Process-related Parameters Research Articles

Experimental and statistical study on the effects of fused filament fabrication parameters on the tensile strength of hybrid PLA/Wood fabricated parts

Fused filament fabrication (FFF) also referred to as fused deposition modeling (FDM) is the most extensively used among all the additive manufacturing (AM) technologies due to its low cost, ease of use and variety of materials commercially available. An FFF printer generates a 3-dimensional object by extruding a stream of heated and semi-melted thermoplastic material, which is deposited onto layer upon layer, working from the bottom up. The literature study reveals that primary attributes of FFF parts like surface texture, mechanical strength, surface roughness, and dimensional accuracy depend on crucial process-related parameters and, therefore, should be set appropriately. This study focuses on the effects of several FFF parameters on the ultimate tensile strength (UTS) of a 3D-printed organic, biocompatible PLA with wood flour. Taguchi’s design involving orthogonal array experiments runs was applied to fabricate the test specimens (according to ASTM D638-10 type I one) and obtain the results corresponding to stress-strain curves and the ultimate tensile strength (UTS). The independent FFF parameters examined are layer thickness (LT), nozzle temperature (NT), raster deposition angle (RDA) and printing speed (PS). The results obtained are further analyzed by implementing standard statistical analyses like contour plots emphasizing 2-way interactions and analysis of variance. Full quadratic models were obtained and proved of high accuracy in terms of predicting the results for the response of UTS. The statistical results are supported with micrographs of failure sections and validated by comparing them with previous studies performed on pure PLA material. Moreover, evaluation experiments were performed to validate the results.

Mechanical Properties of Compression Moulded Aggregate-Reinforced Thermoplastic Composite Scrap

Recycling of thermoplastic composites has drawn a considerable attention in the recent years. However, the main issue with recycled composites is their inferior mechanical properties compared to the virgin ones. In this present study, an alternative route to the traditional mechanical recycling technique of thermoplastic composites has been investigated with the view to increase mechanical properties of the recycled parts. In this regard, the glass/polypropylene laminate offcuts are cut in different grain sizes and processed in bulk form, using compression moulding. Further, the effect of different grain sizes (i.e., different lengths, widths and thicknesses) and other process-related parameters (such as mould coverage) on the tensile properties of recycled aggregate-reinforced composites have been investigated. The tensile properties of all composite samples are tested according to ISO 527-4 test method and the significance of test results is evaluated according to Student’s t-test and Fisher’s F-test respectively. It is observed that the tensile moduli of the recycled panels are close to the equivalent quasi-isotropic continuous fibre-reinforced reference laminate while there is a noteworthy difference in the strengths of the recycled composites. At this stage, the manufactured recycled composites show potential for stiffness-driven application.

Postoperative Pain Assessment in Patients Undergoing Functional Endoscopic Sinus Surgery for Chronic Rhinosinusitis: A Prospective Observational Study.

Post-operative pain management after functional endoscopic sinus surgery for chronic rhinosinusitis is a field with lack of proper guidance and research. Aim of this study was to study post-operative pain and its management. In this prospective observational study 52 patients undergoing functional endoscopic sinus surgery were examined using Quality Improvement in Post-operative Pain Management (QUIPS) questionnaire post-operatively on 1st day, 2nd day and day of 1st follow up permitting standard assessment of all pain related parameters, outcome, and process parameters. Pain was relatively low after surgery throughout with nominal increase on 2nd day and relatively diminishing on 1st follow up day. Positive pain counselling and many other factors affect pain and related parameters in the patients. Opioid analgesics can be effectively avoided in these patients and pain management protocol should be established for all surgeries along with judicious use of analgesics and proper use of positive pain counselling.

Effect of process parameters on flexural strength and surface roughness in fused deposition modeling of PC-ABS material

Fused deposition modeling (FDM) is one of the most commonly used additive manufacturing (AM) technologies, which has found application in industries to meet the challenges of design modifications without significant cost increase and time delays. Process parameters largely affect the quality characteristics of AM parts, such as mechanical strength and surface finish. This article aims to optimize the parameters for enhancing flexural strength and surface finish of FDM parts. A total of 18 test specimens of polycarbonate (PC)-ABS (acrylonitrile–butadiene–styrene) material are printed to analyze the effect of process parameters, viz. layer thickness, build orientation, and infill density on flexural strength and surface finish. Empirical models relating process parameters with responses have been developed by using response surface regression and further analyzed by analysis of variance. Main effect plots and interaction plots are drawn to study the individual and combined effect of process parameters on output variables. Response surface methodology was employed to predict the results of flexural strength 48.2910 MPa and surface roughness 3.5826 µm with an optimal setting of parameters of 0.14-mm layer thickness and 100% infill density along with horizontal build orientation. Experimental results confirm infill density and build orientation as highly significant parameters for impacting flexural strength and surface roughness, respectively.

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

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.

Geometric characteristics of AlSi10Mg ultrathin walls fabricated by selective laser melting with energy density and related process parameters

Geometric characteristics of AlSi10Mg ultrathin walls fabricated by selective laser melting with energy density and related process parameters

Experimental Study of Blast Furnace Hearth Drainage Based on Image Analysis

A smooth hearth drainage is one of the key requirements for maintaining an efficient blast furnace operation. For achieving this, it is necessary to understand the hearth drainage behavior and the effect of related process parameters. To investigate the drainage, a set of experiments is conducted in a 2D Hele−Shaw model, where the influence of the initial accumulated amount of molten liquids (iron and slag), blast pressure, and slag viscosity on the drainage behavior is studied using water and oil as liquids. To quantify the findings, an image analysis‐based algorithm is applied to extract drainage information that is used to analyze the effect of the mentioned process factors on the evolution of the liquid levels and volumes, flow rates, share oil in the outflow, and angle of the interfaces at the outlet. Herein, the implications of the results for the operation of the blast furnace hearth are discussed.

Impact of solar radiation on chemical structure and micromechanical properties of cellulose-based humidity-sensing material Cottonid

Renewable and environmentally responsive materials are an energy- and resource-efficient approach in terms of civil engineering applications, e.g. as so-called smart building skins. To evaluate the influence of different environmental stimuli, like humidity or solar radiation, on the long-term actuation behavior and mechanical robustness of these materials, it is necessary to precisely characterize the magnitude and range of stimuli that trigger reactions and the resulting kinetics of a material, respectively, with suitable testing equipment and techniques. The overall aim is to correlate actuation potential and mechanical properties with process- or application-oriented parameters in terms of demand-oriented stimuli-responsive element production. In this study, the impact of solar radiation as environmental trigger on the cellulose-based humidity-sensing material Cottonid, which is a promising candidate for adaptive and autonomously moving elements, was investigated. For simulating solar radiation in the lab, specimens were exposed to short-wavelength blue light as well as a standardized artificial solar irradiation (CIE Solar ID65) in long-term aging experiments. Photodegradation behavior was analyzed by Fourier-transform infrared as well as electron paramagnetic resonance spectroscopy measurements to assess changes in Cottonid’s chemical composition. Subsequently, changes in micromechanical properties on the respective specimens’ surface were investigated with roughness measurements and ultra-micro-hardness tests to characterize variations in stiffness distribution in comparison to the initial condition. Also, thermal effects during long-term aging were considered and contrasted to pure radiative effects. In addition, to investigate the influence of process-related parameters on Cottonid’s humidity-driven deformation behavior, actuation tests were performed in an alternating climate chamber using a customized specimen holder, instrumented with digital image correlation (DIC). DIC was used for precise actuation strain measurements to comparatively evaluate different influences on the material’s sorption behavior. The infrared absorbance spectra of different aging states of irradiated Cottonid indicate oxidative stress on the surface compared to unaged samples. These findings differ under pure thermal loads. EPR spectra could corroborate these findings as radicals were detected, which were attributed to oxidation processes. Instrumented actuation experiments revealed the influence of processing-related parameters on the sorption behavior of the tested and structurally optimized Cottonid variant. Experimental data supports the definition of an optimal process window for stimuli-responsive element production. Based on these results, tailor-made functional materials shall be generated in the future where stimuli-responsiveness can be adjusted through the manufacturing process.

Learning-based error modeling in FDM 3D printing process

Purpose Additive manufacturing (AM) technologies are gaining immense popularity in the manufacturing sector because of their undisputed ability to construct geometrically complex prototypes and functional parts. However, the reliability of AM processes in providing high-quality products remains an open and challenging task, as it necessitates a deep understanding of the impact of process-related parameters on certain characteristics of the manufactured part. The purpose of this study is to develop a novel method for process parameter selection in order to improve the dimensional accuracy of manufactured specimens via the fused deposition modeling (FDM) process and ensure the efficiency of the procedure. Design/methodology/approach The introduced methodology uses regression-based machine learning algorithms to predict the dimensional deviations between the nominal computer aided design (CAD) model and the produced physical part. To achieve this, a database with measurements of three-dimensional (3D) printed parts possessing primitive geometry was created for the formulation of the predictive models. Additionally, adjustments on the dimensions of the 3D model are also considered to compensate for the overall shape deviations and further improve the accuracy of the process. Findings The validity of the suggested strategy is evaluated in a real-life manufacturing scenario with a complex benchmark model and a freeform shape manufactured in different scaling factors, where various sets of printing conditions have been applied. The experimental results exhibited that the developed regressive models can be effectively used for printing conditions recommendation and compensation of the errors as well. Originality/value The present research paper is the first to apply machine learning-based regression models and compensation strategies to assess the quality of the FDM process.

Process Prediction for Compound Screws by Using Virtual Measurement and Recognizable Performance Evaluation

One of the important values of Industry 4.0 is to integrate people’s needs into the manufacture of enhanced products, systems, and services to achieve greater levels of product customization. This paper presents a prediction method for predicting screw process parameters; taking crystalline and non-crystalline polymers as the molding material, when there is a lack of sufficient historical screw process data to establish a data-driven method, using various screws and polymer materials to predict tool life under different cutting conditions is a challenge. A screw life prediction method is proposed based on the mixed compound screw process parameters method using a dynamic iteration work. To meet the requirements of mass production, this work proposes the combined application of the automatic virtual metrology (AVM) system with the recognizable performance evaluation (RPE) program. The method predicts the injection of compound screws by extracting given cutting conditions and related process parameters characteristics from the senor data by converting sampling inspections with measurement delays from real-time and online routine inspections to automatically and quickly complete method creation production goals.

Black-, gray-, and white-box modeling of biogas production rate from a real-scale anaerobic sludge digestion system in a biological and advanced biological treatment plant

Artificial intelligence-based methodology [artificial neural network (ANN) and fuzzy logic] and a multiple regression-based analysis were conducted for modeling of the biogas production rate from a real full-scale sludge digestion process. In the computational analysis, five process-related parameters such as influent sludge flow rate, total solids content, total volatile solids content, alkalinity, and volatile fatty acids concentration were considered as the input variables for the proposed models. In the ANN-based modeling, a benchmark comparison of 11 backpropagation (BP) algorithms was employed in the first step, and the scaled conjugate gradient algorithm was chosen as the best BP algorithm in terms of their respective mean squared errors. According to the selected BP algorithm (scaled conjugate gradient BP), the number of neurons at the hidden layer was optimized as 14, and the coefficient of determination (R2) was obtained as 0.65 for the optimal three-layer ANN structure (5:14:1). In the second part of the study, a MISO (multiple-input single-output)-type fuzzy-logic model was developed, and five input variables were fuzzified in a knowledge-based manner. For the fuzzy subsets, trapezoidal membership functions were implemented with 10 and 20 levels, and a Mamdani-type fuzzy inference system (FIS) was used to employ 394 rules in the if–then form. The product, summation, and centroid methods were employed, respectively, for implication, aggregation, and defuzzification processes conducted in the FIS. Fuzzy logic-produced forecasts were compared with the estimations obtained from both ANN-based model and a polynomial multiple regression-based model (employed as the third part of this study) derived in this study. Findings of this study clearly indicated that compared to ANN model and conventional multiple regression approach, the proposed MISO fuzzy logic-based model produced smaller deviations and exhibited a superior predictive performance on forecasting biogas production rate from a full-scale treatment plant with a satisfactory R2 value of 0.88.

Application of MABAC in support of decision-making on the key process variables in sheet metal forming

This paper examines a new application in the multi-criteria comparison of approximate boundary regions (MABAC). This method estimates the distance between the target performance of each of the observed options and the approximate area of the solution. An approximate cut-off area is specified for each standard and depends on all alternative values for the observed standard. This method is demonstrated in six steps to support decisions about metal forming applications. The results of previous studies were used to determine the criteria and their weight. In the SPIF process, axial feed, feed, tool diameter, depth and other process parameters in relation to the sample will greatly affect the mechanical quality of the square cups, including maximum Thinning, spring back, wall angle and surface roughness. These parameters are when creating a quality square cup created by VMC. Experiments using the L9 orthogonal array with MABAC and sensitivity analysis are designed to accurately detect these parameters, and the results, including mechanical quality, are analyzed. The best parameters to improve product quality. Material thickness 1.5 mm, feed 100 mm per minute, tool diameter 8 mm, downward movement 0.2 mm. Sensitivity analysis was performed using this model and the results show better results than the initial forming conditions.

Prediction of mechanical properties of hot-rolled strip steel based on PCA-GBDT method

Improving the quality of rolled steel products is the primary task of the entire steel industry. As the main step of rolled steel production, hot rolling has received extensive attention. As far as the hot rolling process is concerned, the chemical composition of steel and related process parameters are the most direct factors affecting the quality of hot rolled steel sheets. Based on Principal Component Analysis (PCA) and Gradient Lifting Decision Tree (GBDT) methods, this paper takes the tensile strength as the research object and constructs a prediction model for the mechanical properties of hot rolled strip steel. Through principal component analysis of characteristic data, 28 variables were reduced into 8 new indicators to be used for GBDT regression analysis. 2489 pieces of data were divided into a training set and a test set at a ratio of 7:3, to be used in the training set to build a regression model, and get a root mean square error of 16.7393. The data in the test set was used to predict the tensile strength value, with the root mean square error reaching 18.2650.

Molecular dynamics modeling of the influence forming process parameters on the structure and morphology of a superconducting spin valve.

This work is a study of the formation processes and the effect of related process parameters of multilayer nanosystems and devices for spintronics. The model system is a superconducting spin valve, which is a multilayer structure consisting of ferromagnetic cobalt nanolayers separated by niobium superconductor nanolayers. The aim was to study the influence of the main technological parameters including temperature, concentration and spatial distribution of deposited atoms over the nanosystem surface on the atomic structure and morphology of the nanosystem. The studies were carried out using the molecular dynamics method using the many-particle potential of the modified embedded-atom method. In the calculation process the temperature was controlled using the Nose–Hoover thermostat. The simulation of the atomic nanolayer formation was performed by alternating the directional deposition of different composition layers under high vacuum and stationary temperature conditions. The structure and thickness of the formed nanolayers and the distribution of elements at their interfaces were studied. The alternating layers of the formed nanosystem and their interfaces are shown to have significantly different atomic structures depending on the main parameters of the deposition process.

Population balance modeling of volume and time dependent spray fluidized bed aggregation kernel using Monte Carlo simulation results

This paper seeks to extend the work of Hussain et al. (A new framework for population balance modeling of spray fluidized bed agglomeration, Particuology 19 (2015) 141–154) to develop a detailed one-dimensional population balance modeling (PBM) of the spatially homogeneous spray fluidized bed aggregation (SFBA) process. A new mathematical model of the volume and time dependent aggregation kernel is developed based on process specific microscopic mechanisms. In addition, the population balance equations of other important process related parameters (total number of available droplets and size distribution of wet particles) are presented. The developed PBM contains a new mathematical model which estimates the death rate of binder droplets due to the drying mechanism. For the verification of the developed PBM, a constant number Monte Carlo (MC) algorithm is used, which simulates important micro-mechanisms of the SFBA process (droplet addition, droplet drying, volume dependent particle collisions, aggregation, and rebound). The MC algorithm is capable of analyzing the effects of each microscopic event on the aggregation behavior. Volume dependency in particle collisions is used, while mimicking the SFBA process in the MC simulation algorithm. Furthermore, the volume and time dependent probability of successful wet position collisions is successfully extracted using the MC simulations and then transferred to the development of the PBM. Finally, the accuracy of the proposed PBM is verified by comparing its results against the predictions of the MC simulations.

Utilization of Alyssum mucilage as a natural coagulant in oily-saline wastewater treatment

Wastewater treatment with natural coagulants is an area that is well-researched and covered in the literature, but new research in this current and ongoing field is still interesting. The present analysis was conducted as the first study to investigate the use of Alyssum mucilage as a new, natural, and cost-effective coagulant for the treatment of synthesized bilge water. To the best of the authors’ knowledge, no previous study has specifically devoted to implementing a systematic analysis of a new coagulant (Alyssum mucilage) within the framework of central composite design-response surface methodology (CCD-RSM), adaptive neuro-fuzzy inference system (ANFIS), and artificial neural network (ANN). Thus, to spot the lights over the mentioned gaps, the current study was undertaken as the first attempt to perform highly comprehensive mathematical, kinetic, and statistical analyses for a quantitative definition of the coagulation-flocculation process of oily-saline wastewater treatment using Alyssum mucilage. In this study, three process-related parameters, such as coagulant dose, contact time, and pH, were introduced for modeling, optimization, and cost analysis of the investigated application. At the optimum conditions (coagulant dose = 40.5 mg/L, pH = 7.05, and contact time = 34.9 min) of Alyssum mucilage, the maximum chemical oxygen demand (COD), turbidity (TU), and surfactant removal efficiencies were obtained as 84.63 %, 96.25 %, and 99 %, respectively. Under the optimum conditions (coagulant dose = 301.8 mg/L, pH = 6.53, and contact time = 23.1 min) of poly aluminum chloride (PAC), used as a comparative polymerized metal coagulant, the maximum COD, TU, and surfactant removal efficiencies were determined as 92.30 %, 99.92 %, and 99 %, respectively. Compared to CCD-RSM, ANFIS and ANN showed high accuracy with R2 values more than 0.990 for both PAC and Alyssum mucilage. The kinetic study revealed that the second-order model performance was superior to the first-order model. Fourier-transform infrared spectroscopy (FTIR) analysis of Alyssum mucilage demonstrated that the functional groups presented in the composition of this coagulant caused coagulation and bonding between the particles. Furthermore, the zeta potentials of bilge water, coagulant, and treated bilge water indicated that the possible mechanism of the coagulation would be adsorption and bridging.

Prediction of Primary Dendrite Arm Spacing in Pulsed Laser Surface Melted Single Crystal Superalloy

Primary dendritic arm spacing (PDAS) is an important microstructure feature of the nickel-base single crystal superalloys. In this paper, a numerical model predicting the PDAS evolution with additive manufacturing parameters using pulsed laser is established, which combines the theoretical PDAS models with the temperature field calculation model during pulsed laser process. Based on this model, processing maps that related process parameters to the evolution of PDAS are generated. To obtain more accurate prediction model, the parameters of different solidification conditions, \(\overline{{G^{ - 0.5} V^{ - 0.25} }}\) and \(\overline{{G}^{-0.5}{V}^{-0.25}}\), are selected to calculate PDAS. The simulation results show that the PDAS increases as the arise of P and t. The processing-PDAS map can accurately predict the evolution of PDAS with pulsed laser process parameters, which is well in accordance with the experimental results. Additionally, the PDAS values calculated by the \(\overline{{G}^{-0.5}{V}^{-0.25}}\) are more in line with the experimental results than those calculated by the \({\bar{G}}^{-0.5}{\bar{V}}^{-0.25}\).

Optimization of Manufacturing Process and Experimental Verification of Vulnerable Part in Reactor

The technical requirements of the control rod driving mechanism of the third generation PWR nuclear power plant are higher. The latch parts are designed with double tooth structure and welded on the wear-resistant surface by cobalt-based alloy surfacing. According to the structural characteristics and manufacturing process difficulties, a special welding device is developed. According to the test and finite element simulation, the related process parameters are optimized. The trial production of the parts was completed, and the parts were tested by metallography, liquid permeation test, hardness test and thermal life test. The results show that the latch parts have high hardness and wear resistance, and meet the requirements of the driving mechanism operation life of the third generation PWR nuclear power plant.

Investigations of the Influence of a Superimposed Oscillation on the Fatigue Strength

Within the scope of the transregional collaborative research centre TCRC73, the effects of an oscillation superimposed forming process for the production of a demonstrator component are investigated. Previous studies in this field were limited to a consideration of the process-related parameters such as the influence of the plastic work and the friction or the component-related parameters such as the influence of the surface quality and the mold filling. This research concentrates on the consideration of the mechanical vibration resistance of components that were manufactured superimposed oscillated. For this purpose, Wöhler tests are conducted in which the fatigue strength of superimposed oscillation pre-stretched test samples and oscillation-free pre-stretched test samples are investigated. First, Wöhler curves are generated in the tensile threshold range for tensile samples made out of the steels DC04 and DP600. Subsequently, tensile specimens are pre-stretched superimposed oscillated and oscillation-free. These specimens are subjected to a tensile threshold load until they break. The influence of the superimposed oscillation forming on the long-term fatigue of components is derived from the comparison of the bearable load cycles. Investigations of the microstructure of the specimens are conducted in order to draw conclusions about the influence on the long-term strength.

Mechanical characterization of functionally graded materials produced by the fused filament fabrication process

Abstract In this research study, the design and fabrication workflow of functionally graded materials (FGMs) were introduced using fused filament fabrication (FFF) process. Rigid polymers were mechanically characterized in three printing directions. Tensile strength and modulus of elasticity values for YZ and XY printing orientation specimens indicated similar results. Printing in the XZ direction showed poor strength values in comparison with other printing directions. Interface analysis with different transition patterns was accomplished and the advantage of gradient transition was achieved. Micrography of specimens was analyzed to investigate the internal structure and was used to validate the test results. Statistical methods were applied to investigate the relationship between microstructural descriptors (volume fractions) and process-related parameters (printing temperatures). The results of the analysis of variance (ANOVA) have confirmed that printing temperature and concentration have a significant impact on tensile test results. The data-driven approach was employed to construct a linear regression models in order to formulate input data for finite element analysis (FEA). As a result of FEA simulation, effective Young’s modulus was calculated using a MATLAB code. FEA-predicted Young’s moduli were validated through experimental test results. The comparison of the numerical method and experimental values showed less than 5% error for FGM specimens printed in 250, 260 and 270 °C temperatures. FFF process with FGM could have potential applications in medical, structural, and automotive industries by locally varying material properties. This study presents a unique method of fabricating FGM structures with low-cost manufacturing process.