Optimal Combination Of Process Parameters Research Articles

Effects of process parameters on additive manufacturing of aluminum porous materials and their optimization using response surface method

This paper deals with the fabrication of porous materials using direct energy deposition, which is a versatile 3D printing technique for metals. To manufacture a porous material, a mixture consisting of AlSi12 powder and a foaming agent (ZrH 2 ) is sprayed onto an aluminum substrate and subsequently fused with the substrate using a high-power laser beam. We examined the effects of the main process parameters, namely the laser power, scanning speed and foaming agent ratio, on the total porosity, average pore size and number of pores of the final product. The results showed that the use of an excessive amount of foaming agent or energy decreases the porosity. In addition, a statistical analysis as well as the optimization of the main process parameters to ensure high porosity was conducted using the response surface method. Based on the central composite design method, the foaming agent ratio was found to have the greatest effect on the porosity, and a porous material with a maximum porosity of 24.83% could be fabricated using the optimal combination of the main process parameters. Finally, a mathematical model was derived to predict the relationship between these parameters and the porosity.

Multitarget optimization method for full-aperture lapping process using temperature-controlled lapping disc

Process parameter optimization is important for improving the lapping effect of optical elements. Based on a temperature-controlled lapping disc, we present a method using fuzzy theory and orthogonal experiment to optimize lapping process parameters. Unlike the theory of optimization target set with a single evaluation criterion, a comprehensive optimization target with multiple evaluation criteria based on a fuzzy algorithm is proposed in this method. The lapping disc temperature, lapping disc rotation speed, and lapping pressure were selected as the optimization process parameters, and the material removal rate (MRR), surface change uniformity (SCU), and fuzzy synthetic index of lapping (FSILAP) were used as the optimization targets to conduct comparative experiments and analysis. The experimental results showed that when using the optimal process parameter combination obtained by the comprehensive index FSILAP as the optimization target, balanced results for MRR and SCU could be obtained. Notably, the result obtained when using a single evaluation criterion as the optimization target was suboptimal. The proposed method is effective and practical for the optimization of lapping process parameters.

Multi-objective optimization of process parameters in TIG-MIG welded AISI 1008 steel for improved structural integrity

This study investigates a parametric multi-objective optimization of the Tungsten Inert Gas-Metal Inert Gas (TIG-MIG) hybrid welding of AISI 1008 mild steel joints. A combined grey relational system theory and the Taguchi method was used for process optimization towards achieving a set of process parameter that maximizes both ultimate tensile strength and 0.2% yield strength for structural applications. An L-9 orthogonal array based on the Taguchi method was adopted for the experimental design matrix. Grey relational grading system was used to establish a single grade for the responses. Mathematical models for first- and second-order regressions were developed and optimum process parameter combination that optimizes the response was obtained. From the results, the gas flow rate had the most significant influence on the responses with a percentage contribution of 39.77%. Also, the second-order regression models had a higher coefficient of determination (R2) compared to the first-order regression for the two responses and, thus, represents the best fit for the process. The grey relational grade was improved by 0.0489 through process optimization. The interactive effects of process parameters and their effects on the responses are also illustrated by response surface plots. This study shows the effectiveness of the grey relational grading system in achieving a multi-objective optimization for the TIG-MIG welding process.

Parameter optimization during single roller burnishing of AA6061-T6 alloy by design of experiments

Roller burnishing is a surface finishing process that involves plastic deformation of the surface without any removal of the material. The level of surface finish that can be attained in roller burnishing operation depends on several process parameters. Processing at the optimum combination of process parameters is critical for achieving a higher yield during manufacturing. Hence, the present study is carried out to obtain the optimized process parameters during roller burnishing of aluminium alloy AA6061-T6, with an objective to produce a high level of surface finish. The Taguchi method has been used to assess the role of burnishing speed, feed rate, and load on the surface finishing of the alloy. Analysis of variance (ANOVA) and signal-to-noise ratio (S/N) have been employed to analyze, obtain significant parameters, and find out optimum combination levels of the process parameters. The results show that the surface quality of the specimens is significantly improved with burnishing parameters. The analysis of the results shows that the burnishing feed rate has the highest impact on the surface roughness with a contribution of 54.51%, followed by load and burnishing speed.

Process parameter study for enhancement of directed energy deposition powder efficiency based on single-track geometry evaluation

Directed energy deposition (DED) is a widespread laser additive manufacturing process characterized by the simultaneous laser and powder delivery. During the layerwise bounding of the material, many complex interconnected physical phenomena take place in a very short time. Presently, one of the main challenges faced by DED is to enhance the powder deposition efficiency. It is defined as the ratio of powder that has been effectively solidified in the part over the total amount of powder that flowed through the nozzle while the laser was on during the deposition process. Increasing the powder efficiency would allow us to minimize the powder waste, minimize the overall costs of the DED process, and therefore reduce the printed part cost. The present work, therefore, proposes to study the influence of laser beam diameter, stand-off distance, and gas/powder settings on the DED powder efficiency. The considered gas/powder settings are carrier and shielding gas volumetric flow rate, powder mass flow rate, and particle diameter. The efficiency is computed by means of the geometry of 316L stainless steel single-track deposits. The track geometries are evaluated based on the deposition width, deposition height, and area of deposition, all extracted from the deposition profiles measured by means of laser triangulation. Optical micrographs of the single-track transversal cross sections are linked to the obtained powder efficiencies. The final aim is to get insight into the effect of process parameters on the powder efficiency and print quality and to identify the optimal process parameter combination in order to maximize the powder efficiency.

Knowledge-driven material design platform based on the whole-process simulation and modeling

In order to realize the agility, collaboration and visualization of alloy material development process, a product development platform based on simulation and modeling technologies is established in this study. In this platform, the whole-process simulation module builds multi-level simulation models based on metallurgical mechanisms from the production line level, the thermo-mechanical coupling field level and the microstructure evolution level. The design knowledge management module represents the multi-source heterogeneous material design knowledge through ontology model, including customers’ requirement knowledge, material component knowledge, process design knowledge and quality inspection knowledge, and utilizes the case-based reasoning approach to reuse the knowledge. The data-driven modeling module applies machine learning algorithms to mine the relationships between product mechanical properties, material components, and process parameters from historical samples, and utilizes multi-objective optimization algorithms to find the optimal combination of process parameters. Application of the developed platform in actual steel mills shows that the proposed method helps to improve the efficiency of product design process.

Effect of Forming Parameters on Profile Thinning of Flexible 3D Multi-Point Stretch Bending

The ABAQUS finite element simulation software is used to simulate the flexible multi-point three-dimensional stretch bending process of aluminum profiles. The effect of process parameters on the web thickness of rectangular profile in flexible multi-point three-dimensional stretch bending is studied by orthogonal experiment and range analysis. The process parameters used in the experiments include pre-stretching value, post-stretching value, the number of multi-point dies and friction coefficient. The optimal combination of process parameters is obtained by numerical simulation and experimental verification. When the aluminum profile is completed flexible multi-point stretch bending according to the best parameters, the thickness thinning of outer web and inner web is the smallest. The experimental result is closed to the numerical simulated results. The effectiveness of the numerical simulation is verified by the corresponding experimental methods.

Comprehensive evaluation of surface integrity and parameter optimization of 45 steel subjected to ultrasonic surface rolling process

In order to explore the influence of ultrasonic surface rolling process (USRP) parameters on 45 steel surface integrity, the significance of static pressure, amplitude, feed rate and rolling times on rolling effect was analyzed. The grey relational degree of the comprehensive index of surface integrity after USRP was obtained by orthogonal test and grey relational analysis, and the optimal combination of process parameters was obtained. The feasibility of process parameter optimization was verified by experiments. The results show that the significant influence of USRP parameters on 45 steel workpiece surface integrity was as follows: rolling times> feed speed> static pressure> amplitude. The optimal process parameters are rotational rolling times 9 times, feed speed 3200 mm⋅min-1, static pressure 300 N and amplitude 6 μm. Under the optimum combination of process parameters, the surface roughness decreases by 70.8 %. The surface hardness and the surface residual compressive stress increase by 16.5 % and 134 % respectively compared with the surface performance parameters of the material before rolling.

Optimization of Cutting Parameters for Milling Process of (4032) Al-Alloy using Taguchi-Based Grey Relational Analysis

The objective of this work is to study the influence of end milling cutting process parameters, tool material and geometry on multi-response outputs for 4032 Al-alloy. This can be done by proposing an approach that combines Taguchi method with grey relational analysis. Three cutting parameters have been selected (spindle speed, feed rate and cut depth) with three levels for each parameter. Three tools with different materials and geometry have been also used to design the experimental tests and runs based on matrix L9. The end milling process with several output characteristics is solved using a grey relational analysis. The results of analysis of variance (ANOVA) showed that the major influencing parameters on multi-objective response were spindle speed and cutting tool with contribution percentage (52.75%, 24%), respectively. In addition, the optimum combination of end milling process parameters was then validated by performing confirmation tests to determine the improvement in multi-response outputs. The confirmation tests obtained a minimum (surface roughness and micro-hardness) and maximum metal removal rate with grey relational grade of 0.784 and improvement percentage of 2.3%.

Microstructural and mechanical studies of feedstock material in continuous extrusion process

The challenge encountered in continuous forming process is the variation in mechanical strength of product formed with respect to process variables like extrusion wheel speed and diameter of product. In this research article, the micro-structural investigation of the aluminum (AA1100) feedstock material of 9.5-mm diameter has been carried out at various extrusion wheel speeds and diameter of product before and after deformation on commercial continuous extrusion setup TBJ350. The mechanical properties like yield strength as well as percentage elongation have been estimated and optimized using two variables with 3 levels through central composite rotatable design (CCRD) method. The mathematical modeling has been carried out to predict the optimum combination of process parameters for obtaining maximum value of yield strength and percentage elongation. The statistical significance of mathematical model is verified through analysis of variance (ANOVA). The optimum value of yield strength is found to be 70.939 MPa at wheel velocity of 8.63 rpm and product diameter of 9 mm respectively, whereas the maximum percentage elongation recorded is 46.457 at wheel velocity of 7.06 rpm and product diameter of 7.18 mm. The outcome may be useful in obtaining the best parametric combination of wheel speed and extrusion ratio for best strength of the product.

Effects of Process Parameters on Surface Straightness of Variable-Section Conical Parts during Hot Power Spinning

How to form high-quality variable-section thin-walled conical parts through power spinning is a key issue for superalloy spinning manufacturing. A study into the hot power spinning deformation law of variable-section thin-walled conical parts and the effects of process parameters on surface straightness of forming quality are delineated in this paper. Through the establishment of finite element (FE) models using the single-factor and orthogonal design of experiments, the effects of four key process parameters on the surface straightness have been investigated and the optimal combination of process parameters have been yielded. These key factors include spinning temperature, roller nose radius, mandrel rotation rate and roller feed ratio. The results of FE simulation have been validated through the comparison of the surface straightness of modeled parts with those measured during a spinning experiment. The results reveal that, among the studied process parameters, the spinning temperature has the greatest influence on the surface straightness, followed by the roller nose radius and mandrel rotation rate, and the roller feed ratio has the least influence on the straightness. Larger mandrel rotation rate, smaller feed ratio and suitable spinning temperature can enhance the surface straightness.

Optimization of Process Parameters for Friction Materials Using Multi-Criteria Decision Making: A Comparative Analysis

Friction materials, mainly made of reinforced composites, should possess excellent physical and tribological properties, such as a higher coefficient of friction, minimum wear, adequate surface hardness, and higher porosity for various automotive brake applications. Attainment of those properties greatly depends on the settings of different input parameters, such as molding pressure, temperature and time, temperature and time of heat treatment/sintering, etc., during the processing of friction materials. In this article, four multi-criteria decision-making (MCDM) tools, i.e., technique for order of preference by similarity to ideal solution (TOPSIS), evaluation based on distance from average solution (EDAS), VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), and multi-objective optimization on the basis of ratio analysis (MOORA) are applied to identify the optimal parametric combinations for two different friction materials. A comparative analysis of the derived results suggests the same optimal parametric mixes for all the MCDM techniques for both case studies. Thus, for the considered case studies, the optimal parametric combinations as molding time = 8 min, molding temperature = 175 °C, molding pressure = 27 MPa, sintering time = 10 h and sintering temperature = 225 °C, and molding pressure = 27.90 MPa, molding temperature = 170 °C, curing time = 8 min, and heat treatment time = 1 h, respectively, would help in attaining the most desired properties of friction materials. Improvements in the predicted response values prove the efficacy of the adopted MCDM techniques in determining the optimal combinations of various process parameters for friction materials.

Multi-Objective Optimization of MIG Welding and Preheat Parameters for 6061-T6 Al Alloy T-Joints Using Artificial Neural Networks Based on FEM

To control the welding residual stress and deformation of metal inert gas (MIG) welding, the influence of welding process parameters and preheat parameters (welding speed, heat input, preheat temperature, and preheat area) is discussed, and a prediction model is established to select the optimal combination of process parameters. Thermomechanical numerical analysis was performed to obtain the residual welding deformation and stress according to a 100 × 150 × 50 × 4 mm aluminum alloy 6061-T6 T-joint. Owing to the complexity of the welding process, an optimal Latin hypercube sampling (OLHS) method was adopted for sampling with uniformity and stratification. Analysis of variance (ANOVA) was used to find the influence degree of welding speed (7.5–9 mm/s), heat input (1500–1700 W), preheat temperature (80–125 °C), and preheat area (12–36 mm). The range of research parameters are according to the material, welding method, thickness of the welding plate, and welding procedure specification. Artificial neural network (ANN) and multi-objective particle swarm optimization (MOPSO) was combined to find the effective parameters to minimize welding deformation and stress. The results showed that preheat temperature and welding speed had the greatest effect on the minimization of welding residual deformation and stress, followed by the preheat area, respectively. The Pareto front was obtained by using the MOPSO algorithm with ε-dominance. The welding residual deformation and stress are the minimum at the same time, when the welding parameters are selected as preheating temperature 85 °C and preheating area 12 mm, welding speed is 8.8 mm/s and heat input is 1535 W, respectively. The optimization results were validated by the finite element (FE) method. The error between the FE results and the Pareto optimal compromise solutions is less than 12.5%. The optimum solutions in the Pareto front can be chosen by designers according to actual demand.

A Cooks Distance Approach to Control the Haz and Arc Length of Tig Mild Steel Weld

The heat affected zone and arc length parameters have a vital role to play in determining the integrity of a weld structure. The cooks distance is a statistical diagnostic employed in this study to select the best optimum combination of welding process parameters. Mild steel plate was the choice material used to produce the weld specimen, which was welded with the Tungsten inert gas method. The RSM model was used to develop an optimal solution that can explain the behavior of the welded joint with respect to the heat affected zone and arc length, different diagnostic techniques were employed which includes the normal probability plot and cooks distance plot. The model developed has sufficient merit as the results obtained shows that the cooks distance values is within the range of 0 and 1 indicating the absence of outlier in the data making the optimal solution highly acceptable.

A Cooks Distance Approach to Control the Haz and Arc Length of Tig Mild Steel Weld

The heat affected zone and arc length parameters have a vital role to play in determining the integrity of a weld structure. The cooks distance is a statistical diagnostic employed in this study to select the best optimum combination of welding process parameters. Mild steel plate was the choice material used to produce the weld specimen, which was welded with the Tungsten inert gas method. The RSM model was used to develop an optimal solution that can explain the behavior of the welded joint with respect to the heat affected zone and arc length, different diagnostic techniques were employed which includes the normal probability plot and cooks distance plot. The model developed has sufficient merit as the results obtained shows that the cooks distance values is within the range of 0 and 1 indicating the absence of outlier in the data making the optimal solution highly acceptable.

Optimization of Technological Parameters of Straw Fiber-Based Plant Fiber Seedling Pot Raw Materials

Straw fiber seedling pots are a promising substitute for plastic seedling pots. The mixing mode of straw fiber affects the mechanical properties of the raw material membrane of the seedling pot. To explore the processing technology of making the raw material membrane of the seedling pot with two kinds of plant fibers in a layered manner, the optimal combination of the process parameters of the raw material membrane of the seedling pot without additives in the production process was studied experimentally. Response surface methodology (RSM) was used to analyze the parameters (beating degree of unbleached softwood kraft pulp fiber, beating degree of wheat straw fiber, wheat straw fiber quality percentage content, and film grammage) with regard to the dry tensile index and Z-direction tensile index of the seedling pot body. The optimal process parameter combination with a certain dry tensile index and Z-direction tensile index of seedling pot raw material was obtained by using four factors and five levels of a quadratic regression orthogonal rotation center combination design parameter optimization experiment. The optimal technical parameters were optimized as wheat straw fiber quality percentage content 70%, film grammage 70 g/m2, unbleached softwood kraft pulp fiber beating degree 47–48 °SR, and wheat straw fiber beating degree 65–75 °SR. With the optimal conditions, the dry tensile index of the seedling bowl raw material film was between 21 and 22 N·(m·g−1), and the Z-direction tensile index was greater than 2.1 N·(m·g−1). Using wheat straw fibers and unbleached sulfite wood pulp fibers as raw materials for seedling pots, the raw material membrane of the seedling pots was made in a layered manner. The experimental study proved this feasibility. With this mixing process of raw materials, the straw fiber-based plant fiber seedling pot would meet the demands of a crop nursery after adding chemical additives. The research results provide a theoretical basis and technical support for the manufacture of the raw material membrane of the seedling pot body.

EXPERIMENTAL INVESTIGATION AND PARAMETRIC OPTIMIZATION OF NEUROSURGICAL BONE GRINDING UNDER BIO-MIMIC ENVIRONMENT

Bone grinding is a craniotomy procedure which is used to remove a bone flap from the skull to expose and create an access for the dissection of tumors. In this study, a computer-controlled neurosurgical bone grinding has been used to explore the effect of various neurosurgical bone grinding parameters, such as cutting forces, torque, grinding force ratio, and temperature generated during bone grinding have been investigated. Bone samples after grinding have been assessed for morphological analysis. Based on the outcomes, a multi-attribute decision-making methodology based on grey relational analysis has been adopted. Regression models have been developed and then validated to ensure the adequacy of the developed models. Subsequently, a comparative analysis of experimental and predicted results have been presented. It is revealed that grinding forces and torque decreased with the escalation of rotational speed from 35,000 revolutions per minute (rpm) to 55,000[Formula: see text]rpm. The optimum combination of process parameters found as rotational speed of 55,000[Formula: see text]rpm, feed rate of 20[Formula: see text]mm/min, and depth of cut of 0.50[Formula: see text]mm.

Experimental study on mechanical and tribological behaviour of Ti-3Al-2.5V alloy at high load condition using response surface methodology

ABSTRACT The present work investigates the effect of process parameters (normal load, sliding velocity, and sliding distance) on wear behaviour of Ti-3Al-2.5 V alloy under dry sliding condition. The wear tests are designed using central composite design (CCD) and are carried out using pin on disc tribometer. Scanning electron microscope (SEM) is used to analyse the worn out surfaces of the Ti-3Al-2.5 V alloy specimens. SEM micrograph of worn samples indicates that at low normal load condition, cracks and smooth wear occur during sliding, whereas some deep grooves and delamination occurred at high normal load condition. The main mechanism responsible for wear of titanium alloy is ploughing, oxidative wear and metallic delamination. The effect of process parameters on wear loss was studied using response surface methodology (RSM) – based mathematical models. Using analysis of variance (ANOVA), an optimal combination of process parameters that minimise the wear loss is determined. The results indicated that among all three factors, normal load is the most significant factor that influences the dry sliding wear behaviour of the alloy. The wear rate of the alloy increases with increasing the normal load and sliding velocity.

Friction stir welding parameter optimization using novel multi objective dragonfly algorithm

Friction stir welding (FSW) is a type of welding in which the joint is made in the solid phase using the heat generated through friction. FSW is used to produce welds with superior strength with low distortion. At present, the FSW technique is enormously applied in aerospace-related applications. In the present study, a multi objective optimization technique based on Dragonfly Algorithm (DFA) is proposed for optimizing the weld strength of FSW of aluminum alloy 6082-T6. The prime purpose of this work is to determine the optimal parameter combination of the input process parameters namely the tool rotational speed, the welding speed, and the tool tilt angle to achieve the optimal tensile strength and the impact strength of the weld. The optimal process parameters are validated and simulated using MATLAB software.

Application of modified PROMETHEE method for coating quality enhancement in thin film deposition processes

ABSTRACT Thin film coatings are extensively used to enhance the physical and chemical properties of various materials by depositing a thin layer of coatings of different materials onto various substrates. Several deposition processes are available which are widely adopted by many manufacturing industries to meet such demands. To exploit the fullest potential of those deposition processes, it is often advised to operate them with the optimal combination of process parameters. In this research, a modified PROMETHEE (preference ranking organisation method for enrichment evaluation) method as an effective multi-objective optimisation tool is applied for identifying the optimal parametric combinations of two thin film deposition processes: silicon carbonitride thin film coating using a thermal chemical vapour deposition process and zirconium-containing diamond-like-carbon thin film coating using an unbalanced magnetron sputtering process. The derived optimal parametric combinations are validated based on developed regression equations, which exhibit that the proposed approach can attain better response values as compared to other popular optimisation techniques. The analysis of variance results and the developed surface plots further illustrate the influence of various deposition parameters on coating quality.