Modeling the Producibility of 3D Printing in Polylactic Acid Using Artificial Neural Networks and Fused Filament Fabrication

Polylactic acid (PLA) is a highly applicable material that is used in 3D printers due to some significant features such as its deformation property and affordable cost. For improvement of the end-use quality, it is of significant importance to enhance the quality of fused filament fabrication (FFF)-printed objects in PLA. The purpose of this investigation was to boost toughness and to reduce the production cost of the FFF-printed tensile test samples with the desired part thickness. To remove the need for numerous and idle printing samples, the response surface method (RSM) was used. Statistical analysis was performed to deal with this concern by considering extruder temperature (ET), infill percentage (IP), and layer thickness (LT) as controlled factors. The artificial intelligence method of artificial neural network (ANN) and ANN-genetic algorithm (ANN-GA) were further developed to estimate the toughness, part thickness, and production-cost-dependent variables. Results were evaluated by correlation coefficient and RMSE values. According to the modeling results, ANN-GA as a hybrid machine learning (ML) technique could enhance the accuracy of modeling by about 7.5, 11.5, and 4.5% for toughness, part thickness, and production cost, respectively, in comparison with those for the single ANN method. On the other hand, the optimization results confirm that the optimized specimen is cost-effective and able to comparatively undergo deformation, which enables the usability of printed PLA objects.

Design guidelines to mitigate distortion in material jetting specimens

Purpose The purpose of this paper is to investigate the effects of layer thickness, aspect ratio, part thickness and build orientation on distortion to have a better understanding of its behavior in material jetting technology. Design/methodology/approach Specimens with two layer thicknesses (14 and 28 µm) were printed in two aspect ratios (2:1) and (10:1), four thickness values (1, 2, 3 and 4 mm) and three build orientations (45d, XY and YX) and scanned with a wide-area 3D surface scanner to quantify distortion. The material used to build the test specimens was a commercially available resin, VeroWhitePlus RGD835. Findings The results of this study showed that all printed specimens by material jetting 3D printers had some level of distortion. The 1-mm thickness specimens, for both layer thicknesses of 14 µm and 28 µm, showed a wide range of anomalies including reverse coil set (RCS), reverse cross bow (RCB), cross bow (CB), wavy edge (WE) and some moderate twisting (T). Similar occurrences were observed for the 2-mm thickness specimens as there were RCS, WE, RCB and T anomalies that show the difference between the thinner specimens (1- and 2-mm) with the thicker ones (3- and 4-mm). In both 3- and 4-mm thickness specimens, there was more consistency in terms of distortion with mainly RCS and RCB anomalies. In total, six different types of flatness anomalies were found to occur with the following incidences: reverse coil set (91 specimens, 63.19%), reverse cross bow (50 specimens, 34.72%), wavy edge (23 specimens, 15.97%), twist (19 specimens, 12.50%), coil set (11 specimens, 7.64%) and cross bow (7 specimens, 4.86%). Originality/value This study expands the research on how the preprocess parameters such as layer thickness and build orientation and the geometrical parameters such as part thickness and aspect ratio cause dimensional distortion. Distortion is a pervasive consequence of the curing process in photopolymerization and explores one of the most common defects that come across in polymeric-based additive manufacturing. In addition to the characterization of the type and magnitude of distortion, the contributions of this work also include establishing the foundation for design guidelines aiming at minimizing distortion in material jetting.

GEOCHEMICAL CHARACTERISTIC OF THE SILURIAN ROCKS FROM THE EASTERN PART OF MOLDAVIAN PLATFORM (ROMANIA)

The scope of this paper is to assess the hydrocarbon potential of Silurian, respectively, the maturity of this stratigraphical interval, based, especially, of the geological and geophysical data derived from eight boreholes located in the north-eastern part of Moldavian Platform - Romania. The main conclusion is that the organic matter contents measured in the core and cutting samples of the different wells are low with the Organic Carbon (TOC) (residual) mainly clearly lower than 1%. Due to the poorness of the sample and the high maturity the petroleum potential and organic matter cannot be assessed. Estimation of the initial TOC allows to differentiate a richer interval at 24 – 27m thick below the top of Silurian. It shows higher TOC with estimated initial TOC reaching 3.6 % weight at the most. The maturity is high and shows a rapid increase with depth between around 2% at 200m and 4% eq. VRr at 1100m. An estimation of the eroded cap-rock is of around 3000m. Mineral carbon content obtained from Rock Eval permits to separate two lithological intervals: carbonated in the upper part (thickness of 250-300m), argillaceous in the lower part.

FORMING OF SHEET DETAILS WITH CURVILINEAR SIDES WITH ELASTOMER PRESSURE

In the article, the authors present a method of constrained bending of sheet parts with curvilinear sides by the elastic media. They describe the process of constrained bending and the scheme of the experiment. After the first transition, a part with thinning in the radius and high side springing is obtained. The second transition, on a low-height rigging, a wave of excess material is first formed in the radius zone, which settles on the contour of the mandrel with a set of thickness. The stages of forming a semi-finished product are given. The experiment showed the feasibility of constrained bending of curved sides, wall thickness measurements showed that the second transition results in a thickening of the part wall. Finite element modeling of a constrained bend is performed to study the effect of excess of the Board on the thickening in the bending zone. Numerical studies of deformation processes have been carried out. An example of a diagram of the part thickness change for various deformation stages as a result of numerical studies is given. Dependence of part height exceeding with loss of stability during shaping is established. Examples of defects - folds are given.

Enhancing 3D Printing Producibility in Polylactic Acid Using Fused Deposition Modelling and Machine Learning

Polylactic acid (PLA) is brittle in nature with extensive deformation property. For improvement of the end-use quality, it is of significant importance to enhance the producibility of fused deposition modeling (FDM)-printed objects in PLA. The purpose of this investigation is to boost toughness and to reduce the production cost of the FDM-printed tensile test samples with the desired part thickness. To attain the research purpose number of experiments are designed and analyzed by the Response Surface Method (RSM). The statistical analysis is performed to deal with this concern considering layer thickness, infill percentage, and extruder temperature as controlled factors. The tensile test specimens are printed based on the designed experiments, and the tensile strength tests are conducted by SANTAM 150 universal testing machine based on ASTM D638. The honeycomb internal fill pattern is applied for the production of light-weight and high-strength specimens. The area under Force- Extension curve up to fracture is acquired as the toughness of the printed specimens. This study also developed a modeling process using ANN and ANN-GA techniques for developing an accurate estimation for toughness, part thickness, and production cost as dependant variables. Results were evaluated by correlation coefficient and RMSE values. According to the modeling results, ANN-GA as a hybrid ML technique could successfully improve the accuracy of modeling about 7.5, 11.5 and 4.5 % for toughness, part thickness, and production cost, respectively, in comparison with those for the single ANN method. In the other side, the optimization results confirm that the optimized specimen is cost-effective and able to comparatively undergo deformation, which develops the usability of printed PLA objects. The research is accomplished under the constraints of PLA compatibility with existing fused deposition modeling setup without changing the functional hardware/software of the machine. Although the mechanical properties and dimensional accuracy of PLA have already been studied, there is little literature on the toughness of the printed PLA with honeycomb internal fill pattern.

Enhancing 3D Printing Producibility in Polylactic Acid Using Fused Filament Fabrication Fused Deposition Modelling and Machine Learning

Polylactic Polylactic acid (PLA) is one of the high applicable material which is used in the 3D printers due to some significant features like its deformation property and affordable costacid (PLA) is brittle in nature with extensive deformation property. For improvement of the end-use quality, it is of significant importance to enhance the quality of Fused Filament Fabrication (FFF)fused deposition modeling (FDM)-printed objects in PLA. The purpose of this investigation is to boost toughness and to reduce the production cost of the FDMFFF-printed tensile test samples with the desired part thickness. Due to prevent from many numerous and idle printing samples the response Surface Method (RSM) is used.To attain the research purpose number of experiments are designed and analyzed by the Response Surface Method (RSM). The statistical analysis is performed to deal with this concern considering extruder temperature (ET), infill percentage (IP), and layer thickness (LT) as controlled factors. The tensile test specimens are printed based on the designed experiments, and the tensile strength tests are conducted by SANTAM 150 universal testing machine based on ASTM D638. The pattern for filling is designed based on honeycomb which is applied to produce lightweight and high-strength specimens. The area under Force- Extension curve up to fracture is acquired as the toughness of the printed specimens. This study also developed a modeling process using artificial neural network (ANN) and artificial neural network- genetic algorithm (ANN-GA) techniques to develop an accurate estimation for toughness, part thickness, and production cost dependent variables. Results were evaluated by correlation coefficient and RMSE values. According to the modeling results, ANN-GA as a hybrid machine learning (ML) technique could could successfully improveenhances the accuracy of modeling about 7.5, 11.5 and 4.5 % for toughness, part thickness, and production cost, respectively, in comparison with those for the single ANN method. On the other side, the optimization results confirm that the optimized specimen is cost-effective and able to comparatively undergo deformation, which enables the usability of printed PLA objects. The research is accomplished under the constraints of PLA compatibility with existing Fused Filament Fabrication fused deposition modeling installation, in the absence of the functional assistant of the machine in the absence of the functional assistant of the machine. Although the mechanical properties and dimensional accuracy of PLA have already been studied, there is little literature on the toughness of the printed PLA with honeycomb internal fill pattern.

Design and optimization of oven vacuum bag (OVB) processing for void air removal in high-performance thermoplastic composites

Oven vacuum bag processing is an emerging process to manufacture high quality thermoplastic parts with void reduction using vacuum consolidation only. This paper models void air removal as a combination of through-the-thickness gas diffusion and in-plane airflow through the interlayer region for a flat plate of finite in-plane dimensions consisting of an arbitrary number of layers. A finite difference model assumes Fickian diffusion, simplifies the microstructure of the multi-layer prepreg stack and allows evaluation of various material and process conditions (inter- and intra-layer void content, temperature and pressure cycle, etc.) on the through-thickness diffusion behavior of the volatiles. In-plane airflow in the intra-layer is modeled using Darcy’s flow and requires high permeability of the interface created by the porous volume in between adjacent layers and the ability to vent the gas at the part edge. The dual mechanism model evaluates part geometries and processing cycles to reduce void content equivalent to autoclave parts with previously generated material properties of AS4/APC2 carbon PEEK prepreg. The modeling results based on the proposed mechanism shows that processing with through-the-thickness diffusion and standard processing cycles limits part thickness to five layers or less for APC2 while in-plane gas reduction can be used to make large parts of up to 10 m in the in-plane direction independent of part thickness. Very large parts require the addition of an intermediate lower temperature dwell cycle where diffusivity is high but interlayer permeability is unaffected allowing gas flow to the part edges for an extended period of time. The edge vent approach ensures a robust process even for as received materials with significant void variability often seen in thermoplastic prepreg tapes.

Investigation of significant factors on deformation with powder bed fusion system

Powder bed fusion (PBF) is one of the most popular techniques in additive manufacturing (AM). The PBF technique of selective laser melting (SLM) consolidates powder layer by layer using a laser as the energy source. This technique ensures the processes capability of fabricating components with internal and external complex geometries, which could be challenging to make with conventional manufacturing methods. However, the cyclic heating and cooling inherent in this process give rise to the buildup of residual stresses, which can distort or completely deform the part. In this work, a screening build with nine factors was designed to investigate the effects of component size, support structure, and energy input on the build completion and average distortion induced by the inherent residual stress. Experimental results indicated that support hatch spacing, part thickness, and support contact spacing played dominant roles in the final quality (i.e. resultant deformation) of the built parts. The identified significant factors from this study can be carefully selected to increase the success rates of single builds and improve the qualities (i.e. geometric accuracy) of the final products.