Significant Input Factors Research Articles

OPTIMAL TENSILE-SHEAR STRENGTH OF GALVANIZED/MILD STEEL (SPCC-SD) DISSIMILAR RESISTANCE SPOT WELDING USING TAGUCHI DOE

This paper demonstrates the optimization of resistance spot welding on different connections of galvanized steel sheets and low carbon steels. The zinc coating on galvanized steel sheets will have an effect to reduce the welding ability in the resistance welding process. The practical Taguchi experimental technics were used by implemented adequately to optimize input factors, namely squeezing time, welding current, welding time and holding time. Statistical software implemented an analysis of variance (ANOVA) and multilinear regression to investigate and evaluate the significant input factors and compare them with the experimental output factors of resistance spot welding. The 'signal to noise ratio' (S/N ratio) results shows that the welding time and the welding current are the most significant factors on the output. The delta values ​​of welding time and welding current are 3.15 and 2.25, respectively. The ANOVA results showed that welding current and welding time are the most contributing factors by 23.5% and 51.4%, respectively. Taguchi recommends an optimal squeezing time of 20 cycles, a welding current of 27 kA, a welding time of 36 cycles, and a hold/cooling time of 15 cycles. The highest output reaches a tensile shear strength of 5762.04 N on the third iteration. The present research has successfully identified significant variable inputs for resistance spot welding, namely welding current and welding time. In the future, the relevant research may use our corresponding results to improve the RSW practical procedure for other significant impacts.

Experimental investigation on WEDM parameters of Ni-rich nitinol plate

Wire Electro discharge machining (WEDM) process is extensively employed to fabricate miniaturized products. Ni-Ti shape memory alloy (Nitinol) finds its primary application in many significant engineering areas and biomedical fields because of its inherent properties, like shape recovery, superior biocompatibility, and elasticity. The current research investigates the shape recoverability characteristics of WEDM machined nitinol. WEDM Experiments were conducted through DOE with significant input factors like the current (I), pulse on time (Ton), and Wire Feed (f). The responses recorded are material removal rate (MRR), wire wear rate (WWR), percentage of shape recovery (R), kerf width (w), and surface roughness (Ra). The mathematical model developed was to predict the response for corresponding input factors, and the variances of the model are analyzed. Significant contributors are identified from ANOVA, and surface plots are drawn to show the interaction effects. The morphological behavior of the surface was analyzed by FESEM and compositional analysis by EDAX. Surface topography is analyzed in surface roughness, and microhardness was measured to understand the process relevant to shape recoverability better. The results reveal that current and pulse on time affect the microhardness and surface roughness, altering the machined alloy’s shape recoverability.

Exergy, energy and environmental evaluation of a biomass-assisted integrated plant for multigeneration fed by various biomass sources

It is expected that fossil fuels will be replaced by renewable energy sources to reduce environmental pollution. Multigenerational integrated plants which generate various useful outputs from the same input are required to utilize these sources more efficiently. An innovative multigeneration system based on biomass for useful outputs including district cooling and heating, bioethanol, biogas, and electricity is proposed in this study. The system includes a biomass combustion unit, Rankine cycle, biofuel production unit, and absorption cooling cycle. 15 types of biomass sources are considered for the combined system and a thermodynamic and environmental analyses are carried out to assess the effects of biomass sources on the multigeneration system. According to the modeling results, using cotton stem leads to the highest CO2 emission (195.3 kg/MWh) while switchgrass produces the lowest (147.0 kg/MWh). The thermodynamic analysis reveals that, for all considered cases, the burner has the maximum exergy destruction rate among all the stages. Moreover, the exergy and energy efficiencies of the plant for different fuels were assessed, and show that the use of rice straw and rice husk exhibit the highest (68.30%) and lowest (62.72%) overall energy efficiencies, respectively. It is also observed that the system using rice straw and larch wood has the highest (45.29%) and lowest (42.86%) overall exergy efficiencies, respectively. The effects of significant input factors are examined on the system performance and emission indicants.

Discerning potable water sources using Monte Carlo based simulation for health risk assessment in Omu-Aran, Nigeria

Human health is vulnerable upon exposure to persistent heavy metals (HMs) in environmental media such as groundwater even at trace levels. Therefore, this study aims to estimate the health risk posed by exposure to HMs using deterministic and probabilistic methods. Water samples were collected from 8 Hand dug wells (HWs) and 8 Boreholes (BHs) within Omu-Aran, Nigeria. The order of magnitude of the average contents for heavy metals are Zn > Fe > Al > Cu > Cr > Mn > Pb in HWs and Zn > Fe > Cu > Al > Cr > Pb > Mn for BHs. Pollution indices reveal that BHs were of better quality while HWs were more polluted. PCA shows contamination of HWs come from anthropogenic sources. Monte Carlo Simulation (MCS) technique was used to evaluate non-carcinogenic and carcinogenic risk in children and adults. The computed Hazard Index (HI) values for children and adults show the likelihood of exceeding the HI value of 1 is greater in children (90%) than in adults (16%) upon exposure to Cr in HWs. Computed lifetime carcinogenic risk (LTCR) values via oral pathways for Pb exposure are well below the 10−4, hence, no carcinogenic risk is expected upon exposure to Pb. Conversely, for HWs, LTCR values for Cr exposure in children ranged from 0 to 2.14*10−4, indicating that the current Cr levels pose a risk, especially for children. Hence, prolonged duration to HMs exposure via ingestion increases the possibility of having cancer. Sensitivity analysis reveal Exposure frequency and HMs concentration are the most significant input factors on the risk estimate. Overall, BHs represent a safe water source for both adults and children, while HWs could pose health risk in children due to elevated HMs levels.

Statistical modeling of hybrid supercapacitor

Energy helps machines to work as per human requirements. Energy can be in any form such as solar energy, wind energy, geo thermal energy and many more. However, utilization of energy depends on application requirements, energy converters, source of energy and most important energy storage system. Some energy storage devices are required that stores the energy from various forms and gives it to load as and when required. Battery, fuel cell and supercapacitor are the examples of it. However, there is need of better electrical energy storage device. Hybrid supercapacitor can replace some of them in near future. Modeling of hybrid supercapacitor could play a significant role to achieve desired characteristics. Hybrid supercapacitor possesses characteristics of both battery and supercapacitor. Modeling of this device can be a game-changer in the energy sector as it will give full insight of issues in it. Statistical modeling can help in getting single device that can replace externally connected battery-supercapacitor combinations. Statistical modeling of the hybrid supercapacitor has been carried out with the most significant input factors related to electrodes and specific capacitance as an output parameter.

Comparative Study on EDM Parameter Optimization for Adsorbed Si3N4–TiN using TOPSIS and GRA Coupled with TLBO Algorithm

Electrical discharge machining is a thermo-physical-based material removal technique. 25 combinations of process variables were formulated with the aid of Taguchi technique for EDM of adsorbed Si3N4–TiN. Machining variables like pulse current, pulse-on time, pulse-off time, dielectric pressure, and spark gap voltage varied, and impact of each variables on the performance metrics (MRR, EWR, SR, ROC, θ, CIR, and CYL) was assessed. MCDM strategies like grey relational analysis and TOPSIS are utilized to find out the ideal arrangement of machining parameters to achieve most acute productivity of the multitude of reactions. Likewise, metaheuristic algorithm in particular GRA combined with teaching-learning-based optimization algorithm is utilized for getting global optimized input factors. Important factors like pulse current, pulse-on time, and spark gap voltage characteristically affect the outputs. It is recognized that the pulse-on time and the pulse current are the most significant input factors than others. The ideal machining parameters in view of GRA and TOPSIS techniques for acquiring better output factors are I, 12 amps; PON, 7 μsec; POFF, 4 μsec; DP, 12 kg/cm2; and SV, 36 volts.

Analysis of wear assessment of FDM printed specimens with PLA, multi-material and ABS via hybrid algorithms

The present work deals with wear assessment of FDM printed specimens with PLA (polylactic acid), Multi-Material, and ABS (acrylonitrile butadiene styrene). As per the American Society for Testing and Material G99 17 standard, the parts have been fabricated. The significant parameters raster angle, infill density, speed, temperature, wall thickness, and material selection have been taken as input factors to fabricate various specimens. The experimental matrix has been designed using optimal custom design for the response surface method, accommodating custom model, categoric elements, and irregular (constrained) regions. A total of 38 experimental runs have been achieved using the present approach. The 38 specimens have been printed with an FDM printer on these significant input factors and tested on the Pin on disc. An artificial neural network has developed a relationship between input factors and output response. GA-ANN has been applied to minimize the wear rate. It is observed that the minimum wear rate achieved 0.155371 mm3/m at PLA material at raster angle 89.258°, infill density 95.207%, temperature 220.009 ℃, speed 40.043 mm/s, and wall thickness 1.198 mm; this data has been validated using Hybrid algorithms.

On the Influence of Electrical Discharge Drilling Parameters and Per-formance Measures of Inconel 718 Superalloy - a Study

In order to achieve higher productivity and product quality, the investigation of machining parameters on Electrical Discharge Drilling and surface characteristic analysis are most critical for manufacturing industries. The intention of this article is to assess the impact on performance matrices including Material Removal Rate, and Surface Roughness of input factors of peak current, pulse-on and off duration while drilling with a rotary hollow copper tool on Inconel 718 under Tungsten powder suspended kerosene. Analysis of Variance has been implemented using MINITAB release 18 software to identify the most significant input factors. An Artificial Neural Network was used for validating the experimental results of the drilling process. The additional intention of this research is to discover the significance of influencing input parameters and analyze the quality surface of the workpiece were observed by microscope tests. The experimental results indicated that the peak current and pulse-on period have an effect on the performance of the drilling process considerably.

Performance of electrokinetic treatment of fine-grained problematic soils

Electrokinetic (EK) treatment is an innovative, cost-effective in situ ground modification technology. The EK treatment uses a combination of low-voltage direct-current, electrodes, and ionic solutions across problematic soil to improve the ground conditions. This study aims to model the effect of changing electrode length (le) on the performance of the EK treatment on the engineering properties of fine-grained problematic soils. The consideration of the changing electrode lengths (le), varying soil depths (ds), and lengthwise anode to cathode distances (dA↔E), in the soil block samples, is in the form of the laboratory model test tank. The significant performance of the experimental tests was with changing electrode lengths of 0.25le (7.5 cm), 0.50le (15.0 cm), 0.75le (22.5 cm), and 1.0le (30.0 cm). The study analyzed the test data obtained from the Atterberg limit and one-dimensional swelling tests at different extraction points of the EK treated soils in the test tanks. Furthermore, the study carefully analyzed the effect of changing electrode length (le) on the performance of the EK treatment. The results of the Design of Experiment (DOE) model analysis revealed that the effect of changing electrode length (le) on the plasticity index (PI), and swelling potential (SP) of the EK treated soils, was significant. For a specific soil depth (ds), the electrode lengths (le) of 0.50le and 0.75le were significantly effective in reducing the PI, and the SP of the EK treated soils. Unlike other studies in the literature, the use of DOE analysis in the present study enabled the detection of the significant input factors and their interactive effects on the PI and the SP, thus, enabling the practicing engineers to navigate accurate design models for large in situ applications.

Analysis of Integrated Preventive Maintenance and Machine Failure in Stochastic Flexible Job Shop Scheduling with Sequence-dependent Setup Time

ABSTRACT When we consider the real-time situation in scheduling problems, it always helps to enhance the manufacturing system and increases the system performance. In this study, the effect of five input parameters, i.e., reliability-centered preventive maintenance, percentage of machine failure (PMF), mean time to repair for random machine breakdown, due date tightness factor, and routing flexibility (RF) on stochastic flexible job shop scheduling problem (SFJSSP) under simultaneously reliability-centered preventive maintenance and random machine breakdown environment with sequence-dependent setup time is evaluated. The effects of input parameters are measured using four different performance measures, i.e., mean flow time (MFT), makespan (Cmax), mean tardiness (MT), and total setups (TS). A statistical response surface methodology is used to assess the performance measures. ANOVA analysis is used to determine the model’s suitability. The results show that PMF and RF are found as the most common significant input factors for all the performance measures. Multi-objective optimization is performed using the desirability function approach to optimize the system performance measures. It is found that the minimum value of MFT, Cmax, MT, and TS performance measures for optimum performance of the SFJSSP are predicted as 123.432, 220,561, 103.399, and 102,171, respectively, with composite desirability, D of 0.916. The confirmatory results show that the error between the predicted and experimental results is less than 5%. Moreover, considering both uncertainties with dynamic jobs arrival environment shows the study’s real-time scheduling scenario and novelty.

A Taguchi Approach in Investigating the Effects of Process Parameters in Drilling of Glass Fibre Reinforced Plastic Composite

Glass fibre reinforced plastic (GFRP) composite has found an important place in the industries replacing many metals and alloys. The joints formed are found to be the weakest portions of the assembled GFRP parts. Conventional drilling is the most widely adapted manufacturing process for an efficient assembly. Moreover, drilling of glass fibre reinforced plastics is difficult to carry out due to the anisotropic, non-homogeneous structure and high abrasiveness of the glass fibre and the complex geometry of the drill. For these reasons, the damage gets induced to the hole/workpiece and the cutting tool. In this study, Taguchi approach is employed to analyse the drilling process. An L25 orthogonal array, signal to noise S/N ratio and analysis of variance (ANOVA) are used to identify the effects of process input parameters on the output factors. The process input parameters are drill diameter, cutting speed and feed rate. The output factors/responses are the thrust force, torque, power, temperature, tool life, surface roughness, deviation from dimensional parameters of the holes, peel-up and push-out delaminations. In every set of experiment, an exhaustive work has been carried out till each drill bit reached its tool life. Optimal process parameters are available from the S/N ratio plots whereas ANOVA has identified the significant input factors and their percentage contribution on the responses.

Electrokinetic treatment of soft soils: experimental study and numerical models

Soft soils have a high compressibility, and low shear strength, and constructions on such soils often require the use of ground-improvement techniques. This paper compares the use of an electrokinetic (EK) treatment of soft soils using the ionic solutions calcium chloride and sodium carbonate. The effects of the ionic-solution type, the EK-treatment duration, the cation exchange capacity (CEC), the specific surface area (Sa), the pH, the electrical conductivity (σ), and the ionic strength (Is) were considered in this study. Examining the parameters and evaluating their effects on soil behavior are difficult and complex. The design of experiments (DOE) software program was used to evaluate the effects of the parameters and determine the significant input factors for the EK treatment on soft soils. The analysis and optimization of the data produced the threshold values using the design-expert® software. In this study, the EK-treated soil with CEC = 4.9 meq 100/g, Sa = 4.5 m2/g , pH = 9.5, σ = 6.0 S/m, Is = 1.55·10-4 mol/L, and electrolyte-type setup of CaCl2-Na2CO3 gave better soil strengthening. The gain in strength is attributed to the flocculation and aggregation of the EK-treated soil particles. The analysis of the data by DOE indicated that it could be used to assess the significant effects of the input factors on the unconfined compressive strength, qu of the EK-treated soft soils.

Sensitivity Analysis of Neural Network Models: Applying Methods of Analysis of Finite Fluctuations

As an initial stage prior to Mathematical Modeling, the information processing should provide qualitative data preparation for the construction of consistent models of technical, economic, social systems and technological processes. The question, concerning choosing the most significant input factors affecting the function of the system, is a very actual and important. This problem could be solved with the application of methods of Sensitivity Analysis. The presented paper has the purpose to show a possible approach to this problem through the method of the Analysis of Finite Fluctuations, based on Lagrange mean value theorem, to study the sensitivity of the model under consideration. The numerical example of comparing the results obtained by Sobol sensitivity coefficients, Garson algorithm and proposed approach showed the sustainability of the introduced method. There is shown, that the proposed approach is stable in the sense of applying different input datasets. In particular, the proposed approach has been applied to the construction of a neural network model identifying any anomalies present in certain medical insurances, in order to define the most significant input factors in the anomaly's detecting, discard the others and get a slim and efficient model.

Factors impacting individual Six Sigma adoption

PurposeOrganizations spend considerable time and money educating individuals on Six Sigma; however, existing literature does not examine Six Sigma adoption at the individual level or the factors that impact individual Six Sigma adoption. The purpose of this paper is to increase the understanding of individual adoption of Six Sigma tools and methodology.Design/methodology/approachThis paper used a single-site field study in a manufacturing organization to empirically test and refine a theory of the factors impacting Six Sigma adoption at the individual level.FindingsReaction to training, project management and project infrastructure were found to be significant input factors for individual Six Sigma adoption with an R2 of 0.482, which indicates that about 48 per cent of the variation in Six Sigma adoption is explained by the input factors. All of the identified input factors were found to have a positive relationship with individual Six Sigma adoption, as well as positive correlations with each other.Research limitations/implicationsThis paper was not a controlled experiment or a longitudinal study, so it is not possible from the results of this research to prove causal relationships, although the literature supports a causal relationship between the input factors and outcome.Practical implicationsThe findings of this paper will be useful to practicing organizations which seek to improve individual Six Sigma adoptions, as well as inform future Six Sigma adoption research.Originality/valueSix Sigma adoption at the organizational level has been well documented in the existing literature. The successful adoption of Six Sigma in an organization is dependent, at least in part, to adoption Six Sigma at the individual level. A review of the existing literature indicates that there has been no research into individual adoption of Six Sigma tools and methodology.

Strength retrieval of artificially cemented bauxite residue using machine learning: an alternative design approach based on response surface methodology

The aim of the present study is to propose an alternative artificial neural network model based on response surface methodology over conventional approach to estimate the unconfined compressive strength of artificially cemented bauxite residue. The artificial neural network model uses molding moisture content (w), curing time (t) and porosity/volumetric lime (η/Lv′) as input parameters and unconfined compressive strength as the output parameter. Bayesian regularization as training function with sigmoid and pure linear at hidden and output layers is used for modeling the artificial neural network. The proposed response surface methodology designed ANN model is comparable with the conventional designed ANN model and can be used effectively with significantly less number of data set. Sensitivity analysis, to make out the significant input factors based on connection-weight approach, is also discussed. Further, neural interpretation diagram is incorporated to study the effects of individual input parameters over the response. Finally, a predictive equation is presented based on response surface methodology designed artificial neural network model for the range of parameters studied.

Assessment of Factors That Affect the Performance of Agricultural Production, in the Case of Amhara Region, Ethiopia

The agricultural sector is the basis of livelihood for a large proportion of society in Ethiopia. In the three political regimes in modern Ethiopia, the Imperial, the military and the Ethiopian people revolutionary democratic front (EPRDF), agriculture has been regarded as a critical sector. The Agricultural Development Led Industrialization (ADLI) is the national policy of the country. Regardless of the government policy attention and investment, there is a long way to go for smallholders to ensure food self-sufficiency. Agriculture is the base of our food, transformation to industrialization, climatic change control system. Agriculture is the soul of our sovereignty for development as well as poverty reduction for individuals and country level. In Ethiopia, population density is high and has been increasing and agricultural land has been decreasing because of fragmenting or converting it into residential plots. To meet the domestic food requirements, use of improved production technologies developed by research is come out to be important. Therefore, the goal of this study was to analyze factors that affect the performance of agricultural products in Amahara region national state and to determine the highly significant input factors for producing high and qualified agricultural outputs. Data regarding total agricultural outputs and its input factors in study area of Amahara region from 2010 to 2018 was obtained from Amhara national state branch of the Ethiopian institute of agricultural sector. Correlation analyses were used to examine the strength of the relationship between each of the determinant factors with total agricultural output, while multiple regression analysis was employed to examine the simultaneous effects of several independent variables on the dependent variable, total agricultural outputs. These analyses were employed through the packages R and Stata to achieve the main objectives of the study. All of the independent variables were highly correlated with the total agricultural output. The overall regression model was highly significant (p-value < 0.01) with F = 45.532. The R-squared value implies that 93.8% percent of the changes in average total agricultural outputs are successfully explained by the variables used in the model of this study. If we take model size into account, 91.8% percent of the variation in average total agricultural output was explained by the values of the independent variables. Specifically, among the independent variables irrigated land, fertilizer, improved seed and pesticides are the most significant factors for total products (p-value < 0.05).

Uncertainty evaluation of wellbore stability model predictions

Abstract There is an increasing concern in the oil and gas industry regarding wellbore stability problems. The need to improve well operations becomes more imperative as the operators move towards more challenging and harsher environments such as ultra-deep waters and high-pressure and high-temperature (HPHT) fields. Different inconsistencies affect many previous wellbore stability analyses, resulting in incorrect results, or results that cannot be extended to other well configurations by well planners. Typical wellbore fracture and collapse models provide single point estimates of the geopressures. The model input data may be uncertain. Failure to capture these uncertainties has led to poor predictions. The purpose of this work is to investigate typical fracture and collapse models with respect to in accuracies in the input data with a stochastic method. Uncertainties in the input data, which include in-situ stresses, rock strength data, and pore pressure will be evaluated, to show how these contribute to the cumulative uncertainties in the model predictions. In this approach, the input parameters are assigned appropriate probability distributions. The distributions are then applied in the wellbore stability models. By means of Monte Carlo simulations, the uncertainties are propagated and the histograms of the outputs are generated. Two types of distributions – triangular and uniform – are applied, to see how the types of input-parameter distributions that are assumed influence the model predictions. Sensitivity analysis is also conducted. This is to ascertain the most significant input factors, which are largely responsible for the cumulative uncertainties or variabilities in the critical fracturing and collapse pressures. The proposed methodology can help in reducing many drilling problems such as circulation loss, stuck pipe, and well collapse. As a result, the industry may save much non-productive time. In addition, well planners will have improved information to make critical decisions.

An individual-based model for &amp;lt;i&amp;gt;Salmonella&amp;lt;/i&amp;gt; transmission along the pig production chain

Abstract. The aim was to develop an individual-based model for the transmission of Salmonella from farrowing farm to slaughterhouse. The present study concentrated on farrowing sows as the initial source of Salmonella transmission. The model was evaluated with a sensitivity analysis using a Plackett-Burman design. Three levels (minimum, default, maximum values) for all input factors were considered. The deviations from the default prevalences caused by the extreme values did not balance each other for several significant input factors. For these factors, the relation between input factor and regarded health states was not linear. Results showed that the probability of effective contact, to restart shedding, the shedding duration and the sow herd prevalences as well as their distribution across farrowing farms determined the Salmonella prevalence at slaughter. The study emphasised the importance of vertical and horizontal transmission. Low Salmonella prevalences obtained after nursing caused prevalences at slaughterhouse up to 12 %.

Sensitivity analysis of the two dimensional application of the Generic Ecological Model (GEM) to algal bloom prediction in the North Sea

Harmful algae can cause damage to co-existing organisms, tourism and farmers. Accurate predictions of algal future composition and abundance as well as when and where algal blooms may occur could help early warning and mitigating. The Generic Ecological Model is an instrument that can be applied to any water system (fresh, transitional or coastal) to calculate the primary production, chlorophyll-a concentration and phytoplankton species composition. It consists of physical, chemical and ecological model components which are coupled together to build one generic and flexible modelling tool. In this paper the model has been analyzed to assess sensitivity of the simulated chlorophyll-a concentration to a subset of ecologically significant input factors. Only a small number of approaches could be considered as suitable for several reasons including the model complexity, engagement of numerous interacting parameters and relatively long time of a single simulation. Thus, sensitivity analysis has been carried out with the use of the Morris method and later enriched by the computation of the correlation ratios of the selected parameters on the model response at more than a few locations in the modelled area. The obtained results are in agreement with expert knowledge of the ecological processes in the North Sea and correspond well with local characteristics.

Comparison of Structural Deterioration Models for Stormwater Drainage Pipes

Abstract: Structural deterioration of pipes is the continuing reduction of load bearing capacity, which can be characterized through structural defects. Structural deterioration has been a major concern for asset managers in maintaining the required performance of stormwater drainage systems in Australia. Condition assessment using closed circuit television (CCTV) inspection is often carried out to assess the deteriorating condition of individual pipes. In this study, two models were developed using ordered probit and neural networks (NNs) techniques for predicting the structural condition of individual pipes. The predictive performances were compared using CCTV data collected for a local government authority in Melbourne, Australia. The significant input factors to the outputs of both models were also identified. The results showed that the NN model was more suitable for modeling structural deterioration than the ordered probit model. The hydraulic condition, pipe size, and pipe location were found to be significant factors for this case study.