A Systematic Survey of FDM Process Parameter Optimization and Their Influence on Part Characteristics <s> Dimensional Accuracy <s> Purpose – The following research seeks to focus on optimizing the fused deposition modeling (FDM) process of RP systems.Design/methodology/approach – Early stages of this study are based on the Taguchi method in establishing rapid prototyping building factors and their various levels. The ultimate tensile strength, dimension accuracy and surface roughness (SR), are analyzed. Through analysis of variance (ANOVA) and contribution approximation, significant building factors of each quality characteristic and optimal factors level combinations of each best quality characteristic are obtained. The main steps are setting the weight for each quality characteristic of the previous Taguchi method, obtaining the estimated multiple building quality characteristics through integrating the Gray theory, and obtaining a set of optimal building factors. Finally, the result is verified by the Gray theory and the technique for order preference by similarity to ideal solution (TOPSIS) evaluation method.Findings – It is prov... <s> BIB001 </s> A Systematic Survey of FDM Process Parameter Optimization and Their Influence on Part Characteristics <s> Dimensional Accuracy <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> Dimensional Accuracy <s> This paper describes an experimental design technique for determining the optimum surface finish and dimensional accuracy of a part built by the Fused Deposition Modeling (FDM) process. The design investigates the effect of the process parameters layer thickness, road width, raster angle and air gap on the surface finish and dimensional accuracy. Experiments were conducted using Taguchi's design of experiments with three levels for each factor. The results are statistically analyzed to determine the significant factors and their interactions. From the ANOVA analysis, it was found that the layer thickness and road width affect the surface quality and part accuracy greatly. Raster angle has little effect. But air gap has more effect on dimensional accuracy and little effect on surface quality. <s> BIB003 </s> A Systematic Survey of FDM Process Parameter Optimization and Their Influence on Part Characteristics <s> Dimensional Accuracy <s> Fused deposition modelling (FDM) is one of rapid prototyping (RP) technologies which uses an additive fabrication approach. Each commercially available FDM model has different types of process parameters for different applications. Some of the desired parts require excellent surface finish as well as good tolerance. The most common parameters requiring setup are the raster angle, tool path, slice thickness, build orientation, and deposition speed. The purpose of this paper is to discuss the process parameters of FDM Prodigy Plus (Stratasys, Inc., Eden Prairie, MN, USA). Various selected parameters were tested and the optimum condition was proposed. The quality of the parts produced was accessed in terms of dimensional accuracy and surface finish. The optimum parameters obtained were then applied in the fabrication of the master pattern prior to silicone rubber moulding (SRM). These parameters would reduce the post processing time. The dimensional accuracy and surface roughness were analyzed using coordinate measuring machine (CMM) and surface roughness tester, respectively. Based on this study, the recommended parameters will improve the quality of the FDM parts produced in terms of dimensional accuracy and surface roughness for the application of SRM. <s> BIB004 </s> A Systematic Survey of FDM Process Parameter Optimization and Their Influence on Part Characteristics <s> Dimensional Accuracy <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> BIB005 </s> A Systematic Survey of FDM Process Parameter Optimization and Their Influence on Part Characteristics <s> Dimensional Accuracy <s> This paper discusses optimization studies on process parameters for fused deposition modelling (FDM). Layer thickness, Orientation angle and fill angle are the process variables considered for optimization. Ultimate tensile strength, surface roughness, dimensional accuracy and manufacturing time were considered as response parameters. Experiments were designed using well known Taguchi's L9 orthogonal array, Taguchi's S/N ratio was used to identify optimum parameter values. Effectiveness of each parameter was investigated using analysis of variance. Furthur, performance of optimum conditions were validated by conducting verification experiment. <s> BIB006 </s> A Systematic Survey of FDM Process Parameter Optimization and Their Influence on Part Characteristics <s> Dimensional Accuracy <s> Abstract Fused deposition modeling (FDM) is one of the widely used additive manufacturing technologies because it has flexibility and ability to build complex parts. The accuracy of parts fabricated by FDM is greatly influenced by various process parameters. FDM process has a complex mechanism in building parts and often poses difficulty in understanding adequately how conflicting FDM parameters will determine part quality and accuracy. Sectors such as medical implant, telecommunication, electronics and aerospace require increasingly higher levels of dimensional accuracy. Thus, traditional methods of ensuring quality do not effectively address global markets and customer’s needs. This study proposes an I-optimality criterion for the optimization of FDM process parameters in order to address the limitations of the commonly used traditional designs. This study also aims to develop mathematical models in order to establish nonlinear relationship between process parameters and dimensional accuracy. The results show that I-optimality criterion is very promising technique in FDM process parameter optimization. Confirmation experiments show that the proposed method has great advantages in the aspect of both accuracy and efficiency compared with traditional methods proposed in previous studies. <s> BIB007 </s> A Systematic Survey of FDM Process Parameter Optimization and Their Influence on Part Characteristics <s> Dimensional Accuracy <s> Abstract The Additive Manufacturing (AM) technology initially was developed as a rapid prototyping tool for visualization and validation of designs. The recent development of AM technologies, such as Fused Deposition Modelling (FDM), is driving it from rapid prototyping to rapid manufacturing. However, building end-user functional parts using FDM proved to be a challenging task. The difficulty arises from the large number of processing parameters that affect the final part design such as: building direction, extrusion temperature, layer height, infill pattern and more. The processing parameters of FDM influence the quality of the parts and their functionality. In addition, a more systemic understanding is required to elaborate on the impact of the FDM processing parameters on the final part's mechanical properties, dimensional accuracy and building time. The presented paper provides an experimental study to investigate the independent effect of each processing parameter on the mechanical properties and dimensional accuracy repeatability of FDM parts. A total of 18 test specimen samples were printed using varying processing parameters. In order to investigate the repeatability and resulted tolerances, the dimensions of these specimens were measured and compared with a 3D CAD model. The presented work utilizes a tensile test per ASTM D638 standards to obtain the mechanical properties of each fabricated sample. In addition, the work provides a Finite Element Analysis (FEA) model for AM parts. The work suggested to simulate their behavior under mechanical loads for future investigation on the coupled effects of processing parameters. <s> BIB008 </s> A Systematic Survey of FDM Process Parameter Optimization and Their Influence on Part Characteristics <s> Dimensional Accuracy <s> Production of technical parts for Mechanical Engineering field is very important. There are many conditions which the produced parts have to satisfy. Very important for functional parts are accuracy of final parts. This accuracy could be evaluated in different ways. For example it can be evaluated shape accuracy or dimensional accuracy. This paper is focused to Fused Deposition Modelling (FDM) technology which is used for Additive Manufacturing process. The parts in additive manufacturing are produced directly from virtual model and digital file which could be obtained by direct modelling with CAD software of by 3D scanning. Published paper deals realized research where the full factor experiment was prepared and measured shape and dimensional accuracy of produced specimens. There are designed two types of specimens with different shapes and dimensions. Digital models are processed with different different 3D printer settings. Measured values are evaluated by statistical methods. <s> BIB009
Dimensional accuracy is an important property for functional parts. Adjusting FDM process parameters has a significant impact on dimensional accuracy. Many researchers worked extensively to determine how process parameters can influence the dimensional accuracy of the printed parts. Wang et al. BIB001 studied the influence of six process parameters on dimensional accuracy. The examined factors were layer thickness, deposition style, support style, deposition orientation in the Z-direction, deposition orientation in the X-direction, and build location. The authors used the Taguchi orthogonal array (L18) for experimental design, and ABS parts were produced by the Dimension BST rapid prototyping machine according to the experimental design. Their results were reviewed using analysis of variance (ANOVA), which indicated that deposition orientation in the Z-direction was the most significant parameter for dimensional accuracy. Sood et al. BIB002 analyzed the impacts of five process parameters (layer thickness, build orientation, raster orientation, raster width, and air gap) and their interactions on dimensional deviations along the length, width, and thickness. Their results showed that layer thickness was the most important factor for width and thickness deviations. However, build orientation was most influential for change in length. The authors also observed some shrinkages which occurred in the length and width directions. However, the thickness of the printed part was found to be higher than the computer-aided design (CAD)-defined thickness along the Z-direction. They found that the optimum setting of parameters was different for dimensional deviations and for different dimensions of a part. For this, they used the gray relational grade to convert three target part characteristics into one. We discuss this method in the optimization section of this paper. Similar to the abovementioned two papers, Nancharaiah et al. BIB003 used Taguchi's design of experiments to examine the impact of layer thickness, raster width, raster orientation, and air gap on dimensional accuracy. ANOVA was used to determine significant parameters and their interactions for dimensional accuracy. The ABS parts were produced using a Prodigy plus FDM machine for different combinations of the analyzed parameters. According to their experimental design, it was shown by ANOVA that raster width and interaction between raster width and raster orientation were two significant terms for dimensional accuracy. Other than that, their results indicated that there was a positive correlation between dimensional accuracy and layer thickness. According to their suggestion based on signal-to-noise (S/N) ratio, a smaller layer thickness is preferred for a higher dimensional accuracy. By using the same machine, Bakar et al. BIB004 produced a complex shaped part (with slots, cubes, cylinders, and rings) by varying layer thickness, shell width, and internal raster width. The conclusion of their results was that the dimensional accuracy of an FDM part also depends on its shape, and a cylindrical shape has high dimensional deviation. Peng et al. BIB005 used the MEM-300 machine to produce ABS parts based on uniform experimental design. From their case study, the controllable parameters were line width compensation, extrusion velocity, filling velocity, and layer thickness. They also concluded from the experimental results that low layer thickness was preferable for improving dimensional accuracy. In addition, they also determined the optimum combinations of these four process parameters using response surface method (RSM), fuzzy inference system (FIS), artificial neural network (ANN), and genetic algorithm (GA) for three response variables including dimensional accuracy. All of these techniques are discussed in the upcoming sections. Akande  analyzed the effect of three process parameters (layer thickness, print speed, and infill density) on dimensional accuracy. The results showed that high dimensional errors took place along the thickness (Z-direction) of a PLA part. The author recommended high layer thickness (0.5 mm), low print speed (16 mm/s), and low infill density (20%) for printing parts with high dimensional accuracy. The influence of layer thickness, raster orientation, and build orientation on dimensional accuracy was investigated by Nidagundi et al. BIB006 , and the ABS parts were manufactured by a Julia 3D printer BIB006 . A Taguchi orthogonal array (L9) and S/N ratio were applied for experimental design and the determination of optimum levels of parameters, respectively. Low layer thickness, 0 • raster orientation, and build orientation were found to be optimum for dimensional deviation reduction, and the most significant parameter was indicated as layer thickness. Mohamed et al. BIB007 considered six process parameters, and all parameters excluding layer thickness had six levels. In their paper, I-optimal design was used for experimental set-up because the number of levels for each parameter was more than three. They also determined a quadratic equation that represented the relationship between process parameters and dimensional accuracy. Their observational results showed that low layer thickness and number of shells were optimal to decrease the dimensional deviation along the length, width, and thickness. Additionally, the other four parameters (raster orientation, raster width, air gap, and build orientation) had a different optimum combination for dimensional accuracy in different directions. Thus, the desirability function approach was used to determine the optimum combination of all parameters for three-dimensional deviations. In 2017, Qattawi et al. BIB008 experimentally examined the impact of two categorical parameters (build orientation and infill pattern) and four numerical parameters (layer thickness, infill density, print speed, and extrusion temperature) on dimensional accuracy. They used PLA as the filament and Makerbot Replicator 2X as the FDM machine. They concluded that layer thickness, extrusion temperature, and build orientation were the three significant parameters among the six parameters for dimensional accuracy. In addition, a low level for both layer thickness and extrusion temperature was preferable to a high level for dimensional accuracy. Tontowi et al.  observed the dimensional change of PLA printed parts by a Wanhao Duplicator 5S Mini FDM machine (WANHAO, Jinhua, China) with three process parameters, namely, layer thickness, raster orientation, and extrusion temperature. Unlike other research, their experimental results concluded that raster orientation was more significant than layer thickness for dimensional accuracy. In their paper, the outcomes of the Taguchi orthogonal array and response surface method were compared, and they concluded that the response surface method gave a better prediction. On the other hand, Wu  showed that low layer thickness was important for high dimensional accuracy. This conclusion is more aligned with the majority of research in this area. Beniak et al. BIB009 analyzed the importance of layer thickness and extrusion temperature for dimensional accuracy. Among layer thickness and extrusion temperature, extrusion temperature was significant for dimensional accuracy and, again, a low level for extrusion temperature was preferable. Short discussion and summary: According to the presented reviewed literature, the layer thickness is one of the most analyzed and influential factors for dimension accuracy. Most of the researchers concluded that high dimensional accuracy is obtained by setting a low layer thickness extrusion temperature and number of shells. In our perspective, further analysis is needed for extrusion temperature and the number of shells for validation of the conclusion. It is observed that shrinkage occurs along X-and Y-directions of build platforms and expansion is experienced along the Z-direction of the build platform. From this, we can conclude that build orientation is also an important parameter of dimensional accuracy. The effects of many process parameters including extrusion temperature, number of shells, infill pattern, and raster width on dimensional accuracy are still unknown. It is important to know the influence of those parameters to produce a part with high dimensional accuracy. Most of the existing research considered only two or three levels of parameters. There is a need to analyze more than three levels of parameters to make a more accurate decision, and to study the impact of the least known parameters on dimensional accuracy (such as extrusion temperature, infill pattern, or nozzle diameter).