is made based on artificial neural network (ANN). <s> BIB002 </s> A Systematic Survey of FDM Process Parameter Optimization and Their Influence on Part Characteristics <s> Process Parameter Optimization <s> Fused deposition modelling (FDM) is a fast growing rapid prototyping (RP) technology due to its ability to build functional parts having complex geometrical shape in reasonable time period. The quality of built parts depends on many process variables. In this study, five important process parameters such as layer thickness, orientation, raster angle, raster width and air gap are considered. Their influence on three responses such as tensile, flexural and impact strength of test specimen is studied. Experiments are conducted based on central composite design (CCD) in order to reduce experimental runs. Empirical models relating response and process parameters are developed. The validity of the models is tested using analysis of variance (ANOVA). Response surface plots for each response is analysed and optimal parameter setting for each response is determined. The major reason for weak strength may be attributed to distortion within or between the layers. Finally, concept of desirability function is used for maximizing all responses simultaneously. <s> BIB003 </s> A Systematic Survey of FDM Process Parameter Optimization and Their Influence on Part Characteristics <s> Process Parameter Optimization <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> BIB004 </s> A Systematic Survey of FDM Process Parameter Optimization and Their Influence on Part Characteristics <s> Process Parameter Optimization <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> BIB005 </s> A Systematic Survey of FDM Process Parameter Optimization and Their Influence on Part Characteristics <s> Process Parameter Optimization <s> Fused deposition modeling (FDM) is gaining distinct advantages because of its ability to fabricate the 3D physical prototypes without the restrictions of geometric complexities, while when it comes to accuracy and efficiency, the advantages of FDM is not distinct, and so how to improve them is worthy of study. Focusing on process parameter optimization, such parameters as line width compensation, extrusion velocity, filling velocity, and layer thickness are selected as control factors, input variables, and dimensional error, warp deformation, and built time are selected as output responses, evaluation indexes. Experiment design is assigned according to uniform experiment design, and then the three output responses are converted with fuzzy inference system to a single comprehensive response. The relation between the comprehensive response and the four input variables is derived with second-order response surface methodology, the correctness of which is further validated with artificial neural network. Fitness function is created using penalty function and is solved with genetic algorithm toolbox in Matlab software. With confirmation test, the results are obtained preferring to the results of the experiment 1 with the best comprehensive response among the 17 experiment runs, which confirms that the proposed approach in this study can effectively improve accuracy and efficiency in the FDM process. <s> BIB006 </s> A Systematic Survey of FDM Process Parameter Optimization and Their Influence on Part Characteristics <s> Process Parameter Optimization <s> Abstract The performance of rapid prototyping (RP) processes is often measured in terms of build time, product quality, dimensional accuracy, cost of production, mechanical and tribological properties of the models and energy consumed in the process. The success of any RP process in terms of these performance measures entails selection of the optimum combination of the influential process parameters. Thus, in this work the single-objective and multi-objective optimization problems of a widely used RP process, namely, fused deposition modeling (FDM), are formulated, and the same are solved using the teaching-learning-based optimization (TLBO) algorithm and non-dominated Sorting TLBO (NSTLBO) algorithm, respectively. The results of the TLBO algorithm are compared with those obtained using genetic algorithm (GA), and quantum behaved particle swarm optimization (QPSO) algorithm. The TLBO algorithm showed better performance as compared to GA and QPSO algorithms. The NSTLBO algorithm proposed to solve the multi-objective optimization problems of the FDM process in this work is a posteriori version of the TLBO algorithm. The NSTLBO algorithm is incorporated with non-dominated sorting concept and crowding distance assignment mechanism to obtain a dense set of Pareto optimal solutions in a single simulation run. The results of the NSTLBO algorithm are compared with those obtained using non-dominated sorting genetic algorithm (NSGA-II) and the desirability function approach. The Pareto-optimal set of solutions for each problem is obtained and reported. These Pareto-optimal set of solutions will help the decision maker in volatile scenarios and are useful for the FDM process. <s> BIB007 </s> A Systematic Survey of FDM Process Parameter Optimization and Their Influence on Part Characteristics <s> Process Parameter Optimization <s> The mechanical properties of a fused deposition modeling (FDM) process product are greatly influenced by many process parameters. The identified parameters namely deposition orientation, layer thickness and deposition style, raster width, and raster gap are more significant factors contributing to the strength of a FDM product. In this paper, tensile strength, flexural strength, and impact strength are considered as three evaluation indexes to characterize the mechanical properties of a FDM part. An experimental research approach based on the Taguchi method was presented and some special specimens were designed. The influences of the five parameters on the three evaluation indexes were analyzed by the use of analysis of variance (ANOVA). Finally, based on the gray relational analysis, a set of optimal process parameter combination was obtained to optimize comprehensive mechanical properties of FDM parts. <s> BIB008 </s> A Systematic Survey of FDM Process Parameter Optimization and Their Influence on Part Characteristics <s> Process Parameter Optimization <s> Rapid prototyping with FDM technology is widely used by industry for producing prototypes. High quality and low processing cost are the main concerns. This study aims to determine the optimal parameter setting of 3D printing based on FDM technology to produce prototypes with high tensile strength with low energy consumption and processing time. The parameters to be optimized are temperature, density and fill angle. The Taguchi method is used to design the expereiment. PCR-TOPSIS is used to obtain the optimal parameter settings simultaneously. The result showed that optimal parameters setting were in level combination of extrusion temperature 210°C, 40% of density and 45° of fill angle. From the three parameters, the most significant parameter is the infill density. <s> BIB009
In most of the presented research, the optimum combination of process parameters was determined from an experimental study, and the experimental outcome that generated a better solution was considered as the optimum solution. However, the combination of process parameters for the best solution may be different from the experimental combinations, and they must be within the allowable range of process parameters. To overcome this shortcoming, different optimization methods were employed by different researchers. In most optimization problems, the objective function is a mathematical model that represents the relationship between process parameters and one single part's characteristic. In multi-objective optimization, a combination of mathematical models is used to represent the relationship between process parameters and multiple parts' characteristics. The constraints are the allowable range of process parameters; generally, this represents the highest and lowest levels of process parameters from the FDM machine or a range of process parameters that are known to produce good part characteristics. In this section, the papers that employed optimization techniques to get the desired combination of process parameters are summarized. Sood et al. BIB004 applied quantum-behaved particle swarm optimization (QPSO) to get the optimum combination of five process parameters for compressive strength. In the paper, a quadratic response surface model for compressive strength was generated by MINITAB R14, and the validity of the model was checked by the normal probability plot of residuals. Other than optimization, the compressive strength obtained by the quadratic model was compared to an ANN prediction. Rayegani et al. BIB005 developed a mathematical model for tensile strength using a group method for data handling (GMDH), and differential evolution (DE) was implemented to generate the optimum combination of build orientation, raster orientation, raster width, and air gap. In some works, characteristics of more than two parts were optimized simultaneously to determine the optimum combination(s) for multiple conflicting characteristics. Sood et al. BIB002 determined the optimum combination of five process parameters (layer thickness, build orientation, raster orientation, raster width, and air gap) for three-directional (length, width, and height) dimensional accuracy. The authors used the Taguchi method for optimization of all dimensional accuracies, and an optimal solution that minimized three-directional dimensional accuracy was determined. In addition, the author also employed an ANN approach for prediction. Liu et al. BIB008 also applied the Taguchi method for evaluating the levels of process parameters (build orientation, layer thickness, raster orientation, raster width, and raster gap) for tensile strength, flexural strength, and impact strength. Sood et al. BIB003 published another paper where the desirability function was applied for multi-objective optimization, instead of the gray Taguchi method. In their research, three mechanical properties (tensile, flexural, and impact strength) were optimized simultaneously. Akande  also applied the desirability function to transform two objectives (surface finishing and dimensional accuracy) into one objective and get an optimum combination of layer thickness, print speed, and infill density. Peng et al. BIB006 used RSM, fuzzy logic, and the genetic algorithm (GA) to optimize dimensional accuracy, warp deformation, and build time. In their work, the fuzzy logic method was applied to convert three objectives (dimensional accuracy, warp deformation, and build time) into a single objective. Furthermore, this single objective optimization problem was later solved using GA. Srivastava et al.  used RSM combined with Fuzzy logic for optimizing the build time and support material volume simultaneously. Rinanto et al. BIB009 used a method called process capability ratio-technique for order performance by similarity to ideal solution (PCR-TOPSIS) to get an optimum combination of extrusion temperature, infill density, and raster orientation for tensile strength, energy consumption, and build time. The above-discussed multi-objective optimization algorithms are able to generate a single optimum solution, and this is a disadvantage of single-solution multi-objective optimization. One of the reasons is that a unique solution may not be valid for multiple responses due to the different users' requirements. Therefore, multi-objective optimization techniques which generate a set of non-dominated solutions, often known as the Pareto frontier, are more suitable for FDM process parameter optimization. There are only limited articles that used multi-objective optimization algorithms that generated a set of optimal solutions. Pandey et al. BIB001 minimized surface roughness and build time simultaneously by considering build orientation as a variable. To solve the optimization problem, the non-dominated sorting genetic algorithm-II (NSGA-II), a heuristic algorithm, was used, and a set of optimum solutions was generated. Gurrala et al.  also applied NSGA-II for optimizing tensile strength and volumetric shrinkage simultaneously, and quadratic equations of both part characteristics were determined using Design Expert®software. Finally, a set of non-dominated optimum solutions was generated by NSGA-II. Rao et al. BIB007 introduced a teaching-learning-based optimization (TLBO) algorithm and non-dominated sorting TLBO (NSTLBO) algorithm in the field of FDM for single-objective optimization and multiple-objective optimization, respectively, and they showed that the algorithms worked better than many heuristic algorithms including QPSO, GA, and NSGA-II. The recent work on FDM process parameter optimization is summarized in Table 3 based on the optimization method employed, process parameters considered, and part characteristics of interest. Please note that, due to the page limitations of this paper, the optimization methods are not discussed in detail. Interested readers are encouraged to review the provided references for more information about the step-by-step optimization procedure for each optimization method. 