scholarly journals Effect of Process Parameters on Energy Consumption, Physical, and Mechanical Properties of Fused Deposition Modeling

Polymers ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2406
Author(s):  
Emmanuel U. Enemuoh ◽  
Stefan Duginski ◽  
Connor Feyen ◽  
Venkata G. Menta
Keyword(s):  
Energy Consumption ◽  
Additive Manufacturing ◽  
Process Parameters ◽  
Fused Deposition Modeling ◽  
Function Optimization ◽  
Free Form ◽  
Production Time ◽  
Fused Deposition ◽  
Deposition Modeling ◽  
The Impact

The application of the fused deposition modeling (FDM) additive manufacturing process has increased in the production of functional parts across all industries. FDM is also being introduced for industrial tooling and fixture applications due to its capabilities in building free-form and complex shapes that are otherwise challenging to manufacture by conventional methods. However, there is not yet a comprehensive understanding of how the FDM process parameters impact the mechanical behavior of engineered products, energy consumption, and other physical properties for different material stocks. Acquiring this information is quite a complex task, given the large variety of possible combinations of materials–additive manufacturing machines–slicing software process parameters. In this study, the knowledge gap is filled by using the Taguchi L27 orthogonal array design of experiments to evaluate the impact of five notable FDM process parameters: infill density, infill pattern, layer thickness, print speed, and shell thickness on energy consumption, production time, part weight, dimensional accuracy, hardness, and tensile strength. Signal-to-noise (S/N) ratio analysis and analysis of variance (ANOVA) were performed on the experimental data to quantify the parameters’ main effects on the responses and establish an optimal combination for the FDM process. The novelty of this work is the simultaneous evaluation of the effects of the FDM process parameters on the quality performances because most studies have considered one or two of the performances alone. The study opens an opportunity for multiobjective function optimization of the FDM process that can be used to effectively minimize resource consumption and production time while maximizing the mechanical and physical characteristics to fit the design requirements of FDM-manufactured products.

Finite Element Analysis of Additive Manufacturing Based on Fused Deposition Modeling: Distortions Prediction and Comparison With Experimental Data

2018 ◽  
Vol 141 (1) ◽  
Author(s):  
Alberto Cattenone ◽  
Simone Morganti ◽  
Gianluca Alaimo ◽  
Ferdinando Auricchio
Keyword(s):  
Additive Manufacturing ◽  
3D Printing ◽  
Fused Deposition Modeling ◽  
Step Size ◽  
Time Step ◽  
Time Step Size ◽  
Fused Deposition ◽  
Deposition Modeling ◽  
The Impact ◽  
Functional Components

Additive manufacturing (or three-dimensional (3D) printing) is constantly growing as an innovative process for the production of complex-shape components. Among the seven recognized 3D printing technologies, fused deposition modeling (FDM) covers a very important role, not only for producing representative 3D models, but, mainly due to the development of innovative material like Peek and Ultem, also for realizing structurally functional components. However, being FDM a production process involving high thermal gradients, non-negligible deformations and residual stresses may affect the 3D printed component. In this work we focus on meso/macroscopic simulations of the FDM process using abaqus software. After describing in detail the methodological process, we investigate the impact of several parameters and modeling choices (e.g., mesh size, material model, time-step size) on simulation outcomes and we validate the obtained results with experimental measurements.

Data Driven Modeling and Optimization for Energy Efficiency in Additive Manufacturing Process With Geometric Accuracy Consideration

2018 ◽  
Author(s):  
Junfeng Ma ◽  
Wenmeng Tian ◽  
Morteza Alizadeh
Keyword(s):  
Energy Consumption ◽  
Additive Manufacturing ◽  
Process Parameters ◽  
Complex Geometry ◽  
Statistical Regression ◽  
Geometric Accuracy ◽  
Fused Deposition ◽  
Optimization Framework ◽  
Accuracy Measure ◽  
The Impact

Despite of its tremendous merits in producing parts with complex geometry and functionally graded materials, additive manufacturing (AM) is inherently an energy expensive process. Prior studies have shown that process parameters, such as printing resolution, printing speed, and printing temperature, are correlated to energy consumption per part. Moreover, part geometric accuracy is another major focus in AM research, and extensive studies have shown that the geometric accuracy of final parts is highly dependent on those process parameters as well. Though both energy consumption and part geometric accuracy heavily depend on the process parameters in AM processes, jointly considering the dual outputs in AM process is not fully investigated. The proposed study aims to obtain a quantitative understanding of the impact of these process parameters on AM energy consumption given part quality requirements, such as geometric accuracy. The study utilizes a MakerGear M2 fused deposition modeling (FDM) 3D printer to complete the designed experiments. By implementing experimental design and statistical regression analysis technologies, the study quantifies the correlation between AM process parameters and energy consumption as well as the final geometric accuracy measure. An optimization framework is proposed to minimize the energy consumption per part. The Kuhn-Tucker non-linear optimization algorithm is used to identify the optimal process parameters. This study is of significance to AM energy consumption in terms of jointly considering energy consumption and final part geometric accuracy in the optimization framework.

scholarly journals Additive Manufacturing of Wood Flour/PHA Composites Using Micro-Screw Extrusion: Effect of Device and Process Parameters on Performance

Polymers ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1107
Author(s):  
Jing Tian ◽  
Run Zhang ◽  
Jiayuan Yang ◽  
Weimin Chou ◽  
Ping Xue ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Process Parameters ◽  
Fused Deposition Modeling ◽  
Wood Flour ◽  
Screw Speed ◽  
Fused Deposition ◽  
Screw Extrusion ◽  
Deposition Modeling ◽  
Influencing Parameters ◽  
Printing Speed

Based on additive manufacturing of wood flour and polyhydroxyalkanoates composites using micro-screw extrusion, device and process parameters were evaluated to achieve a reliable printing. The results show that the anisotropy of samples printed by micro-screw extrusion is less obvious than that of filament extrusion fused deposition modeling. The type of micro-screw, printing speed, layer thickness, and nozzle diameter have significant effects on the performance of printed samples. The linear relationship between the influencing parameters and the screw speed is established, therefore, the performance of printed products can be controlled by the extrusion flow rate related to screw speed.

Additive Manufacturing of CFRP Composites Using Fused Deposition Modeling: Effects of Process Parameters

2016 ◽  
Author(s):  
Fuda Ning ◽  
Weilong Cong ◽  
Zhenyuan Jia ◽  
Fuji Wang ◽  
Meng Zhang
Keyword(s):  
Additive Manufacturing ◽  
Carbon Fiber ◽  
Tensile Properties ◽  
Process Parameters ◽  
Fused Deposition Modeling ◽  
Fused Deposition ◽  
Cfrp Composites ◽  
Cfrp Composite ◽  
Deposition Modeling ◽  
Nozzle Temperature

Fused deposition modeling (FDM) is one of the attractive additive manufacturing (AM) technologies for rapid prototyping with complex structures in a short timeframe. Thermoplastics are currently used as common feedstocks to fabricate prototypes in FDM process. However, FDM-fabricated pure thermoplastic parts cannot be used as load-bearing parts in the actual applications due to their limited tensile strength. Such condition could be improved by developing carbon fiber reinforced plastic (CFRP) composites using FDM for potential industrial end users. It is crucial that proper selections of FDM process parameters during fabricating CFRP composite parts could ensure the part quality and properties. However, the effects of FDM process parameters on the tensile properties of CFRP composites have not been explored. In this paper, CFRP composite specimens with 5 wt% carbon fiber content were fabricated using a FDM machine. Tensile testing was conducted to obtain the tensile properties. The effects of process parameters (including infill speed, nozzle temperature, and layer thickness) on the tensile properties of FDM-fabricated CFRP composite parts were investigated.

scholarly journals Energy and Eco-Impact Evaluation of Fused Deposition Modeling and Injection Molding of Polylactic Acid

2021 ◽  
Vol 13 (4) ◽  
pp. 1875
Author(s):  
Emmanuel Ugo Enemuoh ◽  
Venkata Gireesh Menta ◽  
Abdulaziz Abutunis ◽  
Sean O’Brien ◽  
Labiba Imtiaz Kaya ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Injection Molding ◽  
Polylactic Acid ◽  
Fused Deposition Modeling ◽  
Plastic Injection Molding ◽  
Measurement Models ◽  
Fused Deposition ◽  
Dog Bone ◽  
Deposition Modeling ◽  
Plastic Injection

There is limited knowledge about energy and carbon emission performance comparison between additive fused deposition modeling (FDM) and consolidation plastic injection molding (PIM) forming techniques, despite their recent high industrial applications such as tools and fixtures. In this study, developed empirical models focus on the production phase of the polylactic acid (PLA) thermoplastic polyester life cycle while using FDM and PIM processes to produce American Society for Testing and Materials (ASTM) D638 Type IV dog bone samples to compare their energy consumption and eco-impact. It was established that energy consumption by the FDM layer creation phase dominated the filament extrusion and PLA pellet production phases, with, overwhelmingly, 99% of the total energy consumption in the three production phases combined. During FDM PLA production, about 95.5% of energy consumption was seen during actual FDM part building. This means that the FDM process parameters such as infill percentage, layer thickness, and printing speed can be optimized to significantly improve the energy consumption of the FDM process. Furthermore, plastic injection molding consumed about 38.2% less energy and produced less carbon emissions per one kilogram of PLA formed parts compared to the FDM process. The developed functional unit measurement models can be employed in setting sustainable manufacturing goals for PLA production.

scholarly journals Automated Testing and Characterization of Additive Manufacturing (ATCAM)

2021 ◽  
Author(s):  
Arash Alex Mazhari ◽  
Randall Ticknor ◽  
Sean Swei ◽  
Stanley Krzesniak ◽  
Mircea Teodorescu
Keyword(s):  
Additive Manufacturing ◽  
Fused Deposition Modeling ◽  
Cell System ◽  
Automated Testing ◽  
Fused Deposition ◽  
Deposition Modeling ◽  
Machine Calibration

AbstractThe sensitivity of additive manufacturing (AM) to the variability of feedstock quality, machine calibration, and accuracy drives the need for frequent characterization of fabricated objects for a robust material process. The constant testing is fiscally and logistically intensive, often requiring coupons that are manufactured and tested in independent facilities. As a step toward integrating testing and characterization into the AM process while reducing cost, we propose the automated testing and characterization of AM (ATCAM). ATCAM is configured for fused deposition modeling (FDM) and introduces the concept of dynamic coupons to generate large quantities of basic AM samples. An in situ actuator is printed on the build surface to deploy coupons through impact, which is sensed by a load cell system utilizing machine learning (ML) to correlate AM data. We test ATCAM’s ability to distinguish the quality of three PLA feedstock at differing price points by generating and comparing 3000 dynamic coupons in 10 repetitions of 100 coupon cycles per material. ATCAM correlated the quality of each feedstock and visualized fatigue of in situ actuators over each testing cycle. Three ML algorithms were then compared, with Gradient Boost regression demonstrating a 71% correlation of dynamic coupons to their parent feedstock and provided confidence for the quality of AM data ATCAM generates.

Knowledge-Based Design of Artificial Neural Network Topology for Additive Manufacturing Process Modeling: A New Approach and Case Study for Fused Deposition Modeling

2018 ◽  
Vol 141 (2) ◽  
Author(s):  
Hari P. N. Nagarajan ◽  
Hossein Mokhtarian ◽  
Hesam Jafarian ◽  
Saoussen Dimassi ◽  
Shahriar Bakrani-Balani ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Additive Manufacturing ◽  
Fused Deposition Modeling ◽  
Expert Knowledge ◽  
Fused Deposition ◽  
Knowledge Based ◽  
Deposition Modeling ◽  
Artificial Neural

Additive manufacturing (AM) continues to rise in popularity due to its various advantages over traditional manufacturing processes. AM interests industry, but achieving repeatable production quality remains problematic for many AM technologies. Thus, modeling different process variables in AM using machine learning can be highly beneficial in creating useful knowledge of the process. Such developed artificial neural network (ANN) models would aid designers and manufacturers to make informed decisions about their products and processes. However, it is challenging to define an appropriate ANN topology that captures the AM system behavior. Toward that goal, an approach combining dimensional analysis conceptual modeling (DACM) and classical ANNs is proposed to create a new type of knowledge-based ANN (KB-ANN). This approach integrates existing literature and expert knowledge of the AM process to define a topology for the KB-ANN model. The proposed KB-ANN is a hybrid learning network that encompasses topological zones derived from knowledge of the process and other zones where missing knowledge is modeled using classical ANNs. The usefulness of the method is demonstrated using a case study to model wall thickness, part height, and total part mass in a fused deposition modeling (FDM) process. The KB-ANN-based model for FDM has the same performance with better generalization capabilities using fewer weights trained, when compared to a classical ANN.

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