Influence Of Control Factors Research Articles

Investigating the impact of TiO2 filler on abrasive wear characteristics of bamboo fiber-reinforced epoxy composites using the Taguchi method

Purpose The purpose of this study is to investigate the impact of titanium oxide (TiO2) filler on the abrasive wear properties of bamboo fiber reinforced epoxy composites (BFRCs) using a Taguchi approach. The study aims to enhance the abrasive wear resistance of these composites by introducing TiO2 filler as a potential reinforcement, thus contributing to the development of sustainable and environmentally friendly materials. Design/methodology/approach This study focuses on the fabrication of epoxy/bamboo composites infused with TiO2 particles within the Wt.% range of 0–8 Wt.% using hand layup techniques. The resulting composites were subjected to wear testing according to ASTM G99-05 standards. Statistical analysis of the wear results was carried out using the Taguchi design of experiments (DOE). Additionally, an analysis of variance (ANOVA) was used to determine the influential control factors impacting the specific wear rate (SWR) and coefficient of friction (COF). Findings The study illuminates how integrating TiO2 filler enhances abrasive wear in epoxy/bamboo composites. Statistical analysis of SWR highlights abrasive grit size (grit) as the most influential factor, followed by normal load, Wt.% of TiO2 and sliding distance. Analysis of the COF identifies normal load as the primary influential factor, followed by grit, Wt.% of TiO2 and sliding distance. The Taguchi predictive model closely aligns with experimental results, validating its reliability. The morphological study revealed significant differences between the unfilled and TiO2-filled composites. The inclusion of TiO2 improved wear resistance, as evidenced by reduced surface damage and wear debris. Originality/value This research paper aims to integrate TiO2 filler and bamboo fibers to create an innovative hybrid composite material. TiO2 micro and nanoparticles show promise as filler materials, contributing to improved tribological properties of epoxy composites. The utilization of Taguchi’s DOE and ANOVA for statistical analysis provides valuable guidance for academic researchers and practitioners in optimizing control variables, especially in the context of natural fiber reinforced composites.

Experimental investigation on cooling performance of vortex tube with rectifier using Taguchi method

In this article, an experimental study was carried out to investigate the cooling efficiency of a vortex tube equipped with a rectifier utilizing the Taguchi method for analysis. The Coefficient of Performance (COP) was determined as the quality characteristics. In the current study, the inlet pressure, distance between the rectifier and the hot end, the position of the rectifier, the height of the rectifier, the number of blades, the length of blades, the inclination angle of blades with and without ring were selected as the key control factors and levels. Taguchi L18 orthogonal array was applied to design the experiments. The Signal-to-Noise Ratio (SNR), regression, Analysis of Variance (ANOVA) and residual analysis were applied to optimise the combination for COP of the vortex tube. The tests were also conducted to validate the reliability of the predicted optimisation method. Experimental results indicated that: (i) the predicted optimal value falls within a Confidence Interval (CI) of 95%. The analysis also revealed that the length of the blades could be the most influential control factor, accounting for 36.9% of the cooling performance. (ii) the height of the rectifier shows little impact, only contributing 4.7% to the overall cooling performance. (iii) the order of other control factors on COP was inlet pressure (21%), whether the rectifiers with or without ring (12.8%), distance between the rectifier and the hot end (7.9%), the inclination angle of blades (6.2%) and the number of blades (5.3%).

Mathematical Modelling and Multiresponse Optimization to Minimize Surface Roughness in Drilling Custom 450 Stainless Steel

In the present study, drilling tests were carried out on Custom 450 stainless steel workpieces. The influences of control factors (cutting speed-Vc, feed rate-f and drill bit geometry-D) on the drilled holes’ surface roughness (Ra) and on the size of adhering workpiece (AW) to the drill bit was examined. The results obtained from tests designed based on the Taguchi’s L16 orthogonal array were analysed using ANOVA and grey relational analyses (GRA). Therefore, the control factors and their levels were optimised simultaneously for the quality characteristics (Ra and AW). In addition, mathematical models were also developed using Response Surface Methodology (RSM) in order to estimate the quality characteristics. The used drill bits were examined under digital and scanning electron microscopes and EDX analysis was also carried out on the drill bits. The experimental results showed that the Ra and AW increased with increasing the f. It was also seen that increasing the Vc resulted in decrease in the size of adhering layer and that the drill bit wear became clear at the highest Vc of 60 m/min. According to the ANOVA results, the most effective control factor on Ra was f with 93.11% and Vc with 58.14% on AW. GRA analysis revealed that the most influential control factor was the f and that the optimum levels were 60 m/min Vc, 0.005 m/min f and drill bit 4.

Cognitive modeling the level of university students’ perceptions of distance learning in the COVID-19 pandemic

Introduction. The research problem is the contradiction between the widespread use of distance learning technologies at universities, not only during the COVID-19 pandemic, and the absence of an appropriate system of effective organizing the educational process, taking into account the peculiarities of students' perceptions of distance learning. The aim of the study is to identify and predict the features of the mutual influence of various factors on the level of perception of distance learning by university students using cognitive methodology to develop effective forms of organizing the educational process and management methods. Materials and Methods. The study follows the methodology of cognitive modeling developed by R. Axelrod for the analysis of problems that are difficult to formalize. The empirical data were collected using a survey based on Google forms and Internet technologies. The sample included 188 university students from Omsk and Norilsk. Cognitive models are developed as cognitive maps, which, as a rule, are weighted oriented graphs. Thus, cognitive methodology is a specialized approach designed to structure knowledge about complex systems, taking into account their relationships with the external environment, in order to comprehensively study the features of their functioning and predict state changes. In addition, questionnaires, expert evaluation and computer simulation were used. Results. In the course of the study, the controlling factors influencing the target factor – the level of perception of distance learning by university students in the context of the COVID-19 pandemic, as well as the relationship between these factors and the degree of their mutual influence were identified. Taking into account the data obtained, a cognitive model of the level of university students’ perceptions of distance learning in the context of the COVID-19 pandemic in the form of an oriented graph has been created. The developed cognitive model served as a basis for a series of simulation experiments using specialized software. The results of the simulation experiments are a system of predictions about the impact on the target factor of impulses which influence the control factors with different intensity. Based on the received predications, recommendations on the management of the educational process in the conditions of distance learning have been formulated. Conclusions. The results obtained can be used in the design and development of effective methods and specific didactics of distance learning, taking into account the peculiarities of its perception and the level of psychological comfort of university students. The forecasts of changes in the target factor obtained in the process of modeling under the influence of control factors are the basis for the design and development of effective methods and specific didactics of distance learning.

Numerical analysis of the mechanical behavior of separated joints in underground pipelines rehabilitated by cured-in-place pipes

For the old underground pipelines in Taiwan area, damage to the pipe joints due to factors such as overloading, poor backfilling, and liquefaction is a continual concern. Damage to joints includes fracture and separation, and the consequences of such damage include leaking and subsidence. For the rehabilitation of this type of damaged pipeline, trenchless methods are optimal because of their minimal impact on transportation. Cured-in-place pipe (CIPP) is a popular trenchless rehabilitation method suitable for the cases with variable pipe alignment and cross-sections. However, according to the American Society for Testing and Materials F1216 standards, CIPP rehabilitation is designed for cases where a completely damaged pipeline is to be replaced by a new one, and the guidelines do not provide details on the rehabilitation performance of the damaged parts, which was the focus of this study. In this study, the performance of separated joints after CIPP rehabilitation was numerically analyzed. The numerical model was calibrated by using the laboratory test results, before applied to simulate the rehabilitated pipelines with various geometries and loading conditions. The influence of control factors (e.g., backfill, and interface) on CIPP rehabilitation performance was then analyzed in detail. In order to further understand the effect of different resin materials used for CIPP, this study also conducted experiments and numerical analysis on resins commonly used, including epoxy resin (EP), vinyl ester (VE) and unsaturated polyester (UP). The results of this study can be applied in engineering design optimization as well as industry guideline development.

Development of a CAE Modeling Technique for Heavy Duty Cargo Weight using a DFSS Methodology

<div class="section abstract"><div class="htmlview paragraph">Cargo box is one of the indispensable structures of a pickup truck which makes it capable of transporting heavy cargo weights. This heavy cargo weight plays an important role in durability performance of the box structure when subjected to road load inputs. Finite element representation for huge cargo weight is always challenging, especially in a linear model under dynamic proving ground road load durability analysis using a superposition approach. Any gap in virtual modeling technique can lead to absurd cargo box modes and hence durability results. With the existing computer aided engineering (CAE) approach, durability results could not correlate much with physical testing results. It was crucial to have the right and robust CAE modeling technique to represent the heavy cargo weight to provide the right torsional and cargo modes of the box structure and in turn good durability results.</div><div class="htmlview paragraph">As there are multiple variables owing to heavy cargo weight, Design for Six Sigma (DFSS) methodology was used to develop a robust CAE modeling technique. This project was defined with multiple key control factors considering geometrical and material properties of cargo along with the multiple ways to connect cargo ballast with the box floor bed. The manufacturing variability of box panels was considered as a noise factor. L18 Orthogonal Array matrix was chosen to optimize the variables based on factorial effects of signal to noise (S/N) ratio. Finally, the validation was done for the achieved optimum solutions using numerical tests.</div><div class="htmlview paragraph">This paper describes the DFSS project in detail, and emphasizes on how the DFSS approach could be very effective to predict the influential control factors and how the combination of these factors was utilized to find out the optimum solution. This paper also highlights how the optimized/proposed CAE modeling technique improves the accuracy of the cargo box modal and durability results.</div></div>

Quantitative assessment of the effects of climate, vegetation, soil and groundwater on soil moisture spatiotemporal variability in the Mongolian Plateau

Soil moisture (SM) is a key parameter regulating the hydrothermal balance of global terrestrial ecosystems and plays an important role in local ecological environment, particularly in arid and semiarid areas. However, current studies have so far obtained insufficient knowledge of SM spatiotemporal variability and its primary control factors, which limits our understanding of the feedback effects of SM on surface vegetation and hydrothermal activity. Here, we chose the ecologically fragile Mongolian Plateau (MP) as the study area to quantitatively reveal the soil moisture spatiotemporal variability (SMSTV) and the influence of control factors (climate, vegetation, soil and groundwater) with the help of empirical orthogonal functions (EOFs) and geographical detector models. The results indicated that a significant trend of decreasing SM and one dominant spatial structure (EOF1) of SM was found in the MP from 1982 to 2019, which explained over 54% of the spatial variability in SM, and as the soil depth increased, the EOF1 interpretation capacity increased. In addition, EOF1 is high in the north and east and low in the south and west of the MP and that vegetation cover is also relatively greater in the high-value areas. Overall, groundwater has the greatest influence on SMSTV in the MP (q = 0.89); however, precipitation and potential evapotranspiration remain the main control factors for SMSTV for different ecological zones, while the influence of vegetation elements (NDVI and GPP) cannot be ignored, and soil textures (clay, sand, silt) have the least influence. Meanwhile, SMSTV is explained to a greater extent by the interaction of the factors rather than by a single factor. However, there are differences in the influence mechanisms of each factor on SMSTV. This study provides strong evidence that meteorological forcing is not the only factor that dominates SMSTV and that the dominant factors may vary considerably between ecological zones.

Experiments on Material Proportions for Similar Materials with High Similarity Ratio and Low Strength in Multilayer Shale Deposits

It is important to systematically investigate the similar materials with high similarity ratio and low strength in multilayer shale deposits, to provide a scientific basis and experimental basis for the research of underground mining of multilayer shale deposits. In this paper, using an orthogonal experimental method, the physical and mechanical parameters of different material proportions were analyzed with four control factors of mica powder/standard sand, filling material/bonding material, Portland cement/gypsum, silicone oil ratio. Twenty-five groups of material proportioning schemes were designed, and the density, porosity, compressive strength, and elastic modulus of each group of materials were measured. Through the range analysis and significance analysis, the influence of control factors on the material parameters was explored, and multivariate linear regression analysis of test results was carried to eliminate outliers. The result showed that the physical and mechanical parameters of similar materials prepared according to the proportioning scheme were widely distributed, which can meet the preparation requirements of similar materials with different lithologies. The density and compressive strength were most affected by the ratio of Portland cement/gypsum, the porosity was most affected by the ratio of filling material/bonding material, and the elastic modulus was mainly controlled by the silicone oil ratio. The proportioning scheme was applied to three similar prepared shale materials with large lithology differences. The error between actual similar constant and design similar constant of low strength similar material was less than 1.62%. The physical and mechanical parameters of similar materials were in good agreement with the original rock.

MULTI-RESPONSE OPTIMIZATION ON ABRASIVE WATERJET MACHINING OF GLASS FIBER REINFORCED PLASTICS USING TAGUCHI METHOD COUPLED WITH TOPSIS

This work aims to perform the multi-response optimization for abrasive waterjet machining (AWJM) of glass fiber reinforced plastics (GFRP). The experiments were conducted with AWJM factors like pressure (P), traverse speed (TS), and standoff distance (SOD) at three levels. Taguchi’s L9 orthogonal array (OA) was used to design the experiments. The influence of control factors was evaluated by measuring the surface roughness and taper angle while cutting GFRP. The optimum parameter for an individual response was obtained through Taguchi’s [Formula: see text]/[Formula: see text] and multi-response optimization was performed with TOPSIS. From TOPSIS, the optimal parameter of the pressure of 200 MPa, standoff distance (SOD) of 1.5[Formula: see text]mm, and traverse speed (TS) of 25[Formula: see text]mm/min were found. After optimization, the taper angle was decreased by 1.41%. The influence of cutting variables on the responses was statistically analyzed through analysis of variance. It was observed that the pressure has a significant effect on multi-response characteristics and a contribution of 85.90%. After, AWJM, the surface was examined using SEM analysis and found the deformation and pull-out of fibers.

Correlations between Process and Geometric Parameters in Additive Manufacturing of Austenitic Stainless Steel Components Using 3DPMD

The additive manufacturing of components is characterized by a layered build-up. The stability of the build-up process with regard to the component geometry and the layer thickness is essential for the success of the entire system. A prerequisite for this is the exact knowledge of the interrelationships between the process, construction parameters and the resulting component geometry, respectively. These correlations are determined within the study using the 3D Plasma Metal Deposition Process (3DPMD). For this purpose, the process is first subjected to a system analysis. Possible influencing variables were identified with regard to the question “Which system parameters influence the component geometry?” and then prioritized. Then, the influence of control factors (welding current intensity, welding speed, and powder mass flow) was investigated according to the specifications of the Design of Experiments (DOE) method by means of a full-factorial experimental design and evaluated on the basis of metallographic cross-sections. As a result, it was determined that the system parameter powder mass flow only influences the layer thickness and not the wall thickness and is, therefore, available as a process control variable. In sum, comprehensive knowledge of complex relationships between the control parameters and the component geometry in additive manufacturing using 3DPMD was achieved and forms the basis for further scientific work.

DEM SIMULATION AND EVALUATION OF WELL CELLAR MAKING PERFORMANCE OF OPENER WITH LARGE SOCKET

Well cellar seedling transplanting can effectively avoid series of problems such as long recovery time and poor uniformity of seedlings caused by the server climate conditions outside. Well cellar making is quite difficult in the upland with low moisture content. An opener with large socket was newly designed for the well cellar making. The well cellar making process was simulated by means of discrete element method (DEM), and its verification was qualified by soil bin test, with a relative error 7.46%. Taking arc radius of the opener socket, rotation speed and penetration speed as control factors, and collapse rate of the cellar hole as evaluation index, the influence of control factors on the collapse rate and their significance were obtained by Box-Behnken Design (BBD) test and analysis of variance (ANOVA). A quadratic regression equation of collapse rate with control factors was established, and the optimal parameters of the control factors were obtained as arc radius 128 mm, rotation speed 380 r/min and penetration speed 0.15 m/s, with the minimum collapse rate 22.70%. The collapse rate of the newly designed well cellar opener decreased much compared to the traditional one.

A New Coking Coal Charging Method for 6 m Top-Charged Coke Oven: System Design and Experiment

Coking with low moisture coal is an important link of energy conservation and coke quality improvement in the coking industry. Aiming at the problems of dust emission and bad accumulation in the coking chamber during coal charging, a new system of coking coal charged into 6 m top-charge coke oven was studied and designed, in which a cylinder with telescopic and high temperature resistance was used to fill the coking chamber with a dense phase continuous flow. The coal transport characteristics, dust emission, and accumulation characteristics were studied through the actual operation of the equipment. The results found that the matching of spiral feeding speed and cylinder lifting speed had an important influence on the transport characteristics, when moisture was 5%, and the control dense phase transport conditions were—cylinder lifting speed of 0.02 m/s and spiral feeding speed control range of [0.31 m/s, 0.50 m/s]. The new device was found to reduce dust emission by 90% per square meter, compared to the traditional. The influence of controllable factors on the accumulation characteristics of coal was studied, and the essential conditions for optimal repose angle and bulk density were obtained through an orthogonal test, the prediction model of accumulation characteristics was established.

Parametric optimizationof friction stir spot welded aluminium AA6063 alloy joints

Friction stir spot welding is a solid-state welding process where the joints are formed due to plastic deformation. This technique is economically sound and more efficient when compared to that of other spot welding. In this research Taguchi approach has been applied to determine the most influential control factors which will yield better tensile strength of the joints of friction stir spot welded AA6063 aluminium alloy. In order to evaluate the effect of process parameters such as tool rotational speed, tool profile and axial force on tensile strength of friction stir spot welded AA6063 aluminium alloy. Taguchi method was used to obtain significant and optimum levels of process parameters. Mathematical model has been developed using ANOVA based Taguchi method, which were further used to predict the ultimate tensile strength of friction stir spot welded joints of 6063 aluminium alloys. Rotational speed, tool profile and axial force were found significant parameters in deciding the ultimate tensile strength of the joint. The optimum value of ultimate tensile strength of welded joints obtained from experiment. The results obtained has been analyzed and optimized by analysis of variance-based Taguchi method.

Optimization of Investment Casting Parameters to Improve the Mechanical Properties by Taguchi Method

Investment casting is the most adaptable and durable manufacturing process dating back thousands of years, with a rapidly increasing application. It is used primarily for the development of complex components near net-shape. A wax pattern in the exact replica of the cast part is used here. When the mold has been formed, the wax pattern is melted out. For this purpose this process is called lost wax process. In this present study investment casting process parameter is optimized by using Taguchi approach to improve the mechanical properties of mild steel. A set of experiments were conducted as per Taguchi’s L9 orthogonal array to determine the most influential control factors. For test the performance characteristics, orthogonal range, signal to noise ratio and variance analysis were used. Data values are obtained under the constraint of different process parameters like preheat temperature, pouring temperature, preheat time to investigate their effects on the surface hardness, tensile strength of the final results. The variations in the trend of the mechanical properties were observed. It was found that high mold preheat temperature, higher pouring temperature, maximum preheat time significantly reduce the mechanical properties of the castings.

Optimal Design of Parameters for the Nanofluid/Ultrasonic Atomization Minimal Quantity Lubrication in a Micromilling Process

Nanofluid/ultrasonic atomization minimal quantity lubrication (MQL) was used in an SKD11 steel micromilling process with ultrasonic dispersion. The nanofluid atomizer substantially improved nanofluid reunification and delivery to the cutting zone. The micromilling cutting force and temperature were used to calculate the signal-to-noise ratio (S/N). Normalization of the S/N in the gray relational analysis obtained a gray correlation coefficient, which is used in a fuzzy analysis to obtain a multiple performance characteristic indexes (MPCI). The MPCI is maximized by optimizing the process parameters for multiple quality characteristics and then comparing the optimized parameters with those in the MQL system. Experimental results indicate that the nanofluid parameters containing the density of nanofluid, feed rate, and distance of nozzle are the most influential control factors, while 3.617 N and 117.1 °C, respectively, are the best combination of parameters. Different lubricating methods are also compared in terms of effects on micromilling cutting force, micromilling temperature, micromilling tool wear, and surface burr. Experiments show that nanofluid multiwall carbon nanotube/ultrasonic atomization MQL obtained the best results.

Taguchi design of optimum process parameters for sorption of copper ions using different sorbents.

The sorption of Cu ions on pure fly ash-based geopolymer, fly ash-based geopolymer with Pb ions addition (modified) and zeolite NaX was studied. Taguchi's approach was applied to determine the impact of solute concentration (c), temperature (T), mixing time (t) and sorbent type (S) on the sorption of Cu ions onto different sorbents under batch conditions. Optimum experimental conditions and influence of controllable factors were determined using the larger-the-better approach. The influence in descending order is c > S > t > T, for both removal and loading. Also, the impact of sorbent type and solute concentration on the process equilibrium was examined. The equilibrium amount of Cu retained on the sorbents in equilibrium (qe) was as follows: pure geopolymer - 1.169 mmol g-1, modified geopolymer - 1.186 mmol g-1, and zeolite NaX - 1.695 mmol g-1. The experimental data were modelled using Jovanovic, Khan, Baudu, and Fritz-Schlünder isotherm models and their goodness of fit were compared. The Baudu isotherm model was the most useful in predicting the equilibrium of Cu sorption on pure and modified geopolymer. Goodness of fit of the selected isotherm models for the sorption of Cu ions on zeolite NaX was in the order: Fritz-Schlünder > Khan > Jovanovic > Baudu.

Supersaturated split-plot designs for robust parameter experiments

The intention of robust parameter design (RPD) experiments is to identify the influence of control factors on the variation introduced by the uncontrollable noise factors. Split-plot designs can be effectively applied to decrease the cost in RPD experimentation when there are hard-to-change or hard-to-control factors. In the presence of many factors, either the traditional replicated split-plot designs or the Cartesian-product designs proposed by Taguchi are not of practical use since they require a large number of experimental units; thus, fractional factorial designs, such as supersaturated designs (SSDs) are needed. In this article, we propose a construction for supersaturated split-plot designs suitable for RPD experiments. We implemented the methodology of compound orthogonal arrays using orthogonal arrays of strength greater than 1, for the split-plot units and any relevant balanced SSD for the whole-plot units. The proposed methodology indicates that the experiments conducted in a split-plot point of view can be of tremendous value in RPD since they enable the estimation of the contrasts of interest in a considerably easier way than the designs proposed by Taguchi.

Convergence of Economic Growth in Russian Megacities

Purpose: The article presents the results of an empirical analysis of the economic growth of Russian cities with a population of over 1 million people (megacities). Design/Methodology/Approach: The analyzed indicator is the city product calculated according to the UN methodology for the period from 2010 to 2016. The paper analyses the process of β- and σ-convergence across Russian megacities using methods of spatial econometrics in addition to the traditional β-convergence techniques from the neoclassical theoretical framework. Findings: The dynamics of the coefficient of variation confirmed the presence of σ-convergence in city product. Empirically, positive spatial autocorrelation has been confirmed. Beta-convergence for Russian megacities is found to be significant and the spatial location of megacities significantly affects β convergence. Control factors such as fixed capital investment per capita in 2010, average retail volume per capita in 2010, average annual number of employees of enterprises and organizations in 2010 and the dummy variable introduced for “federal cities” Moscow and St. Petersburg are all found to have positive and statistically significant impact on economic growth. Practical Implications: Policymakers may take the results into account under the planning of economical strategies for megacities and regions in Russia in order to facilitate the regional economic growth and the speed of convergence. Originality/Value: The main contribution of the study is the consideration of the economical growth for the megacities and not for the regions as it often used to be the case in similar studies. The important finding is that megacities‘ economies do converge and the influence of control factors is pronounced.

Multi-objective Optimization of EDM Process with Performance Appraisal of GA based Algorithms in Neural Network Environment

Abstract This work focuses on the material removal rate and electrode wear rate of A2 steel in the EDM process, which are considered as to be the important governing parameters for higher productivity and accuracy. The purpose of this investigation is to minimize the electrode wear rate and maximize the material removal rate by controlling the machining parameters. In this work, an experimental investigation has been carried out by considering four machining control parameters such as discharge current (Ip), pulse duration (Ton), duty cycle (τ or Tau) and voltage (V) by using the full factorial design methodology. Artificial Neural Network (ANN) model has been developed to correlate the data generated from experimental results. To obtain an efficient ANN model and to achieve minimum prediction error, ANN architectures, learning/training algorithms and numbers of hidden neurons are generally varied. However, so far the variations have been made in a random manner. For this reason a full factorial design integrated Analysis of Variance (ANOVA) has been utilized to investigate the influence of control factors on response. Developed ANN model equation of material removal rate and electrode wear rate were subsequently used as the fitness functions in Genetic Algorithm (GA) based multi-objective algorithms. Three advanced GA based optimization techniques, i.e., Non-dominating Sorting Genetic Algorithm-II (NSGA-II), controlled NSGA-II and Strength Pareto Evolutionary Algorithm 2 (SPEA2) were attempted for the considered EDM process. Furthermore, a non-dominated set of solution was obtained to have diversity in the solutions for the EDM process. The result obtained using the ANN method and optimization techniques were confirmed using confirmation experiments.

Parameter Design of High-Resolution E-Jet Micro-Fabrication Process by Taguchi Utility Approach

This article describes how an electrohydrodynamic inkjet, or E-jet, is a high-resolution micro-fabrication technology for flexible electronics application. Its operation depends on several process control parameters like applied voltage, flow rate of the material, stand-off height, type of ink material. High-resolution deposition along with ejection frequency is the performance parameter of E-jet. In this article, an attempt is made to design the process parameters in such way, that it could simultaneously satisfy both the performance characteristics of the E-jet process. A Taguchi robust design based utility concept is applied to optimize the multi-response E-jet process through a case study. Utility analysis converts multi-response scenario into a single response by calculating overall utility value, which is optimized by signal to noise (S/N) analysis. Analysis of variance ANOVA found applied voltage is the most influential control factor in the investigated region. The anticipated improvement in fabrication operation shows the successful implementation of the aforesaid methodology.