Set Of Process Parameters Research Articles

Multiscale microstructure containing nanometer-scale precipitations and stacking faults yields a high-strength Al-5Cu alloy by electron beam freeform fabrication

Electron beam freeform fabrication (EBF3) additive manufacturing technique has been successfully employed to print the non-weldable Al-5Cu thin-walled parts through an optimized set of process parameters. Post-heat treatment is conducted to engineer the desirable microstructure containing multiple nanostructures, i.e., the nanoscale θ" and θ' precipitates, stacking faulted zones and Al-Cu-Cd clusters, leading to a high-strength Al-5Cu alloy with good ductility. The grain formation mechanisms are revealed via a combinatorial simulation and experimentation. It is shown that EBF3 process can produce three different types of grain microstructures: fully equiaxed, fully columnar, and the mixture. In the layerwise melt pool solidification process, wide partially melted zone is observed to favor the formation of equiaxed grains due to its inheritance characteristic. After solution and aging heat treatment, Al-Cu-Cd nanoclusters (∼ 2 nm in Dia.) are generated, which facilitates the formation of nanoscale stacking faulted zones (∼11 nm). Meanwhile, as heterogeneous nucleation sites, Al-Cu-Cd nanoclusters effectively promote θ'-Al2Cu phase (∼ 19 - 114 nm) precipitation to enable the coexistence with fine θ"-Al3Cu precipitates (∼ 7 - 37 nm). The multiscale microstructure, especially incorporating the newly identified nanoscale stacking faulted zones, activates multiple strengthening mechanisms. Consequently, its tensile properties at room temperature reach an ultimate tensile stress of ∼ 496.5 MPa, yield strength of ∼ 435.7 MPa, and elongation of ∼ 9.6%, which are superior to other as-cast and additively manufactured Al-Cu alloys. This work offers a promising processing route to directly fabricate high-strength near-net-shape Al-Cu alloy parts for the aerospace and automotive industries.

The Effect of Laser Shock Peening (LSP) on the Surface Roughness and Fatigue Behavior of Additively Manufactured Ti-6Al-4V Alloy

Laser shock peening (LSP) uses plasma shock waves to induce compressive residual stress at the surface of a component which has the potential to improve its fatigue properties. For AM parts, the existence of internal defects, surface roughness, and tensile residual stresses leads to noticeably lower fatigue strength compared to materials produced through conventional processes. Furthermore, there is a tendency for greater scatter in the fatigue behavior of these parts when compared to traditionally manufactured components. In this study, the effect of LSP on the roughness and fatigue behavior of Ti-6Al-4V alloy constructed through Laser Powder Bed Fusion (L-PBF) technique was investigated. Two types of samples were designed and tested: as-built surface air foil samples for four-point bending tests and machined surface straight gage samples for uniaxial fatigue testing. Two sets of process parameters, optimized and non-optimized, were also used for the fabrication of each sample type. It was found that LSP had negative effects on the smooth (i.e., machined) surface samples, whereas for as-built surfaces the roughness was enhanced by decreasing the sharpness of the deep valleys and partially remelting the loosely bonded particles on the peaks. It was found that the scatter of the fatigue data decreased for optimized machined samples, while no clear improvement was observed in their lives. However, all non-optimized samples showed improvements in fatigue lives after the LSP process.

Исследование упругопластического изгиба листовой заготовки различной толщины при ее гибке с учетом эффекта пружинения

The work analyzes the process of bending the sheet work piece to obtain shaping of sheet metal parts and profiles. The analysis is carried out on the basis of strength calculations of elastoplastic bending of the metal plate. When pressing such a plate, in practice, a spring effect is observed when the curved blank changes its shape under the influence of residual elastic deformations at the end of the pressing process. To compensate for the spring effect, it is necessary to determine the values of elastic and plastic deformations in the sheet blank. The work considers the method of calculation of these values on the basis of available experimental works. Conclusions have been drawn and practical recommendations can be used in the process of setting process parameters for pressing sheet blanks.

Pengaruh Sudut Part Build Orientation Z-Direction Terhadap Kuat Tarik Spesimen Uji Standar Astm D638 Type IV Menggunakan Filamen Pla+

This study will try to investigate variations in the angle of the Z-direction build part by using a 3D Printer with Fused Deposition Modeling (FDM) technology made from PLA + Sugoi. This study uses a 0.4mm nozzle with a Bowden type extruder. This study uses a PLA+ filament from the Sugoi brand with a nozzle of 0.4 mm, diameter filament 1,75 mm, 3D Printer with Fused Deposition Modeling (FDM) technology used is Creality Ender 3 V2 with process parameter settings in slicing software using a layer thickness of 0.30 mm, Top solid Layer 5, Bottom Solid Layer 5, Fill density 100%, cooling speed 100%, and flowrate 100%, nozzle temperature 205°C, and bed temperature 60°C. Variation in angle Part build Z-direction is proven to have a significant difference in Tensile strength with a difference of 10° resulting in a difference in Tensile strength of 15 MPa, where the highest tensile strength is 67.50 MPa with part build Z-direction at an angle of 80°, and the lowest tensile strength of 52.50 MPa with a Z-direction part build angle at an angle of 70°.

Fast Automatized Parameter Adaption Process of CNC Milling Machines under use of Perception based Artificial Intelligence

This paper concerns unpublished results obtained from the SIMKI (2020) R&D project at the Department of Mechanical Engineering at Aalen University of Applied Science, Germany. The following text generally discusses the development results of the AI-based CNC parameter identification and optimisation tool AICNC. The identification tool supports the AI-based optimisation of milling machine process parameters when using unknown material compositions. The process parameters are determined by a specific test pattern designed to be automatically analysed in real time by a pre-trained perception-based deep learning algorithm. The tool provides the advantage of obtaining real-time quality information due to AI-based quality assessment and the automated identification of material-dependent milling process parameter sets, even for unknown processing material.

Optimization of the hot stretch-bending process for TC4 titanium L-section profile

Titanium alloys are used more and more in the field of aerospace because of their low weight and high strength. Compared with pure bending, stretch-bending can effectively reduce springback and improve forming accuracy, and is widely used in the forming of high-quality profile structural parts. In this study, based on the loading method of the arm-type stretch-bending machine, the hot stretch-bending process of the TC4 titanium alloy profile with L-shaped section was simulated by ABAQUS. The effects of profile temperature, tension, and section size on the stretch-bending results were analyzed, and the process parameters were optimized. The experimental verification was carried out on the self-designed hot stretch-bending test machine, and the relative deviation between the experiments and simulations is within 1%. This study has guiding significance for the setting of process parameters of hot stretch-bending of titanium alloy profiles.

Effect of autoclaving on the dimensional stability and surface characteristics of 3D printed PVDF composite-based implants

In the past decade, a lot of work has been reported on the use of polyvinylidene fluoride (PVDF) based thermoplastic composites as energy storage devices, and implant materials. But hitherto little has been reported on the dimensional stability and surface characteristics of 3D-printed PVDF composites after autoclaving for implant applications. In this study, the effect of autoclaving on surface characteristics (Shore-D hardness, surface roughness (Ra), morphological characteristics), and dimensional stability of 3D printed PVDF-hydroxyapatite (HAp)- chitosan (CS) composite has been reported for implant applications. The signal-to-noise (S/N) ratio approach was used to ascertain the best setting of process parameters for 3D printing by fused filament fabrication (FFF) process. This study suggests that the best setting for the FFF process, for the 3D printing of PVDF composite (90%PVDF-8%HAp-2%CS) are the nozzle temperature (NT) of 225°C, raster angle (RA) 0°, and printing speed (PS) 40 mm/s, resulting in Shore-D hardness 48.5 HD (before autoclaving) and 55.0 HD (after autoclaving), dimensional deviation 0.01 mm (after autoclaving). The results are supported by scanning electron microscopy (SEM) and Fourier transmission infrared (FTIR) spectroscopy analysis.

Statistical-experimental modeling of the effect of process parameters on geometric characteristics of laser cladding of stellite 6 on SS316 using second-order regression

Applications of 316 stainless steel can be developed by laser cladding of the Co-based superalloys, which has multiple enhancement properties and creates a small heat affected zone. Quantitive investigation of the effect of process parameters on geometrical features and providing reliable models to achieve the desirable metallurgical bond, minimal structural defects, and predicting the dimensions of the geometry without conducting excessive experimental tests are an important issue. This study evaluate the effects of the main process parameters of laser cladding on geometric characteristics of stellite 6 using the response surface methodology. A quadratic model was developed for each of the geometric characteristics. Laser power, scanning speed, and powder feed rate ranged between 400 and 600 W, 6–10 mm/s and 12–20 gr/min, respectively, were considered as the input variables and height, width, penetration depth and dilution were considered as responses. The experiments were designed using the box-behnken method. The accuracy and fitness of the models were evaluated using analysis of variance method. The results indicated the R2 factors obtained for height, width, penetration depth and dilution are equal to 0.89, 0.98, 0.98 and 0.96 respectively. After obtaining the desirability factor of 0.795, the optimal set of process parameters were selected as laser power of 510 W, scanning speed of 10 mm/s and powder feed rate of 13 gr/min. Verification of the optimization was done by performing experimental test,which maximum 13 % difference between actual values and the predicted values was obtained.

OPTIMASI MULTIOBJEKTIF PARAMETER PROSES 3D PRINTER JENIS FUSED DEPOSITION MODELLING MENGGUNAKAN GREY RELATIONAL ANALYSIS-TAGUCHI

The development of fused deposition modelling (FDM) 3D printer models is increasing where the applications can be found in several aspects such as rapid prototyping, functionally components, and parts for assembly process. These conditions required excellent printing quality. One of the factors that affect the printing quality was process parameter setting. There are several parameters process that are used in 3D printing. Hence, the use of these parameters that recommended by manufacturer often give the average results, not the best results. The recent study purposes are to gain optimal setting that give accuracy in the dimension and suitable surface condition. There are 12 factors with 3 different levels. The study used Taguchi method L-27 and grey relational analysis (GRA) to determine the most efficient combinations that can fulfill multi-objective. The results showed that to acquire the accuracy and total dimension, the following combination are used; LH0,075mm, LW0,45mm, IPLines, WT0,8mm, PT205C, FR85%, RD6,5mm, RS30mm/s, PS30mm/s, OWS15mm/s2, PA10mm/s2, and PJ10mm/s where wall thickness (WT); flow rate (FR); and retraction distance (RD) used as 3 essential factors that can influence the respond of the machine. The best surface roughness gathered with the combination as follow; LH0,075mm, LW0,35mm, IPGrid, WT0,8mm, PT200C, FR95%, RD2mm, RS30mm/s, PS70mm/s, OWS25mm/s, PA3000mm/s2, dan PJ30mm/s where the layer height (LH) was the essential factor that affect the machine respond. The accuracy of the dimension and the most optimum surface roughness can be acquired by the following combinations: LH0,075mm, LW0,35mm, IPGrid, WT0,8mm, PT205C, FR95%, RD4,5mm, RS30mm/s, PS70 m/s, OWS15 mm/s, PA3000 mm/s2, dan PJ10mm/s.

Effect of cutting set variations on structural and functional properties of hamburgers

AbstractMeat grinders are composed of a combination of individual functional elements (e.g., screw conveyor, perforated plates, knives). This setup, and in particular the chosen cutting set, influences the characteristics of ground meat and hamburgers produced. In this study, we took a closer look at the effect of cutting set variations and process parameters on structural, functional, and physicochemical properties of beef hamburgers produced. It was found that the specific mechanical energy input during grinding increased when cutting levels, i.e., a set of one hole plate and one knife, were increased, causing more cell disintegration (r = 0.387, p = 0.02). Surprisingly though, an influence on the functional and quality parameters of the hamburgers could not be found for most parameters tested. The findings indicate that variations in the cutting set affect the process parameters and the stress applied to the meat, but residence times in this zone are too small to cause noticeable effects on the analytical and qualitative properties of hamburgers. As such, there are options for energy and cost optimization of industrial grinding processes without sacrificing quality.

Deformation by design: data-driven approach to predict and modify deformation in thin Ti-6Al-4V sheets using laser peen forming

Abstract The precise bending of sheet metal structures is crucial in various industrial and scientific applications, whether to modify deformation in an existing component or to achieve specific shapes. Laser peen forming (LPF) is proven as an innovative forming process for sheet metal applications. LPF involves inducing mechanical shock waves into a specimen that deforms the affected region to a certain desired curvature. The degree of deformation induced after LPF depends on numerous experimental factors such as laser energy, the number of peening sequences, and the thickness of the specimen. Consequently, comprehending the complex dependencies and selecting the appropriate set of LPF process parameters for application as a forming or correction process is crucial. The main objective of the present work is the development of a data-driven approach to predict the deformation obtained from LPF for various process parameters. Artificial neural network (ANN) was trained, validated, and tested based on experimental data. The deformation obtained from LPF is successfully predicted by the trained ANN. A novel process planning approach is developed to demonstrate the usability of ANN predictions to obtain the desired deformation in a treated region. The successful application of this approach is demonstrated on three benchmark cases for thin Ti-6Al-4V sheets, such as deformation in one direction, bi-directional deformation, and modification of an existing deformation in pre-bent specimens via LPF. Graphical abstract

Thermal Operating Window in Selective Laser Melting Processes

Abstract Selective laser melting (SLM) is one of the most effective methods of additive manufacturing (AM). It is used to manufacture products with very complex geometries using a wide range of materials. Practical process conditions are limited by the occurrence of undesirable melting instabilities that degrade the surface quality and lead to product defects. These disadvantages are related to the thermal limitations of the SLM process. The lower thermal limit is due to the need to completely melt the powder layer and partially remelt the underlying layer again to ensure proper bonding between the layers. Exceeding the upper thermal limit in the molten metal pool may cause extensive evaporation, boiling and ejection of molten metal droplets outside the melting area. The article presents an approach and methodology that enable the determination of thermal limits and the operating window of SLM/selective laser sintering (SLS) processes in a relatively simple way. The studies have been performed using various settings of SLM process parameters. The usefulness of the preliminary determination of thermal limitations and approximate prediction of operating window of SLM has been confirmed experimentally and by more accurate computer simulation.

Multi-objective optimization of fiber laser cutting quality characteristics of glass fiber reinforced plastic (GFRP) materials

Laser cutting can achieve ultra-precision cutting of materials by using appropriate parameters. In order to optimize processing parameters and obtain better processing quality, conducted experimental research on laser cutting of Glass fiber reinforced plastic (GFRP). In this study, the impact of four process parameters (laser power, cutting speed, assistant gas pressure and focus position) on quality characteristics (kerf width, kerf taper and kerf section roughness) was evaluated through analysis of variance (ANOVA), the regression relationship between laser processing parameters and quality characteristics was analyzed. And an integrative model for predicting and optimizing the quality characteristics of fiber laser cutting was constructed. In the proposed integrative model, Back-Propagation Neural Network (BPNN) was used to build a prediction model for quality characteristics. Through Non-dominated Sorting Genetic Algorithm (NSGAII) optimizes and outputs the complete optimal solution set of processing parameters, and finally realizes the nonlinear optimization of multi-objective parameters. The fitness value of BPNN model can reach 97.814%, and the maximum prediction relative error is 8.614%. And the set of suggested optimal solutions can be used as most of them are showing certain improvements in the quality characteristics. The results indicate that the integrative modeling strategy has the ability to predict and optimize laser cutting of composite materials.

Optimization of Process Parameters to Minimize the Surface Roughness of Abrasive Water Jet Machined Jute/Epoxy Composites for Different Fiber Inclinations

Composites materials like jute/epoxy exhibit high hardness and are considered as difficult-to-machine materials. As a result, alternatives to conventional machining become essential to post-process the composites. Accordingly, due to its non-thermal nature, abrasive water jet machining has recently come to be seen as one of the most promising machining methods for composite materials. In the current study, the impact of machining parameters such as traverse speed (TS), standoff distance (SOD) and abrasive mass flow rate (MFR) on machined surface roughness (Ra) has been investigated. In addition, the optimum combination of process parameters to machine a jute fiber-reinforced polymer composite with minimum Ra is predicted. The experimental results are analyzed using Taguchi and Response Surface Methodology (RSM) approaches to determine the optimum set of process parameters to achieve the lowest roughness values. Without making any changes in the machining conditions, the optimum set of values is determined for two conditions by reinforcing the fiber with 45° inclination and 90° inclination. The results reflect the different optimum combinations for each fiber inclination. For 45° fiber inclination, to achieve the minimum Ra value, the predicted combination is TS = 30 mm/min, SOD = 2 mm and MFR = 0.35 kg/min. When the fiber inclination is 90°, the predicted optimum combination is TS = 25 mm/min, SOD = 2 mm, and MFR = 0.35 kg/min. It is evident from the results that the optimum combination will be changed according to the machining conditions as well as material properties. The results confirm the effect of fiber orientation on surface roughness. The specimen with 45° fiber inclination produces a lower Ra with an average of 4.116 µm, and the specimen with 90° fiber inclination generates a higher Ra with an average of 4.961 µm.

Additive manufacturing of Stellite 6 alloy by laser-directed energy deposition: Engineering the crystallographic texture

Additive manufacturing (AM) of Stellite 6 alloy using laser-directed energy deposition (L-DED) technology was explored. The impact of argon and nitrogen as the shielding gas atmosphere and of the laser processing parameters on the microstructural characteristics of the L-DED manufactured single- and double-layers of the Stellite 6 alloy were studied. The microstructural characteristics and mechanical properties of the additively manufactured Stellite 6 alloy were investigated using electron backscatter diffraction (EBSD) analysis and indentation micro-hardness measurements. Also, the tribological behavior of the processed Stellite 6 were studied under different loading conditions. Based on the observed structural evolution including the phase transformations, optimized process parameters were established for the building of the main structural wall of the Stellite 6 alloy. Subsequently, the optimized set of processing parameters was analyzed in connection to the crystallographic textural majority of the manufactured alloy. The overall homogenous solidification microstructure of the constructed wall was characterized along different sections, i.e., bottom, middle, and top regions. At the bottom, the microstructure was dominated by gradients due to the solidification mechanism and the formation of a combined equiaxed/columnar dendritic structural morphology upon rapid cooling, resulting in a heterogeneous distribution of mechanical strength. The microstructure mainly consisted of columnar dendrites, generating a dominant textural component of 100<013> with a J-index of 1.62 in the matrix. After L-DED, the hardness of the alloy was found increased up to 565 HV, with a mean value of about 430 HV, depending on the geometrical location across the manufactured alloy wall. Also, a superior wear resistance of 4.91 × 10−5 mm3/Nm under a maximum load of 50 N was observed as a result of the enhanced mechanical properties. In fact, these findings substantiate an exciting approach to develop material properties with respect to processing design in which an advanced alloy is manufactured in a single step without particular post-processing.

Multi-objective optimization to specify optimal selective laser melting process parameters for SS316 L powder

PurposeThis paper adopts a modified Taguchi approach to develop empirical relationships to the performance characteristics (output responses) in terms of process variables and demonstrated their validity through comparison of test data. The method suggests a few tests as per the orthogonal array and provides complete information for all combinations of levels and process variables. This method also provides the estimated range of output responses so that the scatter in the repeated tests can be assessed prior to the tests.Design/methodology/approachIn order to obtain defect-free products meeting the required specifications, researchers have conducted extensive experiments using powder bed fusion (PBF) process measuring the performance indicators (namely, relative density, surface roughness and hardness) to specify a set of printing parameters (namely, laser power, scanning speed and hatch spacing). A simple and reliable multi-objective optimization method is considered in this paper for specifying a set of optimal process parameters with SS316 L powder. It was reported that test samples printed even with optimal set of input variables revealed irregular shaped, microscopic porosities and improper melt pool formation.FindingsFinally, based on detailed analysis, it is concluded that it is impossible to express the performance indicators, explicitly in terms of equivalent energy density (E_0ˆ*), which is a combination of multiple sets of selective laser melting (SLM) process parameters, with different performance indicators. Empirical relations for the performance indicators are developed in terms of SLM process parameters. Test data are within/close to the expected range.Practical implicationsBased on extensive analysis of the SS316 L data using modified Taguchi approach, the optimized process parameters are laser power = 298 W, scanning speed = 900 mm/s and hatch distance = 0.075 mm, for which the results of surface roughness = 2.77 Ra, relative density = 99.24%, hardness = 334 Hv and equivalent energy density is 4.062. The estimated data for the same are surface roughness is 3.733 Ra, relative density is 99.926%, hardness is 213.64 Hv and equivalent energy density is 3.677.Originality/valueEven though equivalent energy density represents the energy input to the process, the findings of this paper conclude that energy density should no longer be considered as a dependent process parameter, as it provides multiple results for the specified energy density. This aspect has been successfully demonstrated in this paper using test data.

Optimizing process parameters of fused deposition molding for 3D printing customized control of polyetheretherketone scaffolds

Polyetheretherketone (PEEK), with good biocompatibility and similar mechanical properties to natural bone, is extensively employed in the manufacture of prostheses. However, the precision and mechanical properties of current implants are major challenges for clinical applications. In this study, the effect of pore size, raster angle and printing temperature were investigated on length, width, thickness, material consumption, compressive strength and Young’s modulus. Taguchi design of experiment method was used to reduce the number of experiments and optimize the printing process parameters. Finally, predictive analysis was exploited to give the optimal set of process parameters. Experimental results indicated that the approach applied in this work provided more accurate predictions and control of the response variables. The maximum compressive strength and compressive modulus of PEEK scaffolds reached 43.4 MPa and 253.3 MPa, respectively. Therefore, the methodology of present work has the potential to meet the demand of design precision and manufacture of customized bone substitutes.

Reduction of energy and fuel consumption in the hot-rolling steel sector

Furnace oil consumption, electric energy consumption, waste owing to mill scale, and process scrap due to cropping length are essential production indicators that must be monitored for economical production with minimal environmental impact.The study examined the effect of mill setting schedules or roll pass designs on furnace oil consumption, electricity consumption, mill scale generation and end cropping loss in a steel bar hot rolling plant. Rolling experiments were performed in a hot rolling steel plant. The results obtained from the rolling plant experiments were compared with the results obtained with the help of computer simulation.A t-test is an inferential statistic used to determine if there is a significant difference between the means of two groups. The results of plant experimentation and simulation were compared by performing t-test. The simulations results were found to be comparable to experimental results. After validation, simulation experiments were performed for the mill setting process parameters beyond the range possible in the rolling plant. Regression equations linking input process parameters with response parameters were created with the help of statistical analysis.The optimum mill setting parameters for minimum consumption of fuel oil, electrical energy, minimum generation of mill scale and end cropping loss have been attained.

A two-phase approach optimizing productivity for a mAb-producing CHO cell culture process using dynamic response surface methodology models

Fed-batch cell culture processes are common in the biomanufacturing industry due to their ease of development and simplicity of execution in the cGMP environment. One challenge of fed-batch operation is that the physiochemical conditions within the bioreactor do not remain constant throughout a batch but change over time as the viable cell density changes. It is well accepted that fed-batch cultures are multi-phasic, divided into “growth, stationary, and death phases”. Yet, using empirical modeling methods such as response surface models (RSMs) for optimization is still common. While RSMs can identify local performance maxima, they are limited by the time-invariant factors (input parameters) they use. These factors do not change throughout the run. Dynamic RSM (DRSM) models predict the time-dependent impact of the process inputs allowing for the identification of optimized process operations that change with time. Starting with the data sets generated via a traditional Design of Experiments (DoE) design, the DRSM model successfully optimized the peak cell count during the growth phase of culture and then identified a second set of process parameters that optimized productivity during the stationary phase. The resulting process achieved a harvest titer improvement of 28% relative to the base process condition.

Development of eDART-based weight prediction system in injection molding via Taguchi design and fuzzy logic

This research describes developing a Fuzzy Logic based weight prediction system (FL-eWPS) during the process of injection molding. The main purpose is to apply Fuzzy Logic to predict defects during injection molding operations while processing parameters, such as shot size, barrel temperature, cooling time, and holding pressure. The parameters are varied within a shorter range when using Delrin 511 DP plastic from DuPont Engineering Polymers. eDART data logging system was used for real-time data collection for the different parameters by using the sensors during the injection filling stages. A Fuzzy Logic reasoning algorithm was applied to gain the threshold values of weight prediction with various processing parameter settings. During the injection molding process, the FL-eWPS system was shown to predict weight with 99% accuracy.