A Systematic Survey of FDM Process Parameter Optimization and Their Influence on Part Characteristics <s> Discussion and Potential Research Areas <s> Abstract Based on our experience gained from uncertainty quantification (UQ) of traditional manufacturing, this paper discusses UQ for additive manufacturing with a focus on the prediction of material properties. Applications of UQ methods in traditional manufacturing are briefly summarized first. Based on that, we investigate how the state of the art UQ techniques can be applied to AM process to quantify the uncertainty in the material properties due to various sources of uncertainty. The UQ of ultimate tensile strength of a structure obtained from laser sintering of nanoparticles is used as an example to illustrate the proposed UQ framework. <s> BIB001 </s> A Systematic Survey of FDM Process Parameter Optimization and Their Influence on Part Characteristics <s> Discussion and Potential Research Areas <s> This paper presents a comparative study of the tensile mechanical behaviour of pieces produced using the Fused Deposition Modelling (FDM) additive manufacturing technique with respect to the two types of thermoplastic material most widely used in this technique: polylactide (PLA) and acrylonitrile butadiene styrene (ABS). The aim of this study is to compare the effect of layer height, infill density, and layer orientation on the mechanical performance of PLA and ABS test specimens. The variables under study here are tensile yield stress, tensile strength, nominal strain at break, and modulus of elasticity. The results obtained with ABS show a lower variability than those obtained with PLA. In general, the infill percentage is the manufacturing parameter of greatest influence on the results, although the effect is more noticeable in PLA than in ABS. The test specimens manufactured using PLA perform more rigidly and they are found to have greater tensile strength than ABS. The bond between layers in PLA turns out to be extremely strong and is, therefore, highly suitable for use in additive technologies. The methodology proposed is a reference of interest in studies involving the determination of mechanical properties of polymer materials manufactured using these technologies. <s> BIB002
Based on the presented research of process parameters, this section describes the major findings and shortcomings of the existing research and the future direction of research in the FDM process parameters optimization. The future direction of research may contribute to increasing FDM usage in industrial mass production, reducing the shortcomings of the FDM process to ensure process efficiency, and improving or optimizing printer part characteristics. Limited Materials: One of the disadvantages of the FDM process to be employed in industries is that only limited materials can be used as filaments. ABS and PLA are the two most widely analyzed filament materials, and some other materials such as PC and Ultem 9085 are also considered as other good part materials. Comparing ABS and PLA, PLA is rigid and biodegradable; however, ABS is ductile and heat resistant. ABS has a high tendency to warp when compared to PLA, and the warping occurs mainly due to an uneven cooling rate. The comparison of ABS and PLA is given in Table 1 . The impact of process parameters on other materials including nylon, PPSF, and TPU is still unknown. Thus, to increase the use of the FDM process and produce parts with different part characteristics, it may be helpful to consider different materials (other than ABS and PLA) as part materials for research purposes, as well as for functional parts. Research efforts aimed at studying the influence of process parameters on two or more materials are still limited. Rodríguez-Panes et al. BIB002 concluded that process parameters are more significant for PLA when compared to ABS. The comparison of different materials will be helpful to predict the impact of parameters on new materials, as well as the part characteristics of parts built from new materials. From the existing research, it can be concluded that the part characteristics of FDM parts are worse than the part characteristics of traditional manufacturing processes, for instance, injection molding. To compete with traditional manufacturing processes, it is necessary to improve part quality along with the advantages of the additive manufacturing process. There is a need to explore new materials in addition to developing composite or hybrid filament materials, including bio-composite and bioplastic materials. Two or more thermoplastic blends and reinforced thermoplastics may be two good options for developing new filament materials. Process Parameters: The FDM process consists of several parameters, but layer thickness, build orientation, raster orientation, raster width, air gap, and infill density are the most analyzed process parameters. To explore more about the FDM process, there is a need to know about the least analyzed parameters such as infill pattern, number of shells, shell width, raft thickness, nozzle diameters, build platform temperature, cooling rate, etc. Due to the wide range of different equipment used for the FDM process, some of these parameters may not be always controllable, which eventually makes the analysis rather difficult to be performed. From the existing research, the impact of some process parameters is clear. For example, 0 • build orientation maximizes the mechanical properties, dimensional accuracy, and surface finishing and minimizes the build time, as the longest side of a part placed in the XY-plane reduces the number of layers, and the downward speed of the build platform (after each layer deposition) is lower than the print speed. Another parameter, infill density, maximizes the mechanical properties (tensile, compressive, flexural, and impact strength) and build time when high. The infill density is not significant for dimensional accuracy and surface roughness. Although the FDM process has several parameters, and many of them are interdependent, in most of the existing research, only three to six parameters were considered as variables, and other parameters were considered as constants. A process parameter that is considered as a variable may be affected by the constant values of other parameters. Ideally, a more valuable piece of information can be gained by considering as many parameters as variables as possible. However, in reality, this is not something that is easily achievable. A major disadvantage of considering more variables is the number of experimental runs and the cost (time and resources), which increase exponentially. In the existing research, dimensional accuracy, surface roughness, and several mechanical properties could be improved by optimizing the process parameters. There are many other part characteristics, such as thermal properties, environmental properties, other mechanical properties, support materials, and part shape complexity, which are equally important for functional parts. Optimizing those part characteristics is also important for producing higher-quality FDM parts. This could be a potential future research direction in this field. Optimum Combination: In most of the existing research, the optimum combination of process parameters was determined from experimental studies instead of applying numerical optimizations. Thus, an optimum solution is one of the combinations from the experimental data. However, the actual optimal solutions might be different. In addition, there may be some combinations that are not analyzed due to the limited amount of resources, effort, time, and cost. This shortcoming can be overcome by using numerical optimization algorithms. Only a few researchers worked on the numerical optimization of process parameters, and most of them optimized only a single part's characteristics. However, one of the drawbacks is that optimizing a single part's characteristics may not always be a practical solution for real-world applications, as a functional part typically requires fulfilling more than one part's requirements. For this kind of functional part, to get an optimum combination of parameters for multiple parts' characteristics, multi-objective optimization is a useful approach. There is a need for more research efforts on multi-objective process parameter optimizations for the FDM process. In addition to mathematically optimizing process parameters, there are some additional concerns from the practitioners' perspective. Although optimizing process parameters can give decision-makers a full spectrum of process parameter ranges to obtain the desired part characteristics, the combinations of the best process parameters may not always be achievable in certain FDM machines. This is due to either the FDM printers not offering a certain range of parameters, or some process parameters unable to be controlled. In order to verify that the optimum process parameter combination is applicable, a validation experimental process should be carried out with the results obtained from the mathematical parameter optimization. Process Uncertainty: Another consideration that may improve part characteristics is controlling the uncertainties in the FDM process. FDM is a complex process; a CAD design passes through several stages to an STL file before it can be used to build a physical object. Each stage has different sources of uncertainties, for example, during the printing process, extrusion temperature variability, properties of filaments, feed rate variability, the environment, or ambient conditions. Other sources of uncertainties can be found prior to printing or outside of the printing process, for example, FDM software algorithms, its assumptions, or numerical model approximations. By considering the uncertainties of the FDM process during process parameter optimization, a part with consistent quality and less variation can be produced. There are limited research efforts that considered the uncertainties in the FDM process. Hu et al. BIB001 worked on predicting the uncertainty quantification of material properties but not the FDM process. The uncertainty quantification of the FDM process is another good direction of research. Multi-Disciplinary Research: Now more than ever, machine learning algorithms are widely applied to data analysis for prediction or learning purposes. These algorithms are also applicable in the field of FDM for the prediction of part qualities, especially in mass-producing FDM parts. In the FDM process, filaments are heated at a high temperature, and the deposited layers are cooled in the build platform. These heating and cooling processes have a significant impact on part characteristics. A closed-loop feedback system through image processing and machine learning algorithms is applicable to control and monitor FDM process performance. Any process parameter deviation may be adjusted based on an advanced control system with some predictive algorithms. By integrating advancements in other disciplines into the FDM process, the realization of the FDM process being employed in an Industry 4.0 setting will be more viable in the near future.