Combination Of Process Variables Research Articles

Evaluation and analysis of cutting speed, wire wear ratio, and dimensional deviation of wire electric discharge machining of super alloy Udimet-L605 using support vector machine and grey relational analysis

The current study investigates the behavior of wire electric discharge machining (WEDM) of the super alloy Udimet-L605 by employing sophisticated machine learning approaches. The experimental work was designed on the basis of the Taguchi orthogonal L27 array, considering six explanatory variables and evaluating their influences on the cutting speed, wire wear ratio (WWR), and dimensional deviation (DD). A support vector machine (SVM) algorithm using a normalized poly-kernel and a radial-basis flow kernel is recommended for modeling the wire electric discharge machining process. The grey relational analysis (GRA) approach was utilized to obtain the optimal combination of process variables simultaneously, providing the desirable outcome for the cutting speed, WWR, and DD. Scanning electron microscope and energy dispersive X-ray analyses of the samples were performed for the confirmation of the results. An SVM based on the radial-basis kernel model dominated the normalized poly-kernel model. The optimal combination of process variables for a mutually desirable outcome for the cutting speed, WWR, and DD was determined as T on1, T off2, I P1, WT3, SV1, and WF3. The pulse-on time is the significant variable influencing the cutting speed, WWR, and DD. The largest percentage of copper (8.66%) was observed at the highest cutting speed setting of the machine compared to 7.05% of copper at the low cutting speed setting of the machine.

Power–Velocity Process Design Charts for Powder Bed Additive Manufacturing

A current issue in metal-based additive manufacturing (AM) is achieving consistent, desired process outcomes in manufactured parts. When process outcomes such as strength, density, or precision need to meet certain specifications, changes in process variable selection can be made to meet these requirements. However, the changes required to achieve a better part performance may not be intuitive, particularly because process variable changes can simultaneously improve some outcomes while worsening others. There is great potential to design the additive manufacturing process, tailoring process variables based on user requirements for a given part. In this work, the tradeoffs between multiple process outcomes are formalized and the design problem is explored throughout the design space of process variables. Based on user input for each process outcome considered, P–V (power–velocity) process design charts are introduced, which map the process space and identify the best combination of process variables to achieve a user's desired outcome.

Process optimisation and microstructural evolution of friction stir spot-welded Al6061 joints

The joint strength and subsequent microstructural evolution of a friction stir spot-welded AA6061-T6 alloy was investigated according to the process parameters: tool rotation speed, dwell time and pin angle of the tool. A maximum tensile shear load of 2.78 kN was obtained from the joints generated under a combination of process variables like 1000 and 1500 rev min−1, 5 s, 5°. Under a fixed pin angle of 5° and a rotational speed of 1000 rev min−1, an increase in dwell time from 1 to 5 s resulted in a considerable increase in tensile shear load. An empirical process map under a fixed tool design is proposed to determine a feasible range of process conditions.

Optimal milling conditions of aeronautical composite material under temperature, forces and vibration parameters

Optimal conditions for milling carbon/epoxy composite material were established by response surface methodology. The combination of cutting parameters such as cutting speed (Vc) and chip thickness (h) was set at various design points of a central composite design. Significant regression models describing the changes of vibration level, delamination of composite, cutting force, workpiece temperature were developed with the coefficient of determination greater than 0.90. Results suggested that beyond a threshold of vibration, the occurrence of delamination is regular. Vibration criterion was defined from the observations of the workpiece according to the delamination defect. The optimum milling conditions were graphically represented using design contour plots. The best combination of process variables was found, according to the cutting conditions. This new technique should help the operator to select the optimum cutting conditions such as cutting speed and feed rate (chip thickness) in order to avoid damage to the carbon/epoxy composite material T800S/M21 and increase machining productivity.

Multi-Objective Optimization of Squeeze Casting Process using Genetic Algorithm and Particle Swarm Optimization

Abstract The near net shaped manufacturing ability of squeeze casting process requiresto set the process variable combinations at their optimal levels to obtain both aesthetic appearance and internal soundness of the cast parts. The aesthetic and internal soundness of cast parts deal with surface roughness and tensile strength those can readily put the part in service without the requirement of costly secondary manufacturing processes (like polishing, shot blasting, plating, hear treatment etc.). It is difficult to determine the levels of the process variable (that is, pressure duration, squeeze pressure, pouring temperature and die temperature) combinations for extreme values of the responses (that is, surface roughness, yield strength and ultimate tensile strength) due to conflicting requirements. In the present manuscript, three population based search and optimization methods, namely genetic algorithm (GA), particle swarm optimization (PSO) and multi-objective particle swarm optimization based on crowding distance (MOPSO-CD) methods have been used to optimize multiple outputs simultaneously. Further, validation test has been conducted for the optimal casting conditions suggested by GA, PSO and MOPSO-CD. The results showed that PSO outperformed GA with regard to computation time.

Process Optimization for Formulating Trigonella foenum-graecum and Gymnema sylvestre Added Vegetable Cereal Mix Using Response Surface Methodology

Trigonella foenum-graecum seeds and Gymnema sylvestre leaves are health promoting food ingredients which are particularly beneficial for individuals suffering from diabetes mellitus. In order to promote their incorporation in diet, vegetable cereal mix was formulated as a dried food product with extended shelf life, which could be easily reconstituted for consumption. Response surface methodology and central composite design were used in optimization of vegetable cereal mix. The objective was to devise the best combination of process variables and obtain optimal recipe having minimum fat, maximum fiber, in-range carbohydrate and maximum overall acceptability. Three quantitative controllable factors were selected for the experimental design: amount of Trigonella foenum-graecum, soaking time of Trigonella foenum-graecum and amount of Gymnema sylvestre as process parameters. Response variables were fat, fiber, carbohydrate and overall acceptability. Regression models and response surface plots were generated and adequacy was tested with regression coefficients and the lack of fit tests. Sensory, physico-chemical and microbiological analysis were undertaken for optimized recipe. The optimum recipe had 8.0 g Trigonella foenum-graecum with 8.0 hours soaking time and 5.0 g Gymnema sylvestre with 0.48 g fat, 3.01 g crude fiber, 29.84 g carbohydrate and 89.17 overall acceptability score. Results indicated that optimized product obtained good scores for all sensory attributes and had good shelf life. This vegetable cereal mix was nutritionally adequate and had desirable sensory quality.

Prediction of Bead Geometry for Flux Bounded TIG Welding of AA 2219-T87 Aluminum Alloy

To improve penetration capability of Tungsten Inert Gas (TIG) process, Flux Bounded TIG (FBTIG) is investigated in aluminum alloy AA 2219 T87. Characterization of this process is essential to predict and control the weld bead quality. For characterization, design of experiments based on central composite rotatable design is employed for the experiments. Response Surface Methodology (RSM) is used to develop a mathematical model to correlate the bead geometry with controllable process variables. The model developed is checked for its adequacy by regression by using the [Formula: see text]-test and Analysis of Variance (ANOVA). By backward elimination method, reduced model is obtained from the full model. The process parameters are optimized to get desired weld bead geometry. The model is validated by confirmatory tests. The main and interaction effects of the process variables on bead geometry are presented in graphical form. The results are useful to predict the weld bead geometry for different combinations of process variables.

Treatment of POME using Fenton oxidation process: removal efficiency, optimization, and acidity condition

In this study, the performance of a Fenton oxidation process was evaluated for the treatment of high-concentrated palm oil mill effluent (POME). Experiments were designed using the central composite design model of the response surface methodology (RSM). Four independent variables (reaction time, H2O2 concentration, Fe2+ ions concentration, and initial solution pH) and two dependent responses (COD and final solution pH) were investigated. The results show that at a low Fe2+ concentration and pH of about 3, an acceptable COD removal efficiency was achieved. At optimum conditions of pH 3.5 and 90 min reaction time, COD removal exceeded 85% and was successfully optimized by RSM. A significant model (p < 0.0001) was obtained for final pH condition and statistically reached a satisfactory level with a correlation coefficient (R2) of about 0.71. This study, therefore, demonstrates the capacity of Fenton process to successfully remove COD from high-concentrated POME under the right combination of process variables.

Electrospun silk-based nanofibrous scaffolds: fiber diameter and oxygen transfer.

In this study, silk fibroin was extracted from cocoons of silkworms and fabricated into nonwoven mats by electrospinning method. A new model based on the group method of data handling (GMDH) and artificial neural network (ANN) was developed for estimation of the average diameter of electrospun silk fibroin nanofibers. In this regard, concentration, flow rate, voltage, distance, and speed of collector were used as input parameters and average diameter of the fibers was considered as output parameter. Two models were capable to estimate average diameter of fibers with good accuracy. The average absolute relative deviation for GMDH and ANN models was equal to 3.56 and 2.28 %, respectively. Furthermore, due to importance of oxygen delivery to site of injury to promote wound healing, continuity equation for mass transport was employed for prediction of oxygen profile in the system containing wound dressing and skin. The result showed that our prepared wound dressing is capable to pass the oxygen completely to the skin layer and is not acting as a barrier for oxygen delivery to wound site. Since average nanofibers diameter can influence the mat physical, mechanical and biological properties then this model may serve as a useful guide to obtain tailor made and uniform silk nanofibers at various combinations of process variables.

Multi-Objective Optimization of Squeeze Casting Process using Evolutionary Algorithms

The present work focuses on determining optimum squeeze casting process parameters using evolutionary algorithms. Evolutionary algorithms, such as genetic algorithm, particle swarm optimization, and multi objective particle swarm optimization based on crowing distance mechanism, have been used to determine the process variable combinations for the multiple objective functions. In multi-objective optimization, there are no single optimal process variable combination due to conflicting nature of objective functions. Four cases have been considered after assigning different combination of weights to the individual objective function based on the user importance. Confirmation tests have been conducted for the recommended process variable combinations obtained by genetic algorithm (GA), particle swarm optimization (PSO), and multiple objective particle swarm optimization based on crowing distance (MOPSO-CD). The performance of PSO is found to be comparable with that of GA for identifying optimal process variable combinations. However, PSO outperformed GA with regard to computation time.

Modeling and Optimization of Hard Turning Operation on 41Cr4 Alloy Steel Using Response Surface Methodology

Product quality, productivity and organizational goodwill are often the major concern of every production or manufacturing unit. These criteria, more especially product quality, cannot readily and effectively be met through dependence on the skills of an operator. Hence, the need for optimization in order to identify the best process condition, derived from parametric combinations of process variables, for the manufacturing process. The work presented concerns an aspect of a series of hard turning experiments on 41Cr4 alloy structural steel conducted to model, predict and optimize the machining induced vibration, and the surface roughness as functions of the cutting speed, feed rate, and the tool nose radius. The response surface methodology, based on the central composite design of experiment is employed in the study, and analysis of the generated data performed with the aid of Design expert 9 software. A quadratic regression model was suggested as best fits for both the machining induced vibration and surface roughness data. These were confirmed by analyses of variance, which also revealed the tool nose radius and cutting speed, as well as the feed rate and cutting speed to be important factors that determine changes in the machining induced vibration and surface roughness, respectively. The optimum setting of the tool nose radius at 1.72301 mm, feed rate at 0.15 mm/rev, and the cutting speed at 311.075 rev/min minimized the machining induced vibration to a value of 0.08 mm/min² and the surface roughness to a value of 4.74 µmm.

Soft Sensor Design for Estimation of SOFC Stack Temperatures and Oxygen-to-Carbon Ratio

The lifespan of a solid oxide fuel cell (SOFC) stack depends on several factors, such as the internal stack temperature and temperature gradients as well as the fuel gas oxygen-to-carbon (O/C) ratio within the stack. An excessively high stack operating temperature generally accelerates degradation processes while large temperature gradients over the stack increase the thermal stress within the stack which may lead to the delamination of repeating unit components. A too low O/C ratio inflicts carbon deposition which quickly leads to stack breakage. Therefore, monitoring these variables is of vital importance. Although measuring temperatures within the stack and measuring the fuel gas composition at the stack inlet is feasible, this raises the overall equipment cost. In this paper a data-driven design of soft sensors for stack minimum and maximum temperatures as well as the O/C ratio at the anode and pre-reformer inlet is presented. For temperature estimation both dynamic and static models are derived and their performance is compared. The dynamic model is identified using subspace identification, which results in a causal state-space model. The non-causal static model assumes that a combination of process variables at the stack inlet and outlet describe its internal condition. The estimation of gas composition at the inlets, which is required for O/C ratio estimation, is based solely on static relationships. The soft sensors are designed in such a way that adding extra inputs yields no further increase in estimate accuracy. The empirical data required for modelling were obtained from a complete SOFC power generation unit. The results show that estimates of all the relevant variables can be accomplished by simple linear regression models. Keywords: SOFC, soft sensor, O/C estimation, temperature estimation Figure 1

Soft Sensor Design for Estimation of SOFC Stack Temperatures and Oxygen-to-Carbon Ratio

The life span of a solid oxide fuel cell (SOFC) stack depends on several factors, such as internal stack temperature and temperature gradients as well as the fuel gas oxygen-to-carbon (O/C) ratio. An excessive stack temperature generally accelerates the degradation, while large temperature gradients across the stack cause thermal stress, which leads to delamination. Too low O/C ratio inflicts carbon deposition, which quickly leads to stack breakage. Therefore, monitoring of these variables is of vital importance. Although direct sensing of temperatures within the stack as well as fuel gas composition in fuel stream is feasible, it is not desirable due to increased equipment cost. In this paper a data-driven design of soft sensors for minimal and maximal stack temperatures as well as the O/C ratio is presented. Dynamic and static models for stack temperature are identified from data and their performance is compared. The dynamic model is derived by means of the subspace identification, which results in a causal state-space model. The non-causal static model assumes that a combination of process variables at the stack inlet and outlet describe its internal condition. The estimator of O/C ratio is based on static relationships. The soft sensors are designed in such a way that adding extra inputs to the model yields no further increase in accuracy of the estimates. The empirical data required for modelling were obtained from a SOFC power generating unit. The results show that the reconstruction of all the relevant variables can be accomplished by simple linear regression models.

Modelling of squeeze casting process using design of experiments and response surface methodology

The present work makes an attempt to model and analyse squeeze casting process by utilising design of experiments and response surface methodology. The input–output data for developing regression models and test cases is obtained by conducting the experiments. Surface roughness, ultimate tensile strength and yield strength have been measured for different combinations of process variables, namely, squeeze pressure, pressure duration, pouring temperature and die temperature. Two non-linear regression models based on central composite design (CCD) and Box-Behnken design (BBD) of experiments have been developed to establish the input–output relationships. The effects of process variables on the measured responses have been studied using surface plots. The performances of the two non-linear models have been tested for their prediction accuracy with the help of 15 test cases. It is observed that, both CCD and BBD, the non-linear regression models are statistically adequate and capable of making accurate predictions.

Modelling in Squeeze Casting Process-Present State and Future Perspectives

The growing demand in today’s competitive manufacturing environment has encouraged the researchers to develop and apply modelling tools. The development and application of modelling tools help the casting industries to considerably increase productivity and casting quality. Till date there is no universal standard available to model and optimize any of the manufacturing processes. However the present work discusses the advantages and limitations of some conventional and non-conventional modelling tools applied for various casting processes. In addition the research effort made by various authors till date in modelling and optimization of the squeeze casting process has been reported. Furthermore the necessary steps for prediction and optimization are high lightened by identifying the trends in the literature. Ultimately this research paper explores the scope for future research in online control of the process by automatically adjusting the squeeze cast process parameters through reverse prediction by utilizing the soft computing tools namely, Neural Network, Genetic Algorithms, Fuzzy-logic Controllers and their different combinations. The present work also proposed a detailed methodology, starting from the selection of process variables till the best process variable combinations for extreme values of the outputs responsible for better product quality using experimental, prediction and optimization methodology.

Process Optimization for Producing Cowpea Added Instant Kheer Mix Using Response Surface Methodology

Kheer is a cereal based dessert commonly prepared using rice or any other staple cereal, milk and sugar, popular in India and South East Asian countries. In the present endeavour, a modified instant kheer mix was devised wherein cowpea and malted wheat flour were added along with rice, skim milk powder and sugar. These additions were done to enhance the nutritional properties of kheer. Response surface methodology (RSM) and central composite design (CCD) were used in optimization of instant kheer mix. The objective was to devise the best combination of process variables, so as to obtain optimal recipe having high protein as well as overall acceptability. Variants of instant kheer mixes were tried and tested for acceptability using varying amounts of different ingredients. Best variant was selected by a semi trained panel using 9 point hedonic test and taken up for optimization. Process variables were amounts of cowpea, cowpea soaking time and amount of malted wheat flour and responses were protein, crude fiber and overall acceptability. Regression models and response surface plots were generated and adequacy was tested with regression coefficients (R2) and the lack of fit tests. The best recipe of highest desirability was chosen and its organoleptic attributes were evaluated using 5 point composite rating scale. Nutritional analysis of the optimum recipe was done. Results of ANOVA indicated that process variables had a significant effect on the response; protein (p<0.05). Results for regression coefficients suggested a fair fit of the model. The optimum instant kheer mix had 12 g cowpea, with 4 hours soaking of time and 5.01 g malted wheat flour. Its responses were 10.273 g protein, 0.076 g crude fiber and 8.052 overall acceptability liked highly). The optimized recipe was high in protein and had good sensory characteristics.

Optimization of thermophysical properties of Pacific white shrimp (Litopenaeus vannamei) previously treated with freezing-point regulators using response surface methodology.

Three freezing-point regulators (glycine, sodium chloride and D-sorbitol) were employed to optimize thermophysical properties of Pacific white shrimp (Litopenaeus vannamei) using response surface methodology (RSM). The independent variables were glycine content (0.250-1.250%), sodium chloride content (0.500-2.500%) and D-sorbitol content (0.125-0.625%) and analysis of variance showed that the effects of glycine, sodium chloride and D-sorbitol on the thermophysical properties were statistically significant (P < 0.05). The coefficient of determination, R (2) values for initial freezing point (T i ), unfreezable water mass fraction (W u ), apparent specific heat (C app ) and Enthalpy (H) were 0.896 ~ 0.999. The combined effects of these independent variables on T i , W u , C app and H were investigated. The results indicated that T i , C app and H varied curvilinearly with increasing of glycine, sodium chloride and D-sorbitol content whereas W u increased nearly linearly. Based on response plots and desirability functions, the optimum combination of process variables for Pacific white shrimp previously treated with freezing-point regulators were 0.876% for glycine content, 2.298% for sodium chloride content and 0.589% for D-sorbitol content, correspondently the optimized thermophysical properties were T i , - 5.086°C; W u , 17.222%; C app , 41.038 J/g °C and H, 155.942J/g, respectively. Briefly, the application of freezing-point regulators depressed T i and obtained the optimum W u , C app and H, which would be obviously beneficial for the exploitation of various thermal processing and food storage.

Integrated control of solidification microstructure and melt pool dimensions in electron beam wire feed additive manufacturing of Ti-6Al-4V

The ability to deposit a consistent and predictable solidification microstructure can greatly accelerate additive manufacturing (AM) process qualification. Process mapping is an approach that represents process outcomes in terms of process variables. In this work, a solidification microstructure process map was developed using finite element analysis for deposition of single beads of Ti-6Al-4V via electron beam wire feed AM processes. Process variable combinations yielding constant beta grain size and morphology were identified. Comparison with a previously developed process map for melt pool geometry shows that maintaining a constant melt pool cross sectional area will also yield a constant grain size. Additionally, the grain morphology boundaries are similar to curves of constant melt pool aspect ratio. Experimental results support the numerical predictions and identify a proportional size scaling between beta grain widths and melt pool widths. Results further demonstrate that in situ indirect control of solidification microstructure is possible through direct melt pool dimension control.

EFFECT OF PRETREATMENT CONDITIONS ON STRUCTURAL CHARACTERISTICS OF WHEAT STRAW

Structural characteristics of lignocellulosic biomass such as surface area, pore volume, crystallinity, hemicellulose, and lignin content significantly affect the yield of fermentable sugars for bioethanol production. In the present work, the effect of dilute acid pretreatment was studied on structural characteristics of wheat straw, using different combinations of process variables (temperature, time, and acid concentration). Pretreated wheat straw (PWS) exhibited higher available surface area and pore volume along with low hemicellulose and lignin content. Crystallinity index (CrI) of biomass at different pretreatment conditions showed an increased trend followed by sharp decrease at high temperature (190°C) conditions. Maximum increase in surface area (7.1 m2/g compared to 4.0 m2/g for untreated wheat straw) was obtained at pretreatment conditions of 180°C temperature, 0.5% (v/v) acid, and 7 min time. SEM imaging of biomass revealed that pore breaking, compression of pores, and partial pore blocking in the case of high temperature (190°C) pretreatment conditions may be the reason behind decreased surface area of biomass. FT-IR analysis showed almost complete hemicellulose removal and acid-soluble lignin removal after dilute acid pretreatment but insufficient removal of acid insoluble lignin. [Supplementary material is available for this article. Go to the publisher's online edition of Chemical Engineering Communications for the following free supplemental resource: figure showing XRD pattern of biomass with respect to different pretreatment conditions.]

Investigation and analysis on pulsed Nd:YAG laser micro-turning process of aluminium oxide (Al2O3) ceramic at various laser defocusing conditions

Laser micro-turning process is one of the latest promising laser material processing techniques which can be employed for generation of micro-turning surface of particular surface profile and dimensional accuracy on cylindrical workpiece. The present paper addresses the laser micro-turning process of cylindrical shaped 99 % pure aluminium oxide (Al2O3) ceramics of size 10 mm in diameter and 40 mm in length. The experiments have been conducted using response surface methodology design of experiments. The targeted depth was set at 100 μm. Laser average power, pulse frequency, workpiece rotating speed, air pressure and Y feed rate were considered as process variables. After each experiment, surface roughness (Ra) and micro-turning depth deviation have been measured. Empirical models of the responses have been developed based on the test results achieved during measurements of surface roughness and machined depth deviation. Analysis of variance was conducted. Multi-objective optimal parametric combination of process variables has been obtained for achieving least values of responses, i.e. surface roughness and depth deviation. To achieve further high-quality machined surface, machining was done at various defocusing conditions of laser beam and also by varying the number of laser scan passes. Other process parameters were kept constant at optimal parametric combination obtained in multi-objective optimization. From the experimental results, it was revealed that surface roughness decreases with increase in defocusing positions. With increase in number of passes up to a certain limit, surface roughness decreases. However, depth deviation increases with number of laser scan passes at all levels of defocus planes.