Showing papers on "Process variable published in 2020"

Defect structure process maps for laser powder bed fusion additive manufacturing

Abstract: Accurate detection, characterization, and prediction of defects has great potential for immediate impact in the production of fully-dense and defect free metal additive manufacturing (AM) builds. Accordingly, this paper presents Defect Structure Process Maps (DSPMs) as a means of quantifying the role of porosity as an exemplary defect structure in powder bed printed materials. Synchrotron-based micro-computed tomography (μSXCT) was used to demonstrate that metal AM defects follow predictable trends within processing parameter space for laser powder bed fusion (LPBF) materials. Ti-6Al-4 V test blocks were fabricated on an EOS M290 utilizing variations in laser power, scan velocity, and hatch spacing. In general, characteristic under-melting or lack-of-fusion defects were discovered in the low laser power, high scan velocity region of process space via μSXCT. These defects were associated with insufficient overlap between adjacent melt tracks and can be avoided through the application of a lack-of-fusion criterion using melt pool geometric modeling. Large-scale keyhole defects were also successfully mitigated for estimated melt pool morphologies associated with shallow keyhole front wall angles. Process variable selections resulting in deep keyholes, i.e., high laser power and low scan velocity, exhibit a substantial increase of spherical porosity as compared to the nominal (manufacturer recommended) processing parameters for Ti-6Al-4 V. Defects within fully-dense process space were also discovered, and are associated with gas porosity transfer to the AM test blocks during the laser-powder interaction. Overall, this work points to the fact that large-scale defects in LPBF materials can be successfully predicted and thus mitigated/minimized via appropriate selection of processing parameters.

A modified volumetric energy density–based approach for porosity assessment in additive manufacturing process design

Abstract: Soundness of additively manufactured parts depends on a lot of process and geometrical parameters. A wrong process design leads to defects such as lack of fusion or keyhole porosity that have a detrimental effect on the mechanical properties of the printed parts. Process parameter optimization is thus a formidable challenge that requires in general a huge amount of experimental data. Among the others, heat source power and scan speed are the most defects-affecting parameters to be optimized. The energy density is used in literature to quantify their combination. Unfortunately, in different works it was demonstrated that it fails if used as design parameter mainly because it does not take into account the material properties and the interaction between heat source and the powder bed. In this contribution, a modified volumetric energy density equation that takes into account the powder-heat source interaction to optimize the combination of power-scan speed values for porosity assessment in powder bed fusion process design is proposed and verified on both AlSi10Mg alloy and Maraging steel 300.

Application of TGRA-Based Optimisation for Machinability of High-Chromium Tool Steel in the EDM Process

Abstract: It is essential to establish multi-criteria decision-making for computing the optimal process factors required to enhance the performance of the electrical discharge machining (EDM) process. For this purpose, criteria decision-making based on the Taguchi–grey analysis was employed in this study. Several responses such as material removal rates, average surface roughness, microhardness, and average white layer thickness were chosen to evaluate machinability. From the existing factor combinations, the optimum electrical factors that resulted in improved surface performance measures were identified as a peak current of 5 A, gap voltage of 50 V, pulse on time of 18 µs, and pulse off time of 37 µs, with a standard deviation within 4.1%. The maximum high- and low-grade value shows that the peak current affects performance measures, as it is essential in determining the spark energy in the EDM process. Moreover, significantly improved surface performance measures were achieved using the optimal process parameter combinations for the EDM process.

On the application of grey Taguchi method for benchmarking the dimensional accuracy of the PLA fused filament fabrication process

Abstract: The dimensional accuracy of a simple benchmark specimen fabricated with fused filament fabrication (FFF) route is discussed in the present study. FFF is a low-cost 3D-printing process that builds complicated parts by extruding molten plastic. Experimental method was designed according to Taguchi robust design based on an orthogonal array with nine experiments (L9 orthogonal array). The printing material was the polylactic acid (PLA). First, Grey–Taguchi method was used for the identification of the optimal printing parameter levels which result in the best dimensional accuracy for the PLA FFF parts. The printing parameters selected included number of shells, printing temperature, infill rate and printing pattern; they were selected in accordance with relevant studies already published. Then, in the second phase, nine specimens were fabricated using the same optimal printing parameter values determined in the first phase. The tolerance of these specimens was characterized according to international tolerance grades (IT grades). Data analysis showed that nozzle temperature is the dominant parameter. Additionally, the parts printed using the optimized process parameter levels possess good dimensional accuracy, which is compatible with the IT grades specification.

Multi-response Optimization of WEDM Parameters Using an Integrated Approach of RSM–GRA Analysis for Pure Titanium

Abstract: Wire electrical discharge machining is widely used in the application where precision is of prime importance, especially for conductive materials. In this study, central composite design of response surface methodology is implemented to design the experiments for optimization of WEDM process parameter on pure titanium. The identified input process variables are pulse on time (Ton), discharge current and pulse off time (Toff), while surface roughness and material removal rate are the output variables. ANOVA was used to study significance and non-significance factors. Grey relational analysis has been used for obtaining an optimal parameter setting for WEDM process to maximize the cutting rate while reducing surface roughness for pure titanium, which is the most preferred material for aerospace and biomedical application. The optimized process parameters were found at Ton of 6 µs, Toff of 4 µs and discharge current of 6 A after implementing GRA technique. A very close relation has been obtained at an optimal condition using GRA after the validation trial.

An integrated artificial neural network and Taguchi approach to optimize the squeeze cast process parameters of AA6061/Al2O3/SiC/Gr hybrid composites prepared by novel encapsulation feeding technique

Abstract: Aluminum matrix composites known for their superior mechanical properties finds its use as liners in engine cylinders, discs, drum brakes, and pistons in automotive applications. This paper investigates the influence of squeeze casting process parameters on AA6061/Al2O3/SiC/Gr hybrid metal matrix composite using the encapsulated feeding technique. Four levels of factors selected for the L16 orthogonal array to optimize the process parameters were squeeze pressure (60, 80, 100, 120 MPa), melt temperature (700,750,800,850 °C), die temperature (100, 150, 200, 250 °C) and pressure holding time (5, 10, 15, 20 s). Hardness and tensile strength were measured for the designed experiments. Scanning electron microscope with energy-dispersive X-ray spectroscopy identified surface morphologies and elemental analysis. Optimized results were predicted using the artificial neural network. The melt temperature and squeeze pressure exhibited a significant contribution in controlling the mechanical behaviour of the hybrid composites. Taguchi analysis suggested that SP3, MT2, DT4 and HT2 casting conditions presented the optimal process parameter level that showed the maximum hardness of 131 HV and the tensile strength of 329 MPa. Scanning electron microscopy and energy-dispersive X-ray spectroscopy showed uniform distribution of reinforcement in the encapsulation process compared with the regular feeding technique. ANN predicted the hardness and tensile strength with 95 % accuracy. Compared with the regression model and experimental data, the ANN prediction was more accurate. The defined hybrid metal matrix composite stands out as the substitute for AA6061 alloy that meets the demands of the modern automotive industry in engine cylinder liner applications.

Investigation of Melt Pool Geometry Control in Additive Manufacturing Using Hybrid Modeling

Abstract: Metal additive manufacturing (AM) works on the principle of consolidating feedstock material in layers towards the fabrication of complex objects through localized melting and resolidification using high-power energy sources. Powder bed fusion and directed energy deposition are two widespread metal AM processes that are currently in use. During layer-by-layer fabrication, as the components continue to gain thermal energy, the melt pool geometry undergoes substantial changes if the process parameters are not appropriately adjusted on-the-fly. Although control of melt pool geometry via feedback or feedforward methods is a possibility, the time needed for changes in process parameters to translate into adjustments in melt pool geometry is of critical concern. A second option is to implement multi-physics simulation models that can provide estimates of temporal process parameter evolution. However, such models are computationally near intractable when they are coupled with an optimization framework for finding process parameters that can retain the desired melt pool geometry as a function of time. To address these challenges, a hybrid framework involving machine learning-assisted process modeling and optimization for controlling the melt pool geometry during the build process is developed and validated using experimental observations. A widely used 3D analytical model capable of predicting the thermal distribution in a moving melt pool is implemented and, thereafter, a nonparametric Bayesian, namely, Gaussian Process (GP), model is used for the prediction of time-dependent melt pool geometry (e.g., dimensions) at different values of the process parameters with excellent accuracy along with uncertainty quantification at the prediction points. Finally, a surrogate-assisted statistical learning and optimization architecture involving GP-based modeling and Bayesian Optimization (BO) is employed for predicting the optimal set of process parameters as the scan progresses to keep the melt pool dimensions at desired values. The results demonstrate that a model-based optimization can be significantly accelerated using tools of machine learning in a data-driven setting and reliable a priori estimates of process parameter evolution can be generated to obtain desired melt pool dimensions for the entire build process.

Dimensional Errors Due to Overhanging Features in Laser Powder Bed Fusion Parts Made of Ti-6Al-4V

Abstract: The rise in popularity of Additive Manufacturing technologies and their increased adoption for manufacturing have created a requirement for their fast development and maturity. However, there is still room for improvement when compared with conventional manufacturing in terms of the predictability, quality, and robustness. Statistical analysis has proven to be an excellent tool for developing process knowledge and optimizing different processes efficiently and effectively. This paper uses a novel method for printing overhanging features in Ti-6Al-4V metal parts, by varying process parameters only within the down-facing area, and establishes a methodology for predicting dimensional errors in flat 45° down-facing surfaces. Using the process parameters laser power, scan speed, scan spacing, scan pattern, and layer thickness, a quadratic regression equation is developed and tested. An Analysis of variance (ANOVA) analysis concluded that, within the down-facing area, the laser power is the most significant process parameter, followed by the layer thickness and scan speed. Comparatively, the scanning pattern is determined to be insignificant, which is explained by the small down-facing area where the various scanning patterns play no role. This paper also discusses the interaction effects between parameters. Some thoughts on the next steps to be taken for further validation are discussed.

FDM Process Parameter Optimization by Taguchi Technique for Augmenting the Mechanical Properties of Nylon–Aramid Composite Used as Filament Material

Abstract: Fused deposition modelling (FDM) is one such technique of additive manufacturing (AM) that deposits the extruded thermoplastic material layer by layer to build the desired part. The study is focused on the introduction of new thermoplastic material that widens the application of FDM process and also to use the part for functional purpose rather than just the prototype. Nylon is used as the feed filament material for FDM due to its higher mechanical properties and wear resistant characteristics that are often used as sliding bearing. The properties of nylon are further enhanced by adding the aramid short fibres. In this investigation, the process parameter optimization of FDM process is performed by using Gray Taguchi technique for the quality and mechanical characteristics enhancement. Layer thickness, print temperature, raster angle, infill part density and infill pattern style were considered as the influencing factors for optimization. Mechanical properties including tensile strength, flexural strength, impact strength and compression strength (responses) were studied for the designed experiments which were conducted according to ASTM standards. Analysis of variance was performed using Minitab 18 software to understand the signal-to-noise ratio for the respective objective. The overall combined objective is framed by providing equal importance to all the four responses. From the analysis, the following factors were identified as the optimum settings, layer thickness of 0.4 mm, print temperature of 300 °C, infill part density of 90%, raster angle of 90° and infill pattern style of rectilinear. The results from the test sample printed at the determined optimum setting has exhibited tensile strength of 51.455 MPa, flexural strength as 98.164 MPa impact strength of 0.637 MJ/sq m, compressive strength as 19.42 MPa. The test result of the parts printed from pure nylon as per the prescribed standard setting exhibited tensile strength of 48 MPa, flexural strength as 80.5 MPa, impact strength as 0.51 MJ/sq m and compression strength as 18.12 MPa. A significant increase by 7.2% in tensile strength, 22.7% in flexural strength, 27.4% in impact strength and 7.5% in compressive strength were noticed. From the investigation, it was possible to conclude that even short fibre composites can also be used as FDM raw materials and valid predictions can be made using regression equations with very less error and is justified by experimental trails.

Effect of powder reuse on mechanical properties of Ti-6Al-4V produced through selective laser melting

Abstract: Metallic powder reuse presents attractive economic and environmental advantages for direct metal laser sintering (DMLS). However, continuous recycling of powder raises concerns of powder quality and sintered part performance, and complicates process validation. Efforts to examine the mechanical response of parts built with reused feedstocks are increasingly common in the technical literature, but none have optimized process parameters in DMLS to control for changes in material properties. In this paper, titanium powder reuse was investigated with the objective of optimizing the additive manufacturing (AM) process for reuse. Virgin Ti-6Al-4V powder was cycled a total of eight times through conditions representative of industrial DMLS machines. A full 23-factorial design of experiments (DOE) approach was employed to identify how parameter settings affect mechanical behavior, and include reuse as a process variable. The independent factors (laser power, laser speed, and hatch distance) did not significantly affect mechanical properties; however, measurements of ductility were found to be influenced by some interaction between the factors. These results were attributed to the narrow operating envelope which was required for successfully sintered specimens. Density and chemistry measurements further demonstrated no significant change with respect to reuse. The findings suggest that titanium powder can be reused up to eight times without any noticeable loss in strength or ductility.

Evaluating FDM Process Parameter Sensitive Mechanical Performance of Elastomers at Various Strain Rates of Loading

Abstract: To optimize the mechanical performance of fused deposition modelling (FDM) fabricated parts, it is necessary to evaluate the influence of process parameters on the resulting mechanical performance. The main focus of the study was to characterize the influence of the initial process parameters on the mechanical performance of thermoplastic polyurethane under a quasi-static and high strain rate (~2500 s−1). The effects of infill percentage, layer height, and raster orientation on the mechanical properties of an FDM-fabricated part were evaluated. At a quasi-static rate of loading, layer height was found to be the most significant factor (36.5% enhancement in tensile strength). As the layer height of the sample increased from 0.1 to 0.4 mm, the resulting tensile strength sample was decreased by 36.5%. At a high-strain rate of loading, infill percentage was found to be the most critical factor influencing the mechanical strength of the sample (12.4% enhancement of compressive strength at 100% as compared to 80% infill). Furthermore, statistical analysis revealed the presence of significant interactions between the input parameters. Finally, using an artificial neural networking approach, we evaluated a regression model that related the process parameters (input factors) to the resulting strength of the samples.

Microstructural control during laser powder fusion to create graded microstructure Ni-superalloy components

Abstract: This work explores the feasibility of additively manufacturing tailored microstructures through varying process parameters, to eventually control the mechanical properties and performance. The investigation focuses on controlling the heat input and thermal history during laser-powder bed fusion of IN718 through process parameter manipulation; notably the heat input parameters (power, scan speed, and hatch spacing) and island scanning parameters (island size, shift, and island overlap). The changes in preferred orientation, morphology and grain size were characterised in both the transverse and build cross-sections using scanning electron microscopy (SEM) and electron back scatter diffraction (EBSD), while the texture development was comparatively characterised using X-ray diffraction (XRD). The solidification cell size was quantified to estimate the influence of the process parameters on the cooling rates. This was also rationalised using a thermal model resolving the scan characteristics to provide the transient temperature distribution to a numerical grain growth model. Based on the obtained microstructures, graded microstructures were generated using the island strategy and identical laser parameters throughout but changing subtle features such as the island size and shift. A suitable post-process heat treatment was applied to retain the tailored microstructures, while obtaining the required hardness.

Wire-Based Additive Manufacturing of Ti-6Al-4V Using Electron Beam Technique

Abstract: Electron beam freeform fabrication is a wire feed direct energy deposition additive manufacturing process, where the vacuum condition ensures excellent shielding against the atmosphere and enables processing of highly reactive materials. In this work, this technique is applied for the α + β-titanium alloy Ti-6Al-4V to determine suitable process parameter for robust building. The correlation between dimensions and the dilution of single beads based on selected process parameters, leads to an overlapping distance in the range of 70–75% of the bead width, resulting in a multi-bead layer with a uniform height and with a linear build-up rate. Moreover, the stacking of layers with different numbers of tracks using an alternating symmetric welding sequence allows the manufacturing of simple structures like walls and blocks. Microscopy investigations reveal that the primary structure consists of epitaxial grown columnar prior β-grains, with some randomly scattered macro and micropores. The developed microstructure consists of a mixture of martensitic and finer α-lamellar structure with a moderate and uniform hardness of 334 HV, an ultimate tensile strength of 953 MPa and rather low fracture elongation of 4.5%. A subsequent stress relief heat treatment leads to a uniform hardness distribution and an extended fracture elongation of 9.5%, with a decrease of the ultimate strength to 881 MPa due to the fine α-lamellar structure produced during the heat treatment. Residual stresses measured by energy dispersive X-ray diffraction shows after deposition 200–450 MPa in tension in the longitudinal direction, while the stresses reach almost zero when the stress relief treatment is carried out.

Predicting the Printability in Selective Laser Melting with a Supervised Machine Learning Method.

Abstract: Though selective laser melting (SLM) has a rapidly increasing market these years, the quality of the SLM-fabricated part is extremely dependent on the process parameters. However, the current metallographic examination method to find the parameter window is time-consuming and involves subjective assessments of the experimenters. Here, we proposed a supervised machine learning (ML) method to detect the track defect and predict the printability of material in SLM intelligently. The printed tracks were classified into five types based on the measured surface morphologies and characteristics. The classification results were used as the target output of the ML model. Four indicators had been calculated to evaluate the quality of the tracks quantitatively, serving as input variables of the model. The data-driven model can determine the defect-free process parameter combination, which significantly improves the efficiency in searching the process parameter window and has great potential for the application in the unmanned factory in the future.

Influence of laser beam profile on the selective laser melting process of AlSi10Mg

Abstract: Selective laser melting (SLM) offers great potential to manufacture customized and complex metallic parts. Major drawbacks that limit its industrial application are the high cost of the process that is related to low process speeds and issues with reproducibility. One important process parameter that has the potential to increase the reproducibility and speed of the process is the laser beam intensity profile. Since its influence has not been sufficiently investigated, the goal of this study is to analyze the effect of the beam profile on the SLM process of AlSi10Mg. Single tracks and density cubes are manufactured with different process parameters and two beam profiles (standard Gaussian and Donut beam profiles) and analyzed with respect to appearance, the size of melt tracks, porosity, and the types of defect. The results reveal several advantages of the Donut beam profile such as fewer defects and a significantly broader process window that promises a more robust process.

Sensitivity analysis of the artificial neural networks in a system for durability prediction of forging tools to forgings made of C45 steel

Abstract: The article presents the results of a sensitivity analysis of artificial neural networks developed for a system which predicts the durability of forging tools used in the selected hot die forging process. The developed system makes it possible to calculate the geometric loss of the examined tool for the given values of its operating parameters (number of forgings, tool temperature at selected points, type of the applied protective layer, pressure and path of friction) and estimates the intensity of the occurrence of typical mechanisms of tool destruction, i.e. thermo-mechanical fatigue, mechanical wear, abrasive wear and plastic deformation. Nine neural networks operate in the developed system. Five of them determine the geometric loss of the material used for tools operating with protective layers, including a nitrided layer, a pad welded layer and three hybrid layers, i.e. AlCrTiSiN, Cr/CrN and Cr/AlCrTiN. Four networks make calculations determining the intensity of the occurrence of typical destructive mechanisms. The developed sensitivity analysis allows for each neural network to show which input parameters are most important and have the greatest impact on the explained variables. This is determined based on the network error analysis in the case of elimination of individual variables from the input data. The greater the network error calculated after rejecting an input variable relative to the error obtained for the network with all the input variables, the more sensitive the network to the lack of this variable. The best compliance was obtained for the first developed set of networks regarding the geometric loss of material, while the lowest compliance was obtained for the second developed set of networks regarding the applied protective layers, and in particular for plastic deformation and mechanical fatigue, probably due to the smallest size of these sets in the knowledge base. The obtained results of this analysis are important for the system operation, i.e. supporting the technologist’s decision in the selection of such process parameter values that will increase the die’s lifetime.

Modelling of material removal in abrasive belt grinding process : a regression approach

Abstract: This article explores the effects of parameters such as cutting speed, force, polymer wheel hardness, feed, and grit size in the abrasive belt grinding process to model material removal. The process has high uncertainty during the interaction between the abrasives and the underneath surface, therefore the theoretical material removal models developed in belt grinding involve assumptions. A conclusive material removal model can be developed in such a dynamic process involving multiple parameters using statistical regression techniques. Six different regression modelling methodologies, namely multiple linear regression, stepwise regression, artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR) and random forests (RF) have been applied to the experimental data determined using the Taguchi design of experiments (DoE). The results obtained by the six models have been assessed and compared. All five models, except multiple linear regression, demonstrated a relatively low prediction error. Regarding the influence of the examined belt grinding parameters on the material removal, inference from some statistical models shows that the grit size has the most substantial effect. The proposed regression models can likely be applied for achieving desired material removal by defining process parameter levels without the need to conduct physical belt grinding experiments.

Selection of optimal process parameters and assessment of its effect in micro-friction stir welding of AA6061-T6 sheets

Abstract: Selection of appropriate welding parameters in micro-friction stir welding (micro-FSW) is challenging and a practical problem. In this regard, an assessment of variation in the favourable process parameters due to downscaling of sheet thickness can prove beneficial. In this work, FSW in 1- and 0.5-mm-thick AA6061-T6 sheets was compared for suitable process parameters based on the tensile strength of the weld. Comparatively high tool rotational speed combined with a high speed of tool travel and low plunge depth was found to be suitable welding parameters in micro-FSW (0.5-mm sheet). The effect of using this favourable set of process parameter was also assessed on frictional heat input requirement per unit weld volume, deformation conditions during welding (predicted using FEM simulation) and weld’s mechanical properties and microstructural characteristics in each sheet thickness. A high frictional heat input per unit weld volume was found in micro-FSW. A reduced weld zone temperature, an increased value of strain rate and a higher cooling rate were found in micro-FSW which led to a smaller weld nugget grain size, an improvement in weld’s ultimate tensile strength (UTS) but a drop in its ductility in this case.

Intelligent optimization system for powder bed fusion of processable thermoplastics

Abstract: Powder bed fusion (PBF) represents a class of additive manufacturing processes with the unique advantage of being able to fabricate functional products with complex three-dimensional geometries. PBF has been broadly applied in highly value-added industries, including the biomedical device and aerospace industries. However, it is challenging to construct a comprehensive knowledgebase to guide material selection and process optimization decisions to satisfy the product standards of various industries based on a poor understanding of process-structure-property/performance relationships for each type of thermoplastic. In this paper, an intelligent optimization system is proposed to establish quantitative relationships between process parameters and multiple optimization objectives, including mechanical properties, productivity, energy efficiency, and degree of material degradation. Polyurethane is considered as a representative thermoplastic because it is sensitive to thermal-induced degradation and has a relatively narrow process window. Material and powder properties as functions of temperature are investigated using systematic material screening. Numerical models are created to analyze the interactions between laser beams and polymeric powders by considering the effects of chamber thermal conditions, laser parameters, temperature-dependent properties, and phase transitions of polymers, as well as laser beam characteristics. The theoretically predicted features of melting pools are validated experimentally and then utilized to develop quantitative relationships between process parameters and multiple optimization objectives. The established relationships can guide process parameter optimization and material selection decisions for polymer PBF.