Best Combination Of Process Parameters Research Articles

Microstructure and Mechanical Properties of Cermet Composite Coating on TC4 Surface

In order to improve the surface quality and performance of TC4, the TC4-HfC-TaC metal-ceramic composite cladding coating was prepared on the surface of TC4 by using different process parameters by laser cladding technology. Through digital testing equipment, the macro morphology, molten pool size, dilution rate, Rockwell hardness (HRC), geometry, microstructure and other comprehensive properties of the coating are studied and analyzed. The results show that the performance characterization of TC4-HfC-TaC cladding layer is closely related to the melting state of refractory carbide ceramic phase HfC and TaC powder, when the laser beam energy density is not enough to completely melt the ceramic phase powder, with the increase of energy density, the coating surface tends to be flat, the molten pool size and dilution rate increase, the hardness gradually increases, the metallographic structure is refined uniformly, and the surface quality is improved. Excessive energy density of the laser beam can easily lead to excessive dilution rate of the cladding layer, reduced coating hardness, coarse metallographic structure, and reduced surface quality. Finally, the best combination of process parameters was screened out: laser power 3000 W, scanning speed 300 mm/min, spot size 2.5 × 11.5 mm, rectangular spot lap rate 40%, preset powder thickness 1.5 mm, at which time the coating hardness increased to 63.22 HRC. It is about 2.1 times the hardness of TC4 substrate, with the best performance, and the metallographic structure is densely arranged fine cell-like crystals, with outstanding microscopic characteristics, which realizes the purpose of preparing high-quality TC4-HfC-TaC cermet composite cladding coating on the surface of TC4 titanium alloy.

Study of Gas Film Characteristics in Electrochemical Discharge Machining and Their Effects on Discharge Energy Distribution.

Glass is a hard and brittle insulating material that is widely used in optics, biomedicine, and microelectromechanical systems. The electrochemical discharge process, which involves an effective microfabrication technology for insulating hard and brittle materials, can be used to perform effective microstructural processing on glass. The gas film is the most important medium in this process, and its quality is an important factor in the formation of good surface microstructures. This study focuses on the gas film properties and their influence on the discharge energy distribution. In this study, a complete factorial design of experiments (DOE) was used, with three factors and three levels of voltage, duty cycle, and frequency as the influencing factors and gas film thickness as the response for the experimental study, to obtain the best combination of process parameters that would result in the best gas film quality. In addition, experiments and simulations of microhole processing on two types of glass, quartz glass and K9 optical glass, were conducted for the first time to characterize the discharge energy distribution of the gas film based on the radial overcut, depth-to-diameter ratio, and roundness error, and to analyze the gas film characteristics and their effects on the discharge energy distribution. The experimental results demonstrated the optimal combination of process parameters, at a voltage of 50 V, a frequency of 20 kHz and a duty cycle of 80%, that achieved a better gas film quality and a more uniform discharge energy distribution. A thin and stable gas film with a thickness of 189 μm was obtained with the optimal combination of parameters, which was 149 μm less than the extreme combination of parameters (60 V, 25 kHz, 60%). These studies resulted in an 81 μm reduction in radial overcut, a roundness error reduced by 14, and a 49% increase in the depth-shallow ratio for a microhole machined on quartz glass.

Enhancement of mechanical and surface properties by ball burnishing process on polymeric components obtained by FFF

PurposeThis paper aims to investigate and deliver experimental evidence to establish ball burnishing (BB) as an effective procedure for processing fused filament fabricated parts (FFF). This study, which is a novel contribution to applying BB on FFF parts of materials with different properties, demonstrates the validity of this technology on polymers and provides generalizations for its implementation.Design/methodology/approachA BB tool has been designed and validated. Statistical models have been used to determine the process parameters that provide the best results. In addition, the process’ impacts on the dimensional accuracy, quality, hardness and mechanical performance of the treated parts under static bending and fatigue testing have been quantified and compared to the untreated samples.FindingsThis study shows the best combination of process parameters for two printing orientations which have been decisive in obtaining successful results. These positive results allow stating procedure guidelines and recommendations for use in the industrial environment.Originality/valueThis paper fulfills an identified need to enhance FFF parts' surface and mechanical properties, as more experimental evidence of studies demonstrating this technology's validity in additive manufacturing is yet to be found.

Mechanical characterization of particle reinforced jute fiber composite and development of hybrid Grey-ANFIS predictive model

ABSTRACT Natural fiber composites are a potential material in a range of engineering applications because of their excellent properties, which include reduced weight, better strength, and economic affordability. Aside from these features, these materials are biodegradable and renewable. The intent of this study is to look through into mechanical properties of aluminum oxide (Al2O3), boron carbide (B4C), and silicon carbide (SiC) particle-filled jute fiber polymer composite. The response surface methodology (RSM) with three levels/three factors is used to achieve the different combinations of process variables required to fabricate the desired polymer composites. In this regard, the effect of process variables on tensile characteristics, increase in weight %, and flexural characteristics is examined in detail. Further, the best combination of process parameters is chosen to produce composites with the desired mechanical qualities. The significance of such variables on each output variable is assessed using analysis of variance. A hybrid grey-based adaptive neuro-fuzzy inference system model is constructed for establishing multiple performance indexes. From the validation outcomes obtained, it is proved that the evolved model is proficient for precise prediction.

A STUDY OF THE EFFECT OF FIBER CONTENT AND FIBER ORIENTATION ON THE TENSILE STRENGTH OF GLASS FIBER REINFORCED EPOXY COMPOSITE FOR PIPE PRODUCTION: A STATISTICAL APPROACH

This study was aimed at optimizing the development parameters of a glass fiber reinforced epoxy composite for pipeline application. In this study, the Taguchi experimental design technique was applied to determine the best combination of process parameters (fiber content and orientation) for optimum tensile strength. The optimum tensile strength of 209.65MPa was reported at the fiber content of 50% and Fiber orientation of 45º. Analysis of variance also showed that in optimizing for high tensile strength, the glass fiber content and the fiber orientation are significant factors. The material at optimum tensile properties was applied in the simulation of the behavior of a high-pressured gas pipeline and results proved the reliability of the material in such application.

Study on process and parameter optimization of selective laser sintering of SiC composite powder

This study aims to utilize silicon carbide (SiC) powder and phenol-formaldehyde resin (PF) as the primary raw materials to prepare phenol-formaldehyde coated silicon carbide composite (PCSC) using a thermal coating method. Then, single-factor experiment and orthogonal experiment were used to optimize the process parameters of selective laser sintering (SLS). Finally, SiC precursors were formed using optimized process parameters with excellent density and dimensional accuracies. The results show that the best combination of process parameters is: laser power of 25W, scanning speed of 1.7m/s, scanning spacing of 0.12mm and layer thickness of 0.16mm.

Experimental Investigation of Milling Performance of Silicon Nitride Ceramic Subject to Different Assisted Systems.

In this study, silicon nitride milling experiments are carried out using Polycrystalline Diamond (PCD) end mill rods under unassisted, hybrid-assisted (combination of laser assisted and three axis ultrasound), and laser-assisted systems to examine the cutting performance and machined surface quality of different cutting tools. The best combination of process parameters for silicon nitride composites milling are obtained using the Taguchi method. The effects of spindle speed, radial depth of cut, and feed rate on surface roughness, cutting force, edge topography, and tool wear of silicon nitride surfaces are investigated. The results reveal that hybrid-assisted produces superior surface roughness, longer tool life, fewer machining defects, and lower cutting force than unassisted. Best results of triaxial ultrasonic-assisted combined with laser on cutting performance are achieved as the ultrasonic waves help to vibrate the cutting tool and workpiece simultaneously, which helps to effectively remove chips and lowers the cutting force. When compared to unassisted milling, laser-assisted and hybrid-assisted milling improve total average surface roughness by 42% and 66%, and total cutting forces by 26% and 46%, respectively. The best processing parameters obtained in this study are high spindle speed (12,000 rpm), low feed rate (500 mm/min), and low cutting depth (0.02 mm).

The Tobacco Leaf Redrying Process Parameter Optimization Based on IPSO Hybrid Adaptive Penalty Function

In the tobacco redrying process, process parameter settings are greatly influenced by ambient temperature and humidity, and the moisture content of the tobacco leaf. In the face of complex and variable tobacco leaf characteristics, it is difficult to accurately adapt the process parameters to fluctuations in the incoming material characteristics by manual experience alone. Therefore, an improved optimization method combining an improved particle swarm optimization algorithm (IPSO) and an adaptive penalty function is proposed, which can adaptively recommend the best combination of process parameters according to the dynamic incoming characteristics of the tobacco leaf, to reduce the deviation in the outlet moisture and temperature of the roaster under different processing standards of the tobacco leaf. Firstly, the Radial Basis Function (RBF) Neural Network is used to fit the relationship between process parameters and roaster exit moisture content and temperature. Then, taking the standard tobacco leaf redrying export quality as the optimization goal, the optimization algorithm is used to search for the optimal solution. From the high-dimensional nature of the process operating conditions, the difficulty of this study lies in searching for the optimal solution under complex nonlinear constraints of multiple processes. To improve the convergence speed and accuracy of the searching algorithm, the position update method of the particle swarm optimization algorithm is improved, and the adaptive penalty function is combined to search for the optimal global solution to the optimization problem. Redrying experiments are conducted using the method proposed in this paper. Compared with the manual regulation of outlet moisture and temperature, the fluctuation range values are reduced by 7.5% and 11.8%, respectively, which has good application prospects and promotion value.

Optimization of Selective Laser Melting Parameter for Invar Material by Using JAYA Algorithm: Comparison with TLBO, GA and JAYA.

In this study, the hardness and surface roughness of selective laser-melted parts have been evaluated by considering a wide variety of input parameters. The Invar-36 has been considered a workpiece material that is mainly used in the aerospace industry for making parts as well as widely used in bimetallic thermostats. It is the mechanical properties and metallurgical properties of parts that drive the final product's quality in today's competitive marketplace. The study aims to examine how laser power, scanning speed, and orientation influence fabricated specimens. Using ANOVA, the established models were tested and the parameters were evaluated for their significance in predicting response. In the next step, the fuzzy-based JAYA algorithm has been implemented to determine which parameter is optimal in the proposed study. In addition, the optimal parametric combination obtained by the JAYA algorithm was compared with the optimal parametric combination obtained by TLBO and genetic algorithm (GA) to establish the effectiveness of the JAYA algorithm. Based on the results, an orientation of 90°, 136 KW of laser power, and 650 mm/s scanning speed were found to be the best combination of process parameters for generating the desired hardness and roughness for the Invar-36 material.

Process parameter modeling and multi-response optimization of wire electrical discharge machining NiTi shape memory alloy

In the present work, the effects of different wire electrical discharge machining (WEDM) process parameters on nickel titanium shape memory alloy (NiTi-SMA) properties were investigated. Based on the orthogonal experimental design of the Taguchi method, the influence of five process parameters including peak current, discharge frequency, wire tension, flushing pressure, wire speed on the cutting speed (CS) and kerf width (KW) was analyzed. After 27 experimental runs, a multiple regression (MLR) model and a back-propagation neural network (BPNN) model were established to predict the CS and KW properties under different input conditions of process parameters. Utilize the programming language R for statistical testing of models and determination of important control factors. The Bat Algorithm (BA) searches for the best combination of process parameters. The experimental results and the proposed optimization method show that the prediction errors of the two combined optimization methods of MLR-BA and BPNN-BA are controlled by ± 2%. It has been proved that these two combined optimization methods are practical tools for optimizing the processing parameters of WEDM. Statistical analysis and observation results show that the peak current is the main parameter affecting the machining efficiency and surface quality of WEDM.

A Study on the Residual Stress of the Co-Based Alloy Plasma Cladding Layer.

The distribution law of residual stress in multi-channel scanned plasma cladding of Co-based alloy under different process parameters was studied by means of simulation and tests, and the optimum process parameters were optimized. The simulation model of the plasma cladding stress field was established by ABAQUS software, and the influence law of the working current, scanning speed, and scanning mode on the residual stress of the Co-based alloy multi-channel scanning was studied. A set of optimal cladding process parameters were obtained. The residual stress of the cladding layer was measured by the blind hole method and compared with the stress value in the finite element model. The results show that there is residual tensile stress on the surface of the cladding layer. The residual stress along the direction of the scanning path is greater than that along the direction of the scan sequence. The residual stress increases with the increase of the working current. The scanning speed is greater, and the residual stress is smaller. The residual stress of the short-edge scanning is greater than that of the long-edge scanning. The residual stress of the successive scanning is greater than that of the reciprocating scanning. The long-edge reciprocating scanning is the best scanning mode. The best combination of process parameters is the working current of 90 A, the scanning speed of 100 mm/min, and the long-edge reciprocating scanning mode.

Influence of process parameters on mechanical and microstructural properties of friction stir welded AA5052

ABSTRACT The current research work explores the process parameters namely tool shoulder diameter, tool pin design, tool rotational and welding speed and its influence on mechanical and metallurgical qualities of aerospace-grade 5052 aluminium alloy. The research observation shows that the intermediate extent of process parameters indicates enhanced mechanical qualities compared to the lower and higher regimes. When considering different individual tool pin profiles, square pin (SQ) tool geometry yields the highest ultimate tensile strength in comparison with threaded cylindrical, tapered cylindrical and straight cylindrical tools. A set of the best combination of process parameters with 24 mm shoulder diameter SQ pin tool geometry at tool rotational speed of 1000 rev/min along with the welding speed of 40 mm/min produced defect-free joint with excellent mechanical characteristics with an ultimate tensile strength of 92% compared to the base metal. Microstructural analysis of the best welded specimen (considering the above parameters) revealed 81% finer grain compared to base material that leads to enhanced mechanical attributes. Fractography detects ductile fracture of the welded specimen. The effects of individual process parameters on grain size, micro-hardness, flexural strength, bending angle and percentage of elongation of each sample are represented in this research article.

Influence of process parameters on mechanical and microstructural properties of friction stir welded AA5052

ABSTRACT The current research work explores the process parameters, namely, tool shoulder diameter, tool pin design, tool rotational as well as welding speed and its influence on mechanical and metallurgical qualities of aerospace grade 5052 aluminium alloy. The research observation shows the intermediate extent of process parameters indicates enhanced mechanical qualities compared to the lower and higher regimes. With considering different individual tools pin profiles, square pin (SQ) tool geometry yields the highest ultimate tensile strength in comparison with threaded cylindrical, tapered cylindrical, and straight cylindrical tool. A set of the best combination of process parameters with 24 mm shoulder diameter, SQ pin tool geometry at tool rotational speed of 1000 rev/min along with the welding speed of 40 mm/min, produced defect-free joint with excellent mechanical characteristics with an ultimate tensile strength of 92% compared to the base metal. Microstructural analysis of the best welded specimen (considering the above parameters) revealed 81% finer grain compared to base material that leads to enhanced mechanical attributes. Fractography detects ductile fracture of the welded specimen. The effect of individual process parameters on grain size, micro-hardness, flexural strength, bending angle and percentage of elongation of each sample are represented in this research article.

Fatigue life optimization for 17-4Ph steel produced by selective laser melting

PurposeThe mechanical characterization of selective laser melting (SLM) parts is an industrial challenge. This paper aims to propose a methodology to control the fatigue life of 17-4Ph stainless steel by selecting the most relevant manufacturing parameters: i.e. laser power, laser travel speed, hatch spacing and laser defocusing.Design/methodology/approachA rough and refined design of experiment (DOE) is carried out to target the best combination of process parameters. A response surface model is then constructed to predict the parameter combination that optimizes the fatigue performance.FindingsThis study results show that the fatigue limit of the specimens manufactured by SLM (471.7 MPa at 107 cycles) has reached near 90% of the value found in samples machined from a bar. This demonstrates the applicability of the method proposed to optimize the SLM process and control the fatigue life of 17-4Ph stainless steel. The study results are compared with other research works and provide an increase of 18% to the fatigue limit.Originality/valueThis study showcases a DOE methodology to optimize the SLM parameters to achieve fatigue performance as great as that of solid 17-4Ph stainless steel.

Process Parameter Optimization in Metal Laser-Based Powder Bed Fusion Using Image Processing and Statistical Analyses

The powder bed fusion additive manufacturing process has received widespread interest because of its capability to manufacture components with a complicated design and better surface finish compared to other additive techniques. Process optimization to obtain high quality parts is still a concern, which is impeding the full-scale production of materials. Therefore, it is of paramount importance to identify the best combination of process parameters that produces parts with the least defects and best features. This work focuses on gaining useful information about several features of the bead area, such as contact angle, porosity, voids, melt pool size and keyhole that were achieved using several combinations of laser power and scan speed to produce single scan lines. These features are identified and quantified using process learning, which is then used to conduct a comprehensive statistical analysis that allows to estimate the effect of the process parameters, such as laser power and scan speed on the output features. Both single and multi-response analyses are applied to analyze the response parameters, such as contact angle, porosity and melt pool size individually as well as in a collective manner. Laser power has been observed to have a more influential effect on all the features. A multi-response analysis showed that 150 W of laser power and 200 mm/s produced a bead with the best possible features.

Characterization and Optimization of Process Parameters for Directed Energy Deposition Powder-Fed Laser System

Laser-directed energy deposition (L-DED) is a type of additive manufacturing (AM) technology that allows the manufacture of complex geometry components in a layer-by-layer way and can be considered an emerging manufacturing technique. The process efficiency and properties of produced parts are closely linked to its parameters, i.e., laser power, deposition speed, material flow rate and, inert gas flows. These operational parameters usually are different depending on the machine and material employed. The best combination of process parameters is fundamental to obtain the best characteristics together with process sustainability for each manufactured component. At the present work, an L-DED head and a powder feeding system are combined in a particular machine to deposit Inconel 625 on a substrate made of AISI 304 stainless steel. A combination of analytical studies, CFD simulations, and experimental tests was carried out, finding a process setting that offers a higher concentration of particles, quality depositions, and optimal cooling rates, reducing gas and material consumption during the process. After some in-situ tests, the best results were employed to fabricate thin wall structures and solid components. The specimens were characterized by laser confocal microscopy, roughness, and Vickers microhardness measurements, finding exciting results with a significant reduction in gas consumption and metal powder usage.

Density and shrinkage evaluation of AISI 316L parts printed via FDM process

ABSTRACT Metal FDM process is a valid alternative to costly, energy consuming and less accessible metal Additive Manufacturing technologies. Metal parts were fabricated by means of a low-cost FDM machine with a filament composed of 316 L stainless steel particles and polymeric binder mixture, followed by debinding and sintering treatments. The objective of the study is to investigate the possible influence of some relevant FDM printing parameters on dimensional shrinkage and bulk density of the metal samples. The experimental analysis was conducted by means of a statistical method (ANOVA). Besides, a porosity evaluation was carried out to identify the real density values and the amount of opened and closed porosities of samples characterized with highest and lowest bulk density. Lines infill pattern, extrusion speed set to 20 mm/s and 0.1 mm as layer thickness was the combination of process parameters that ensured the highest nominal density (i.e. 95% of the monolithic 316 L nominal density). The statistical analysis did not show any effects due to the nozzle temperature on the bulk density. X and Y shrinkages settled around 16%, while shrinkages along Z-axis showed a greater variability. The samples with the worst bulk density showed a higher percentage of opened porosity, while the percentage of opened porosity for samples with the best combination of process parameters resulted less than 3%.

A novel investigation into edge effect reduction of 4340 steel spur gear during induction hardening process

Induction hardening, a promising approach for selective hardening of metal parts, is widely used for surface hardening, where a hard surface is required alongside a tough core. Regarding the complexity of this process, parts’ geometry deeply affects the temperature distribution and hardness profile accordingly. In this study, two magnetic flux concentrators are introduced to our induction machine set in order to control the magnetic flux and consequently hardness profile (case depth) of spur gears. The performance of magnetic flux concentrators is examined by the effect of machine parameters on the case depth and the edge effect of AISI 4340 steel-made spur gear. Design of experiments based on Taguchi method is primarily used to optimize the number of experimental trials. Then, the hardness profiles of heat-treated gears at the tip and root of gears are measured by microindentation hardness tests. The results are analyzed using analysis of variance (ANOVA) and response surface methodology (RSM) to determine the main effect of process parameters, also the best combination of process parameters that maximizes the case depth and minimizes the undesirable feature of edge effect. Finally, the predicted case depth models versus process parameters are developed based on linear regression method. To this end, four predictive models of case depth at tip and root in the edge plane and middle plane of spur gears are generated. Results imply that maximum case depth with minimum edge effect at root and tip is achieved by setting up the highest machine power, longest heating time, and minimum axial gap between concentrators and the spur gear. This study provides a good exploration of case depth in presence of magnetic flux concentrators under various process parameters and gives a reliable guideline towards edge effect during induction hardening process.

High-speed machining parametric optimization of 15CDV6 HSLA steel under minimum quantity and flood lubrication

High-speed machining (HSM) maintains a high interest in the preparation of metal parts for optimum results, but with the application of HSM, the sustainability issue becomes important. To overcome the problem, minimum quantity lubrication (MQL) during HSM is one of the innovative and challenging tasks during conventional cutting (milling) to improve quality, productivity, and strength under the umbrella of sustainability. The objective of this research is to achieve sustainable machining by simultaneously optimizing sustainable machining drivers during the HSM of 15CDV6 HSLA steel under MQL and flood lubrication. The response surface methodology has been applied for the development of mathematical models and selecting the best combination of process parameters to optimized responses, i.e. surface roughness, material removal rate, and strength. Optimization associated with sustainability produced compromising optimal results (Min. Ra 0.131 µm, Max. MRR 0.64 cm3/min, and Max. ST 1132 MPa) at the highest cutting speed 270 m/min and the lowest feed rate 0.09 mm/rev and depth of cut 0.15 mm under MQL. The comparative investigation exposed that significant improvement in Ra (1.1-16.6 %) and ST (1.3-2.3 %) of the material using MQL has been witnessed and gives a strong indication that MQL is the best substitute than the flood lubrication. The scientific contribution of the approach is to develop mathematical models under MQL and flood lubrication that will aid practitioners to choose input parameters for desired responses without experimentations. The work would be beneficial in the field of aviation, defense, and aeronautical applications due to the excellent mechanical properties of 15CDV6 HSLA steel.

Optimization of weld strength for laser welding of steel to PMMA using Taguchi design method

Dissimilar joining of 304 stainless steel to PMMA is accomplished by a Nd: YAG pulsed laser. The light and process were represented by laser peak power, welding speed, defocus, pulse duration, pulse frequency and Ar gas flow rate, as these experiment parameters have significant effects on joints strength, weld width, joints quality and appearance. The welding parameters were optimized by an orthogonal experiment design, then the validation and optimization experiments were carried out to obtain the best combination of process parameters. A maximum weld strength of the joints of 974 N was achieved (the joints efficiency reached 70% of the base PMMA), much larger than ever reported. Bead profiles such as bead width and mechanical strength were investigated, and five types of 304 stainless steel-PMMA welded joints and three kinds of fracture model have been proposed.