Optimal Combination Of Process Parameters Research Articles

Optimal Parameters Selection in Advanced Multi-Metallic Co-Extrusion Based on Independent MCDM Analytical Approaches and Numerical Simulation

Multi-material co-extrusion is a complex thermo-mechanical forming process used to obtain bimetallic billets. Its complexity is due to the combination of diffusion phenomena in the interface of both materials together with the high temperature and pressure generated and the different flow stress characteristics created by the joining of dissimilar materials. Accordingly, the selection of optimal process parameters becomes key to ensure process feasibility. In this work, a comparison among different multi-criteria decision making (MCDM) methodologies, together with different weighting methods, were applied to the simulation results by using DEFORM3D© software to select the optimal combination of process parameters to fulfil the criteria of minimum damage, extrusion force, and tool wear, together with the maximum reduction in the average grain size.

Effects of machining parameters on enhancing Alpha-Beta Titanium alloy using Taguchi-grey relational analysis for aerospace applications

The need for enhancement of productivity and quality of the machined titanium alloy components is paramount to meet the high demands of aerospace parts. Various optimization techniques are being used widely to find suitable machining parameters to improve productivity and reduce surface defects. The present study aims to derive an optimal combination of process parameters during the end milling operation of Ti-6Al-4V alloy for enhancing performance measures which are surface roughness, specific cutting energy and material removal rate (MRR). The Grey relational optimization techniques along with Taguchi method has been utilized for finding the optimum machining parameters. It has been found that the axial depth of cut has the most significant factor on machining characteristics in end milling process of titanium alloy. Using Taguchi-Grey relational analysis technique, optimal process parameter combination has been computed and the performance enhancement using the optimal process combination has been verified by the confirmation test

An Improved Surface Treatment Process of 304 Stainless Steel Based on Low-Temperature Chromizing and Ultrasonic Vibration Extrusion

The ultrasonic vibration extrusion process is a widely used surface treatment process of stainless steel, e.g., 304 stainless steel, to improve the surface quality and increase hardness and wear resistance. However, for high-hardness 304 stainless steel, the traditional process, i.e., a single ultrasonic vibration extrusion process, does not fulfil its application requirements. To cope with this problem, this paper proposes an improved surface treatment method based on low-temperature chromizing and ultrasonic vibration extrusion to obtain the expected surface quality of 304 stainless steel. Using orthogonal design and multivariate regression, the influence of ultrasonic impact parameters on the surface integrity of 304 stainless steel was studied in this work. Finally, the experimental results show that the hardness of the surface processed by the proposed method is increased by about 2.55 times compared with the ultrasonic vibration extrusion process, and the surface roughness of the composite process is reduced by an average of 60.8% compared with that of unfinished surface. In addition, the optimal combination of process parameters is obtained: the spindle speed of 240 rpm, the feed of 0.1 mm/r, and the static extrusion of 40 μm, which can provide the optimal process parameter support for the surface treatment of 304 stainless steel.

Microstructure and mechanical properties of M2 high speed steel produced by electron beam melting

M2 high-carbon and high-alloy steel was prepared by electron beam melting (EBM) in this study. The optimal process parameters were explored by varying the beam current, scan speed, and scanning spacing. The microstructure and properties of manufactured samples, including chemical composition, grain morphology, texture, phase composition, the relative density, compressive strength and ductility were examined by means of SEM, TEM, EBSD, as well as compression testing. The results show that the line energy is closely related to the surface morphology and microstructure of the formed samples. High line energy leads to bulging of the sample surface. Decrease of the line energy results in an increase of the cooling rate that contributes to the refinement of grains and carbides of the as-built samples. However, when the line energy is too low, spheroidizing phenomenon takes place at the sample surface, and the density of sample is decreased significantly. The optimal combination of process parameters is achieved with 7.8 mA scanning current, 2.4 m/s scanning speed and 0.1 mm scanning spacing. The relative density of the sample can reach to 99.6%, with 793HV microhardness and 2860 MPa compressive strength. Due to the rapid cooling and gradient “annealing” process during EBM, the microstructure of the single-scan layer presents a “sandwich” structure composed of fine equiaxed crystals, columnar dendrites and large equiaxed crystals in the heat-affected zone.

The Use of TOPSIS Method for Multi-Objective Optimization in Milling Ti-MMC

This paper presents the use of TOPSIS, a multi-criteria decision-making model combined with the Taguchi method to find the optimum milling parameters. TOPSIS is the Technique for Order Preference by Similarity to the Ideal Solution and shows the value of closeness to the positive ideal solution. This study shows the optimum combination of process parameters using the shortest distance from the ideal solution. The surface roughness and flank tool wear were considered the objectives for simultaneous optimization. After converting multiple responses into a single response, the Taguchi method was used to analyze and determine the optimum machining parameters. According to reported studies, the initial wear behavior and initial cutting conditions have significant effects on the tool wear progress. Several initial cutting parameters can contribute to tool life and therefore can be used to improve both tool life and surface roughness. However, the cutting speed may significantly affect tool wear and ultimate tool life. In this study, an innovative solution was proposed for interrupted machining with two different cutting speeds. The first level cutting speed was used for 1 s and the second level was used for the rest of the process. The experimental results indicate that the initial speed followed by the feed rate significantly affects tool life. In addition, using the proposed strategy with different levels of cutting speed during machining operations led to improved tool life and surface roughness compared to conventional machining with uniform cutting speed throughout the entire process.

Experimental Research and Optimization of Ti-6Al-4V Alloy Microgroove Machining Based on Waterjet-Guided High-Power Laser.

In order to improve the tribological properties of Ti-6Al-4V alloy and further broaden the application scope of titanium alloy materials in the industrial field, a preparation method of a waterjet-guided high-power laser processing surface microgroove was studied. In this paper, a multifocus coupling lens was innovatively designed to replace the spherical lens in the traditional waterjet-guided laser coupling device, which avoids the gas explosion phenomenon in the coupling of the high-power laser and waterjet, and realizes the high-quality coupling of the high-power laser and water beam fiber. Then, with the microgroove morphology as the response target, the single-factor test and response surface test of the water-guided laser processing microgroove were carried out. Based on the experimental results, an approximate mathematical model of the response surface between the process parameters and the microgroove topography target was constructed, and the quantitative relationship between the waterjet-guided laser processing parameters and the target response was studied. At the same time, the optimal combination of process parameters was obtained by multiobjective optimization, so as to effectively improve the microgroove morphology. This technology provides method guidance and a decision-making reference for subsequent waterjet-guided laser processing of titanium alloy surface functional microstructures.

Research on Optimization of Injection Molding Process Parameters of Automobile Plastic Front-End Frame

The main objective of the present article is to obtain the optimum technological parameters of the automobile plastic front-end frame. Moldflow software was used to simulate the injection molding of the automobile plastic front-end frame bracket in this study. The uniformity experiment was designed and completed. Five injection molding process parameters, including mold temperature, melt temperature, packing pressure, injection time, and packing time, were selected as experimental factors. Volume shrinkage and warpage amount were selected as quality evaluation indexes. Statistical Product Service Solutions (SPSS) software was used to perform linear regression analysis on the test results, respectively, and the linear regression equation corresponding to the two evaluation indexes was obtained. Then, regression equation analysis was carried out to obtain the optimal process parameter combination of the volume shrinkage and the warpage amount. A back propagation (BP) neural network model with input as a process parameter and output as an evaluation index is established by MATLAB and optimized by a genetic algorithm (GA). Finally, the optimized neural network model was used to predict the combination of process parameters with the minimum volume shrinkage and warpage amount. Based on the performed simulations, the minimum volume shrinkage and the minimum amount of warping in the optimal design were 13% and 0.7062 mm, respectively. The corresponding combination of process parameters was the mold temperature of 75.5°C, the melt temperature of 285.32°C, the packing pressure of 42.32 MPa, the injection time of 3.45 s, and the packing time of 60 s. According to the optimal process parameters, the volume shrinkage and warpage amount of the automobile plastic front-end frame have reached the optimal state at the same time, which improves the quality of injection molding and reduces production and processing costs. It has a certain guiding significance.

Analysis and optimization of FFF process parameters to enhance the mechanical properties of 3D printed PLA products

Abstract In this work, the combined effects of fused filament fabrication (FFF) process parameters on the mechanical properties of 3D printed PLA products have been determined by focusing on the tensile strength at R 2 (97.29%). ASTM D638 test standard is used for the preparation of specimens for tensile tests. The optimization technique has been used to determine the optimal combinations of FFF process parameters for the validation of experimental tensile tests and computational fluid dynamics (CFD) simulations. From the results obtained the optimum cooling fan speed of 79.3%, extrusion temperature of 214.4 °C, printing speed of 75.9 mm/s, raster width of 0.4814 mm, and shell number 5 were determined with a 2.266% error of the tensile strength (45.06 MPa). SEM morphology examination shows that the fabricated part cooled at 80% cooling fan speed illustrates good inter-layer bond strength which is also confirmed by CFD temperature distributions analysis.

OPTIMIZATION OF INJECTION MOLDING QUALITY BASED ON BP NEURAL NETWORK AND PSO

An electronic product shell is prone to uneven shrinkage, warpage and sink marks, resulting in a large number of unqualified products and increased costs. Therefore, volumetric shrinkage, warpage deformation and sink mark index are selected as optimization goals. Based on the orthogonal test and entropy weight method, a multi-objective optimization was transformed into a comprehensive evaluation optimization. A BP neural network combined with a particle swarm optimization algorithm was used to obtain the optimal combination of process parameters, simulated by Moldflow to reduce volumetric shrinkage to 3.46 %, warpage deformation to 2.538 mm, and the sink mark index to 1.87 % so as to improve the injection molding quality of the plastic parts and meet the requirements of qualified parts. The combination of the BP neural network and particle swarm optimization algorithm can prevent the defects such as large shrinkage, warpage and sink marks

Process Optimization of Non-Holonomic Surface Milling for Al-Si7Mg Component

Milling process enhancement for an aluminium alloy component with a large bore and variable wall thickness for incomplete circular surfaces, research on the optimization of milling process parameters based on machining stability and machining deformation control was carried out. Analysis of the formation mechanism of the regeneration effect caused by casting defects in cast aluminium parts, the stability lobe diagrams are plotted by the zero-order frequency domain analysis method, and the effects of radial depth of cut, cis-reverse milling and the dynamics parameters of the milling system on the boundary range of the stability lobe diagram were investigated. By orthogonal simulation analysis of milling deformation, determine the primary and secondary relationship between the influence of each process parameter on the machining deformation and the optimal combination of each process parameter in the test range. The results show that in the milling process, reducing the radial depth of cut, using a more rigid milling cutter and reverse milling can all be effective in improving the stability of the process; The significant influence of process parameters on the amount of machining distortion is: radial depth of cut > feed rate > cutter’s speed, the optimal combination of process parameters is: cutter’s speed 600 r/min, radial depth of cut 0.2 mm, feed rate 150 mm/min.The process parameters are used for machining verification, the surface roughness of the annular hole was improved from 6.3 to 3.2 before the optimization, and the coaxiality was improved from 0.12mm to 0.05mm before the optimization. The results show that Optimised milling process parameters improve part quality.

Optimization design of process parameters for cold and hot composite roll forming of the AHSS square tube using response surface methodology

Advanced high-strength steel (AHSS) is a highly competitive material for the automobile industry to resolve the challenge of weight reduction and passenger safety. Cold and hot composite roll forming is a newly developed manufacturing technology, which aims to overcome the defects such as microcrack, severe work hardening, thickness reduction, and large corner radius in the traditional cold roll forming process of AHSS. In this paper, the mathematical models that represent the effect of line velocity, heating power, and deformation amount on the yield strength, outer corner radius, and microcrack length in the corner section of cold and hot composite roll-formed AHSS square tube are investigated by the response surface methodology with Box-Behnken design. The analysis of variance shows that the linear velocity has the greatest influence on the three responses, followed by the heating power and deformation amount. Furthermore, the experimental verification is carried out under the optimal combination of process parameters to manufacture the AHSS square tube with the desired performance. Compared to the cold roll-formed AHSS square tube, the product of strength and elongation in the corner section is increased by 53%, the corner thickness is increased by 60%, the outer corner radius is decreased by 94%, and the microcrack defect is not generated. The average magnitude of errors between the predicted and actual values is about 5%, which implies that the optimization design is accurate and reliable.

Process Optimization of Inconel 718 Alloy Produced by Laser Powder Bed Fusion

To cut the cost of the laser powder bed fusion (LPBF) process, which is much higher than that of the traditional manufacturing process, an effective implementation of optimization analysis is needed. The study investigated the optimization of the LPBF Inconel 718 alloy with the Taguchi method and principal component analysis (PCA), covering four control factors at three levels in the manufacturing process. It focused on four mechanical properties, namely tensile strength, elongation, impact energy, and hardness. The results show that the highest tensile strength is obtainable at a laser power of 140 W, scanning speed of 800 mm/s, scanning pitch of 70 μm, and interlayer angle of 45 degrees. The optimal combination of process parameters for multiobjective optimization is just the same as that for single-objective optimization for tensile strength. The difference between the predicted and experimental average tensile strength is 1.2%, and the error of the predicted optimal strength index is 12.6%. The most important control factor for tensile strength and multiple responses is the angle between layers, with a contribution rate exceeding 90%. With a given volume energy density of the LPBF process, the higher the power and scanning speed, the higher the accumulated energy and the larger the amount of dendritic or cellular crystals formed.

Prediction and Optimization of Surface Roughness for Laser-Assisted Machining SiC Ceramics Based on Improved Support Vector Regression.

In this paper, the surface roughness of SiC ceramics was investigated in laser-assisted machining (LAM) processes; machine learning was used to predict surface roughness and to optimize the process parameters, and therefore, to ultimately improve the surface quality of a workpiece and obtain excellent serviceability. First, single-factor turning experiments were carried out on SiC ceramics using LAM according to the material removal mechanism to investigate the variation trend of the effects of different laser powers, rotational speeds, feed rates, and cutting depths on surface roughness. Then, laser power, rotational speed, feed rate and cutting depth were selected as the four factors, and the surface roughness was used as the target value for the orthogonal experiments. The results of the single-factor experiments and the orthogonal experiments were combined to construct a prediction model based on the combination of the grey wolf optimization (GWO) algorithm and support vector regression (SVR). The coefficient of determination (R2) of the optimized prediction model reached 0.98676 with an average relative error of less than 2.624%. Finally, the GWO algorithm was used to optimize the global parameters of the prediction model again, and the optimal combination of process parameters was determined and verified by experiments. The actual minimum surface roughness (Ra) value was 0.418 μm, and the relative error was less than 1.91% as compared with the predicted value of the model. Therefore, the prediction model based on GWO-SVR can achieve accurate prediction of the surface roughness of SiC ceramics in LAM and can obtain the optimum surface roughness using parameter optimization.

Study on the optimal process parameters for stripping the X-ETFE insulation layer of aviation wires by a small semiconductor laser

A reasonable laser wire stripping process parameter set is a prerequisite for high-quality and high-efficiency wire stripping. For the X-ETFE insulated wire used in aviation, this study explores the method of obtaining the optimal stripping process parameters based on a 405 nm wavelength semiconductor laser. Build energy conversion model, mobile heat source model, and finite element simulation numerical model of the laser stripping insulating layer process. Based on the single-factor analysis method, simulate the effect of laser power, scanning speed, and processing time on the kerf width, heat-affected zone width, and cutting seam depth of the insulating layer. The results show that increasing the laser power to improve the stripping efficiency will also increase the width of the kerf and heat-affected zone, and increasing the scanning speed can effectively reduce the kerf width. A reasonable combination of laser power and scanning speed can improve stripping efficiency while ensuring quality. According to the single-factor simulation analysis results, select the possible value levels for each parameter to carry out the actual laser wire stripping orthogonal test. There is a good correspondence between the range analysis results of the test data of each stripping quality evaluation index and the conclusions obtained from the single-factor simulation, which verifies the reliability of getting the optimal combination of laser stripping process parameters through the orthogonal test.

Research on the Injection Mold Design and Molding Process Parameter Optimization of a Car Door Inner Panel

Numerical simulation of the injection molding of the inner panel of the car door and the optimization of its process parameters were completed. The inner panel and its injection mold were designed by UG software and simulated by the filling and cooling module of the Moldflow software. Different gate schemes were selected to compare quality indicators such as filling time, air pockets, and weld lines to obtain the optimal number of gates. We established the objective function model with gate position as the independent variable, and used a multi-population genetic algorithm to solve the optimal position of the function model to get the best gate position. The mold temperature, melt temperature, cooling time, holding pressure, and holding time were selected as the influencing factors, and volume shrinkage and warpage deformation were selected as the evaluation indicators to design and complete the orthogonal test. The test data were simulated by Moldflow, and the optimal combination of process parameters was determined by range and variance analysis. The BP neural network model related to the molding process parameters, volume shrinkage, and warpage deformation was built, and the trained network model was optimized with the ant colony algorithm. The optimal parameter combination was: mold temperature 76 ° C , melt temperature 205 ° C , cooling time 23.8s, holding pressure 54.7Mpa, and holding time 22.1s. The simulation results showed that volume shrinkage was 13.32% and warpage deformation was 4.315 mm. The design of an injection mold for the car door inner panel was completed and its molding process parameters were optimized.

Optimal Mechanical Exclusion Devices for Management of Khapra Beetle in Stored Wheat

The effect of mechanical exclusion devices and its number in combination viz. probe trap (A; 1-3), two-in-one trap with 3 mm perforations (B; 1-5) and two-in-one trap with 2 mm perforations (C; 1-3) were investigated to test the efficiency of these traps for trapping larvae and adults of khapra beetle Trogoderma granarium Everts. Colour change and weight loss (%) happening in wheat stored in bins of 50 kg capacity for six months were used as the criteria. The experiments were planned using a three-factor-three-level Box and Behnken design of response surface methodology and were optimized for maximum trapping, with minimum change in colour and weight loss. Analysis of variance (ANOVA) revealed significantly (p<0.05) higher effects of quantity (number) of two in one trap with 3 mm perforations followed by two in one trap with 2 mm perforation on total trapping efficiency. Colour change and weight loss in stored wheat exhibited a negative relationship with trap capture. Among the process parameters studied, the probe trap and its number in experimental combination demonstrated a significantly lower effect on the response variables. The optimal combinations of mechanical exclusion process parameters obtained by numerical optimization technique for trapping larvae and adults were 3:4:3 and 2:5:3 (probe trap: two in one model trap 3 mm: two in one model trap 2 mm) with desirability of 0.61 and 0.63, respectively.

Forming Quality Research on the Variable-Diameter Section of the Hollow Axle in Three-Roll Skew Rolling.

The hollow axle is the key component of the high-speed train, and the realization of high-quality forming is the key to ensure the safety of train operation. In this paper, the specimen of the variable-diameter section of the hollow axle is taken as the research object, and the generation mechanism of surface spiral mark defects and the formula of spiral mark depth of variable-diameter section in the TRSR (three-roll skew rolling) process with variable roll spacing are explored. The external roundness error and the function C to measure the wall thickness uniformity of the cross-section were taken as the evaluation indexes and the single-factor simulation experiment was established and simulated in the software Simufact.forming16.0 to obtain the influence law of each process parameter on the external roundness error and wall thickness uniformity of the rolled piece. Orthogonal tests were designed and the order and optimal combination of process parameters on the forming quality were obtained by range analysis and ANOVA analysis. The research results provide theoretical guidance for improving the forming quality of the variable-diameter section of the hollow axle in three-roll skew rolling, and promote the transformation of the TRSR process to high performance and accurate forming.

Analysis of surface quality and optimization of process parameters in laser-assisted cutting of SiC ceramics

In order to improve the surface quality of laser-assisted turning silicon carbide (SiC) ceramics, the effects of laser power, rotational speed, cutting depth and feed speed on surface quality are investigated. Taking the surface roughness as the characterization value, the reasonable selection range of each factor is determined by the numerical simulation results of temperature field and the single factor experiment results; orthogonal experiments and range analysis are performed on laser power, rotational speed, cutting depth and feed speed, the primary and secondary order of influence on surface roughness and the optimal combination of process parameters are obtained. The surface topography of the cutting area was observed by 3D digital microscope and the surface roughness is calculated. Taking surface roughness as the evaluation index, the reasonable selection range of each factor is obtained through temperature field simulation and single factor experiment results analysis as follows: laser power 230 ∼ 245 W, rotational speed 1500 ∼ 1650 r min−1, cutting depth 0.1 ∼ 0.2 mm, and feed speed 2 ∼ 4 mm min−1. Through orthogonal experiment and range analysis, the order of the significant degree of the influence of various process parameters on surface roughness and the optimal combination of process parameters are obtained. The order of the significant degree of the influence of various process parameters on surface roughness Ra is as follows: laser power (P) > cutting depth (ap) > feed speed (f) > rotational speed (n); the optimal combination of process parameters is as follows: laser power 235 W, rotational speed 1620 r min−1, cutting depth 0.1 mm and feed speed 2 mm min−1. Compared with the surface quality of original workpiece, the surface of the SiC workpiece processed under the optimal combination of process parameters has no cracks and damage marks, and the surface roughness is reduced to 0.293 μm, which significantly improves the surface quality of the SiC workpiece.

Research on Lightweight Injection Molding (CAE) and Numerical Simulation Calculate of New Energy Vehicle

Abstract In this paper, the new energy automobile front-end frame of a factory is taken as the research object, the polypropylene plastic is used to replace the traditional sheet metal material, and the hyper mesh is used to load the front-end frame’s six loading loads, which are the locking limit tension and the cushion impact force, respectively, pedestrian impact strength, condenser support strength, radiator support strength, oil cooler support strength, ensure to achieve certain mechanical properties before production. The maximum warpage deformation is 1.023 mm by mold flow analysis with Moldflow. It does not meet the needs of manufacturers, so it is necessary to optimize the process parameters. We use the mold temperature, melt temperature, holding pressure, injection time, and holding time as test factors, and take the amount of warpage as the optimization goal. Then this paper uses the orthogonal test method to design the test plan and establishes 25 groups of 5-level 5-factor orthogonal tests. Moreover, the effect of the deformation on the warpage is analyzed. These are all to obtain the minimum warpage deformation and find the optimal combination of process parameters. Verification by numerical simulation shows that the warpage is reduced to 0.632 mm. By analyzing the range and variance, the combination of process parameters is optimized, and the warpage is effectively reduced. In the premise of satisfying mechanical properties, plastic instead of steel, to achieve a lightweight design. It provides correct theoretical guidance for setting the actual injection molding process parameters.

Preparation and mechanical optimization of a two‐layer silk/magnesium wires braided porous artificial nerve guidance conduit

Peripheral nerve injures have long been a tricky problem in surgery and a feasible treatment is the transplantation of nerve guidance conduits (NGCs). This study presents a two-layer composite NGC with fair mechanical properties and good biocompatibility. The inner layer was made of degummed silk yarns/magnesium wires using braiding technology, and the outer layer was made from mixed solution of silk fibroin/chitosan (SF/CS) using freeze-drying treatment. Orthogonal experimental design was applied to rationally design the braided structural layer and obtain the optimal combination of technical process parameters. Meanwhile, the SF/CS porous outer layer was optimized from three concentrations of SF/CS solution. In vitro and in vivo study suggested that the textile-forming scaffold exhibited good biocompatibility and no toxicity. During 4 weeks' degradation, the skeleton of conduits retained its shape, and magnesium ions released from degraded magnesium wires contributed to sustainable release and uniform dispersion, proliferation and adhesion of Schwann cells, indicating potential approach in the development of NGCs.