Optimization of FDM parameters for improving part quality, productivity and sustainability of the process using Taguchi methodology and desirability approach

Camposeco-Negrete, Carmita

doi:10.1007/s40964-020-00115-9

Cited by 43 publications

(40 citation statements)

References 5 publications

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“…However, the method has proven itself many times in many applications and scenarios. This is also the case in the work of Gomez-Gras et al [26] and Camposeco-Negrete [23], where the aim is to optimize the additive manufacturing process FDM regarding certain parameters such as layer thickness, fill density, fill structure, etc. Other works worth mentioning in this context are those of Durão et al [27] and Zandi et al [28] who followed a similar approach using the Taguchi method to optimize the FDM additive manufacturing process.…”

Section: Methodsmentioning

confidence: 89%

“…The method, developed by Dr. Genichi Taguchi in Japan, focuses on the improvement the quality of products or processes. The objective is to reduce the deviations of a process from the nominal value, even if it is within the tolerances, since even these deviations no longer produce an ideal product [23]. This approach is very applicable to the recycling and subsequent use of filament in additive manufacturing since target deviations in both the recycling and printing processes result in a defective part and quality losses [24].…”

Section: Introductionmentioning

confidence: 99%

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Process parameter determination for small recycling plants for the production of filament for FFF printing using the Taguchi method

et al. 2021

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With the increasing use of the filament fabrication process, the in-house production of filament and the recycling of PLA waste is becoming more and more important. Small desktop filament extruders with associated peripherals enable small businesses and private users to carry out these recycling processes. Determing the right process parameters is of crucial importance here. These are usually only issued by the polymer manufacturer and the machine manufacturer. However, the development of own process parameters is important for new polymer compounds, as well as polymers with unknown manufacturers, as is typical in recycling. The common Taguchi method, which is used for process optimisation within the FFF process, was also used in this article to produce improved parameter sets for the production of filament using a single screw extruder (3devo Precision) with four heating zones. In this experimental field, the Taguchi method did not prove promising. Due to the small dimensions and compact design of such desktop filament extrusion machines, it was found that the setting parameters cannot be considered independently. The main parameters influencing the process were identified as the extruder screw speed, the cooling capacity and the temperature of the heating coil at the hopper. Nevertheless, parameter sets for PLA pellets and recycled PLA could be developed which have a better performance in terms of homogeneity of the diameter over time compared to the previously available parameter sets.

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Section: Methodsmentioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Process parameter determination for small recycling plants for the production of filament for FFF printing using the Taguchi method

et al. 2021

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“…Energy consumption [12]- [21] Carbon emission [16]- [18], [20]- [22] Material and/or tool waste [19], [21], [23] Economic Cost [17], [21], [22] Productivity [12]- [14], [20]- [22], [24] Quality [12]- [15], [19], [21], [23]- [26] Social Health and safety [18] Labor and workforce training [21] On the contrary, the a posteriori approach, firstly, brings the set of non-dominated solutions (which are optimal in the wide sense that no other solution in the considered search space, can improve one of the objectives without worsening, at least, another one), which is known as the Pareto front and, after that, allows choosing the most convenient alternative from these solutions [27]. Pareto-based techniques have become the most suitable choice for solving multi-objective optimization problems [28] and has been widely applied for practical manufacturing cases [29].…”

Section: Environmentalmentioning

confidence: 99%

Towards Sustainability of Manufacturing Processes by Multiobjective Optimization: A Case Study on a Submerged Arc Welding Process

et al. 2020

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Optimization on the basis of sustainability brings important benefits to manufacturing process as sustainable productions constitute a crucial aspect in modern manufacturing. This paper presents a new formalized framework for optimizing the sustainability of manufacturing processes. Unlike previous approaches, the proposed technique combines a methodology for selecting the sustainability indicators and a multi-objective optimization for improving the three sustainability pillars (economy, environment and society). While selecting the significant sustainability indicators in the considered manufacturing process relies on the ABC judgment method, the Saaty's method enables weighting the chosen indicators in order to combine them into suitable economic, environmental and social sustainability indexes. Other technological aspects, usually taken as objectives in previous works, are considered constraints in the proposed approach. The optimization is performed by using nature inspired heuristics, which return the set of non-dominated solutions (also known as Pareto front), from which the most convenient alternative is chosen by the decision maker, depending on the specific conditions of the process. For illustrating the usage of the proposed framework, it is applied to the optimization of a submerged arc welding process. Compared with currently used welding parameters, the computed optimal solution outperforms the economic and environmental sustainability while keeps equal the social impact. The results show not only the effectiveness of the proposed approach, but also its flexibility by giving a set of possible solutions which can be chosen depending on how are ranked the sustainability pillars.

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“…Some studies have proposed different approaches for FDM process parameter optimization. Camposeco-Negrete [9] investigated the effects of FDM process conditions on the processing time, energy consumption, and dimensional accuracy of FDM parts using the Taguchi method and desirability function. The results showed that layer thickness, road width, and printing plane were dominant factors.…”

Section: Introductionmentioning

confidence: 99%

Modeling, analysis, and optimization of dimensional accuracy of FDM-fabricated parts using definitive screening design and deep learning feedforward artificial neural network

2021

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Additive manufacturing (AM) technologies such as fused deposition modeling (FDM) rely on the quality of manufactured products and the process capability. Currently, the dimensional accuracy and stability of any AM process is essential for ensuring that customer specifications are satisfied at the highest standard, and variations are controlled without significantly affecting the functioning of processes, machines, and product structures. This study aims to investigate the effects of FDM fabrication conditions on the dimensional accuracy of cylindrical parts. In this study, a new class of experimental design techniques for integrated second-order definitive screening design (DSD) and an artificial neural network (ANN) are proposed for designing experiments to evaluate and predict the effects of six important operating variables. By determining the optimum fabrication conditions to obtain better dimensional accuracies for cylindrical parts, the time consumption and number of complex experiments are reduced considerably in this study. The optimum fabrication conditions generated through a second-order DSD are verified with experimental measurements. The results indicate that the slice thickness, part print direction, and number of perimeters significantly affect the percentage of length difference, whereas the percentage of diameter difference is significantly affected by the raster-to-raster air gap, bead width, number of perimeters, and part print direction. Furthermore, the results demonstrate that a second-order DSD integrated with an ANN is a more attractive and promising methodology for AM applications.

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Optimization of FDM parameters for improving part quality, productivity and sustainability of the process using Taguchi methodology and desirability approach

Cited by 43 publications

References 5 publications

Process parameter determination for small recycling plants for the production of filament for FFF printing using the Taguchi method

Process parameter determination for small recycling plants for the production of filament for FFF printing using the Taguchi method

Towards Sustainability of Manufacturing Processes by Multiobjective Optimization: A Case Study on a Submerged Arc Welding Process

Modeling, analysis, and optimization of dimensional accuracy of FDM-fabricated parts using definitive screening design and deep learning feedforward artificial neural network

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