Showing papers on "Process variable published in 2021"

Comparative Study of the Sensitivity of PLA, ABS, PEEK, and PETG’s Mechanical Properties to FDM Printing Process Parameters

Abstract: Significant advances in fused deposition modeling (FDM), as well as its myriad applications, have led to its growing prominence among additive manufacturing (AM) technologies. When the technology was first developed, it was used for rapid prototyping to examine and analyze a product in the design stage. FDM facilitates rapid production, requires inexpensive tools, and can fabricate complex-shaped parts; it, therefore, became popular and its use widespread. However, various FDM processing parameters have proven to affect the printed part’s mechanical properties to different extents. The values for the printing process parameters are carefully selected based on the part’s application. This study investigates the effects of four process parameters (raster angle, layer thickness, infill percentage, and printing speed) on the mechanical behavior of printed parts that are based on available literature data. These process parameter’s influence on part’s mechanical properties varies depending on the FDM material. The study focuses on four FDM materials: polylactic acid (PLA), acrylonitrile butadiene styrene (ABS), polyether ether ketone (PEEK), and polyethylene terephthalate glycol (PETG). This paper summarizes the state-of-the-art literature to show how sensitive the material’s mechanical properties are to each process parameter. The effect of each parameter on each material was quantified and ranked using analysis of variance (ANOVA). The results show that infill percentage then layer thickness are the most influential process parameter on most of the material’s mechanical properties. In addition, this work identifies gaps in existing studies and highlights opportunities for future research.

Prediction modelling and process optimization for forming multi-layer cladding structures with laser directed energy deposition

Abstract: In the laser Directed Energy Deposition (DED) manufacturing of parts, severe deformation caused by the uneven geometric characteristics of a multi-layer deposition fusion area occurs extremely easily due to the complexity and insufficient understanding of the process. The above results in a significant decline in the overall geometric quality of the parts. To study the influence of the laser DED process parameters on the geometric characteristics of multi-layer cladding, a response surface method was used to carry out an experimental study of the multi-layer cladding structures. Statistical prediction models of multi-layer cladding manufacturing were established among the laser power, scanning speed, powder feeding rate, lap ratio and cladding width, cladding zone area, fusion zone area, and flatness ratio. The accuracy, adaptability and relevance of the models were verified. The influence of each process parameter on the geometric characteristics of the multi-layer cladding structure was analysed, and the laser DED process parameters were optimized. Experimental results show that the surface forming quality of specimens processed with optimized process parameters is good. Relative errors between the experimental and predicted values of the geometric characteristics of specimens are less than 9%; thus, the prediction models can suitably predict and control the geometric characteristics of a multi-layer cladding structure. The proposed optimization of process parameters can significantly improve the geometric quality of parts and has substantial value in engineering applications.

Compositional grading of a 316L-Cu multi-material part using machine learning for the determination of selective laser melting process parameters

Abstract: The aim of this work is to propose a methodology for rapidly predicting suitable process parameters for additive manufacturing of a 316L-Cu multi-material part with a compositional gradient by using machine learning. Specifically, an algorithm based on a multivariate Gaussian process is developed to predict part density and surface roughness for a given set of laser power, velocity, and hatch spacing values. The training data for the algorithm is collected using a high-throughput experimentation method that allows for rapid measurement of part density, and surface roughness. After the model is validated using leave-one-out cross validation method, process parameter maps are generated for 316L-Cu parts manufactured using selective laser melting with premixed powder at mass fractions of 0.25, 0.50, and 0.75. A set of suitable process parameters are predicted using the process maps. It is shown that process parameters are a nonlinear function of gradient composition and neither process parameters of 316L or Cu are suitable for the graded region of a 316L-Cu multi-material part. Generated process maps provide a firsthand knowledge of process-property relationships for regions of compositional grading in 316L-Cu parts.

Selective Laser Melting of 316L Austenitic Stainless Steel: Detailed Process Understanding Using Multiphysics Simulation and Experimentation

Abstract: The parameter sets used during the selective laser melting (SLM) process directly affect the final product through the resulting melt-pool temperature. Achieving the optimum set of parameters is usually done experimentally, which is a costly and time-consuming process. Additionally, controlling the deviation of the melt-pool temperature from the specified value during the process ensures that the final product has a homogeneous microstructure. This study proposes a multiphysics numerical model that explores the factors affecting the production of parts in the SLM process and the mathematical relationships between them, using stainless steel 316L powder. The effect of laser power and laser spot diameter on the temperature of the melt-pool at different scanning velocities were studied. Thus, mathematical expressions were obtained to relate process parameters to melt-pool temperature. The resulting mathematical relationships are the basic elements to design a controller to instantly control the melt-pool temperature during the process. In the study, test samples were produced using simulated parameters to validate the simulation approach. Samples produced using simulated parameter sets resulting in temperatures of 2000 K and above had acceptable microstructures. Evaporation defects caused by extreme temperatures, unmelted powder defects due to insufficient temperature, and homogenous microstructures for suitable parameter sets predicted by the simulations were obtained in the experimental results, and the model was validated.

Fatigue property prediction of additively manufactured Ti-6Al-4V using probabilistic physics-guided learning

Abstract: The probabilistic fatigue properties of additively manufactured (AM) Ti-6Al-4V using selective laser melted (SLM) process is analyzed considering the effects of process parameters. The Probabilistic Physics-guided Neural Network (PPgNN) is proposed for the modeling. With this developed model, both mean and variance of the fatigue life can be learned. The PPgNN contains constraints on model parameters to obtain the probabilistic stress-life relationships (P-S-N curves) with the physics-consistent curvature and nonconstant variance. The PPgNN model is also able to be trained using the data set with missing data for more reliable predictions. Experimental fatigue data are collected from extensive literatures for AM Ti-6Al-4V in as-built and annealed condition subjected to various process parameters (scanning speed, laser power, hatch space, layer thickness, heat temperature, heat time). The PPgNN model is validated using the experimental data. Next, a group of models with the same architecture and training data but different initial neural network biases and weights are obtained to account for the the randomness of NN. Following this, the predictive performance is compared between models training using all data (both complete and incomplete) and only complete data. Finally, global sensitivity analysis using the Delta Moment-Independent Measure is conducted to investigate the importance of process parameters. By only varying one process parameter and keeping the remaining ones fixed, the effect of each parameter on probabilistic fatigue lives is studied.

Statistical and Experimental Analysis of Process Parameters of 3D Nylon Printed Parts by Fused Deposition Modeling: Response Surface Modeling and Optimization

Abstract: In the current study, the additive manufacturing of nylon by fused deposition modeling is conducted based on statistical analysis. Besides, the aim of this study is the influence of process parameters, namely layer thickness (0.15 mm-0.35 mm), infill percentage (15-55%), and the number of contours (2-6) on the maximum failure load, parts weight, elongation at break, and build time. The experiment approach was used to optimize process parameters based on the statistical evaluates to reach the best objective function. The minimum value of build time and maximize of the failure load were considered as objective functions. The response surface method is regarded as an optimization process parameter, and optimum conditions were studied by experimental research to evaluate efficiency. Based on the results, the layer thickness is the significant primary variable for all responses. The experimental evaluation showed that the maximum values of failure load and elongation were obtained by changing the layer thickness from the lowest to the highest. By reduction in layer thickness at the same printing speed, the cooling rate increases, which results in greater strength and less elongation. As a result, it could be concluded that by increasing the number of contour layers from 2 to 6, the maximum failure force increased 42%. Increasing the contours due to the similar effect to increasing the infill density, increases the failure force and production time, which is also confirmed by the ANOVA.

Optimal ultrasonication process time remains constant for a specific nanoemulsion size reduction system.

Abstract: This paper theorizes the existence of a constant optimum ultrasound process time for any size-reduction operation, independent of process parameters, and dependent on product parameters. We test the concept using the case of ‘ultrasonic preparation of oil-in-water nanoemulsions’ as model system. The system parameters during ultrasonication of a hempseed oil nanoemulsion was evaluated by a response surface methodology, comprising lecithin and poloxamer-188 as surfactants. Results revealed that the particle size and emulsion stability was affected significantly (p 0.05) affected by process parameter (‘ultrasonication process time’). Next, other process parameters (emulsion volume and ultrasonic amplitude) were tested using kinetic experiments. Magnitude of particle size reduction decreased with increasing ‘ultrasonication process time’ according to a first order relationship, until a minimum particle size was reached; beyond which ultrasonication no longer resulted in detectable decrease in particle size. It was found that the optimal ultrasonication process time (defined as time taken to achieve 99% of the ‘maximum possible size reduction’) was 10 min, and was roughly constant regardless of the process parameters (sample volume and ultrasonic amplitude). Finally, the existence of this constant optimal ultrasonication process time was proven for another emulsion system (olive oil and tween 80). Based on the results of these case studies, it could be theorized that a constant optimum ultrasonication process time exists for the ultrasonication-based size-reduction processes, dependent only on product parameters.

Process characteristics, particle behavior and coating properties during HVOF spraying of conventional, fine and nanostructured WC-12Co powders

Abstract: In recent years, great effort has been taken in science and industry to find novel material-related solutions, which provide improved properties for future technological applications. One of these approaches is the use of fine structured and nanostructured materials. Within the field of wear protection, the use of fine or nanostructured WC-Co powder feedstock in the thermal spray process enables the application of highly wear resistant, thin near net-shape coatings on parts with complex geometries. In this study, the processing of WC-12Co powders by means of High Velocity Oxy-Fuel (HVOF) flame spraying is fundamentally investigated and the results are compared to those obtained with conventional powders. The influence of process parameter and scaling effects on the spray process and the thermo-kinetic particle behavior in the flame, the heating of the substrate as well as on the coating properties, the microstructure, the behavior of elements and phases and the residual stress is discussed comprehensively. The investigations of this work have shown that HVOF spraying of fine and nanostructured WC-12Co powders instead of conventional ones leads to a significant alteration of the thermo-kinetic spray conditions. Under optimized spray conditions, achieved by the use of special spray equipment and statistical design of experiments (DoE), improvements in terms of the economy of the spray process (higher deposition efficiencies) and the mechanical properties (higher microhardness and fracture toughness, lower porosity and roughness) can be achieved.

Experimental investigation & optimisation of wire electrical discharge machining process parameters for Ni49Ti51 shape memory alloy

Abstract: Shape Memory Alloys (SMAs) are unique class of modern material with various functional properties such as pseudo-elasticity, biocompatibility, high specific strength, high corrosion resistance and high anti-fatigue property. The machining of these SMAs is difficult by conventional machining processes due to strain-hardening effect which changes the properties of materials and it is found that non-conventional machining processes are more suitable to machine them. In present investigations, the experiments were performed on wire electrical discharge machining (WEDM) to study the interaction effects of the process parameter on surface characteristics of Ni49Ti51 SMA’s by brass tool electrode. Peak current, pulse on time, pulse off time, wire tension and wire feed rate were taken as input parameters and their effect were analysed on material removal rate. Artificial neural network was adopted to develop and to train the experimental data using back-propagation neural network (BPNN) approach. The response surface methodology (RSM) was adopted to develop the second order mathematical based quadratic models. The recast layer formation and surface of machined materials were also analysed by the SEM characterization. It was noticed that the machined surface contains the surface cracks and uneven distribution of crater on the surface.

A Critical Review on Effect of Process Parameters on Mechanical and Microstructural Properties of Powder-Bed Fusion Additive Manufacturing of SS316L.

Abstract: Additive manufacturing (AM) is one of the recently studied research areas, due to its ability to eliminate different subtractive manufacturing limitations, such as difficultly in fabricating complex parts, material wastage, and numbers of sequential operations. Laser-powder bed fusion (L-PBF) AM for SS316L is known for complex part production due to layer-by-layer deposition and is extensively used in the aerospace, automobile, and medical sectors. The process parameter selection is crucial for deciding the overall quality of the SS316L build component with L-PBF AM. This review critically elaborates the effect of various input parameters, i.e., laser power, scanning speed, hatch spacing, and layer thickness, on various mechanical properties of AM SS316L, such as tensile strength, hardness, and the effect of porosity, along with the microstructure evolution. The effect of other AM parameters, such as the build orientation, pre-heating temperature, and particle size, on the build properties is also discussed. The scope of this review also concerns the challenges in practical applications of AM SS316L. Hence, the residual stress formation, their influence on the mechanical properties and corrosion behavior of the AM build part for bio implant application is also considered. This review involves a detailed comparison of properties achievable with different AM techniques and various post-processing techniques, such as heat treatment and grain refinement effects on properties. This review would help in selecting suitable process parameters for various human body implants and many different applications. This study would also help to better understand the effect of each process parameter of PBF-AM on the SS316L build part quality.

Application of artificial intelligence to optimize the process parameters effects on tensile properties of Ti-6Al-4V fabricated by laser powder-bed fusion

Abstract: Laser powder-bed fusion (LPBF) process, as one of the most widely used technologies of additive manufacturing, enables fabrication of parts with intricate geometries. The choice of process parameters in this technology plays a major role in defining the microstructural, mechanical and surface properties of the fabricated parts. In this study, the effects of LPBF process parameters on static tensile properties (including yield strength and ultimate tensile strength and elongation) of Ti-6Al-4V samples were investigated using artificial intelligence methods. Deep learning approach was employed by using neural networks for prediction, optimization and parametric and sensitivity analyses. Relevant experimental data available in the literature were collected to feed the network. Stacked auto-encoder was assigned to the networks for high accuracy pre-training. LPBF process parameters including laser power, scanning speed, hatch spacing, layer thickness and sample direction were regarded as inputs while yield strength, ultimate strength and elongation were considered as outputs of the neural networks. The obtained results indicate the high potential of neural networks to be used as a powerful tool for process parameter optimization for enhanced mechanical performance of additive manufactured parts.

FSSW process parameter optimization for AA2024 and AA7075 alloy

Abstract: The novelty of the present work is the investigation of friction stir spot welding between AA2024 and AA7075 aluminum plates performed by Box-Behnken technique of response surface methodology. The ...

A comparative analysis of laser additive manufacturing of high layer thickness pure Ti and Inconel 718 alloy materials using finite element method

Abstract: Investigation of the selective laser melting (SLM) process, using finite element method, to understand the influences of laser power and scanning speed on the heat flow and melt-pool dimensions is a challenging task. Most of the existing studies are focused on the study of thin layer thickness and comparative study of same materials under different manufacturing conditions. The present work is focused on comparative analysis of thermal cycles and complex melt-pool behavior of a high layer thickness multi-layer laser additive manufacturing (LAM) of pure Titanium (Ti) and Inconel 718. A transient 3D finite-element model is developed to perform a quantitative comparative study on two materials to examine the temperature distribution and disparities in melt-pool behaviours under similar processing conditions. It is observed that the layers are properly melted and sintered for the considered process parameters. The temperature and melt-pool increases as laser power move in the same layer and when new layers are added. The same is observed when the laser power increases, and opposite is observed for increasing scanning speed while keeping other parameters constant. It is also found that Inconel 718 alloy has a higher maximum temperature than Ti material for the same process parameter and hence higher melt-pool dimensions.

Effect of process parameters on machinability, hemocompatibility and surface integrity of SS 316L using R-MRAFF

Abstract: Surface finish at nanometric scale is imperative for biomedical implants made up of stainless steel 316L (SS 316L) and titanium alloy to ensure the biocompatibility. However, such fine finishing is quite difficult to process. A rotational magnetorheological abrasive flow finishing process is used for nano-finishing. Rotational magnetorheological abrasive flow finishing (R-MRAFF) is one of the processes to achieve nano-level finishing. In this present study, a rotating die has been developed to perform the R-MRAFF processes with four rectangular-shaped permanent magnets held at an angle of 90° to one another. A new method for producing a variable magnetic field was investigated in this study by increasing or decreasing the distance between permanent magnets in the rotating die and workpiece fixtures. The increase in the distance between permanent magnets in rotating die and workpiece fixture leads to a decline in the value of material removal rate (MRR) as well as a percentage reduction in surface roughness (%ΔRa). This validates the importance of magnetic field intensity in the R-MRAFF process. A central composite design (CCD) in the response surface methodology (RSM) is employed to study the effect of process parameters on the response variables. The effect of the process parameter was evidenced with the help of the ANOVA technique. Optimization was carried out with constraints of maximizing both output response variables, the optimum MRR and %ΔRa, which were found as 2.02 mg/s and 50.66%. To quantify the effect of surface roughness on hemocompatibility, a platelet adhesion study was investigated. The platelet adhered area starts to decrease with the reduction in the surface roughness from 319 to 167 nm.

Development of a high-speed laser material deposition process for additive manufacturing

Abstract: Extreme high-speed laser material deposition (EHLA) is a variant of laser material deposition (LMD) with a modified powder gas jet focus. EHLA allows deposition at high process speeds in the range of several hundred meters per minute and the deposition of layers as thin as 25 μm. Feed rates can be in the range of 50 g/min or above. The processing of material combinations with poor weldability or different melting points is possible, with simultaneously smaller heat-affected zone and dilution zone compared to conventional LMD. Until now, EHLA has mainly been used for applying wear-resistant and corrosion-resistant coatings to rotationally symmetric parts, where high process speeds are achieved by rotation of the substrate. Developing EHLA for additive manufacturing (AM) aims at manufacturing highly individualized parts with high volume buildup rates and finer structural resolution compared to conventional LMD. The main steps toward harnessing EHLA for AM, termed EHLA 3D, are (a) process parameter development for volume buildup through deposition of multiple layers on top of each other and (b) the development of system technology that allows relative spatial movement between the powder nozzle and the substrate at high speeds and high accuracy in three dimensions. With a fast back-and-forth motion, high acceleration rates are necessary to ensure high volume buildup rates and high powder deposition efficiency. In this work, a specially designed tripod machine concept that has been developed in cooperation at Fraunhofer ILT is introduced. Theoretical considerations concerning achievable powder deposition efficiency as a function of process speed and acceleration of the handling system are presented. Furthermore, the results of process parameter development for up to 40 m/min with the iron-base alloy 1.4404 are shown. Virtually pore-free deposition of cuboid volumes is demonstrated. The mechanical values of the additively manufactured volumes are compared to the values for solution-annealed material, conventional LMD-manufactured samples, and samples produced by laser powder bed fusion (LPBF). Finally, an outlook on future research activities required for further development of EHLA 3D is given, covering system technology, buildup strategies, software integration, and possible applications of the new technology in repair, coating, and (hybrid-)additive contexts.

A model of bead size based on the dynamic response of CMT-based wire and arc additive manufacturing process parameters

Abstract: This paper aims at fabricating large metallic components with high deposition rates, low equipment costs through wire and wire and arc additive manufacturing (WAAM) method, in order to achieve the morphology and mechanical properties of manufacturing process, a bead morphology prediction model with high precision for ideal deposition of every pass was established.,The dynamic response of the process parameters on the bead width and bead height of cold metal transfer (CMT)-based AM was analyzed. A laser profile scanner was used to continuously capture the morphology variation. A prediction model of the deposition bead morphology was established using response surface optimization. Moreover, the validity of the model was examined using 15 groups of quadratic regression analyzes.,The relative errors of the predicted bead width and height were all less than 5% compared with the experimental measurements. The model was then preliminarily used with necessary modifications, such as further considering the interlayer process parameters, to guide the fabrication of complex three-dimensional components.,The morphology prediction of WAAMed bead is a critical issue. Most research has focused on the formability and defects in CMT-based WAAM and little research on the effect of process parameters on the morphology of the deposited layer in CMT-based WAAM has been conducted. To test the sensitivities of the processing parameters to bead size, the dynamic response of key parameters was investigated. A regression model was established to guide the process parameter optimization for subsequent multi-layer or component deposition.

Cold spray deformation and deposition of blended feedstock powders not necessarily obey the rule of mixture

Abstract: Cold spray additive manufacturing has the potential to be used in repair and manufacturing of a wide variety of parts and coatings including composite coatings. Understanding materials' fundamental behaviour during deposition is essential for the process development which was not addressed enough for mixed powders. One objective of this study is to explore the process-structure relation of mixed Ni-Ti powders in cold spray to provide a methodology to achieve a coating with the composition of interest. Single/multi-particle impact experiments and FEM modelling were employed to investigate the particle deformation and microstructure evolution using SEM, EBSD and EDS. The sprays of mixed powders were performed using a wide process parameter window and the deposition characteristics and composition variation as a function of process parameters were explored. The experiments and modelling showed that splats in dissimilar multi-particle impacts experience more deformation than those of similar materials, which in microstructural scale translates to variation of deformation mechanism from single-component coatings. This results in higher degree of grain refinement, specifically for Ti in the composite coating with an average grain size of 500 nm. Experiments showed that the DE of mixed powder deviates from the rule of mixture, which means the critical velocity of Ni-Ti powder is different from the single-component Ni and Ti coatings due to material interactions. Findings showed there is a process parameter criteria for which relative DEs of components, and thereby composition of composite coatings, remain at almost constant ratios. η (impact velocity/critical velocity ratio) is used as a measure to predict qualitative composition variation as a function of spraying parameters and powder characteristics.

A multivariate meltpool stability criterion for fabrication of complex geometries in electron beam powder bed fusion

Abstract: Process parameters for manufacturing of complex parts in electron beam powder bed fusion are usually derived from material-specific process windows, which are established by fabrication and evaluation of standardized cuboid specimen. The mechanisms defining low- and high-energy boundaries of the process window are well understood. The boundary emerging at high scan speeds and beam power however, was observed but the underlying mechanism is not further discussed. In addition, appropriate methodologies to transfer process parameters from standardized process windows to complex geometries are not readily available, as the meltpool geometry is significantly affected by the scan length. This work introduces and verifies a characteristic scan length dependent process parameter limit for the fabrication of complex geometries. Electron-optical process monitoring enables the surface characterization for a wide range of process parameter and the subsequent identification of the process parameter limit as a linear function of scan length. A semi-analytical heat conduction model is used to examine the corresponding meltpool geometries. The underlying mechanism is determined as the meltpool stability limit, which occurs, when the aspect ratio of the meltpool reaches the threshold for a liquid film instability. Based on the meltpool geometries for different scan lengths, an analytical relationship for the process parameter limit as a function of scan length is proposed. This relationship may be used as a guideline for the selection of process parameters and the development of new scan strategies for the fabrication of complex geometries.