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Showing papers on "Process variable published in 2019"


Journal ArticleDOI
29 Jul 2019
TL;DR: This paper intensively reviews state-of-the-art literature on the influence of parameters on part qualities and the existing work on process parameter optimization and directions for future research in this field are suggested.
Abstract: Fused deposition modeling (FDM) is an additive manufacturing (AM) process that is often used to fabricate geometrically complex shaped prototypes and parts. It is gaining popularity as it reduces cycle time for product development without the need for expensive tools. However, the commercialization of FDM technology in various industrial applications is currently limited due to several shortcomings, such as insufficient mechanical properties, poor surface quality, and low dimensional accuracy. The qualities of FDM-produced products are affected by various process parameters, for example, layer thickness, build orientation, raster width, or print speed. The setting of process parameters and their range depends on the section of FDM machines. Filament materials, nozzle dimensions, and the type of machine determine the range of various parameters. The optimum setting of parameters is deemed to improve the qualities of three-dimensional (3D) printed parts and may reduce post-production work. This paper intensively reviews state-of-the-art literature on the influence of parameters on part qualities and the existing work on process parameter optimization. Additionally, the shortcomings of existing works are identified, challenges and opportunities to work in this field are evaluated, and directions for future research in this field are suggested.

252 citations


Journal ArticleDOI
TL;DR: These concrete process-property relationships should provide a way to achieve new knowledge about the electrostatic energy-fluid interactions, and to meanwhile improve researchers’ capability to optimize the coaxial process conditions to achieve the desired nanoproducts.
Abstract: The concrete relationship between the process parameters and nanoproduct properties is an important challenge for applying nanotechnology to produce functional nanomaterials. In this study, the relationships between series of process parameters and the medicated nanofibers' diameter were investigated. With an electrospinnable solution of hydroxypropyl methylcellulose (HPMC) and ketoprofen as the core fluid, four kinds of nanofibers were prepared with ethanol as a sheath fluid and under the variable applied voltages. Based on these nanofibers, a series of relationships between the process parameters and the nanofibers' diameters (D) were disclosed, such as with the height of the Taylor cone (H, D = 125 + 363H), with the angle of the Taylor cone (ɑ, D = 1576 - 19ɑ), with the length of the straight fluid jet (L, D = 285 + 209L), and with the spreading angle of the instable region (θ, D = 2342 - 43θ). In vitro dissolution tests verified that the smaller the diameters, the faster ketoprofen (KET) was released from the HPMC nanofibers. These concrete process-property relationships should provide a way to achieve new knowledge about the electrostatic energy-fluid interactions, and to meanwhile improve researchers' capability to optimize the coaxial process conditions to achieve the desired nanoproducts.

94 citations


Journal ArticleDOI
TL;DR: In this paper, the first description of the process parameter relationship to the microstructure/nanostructure and mechanical properties of Aluminum Alloy 6061 AFS-D deposits was provided.

88 citations


Journal ArticleDOI
TL;DR: In this paper, the size and morphology of the resulting microstructure is a function of two well-known parameters: the temperature gradient within the liquid phase (G) and the velocity of the solidification front (R).
Abstract: Laser powder bed fusion additive manufacturing (L-PBF AM) offers great potential for local microstructure control. During this process, solidification occurs in conditions that are far from equilibrium and possesses – in the majority of cases – a strong directionality. In general, the size and morphology of the resulting microstructure is a function of two well-known parameters: the temperature gradient within the liquid phase (G) and the velocity of the solidification front (R). To provide guidance in selecting appropriate, systematically defined, process parameters for L-PBF of 316L stainless steel square pillars, we developed an intentionally simple thermal model to express these two parameters, G and R, as a function of selected process variables (laser scan speed, laser power) and material properties (thermal diffusivity). Results from both microstructural and mechanical characterization of the pillars indicate that high-strength, fully-dense parts with a highly oriented cellular microstructure can be obtained when using significantly different sets of process parameters. Furthermore, despite its simplicity, the numerical model correlates well with experimental evidence and confirms that rather than creating variable microstructures, the process parameter constraints actually lead to a stable cellular microstructure regardless of the wide process window studied.

86 citations


Journal ArticleDOI
TL;DR: In this article, multiple melt pool cross-sections are measured at multiple process parameter combinations for the Inconel 718 alloy in a Laser Powder Bed Fusion (L-PBF) process.
Abstract: Expanding on prior process mapping work by the authors, multiple melt pool cross-sections are measured at multiple process parameter combinations for the Inconel 718 alloy in a Laser Powder Bed Fusion (L-PBF) process. Collection of such data enables the study of the variability of melt pool geometry (e.g. width, depth, and cross-sectional area) across process space. Furthermore, the statistical distribution of the measured melt pool geometries is compared to that of an equivalent normal distribution and intriguing outliers are observed. The cross-sectional morphology of the melt pools are associated with defects such as keyholing porosity and balling and the variability of the defects is quantified. The final product of this work is a robust description of L-PBF In718 melt pool behavior, based on ex-situ observations, which can be linked to in-situ observations of melt pool morphology in future work.

86 citations


Journal ArticleDOI
TL;DR: In this paper, a process map was developed in an effort to improve the understanding of dry granulation of pharmaceutical excipients by roll compaction process, and to implement the quality-by-design (QbD) approach.

79 citations


Journal ArticleDOI
TL;DR: In this article, the authors investigated the process variable effects on the damage and deformational behavior of fused deposition modeling (FDM) three-dimensional (3D)-printed specimens by performing tensile tests and inverse identification analyses.
Abstract: In this paper, we investigated the process variable effects on the damage and deformational behavior of fused deposition modeling (FDM) three-dimensional (3D)-printed specimens by performing tensile tests and inverse identification analyses. A characterization of the effects of different parametric variations of 3D-printed specimens on fracture properties are a matter of considerable significance that are often overlooked. By combining the infill density and the layer thickness options that are available in the 3D printer machine, six groups with different structural configurations can be obtained. The data and images obtained from experiments are employed to investigate the failure mechanism of 3D-printed specimens and demonstrate the relationship that exists between structural variations and fracture mechanical properties. On the basis of experimental results, a Gurson-type porous plasticity model was used within a 3D continuum finite element model to characterize the process–damage parameter relationship through an inverse identification process.

74 citations


Journal ArticleDOI
TL;DR: Results showed that various hybrid statistical tools such as RSM-GA, ANN and ANN-GA are very adequate tools for FDM process parameter optimization.
Abstract: In this study, significant process parameters (layer thickness, build orientation, infill density and number of contours) are optimized for enhancing the magnitude/dimensional preciseness of fused deposition modeling (FDM) devise units. Hybrid statistical tools such as response surface methodology–genetic algorithm (RSM–GA), artificial neural network (ANN) and artificial neural network-genetic algorithm (ANN-GA) in MAT LAB 16.0 are utilized for training and optimization. An attempt has been made to build up a mathematical model in order to set up an indirect correlation between various FDM process parameters and magnitude preciseness. Sequentially to verify the different developed models and the optimum process parameters setting validation tests were also performed. The results showed that various hybrid statistical tools such as RSM-GA, ANN and ANN-GA are very adequate tools for FDM process parameter optimization. The minimum percentage variation in length = 0.06409%, width = 0.03961% and thickness = 0.85689% can be obtained by using ANN-GA.

61 citations


Journal ArticleDOI
TL;DR: In this paper, new aspects of the optimization of the selective laser melting process are investigated for AM Ti-6Al-4V, focusing on the amount of near-surface residual stress (RS), often blamed for the failure of components, and the porosity characteristics (amount and spatial distribution).
Abstract: While the volumetric energy density is commonly used to qualify a process parameter set, and to quantify its influence on the microstructure and performance of additively manufactured (AM) materials and components, it has been already shown that this description is by no means exhaustive. In this work, new aspects of the optimization of the selective laser melting process are investigated for AM Ti-6Al-4V. We focus on the amount of near-surface residual stress (RS), often blamed for the failure of components, and on the porosity characteristics (amount and spatial distribution). First, using synchrotron x-ray diffraction we show that higher RS in the subsurface region is generated if a lower energy density is used. Second, we show that laser de-focusing and sample positioning inside the build chamber also play an eminent role, and we quantify this influence. In parallel, using X-ray Computed Tomography, we observe that porosity is mainly concentrated in the contour region, except in the case where the laser speed is small. The low values of porosity (less than 1%) do not influence RS.

60 citations


Journal ArticleDOI
TL;DR: A new approach to investigate the electrohydrodynamic process and manipulate the functions of nanoproducts through process-property-performance relationships is presented, giving a hint that process parameters can be exploited as useful tools for accurately predicting and tailoring the resultant nanofibers' D, and in turn their functional performances.

53 citations


Journal ArticleDOI
TL;DR: In this paper, the weak coupling of a Finite Element (FE) model with a cellular automaton (CA) model was used to predict the microstructure evolution during SEBM.

Journal ArticleDOI
TL;DR: The proposed chart is a homogeneously weighted moving average type control chart that uses both the process and auxiliary variables in the form of a regression estimator to provide an efficient and unbiased estimate of the mean of the process variable.
Abstract: In this paper, we propose an efficient control chart for monitoring small shifts in a process mean for scenarios where the process variable is observed with a correlated auxiliary variable. The proposed chart, called an auxiliary homogeneously weighted moving average (AHWMA) chart, is a homogeneously weighted moving average type control chart that uses both the process and auxiliary variables in the form of a regression estimator to provide an efficient and unbiased estimate of the mean of the process variable. We provide the design structure of the chart and examine its performance in terms of its run length properties. Using a simulation study, we compare its run length performance with several existing methods for detecting a small shift in the process mean. Our simulation results show that the proposed chart is more efficient in detecting a small shift in the process mean than its competitors. We provide a detailed study of the chart's robustness to non-normal distributions and show that the chart may also be designed to be less sensitive to non-normality. We give some recommendations on the application of the chart when the process parameters are unknown and provide an example to show the implementation of the proposed new technique.

Journal ArticleDOI
TL;DR: In this article, the influence of a set of printing conditions and parameters, namely, envelope temperature, extrusion temperature, forced cooling and extrusion rate, on the parts performance was evaluated by printing a series of test specimens that are morphologically characterized and mechanically tested.
Abstract: The performance of parts produced by fused filament fabrication is directly related to the printing conditions and to the rheological phenomena inherent to the process, specifically the bonding between adjacent extruded paths/raster. This paper aims to study the influence of a set of printing conditions and parameters, namely, envelope temperature, extrusion temperature, forced cooling and extrusion rate, on the parts performance.,The influence of these parameters is evaluated by printing a set of test specimens that are morphologically characterized and mechanically tested. At the morphological level, the external dimensions and the voids content of the printed specimens are evaluated. The bonding quality between adjacent extruded paths is assessed through the mechanical performance of test specimens, subjected to tensile loads. These specimens are printed with all raster oriented at 90o relative to the tensile axis.,The best performance, resulting from a compromise between surface quality, dimensional accuracy and mechanical performance, is achieved with a heated printing environment and with no use of forced cooling. In addition, for all the conditions tested, the highest dimensional accuracy is achieved in dimensions defined in the printing plane.,This work provides a relevant result as the majority of the current printers comes without enclosure or misses the heating and envelope temperature control systems, which proved to be one of the most influential process parameter.

Journal ArticleDOI
TL;DR: In this article, a simple approach for rapid process development using normalized process maps is proposed, using plots of normalized energy density vs. normalized hatch spacing, which is further refined using analytical heat transfer models to predict melt pool size.
Abstract: There is growing interest in Laser Powder Bed Fusion (L-PBF) or Selective Laser Melting (SLM) manufacturing of high conductivity metals such as copper and refractory metals. SLM manufacturing of high thermal conductivity metals is particularly difficult. In case of refractory metals, the difficulty is amplified because of their high melting point and brittle behaviour. Rapid process development strategies are essential to identify suitable process parameters for achieving minimum porosities in these alloys, yet current strategies suffer from several limitations. We propose a simple approach for rapid process development using normalized process maps. Using plots of normalized energy density vs. normalized hatch spacing, we identify a wide processability window. This is further refined using analytical heat transfer models to predict melt pool size. Final optimization of the parameters is achieved by experiments based on statistical Design of Experiments concepts. In this article we demonstrate the use of our proposed approach for development of process parameters (hatch spacing, layer thickness, exposure time and point distance) for SLM manufacturing of molybdenum and aluminium. Relative densities of 97.4% and 99.7% are achieved using 200 W pulsed laser and 400 W continuous laser respectively, for molybdenum and aluminium, demonstrating the effectiveness of our approach for SLM processing of high conductivity materials.

Journal ArticleDOI
TL;DR: The bonding mechanism of hybrid AA5024/(GF‐)CF‐PEEK joints is explained and the kinematics of bonding formation is presented in detail, with the help of detailed process parameter recording and microscopic investigations.

Journal ArticleDOI
TL;DR: An opportunity to fully automate the approach to process optimisation by applying labels to the data is indicated, an approach that could also potentially be suited for on-the-fly process Optimisation.
Abstract: Metal-based additive manufacturing is a relatively new technology used to fabricate metal objects within an entirely digital workflow. However, only a small number of different metals are proven for this process. This is partly due to the need to find a new set of parameters which can be used to successfully build an object for every new alloy investigated. There are dozens of variables which contribute to a successful set of parameters and process parameter optimisation is currently a manual process which relies on human judgement.,Here, the authors demonstrate the application of machine learning as an alternative method to determine this set of process parameters, the subject of this test is the processing of pure copper in a laser powder bed fusion printer. Data in the form of optical images were collected over the course of traditional parameter optimisation. These images were segmented and fed into a convolutional autoencoder and then clustered to find the clusters which best represented a high-quality result. The clusters were manually scored according to their quality and the results applied to the original set of parameters.,It was found that the machine-learned clustering and subsequent scoring reflected many of the observations which were found in the traditional parameter optimisation process.,This exercise, as well as demonstrating the effectiveness of the ML approach, indicates an opportunity to fully automate the approach to process optimisation by applying labels to the data, hence, an approach that could also potentially be suited for on-the-fly process optimisation.,Opens in a new window.

Journal ArticleDOI
TL;DR: Real time AI based control of process parameters in injection molding cycle established relationship between failure and parameters and an automotive product in real industry is chosen for data acquisition, implementation and validation of entire AI based system.

Journal ArticleDOI
TL;DR: In this paper, the effects of altering process parameters on microstructure, porosity, and mechanical performance of Inconel 718 were investigated, and the results showed that process parameter modifications that result in porosity formation can significantly reduce fatigue life, while micro-structure changes were minimal and had little effect on tensile properties.
Abstract: Additive manufacturing (AM) allows for the fabrication of complex parts via layer-by-layer melting of metal powder Laser powder-bed AM processes use a variety of process parameters including beam power, beam velocity, and hatch spacing to control melting Alterations to these parameters have often been attempted to reduce porosity, for example, but less work has been done to on comprehensive effects of process parameter modifications This study looks at the effects of altering these parameters on microstructure, porosity, and mechanical performance of Inconel 718 The results showed that process parameter modifications that result in porosity formation can significantly reduce fatigue life, while microstructure changes were minimal and had little effect on tensile properties The precipitate structure was not found to be changed significantly These results can inform future process parameter modifications, as well as heat treatments to optimize mechanical properties

Journal ArticleDOI
29 Aug 2019-PLOS ONE
TL;DR: Key findings indicate that the relationships between PBF process parameters and ultimate Ti-6Al-4V properties are not straightforward as expected, and that wide ranges of porosity and corrosion resistance can be achieved through relatively minor changes in process parameters used, indicating volumetric energy density is a poor predictor of PBF Ti- 6Al- 4V properties.
Abstract: Ti-6Al-4V is commonly used in orthopaedic implants, and fabrication techniques such as Powder Bed Fusion (PBF) are becoming increasingly popular for the net-shape production of such implants, as PBF allows for complex customisation and minimal material wastage. Present research into PBF fabricated Ti-6Al-4V focuses on new design strategies (e.g. designing pores, struts or lattices) or mechanical property optimisation through process parameter control–however, it is pertinent to examine the effects of altering PBF process parameters on properties relating to bioactivity. Herein, changes in Ti-6Al-4V microstructure, mechanical properties and surface characteristics were examined as a result of varying PBF process parameters, with a view to understanding how to tune Ti-6Al-4V bio-activity during the fabrication stage itself. The interplay between various PBF laser scan speeds and laser powers influenced Ti-6Al-4V hardness, porosity, roughness and corrosion resistance, in a manner not clearly described by the commonly used volumetric energy density (VED) design variable. Key findings indicate that the relationships between PBF process parameters and ultimate Ti-6Al-4V properties are not straightforward as expected, and that wide ranges of porosity (0.03 ± 0.01% to 32.59 ± 2.72%) and corrosion resistance can be achieved through relatively minor changes in process parameters used–indicating volumetric energy density is a poor predictor of PBF Ti-6Al-4V properties. While variations in electrochemical behaviour with respect to the process parameters used in the PBF fabrication of Ti-6Al-4V have previously been reported, this study presents data regarding important surface characteristics over a large process window, reflecting the full capabilities of current PBF machinery.

Journal ArticleDOI
TL;DR: In this article, the impact of process parameters such as laser power, scanning speed and powder feeding rate on the coating geometry was investigated with an experimental design technique analysis using the ANOVA (Analysis of variance) method.

Journal ArticleDOI
TL;DR: A performance-relevant full decomposition of slow feature analysis termed PFDSFA is proposed for process monitoring under closed-loop control by simultaneously considering the influences of process variations on process performance and dynamics and achieves comprehensive process monitoring of process static and dynamic characteristics.

Journal ArticleDOI
TL;DR: In this paper, the key quality performance of the magnetorheological finishing process in achieving nanolevel finish on Ti6Al4V discs was explored, and a response surface model was developed.
Abstract: 3-D components used in today’s industries need fine surface characteristics as a functional requirement. Therefore, it is necessary to improve surface characteristics before putting them into useful applications, by achieving superior surface finish very close to dimensional precision. Magnetorheological fluid-based finishing processes are efficient in achieving ultrafine surfaces. This paper aims to explore the key quality performance of the magnetorheological finishing process in achieving nanolevel finish on Ti6Al4V discs. Sequential experimental design through statistical design of experiments was employed and response surface model was developed. Concentration of abrasive particles and wheel speed were considered as independent process parameters for the present study. To have uniform (or minimum variation) surface roughness values on the entire surface, a negative replica of the workpiece has been fabricated as a tool, and magnetic field was used to create magnetorheological effect. Using a template, surface roughness was measured at the same points before and after finishing. Initial Ra value was found to be the critical process parameter for finishing Ti6Al4V workpiece by magnetorheological finishing. % change in Ra is significantly affected by concentration of abrasive particles (≈50%) followed by initial Ra (≈ 38%). Finishing rate is significantly affected by initial Ra (≈61%) followed by concentration of abrasive particles (≈33%). An area roughness of 49 nm was achieved in the present study.

Journal ArticleDOI
TL;DR: In this paper, a closed chamber was designed and constructed to maintain the chamber temperature and increase the modeling space for reducing the warpage of acrylonitrile butadiene styrene (ABS) filament.
Abstract: Fused deposition modeling (FDM) is a well-known technology that is capable of fabricating three-dimensional prototypes with very complex geometries. However, the physical model built with acrylonitrile butadiene styrene (ABS) filament using a low-cost FDM machine is not satisfactory for most general engineering purposes due to warpage. Thus, minimizing the warpage of the ABS prototypes built with a low-cost FDM machine is a promising research issue. In this study, a closed chamber was designed and constructed to maintain the chamber temperature and increase the modeling space. It was found that the modeling space was increased by approximately 2.75 times. The optimal process parameters for reducing the warpage of ABS prototypes were also investigated using the Taguchi method. The dominant factor affecting the warpage of ABS prototypes is the bed temperature, followed by chamber temperature. The optimal process parameters for reducing the warpage of ABS prototypes are nozzle temperature of 230 °C, bed temperature of 93 °C, print speed of 60 mm/s, and chamber temperature of 43 °C. The optimal process parameter was also evaluated via the verification test. The optimal process parameters were also examined experimentally by a verification test.

Journal ArticleDOI
TL;DR: This study focuses on drilling woven CFRP laminates with four different tool geometries, analyzing the influence of the cutting parameters on the cutting forces and delamination damage.

Journal ArticleDOI
TL;DR: In this article, the impact of process parameters on tensile strength, mesostructure and in-process crystallinity of poly(lactic acid) specimens has been investigated using a statistically designed experiment.
Abstract: This study aims to investigate the impact of layer thickness, extrusion temperature, extrusion speed and build plate temperature on the tensile strength, crystallinity achieved during fabrication (herein, in-process crystallinity) and mesostructure of Poly(lactic acid) specimens. Both tensile strength and in-process crystallinity were optimized and verified as the function of processing parameters, and their relationship was thoroughly examined.,The four key technological parameters were systematically varied as factors on three levels, using the statistically designed experiment. Surface response methodology was used to optimize tensile strength and crystallinity for the given ranges of input factors. Optimized factor settings were used in a set of confirmation runs, where the result of optimization was experimentally confirmed. Material characterization was performed using differential scanning calorimetry and X-ray diffraction analysis, while the effect of processing parameters on mesostructure was examined by scanning electron microscopy.,Layer thickness and its quadratic effect are dominant contributors to tensile strength. Significant interaction between layer thickness and extrusion speed implies that these parameters should always be varied simultaneously within designed experiment to obtain adequate process model. As regards, the in-process crystallinity, extrusion speed is part of two significant interactions with plate temperature and layer thickness, respectively. Quality of mesostructure is vital contributor to tensile strength during FDM process, while the in-process crystallinity exhibited no impact, remaining below the 20 per cent margin regardless of process parameter settings.,According to available literature, there have been no previously published investigations which studied the effect of process parameters on tensile strength, mesostructure and in-process crystallinity through systematic variation of four critical processing parameters.

Journal ArticleDOI
TL;DR: In this article, a sequential multi-objective accelerated process optimization (m-APO) method was proposed to accelerate the master multiobjective process optimization problem by decomposing the master problem into a sequence of single-objectivity subproblems.

Journal ArticleDOI
14 Aug 2019-Polymers
TL;DR: This study developed a scientific method for optimal parameter adjustment, analyzing and interpreting the injection speed, injection pressure, cavity pressure, and the profile of the injection screw position, which can effectively reduce the warpage of the IC-tray.
Abstract: Injection molding is a mature technology that has been used for decades; factors including processed raw materials, molds and machines, and the processing parameters can cause significant changes in product quality. Traditionally, researchers have attempted to improve injection molding quality by controlling screw position, injection and packing pressures, and mold and barrel temperatures. However, even when high precision control is applied, the geometry of the molded part tends to vary between different shots. Therefore, further research is needed to properly understand the factors affecting the melt in each cycle so that more effective control strategies can be implemented. In the past, injection molding was a "black box", so when based on statistical experimental methods, computer-aided simulations or operator experience, the setting of ideal process parameters was often time consuming and limited. Using advanced sensing technology, the understanding of the injection molding process is transformed into a "grey box" that reveals the physical information about the flow behavior of the molten resin in the cavity. Using the process parameter setting data provided by the machine, this study developed a scientific method for optimal parameter adjustment, analyzing and interpreting the injection speed, injection pressure, cavity pressure, and the profile of the injection screw position. In addition, the main parameters for each phase are determined separately, including injection speed/pressure during the mold filling phase, velocity-to-pressure switching point, packing pressure and time. In this study, the IC tray was taken as an example. The experimental results show that the method can effectively reduce the warpage of the IC-tray from 0.67 mm to 0.20 mm. In addition, the parameters profiles obtained by parameter optimization can be applied for continuous mass production and process monitoring.

Journal ArticleDOI
TL;DR: In this paper, a flexible additive manufacturing platform for big plastic objects has been realized mounting an industrial screw-based extruder on an anthropomorphic robot, with the aim of ensuring a regular deposited layer geometry.
Abstract: Complex parts can be successfully manufactured by means of Additive Manufacturing (AM) techniques based on thermoplastic polymer extrusion, whose use for mass production is restricted by their slow printing speed. To address this limitation, a flexible AM platform for big plastic objects has been realized mounting an industrial screw-based extruder on an anthropomorphic robot. An experimental campaign has been performed to set a suitable range of relevant process parameters, with the aim of ensuring a regular deposited layer geometry. Moreover, a closed-loop control strategy has been developed to correct the robot height based on data measured during the material deposition, thus further improving the process parameter setting and compensating the material shrinkage or other unexpected defects. Eventually, an online re-slicing algorithm has been implemented to preserve the desired height of the manufactured object, despite the layer height changes. The proposed approach allows a deposition flow rate up to 1250 cm3/h within a building volume limited only by the robot workspace.

Journal ArticleDOI
TL;DR: In this article, the influence of process parameters (peak pressure, pressure path, and blank holding force) on formability of 1 mm thick AA5182 aluminum alloy sheets in deep drawing of square cups by hydroforming was investigated through numerical simulations and validated with experimental work.
Abstract: The formability of 1 mm thick AA5182 aluminum alloy sheets in deep drawing of square cups by hydroforming was studied. The influence of process parameters (peak pressure, pressure path, and blank holding force) on formability was investigated through numerical simulations and validated with experimental work. The experiments were designed using the Taguchi method. The minimum thickness in the formed cups (at the bottom corners) and the minimum corner radius that can be achieved were considered as the criteria for evaluation of formability. The peak pressure was the most important process parameter affecting thinning and the minimum corner radius that can be achieved. The variation of the pressure path had the least effect on formability. Regression models were developed for prediction of minimum thickness in the cup and the corner radius as a function of peak pressure and blank holding force.

Journal ArticleDOI
TL;DR: Results illustrate that the proposed integrated process parameter optimization framework is effective for obtaining the optimal process parameters and can be used in LW-MF for practical production.
Abstract: Magnetic field assisted laser welding (LW-MF) shows great potential in the jointing of large structures. The quality of the welding joint in LW-MF largely depends on the selection of process parameters. In this study, an integrated process parameter optimization framework is developed for magnetic field assisted laser welding. Firstly, Taguchi method is selected to generate sample points and the LW-MF experiments are carried out to obtain the bead geometrical characteristics. Secondly, a sample-sorted SVR (SS-SVR) metamodeling approach is developed to make full use of the already-acquired prediction error information for fitting the relationships between multiple process parameters and the bead geometrical characteristics. A detailed comparison between the developed SS-SVR metamodeling approach and existing SVR metamodeling approach for prediction accuracy is performed. Then, the particle swarm optimization is used to solve the process parameters optimization problem, in which the objective function values are predicted by the developed SS-SVR metamodel. Finally, verification experiment is conducted to verify the reliability of the obtained optimal process parameters. Results illustrate that the proposed integrated process parameter optimization framework is effective for obtaining the optimal process parameters and can be used in LW-MF for practical production.