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The purpose of this paper is to develop a mathematical model for surface roughness and delamination through response surface methodology (RSM) and analyse the influences of the entire individual input machining parameters (cutting speed, fibre orientation angle, depth of cut and feed rate) on the responses in milling of glass fibre reinforced plastics (GFRP) composites with solid carbide end mill cutter coated with PCD.

Four factors, five level central composites and a rotatable design matrix in response surface methodology were employed to carry out the experimental investigation. “Design Expert 8.0” software was used for regression and graphical analysis of the data were collected. The optimum values of the selected variables were obtained by solving the regression equation and by analyzing the response surface contour plots. Analysis of variance (ANOVA) was applied to check the validity of the model and for finding the significant parameters.

The developed second order response surface model was used to calculate the surface roughness and delamination of the machined surfaces at different cutting conditions with the chosen range with 95 per cent confidence intervals. Using such a model, remarkable savings in time and cost can be obtained.

The effect of fibre orientation during milling of GFRP laminates using RSM has not been previously attempted for analysis.

The purpose of this paper is to produce Al6061 metal matrix composites reinforced with silicon carbide (SiC) and graphite particulates and study their wear behavior and also to develop artificial neural network model to predict the mass loss of hybrid composites.

The hybrid composites were produced by using stir casting process. The experiments were conducted based on the central composite rotatable design matrix using pin‐on‐disc wear testing machine. The set of data collected from the experimental values were used to train a back propagation (BP) learning algorithm with one hidden layer network. In artificial neural network (ANN) training module, four input vectors were used in the construction of proposed network namely, weight percentage of SiC particles, weight percentage of graphite particles, applied load and sliding distance. Mass loss was the output to be obtained from the proposed network. After training process, the test data collected from the experimental values were used to check the accuracy of proposed ANN model.

The results show that the well trained one hidden layer network have smaller training errors and much better generalization performance and can be successfully used for the prediction of mass loss of hybrid aluminium metal matrix composites.

In this paper the ANN method was adopted to predict the mass loss of hybrid composites. It was found that artificial neural network can be successfully used for prediction of mass loss of composites.

This paper aims to study the comparison between a response surface methodology (RSM) and artificial neural network (ANN) in the modelling and prediction of surface roughness during endmilling of glass-fibre-reinforced polymer composites.

Aiming to achieve this goal, several milling experiments were performed with polycrystalline diamond inserts at different machining parameters, namely, feed rate, cutting speed, depth of cut and fibre orientation angle. Mathematical model is created using central composite face-centred second-order in RSM and the adequacy of the model was verified using analysis of variance. ANN model is created using the back propagation algorithm.

With regard to the machining test, it was observed that feed rate is the dominant parameter that affects the surface roughness, followed by the fibre orientation. The comparison results show that models provide accurate prediction of surface roughness in which ANN performs better than RSM.

The data predicted from ANN are very nearer to experimental results compared to RSM; therefore, this ANN model can be used to determine the surface roughness for various fibre-reinforced polymer composites and also for various machining parameters.

One part of putting a new wave solder machine into service is determining the optimum settings for the machine's operating parameters. Generally this is done on the basis of ‘common’ knowledge or by simply using whatever settings were used on the old machine. In this case, a full factorial statistical experiment was designed and performed to determine the optimum settings for conveyor speed, board topside temperature and solder pot temperature. Using this technique it was possible to determine the size and shape of the optimum processing ‘window’ by constructing contour plots from the data. The experiment was repeated for a different assembly to determine the sensitivity of the parameter settings to board thickness and component density.

This paper aims to discuss the results of material tests conducted on specimens manufactured from AZ31 alloy powder by selective laser melting (SLM) technology. The manufactured specimens were then subjected to porosity assessment, microstructure analysis as well as to mechanical and corrosion tests.

SLM process was optimized using the design of experiments tools. Experiments aimed at selecting optimum process parameters were carried out in accordance with a five-level rotatable central composite design.

The porosity results showed very low values of <1 per cent, whereas mechanical properties were close to the values reported for the reference wrought AZ31 alloy in hot-rolled state. A fine-grained microstructure was observed with a large range of grain size, which enhanced the material’s mechanical properties. Corrosion characteristics of the SLM-manufactured material exceed those determined for the wrought material.

The results presented in this paper drive interest in magnesium alloys used in additive manufacturing processes. Low porosity, good mechanical properties, form of the microstructure and, most importantly, improved corrosion characteristics suggest that SLM provides great potential for the manufacture of ultralight structures, including resorbable metallic implants.

Snack industry is one of the fastest growing food sectors globally, and people are nowadays conscious about intake of healthy snacks on regular basis. There is enormous variety of ready-to-snacks available in the market. Brown rice though highly nutritious in comparison to polished rice is consumed meagerly by masses. Each raw material/ingredient used in extrusion cooking requires specific control of processing variables to meet acceptable product characteristics and consumer demands, which in turn necessitates the need to optimize the conditions for development of brown-rice-based snacks. The aim of this study was to optimize the extrusion cooking conditions for development of brown-rice-based extrudates.

Extrusion conditions were optimized through design expert using central composite rotatable design (CCRD) experimental design. The effect of feed moisture (10–22%), screw speed (215–385 rpm) and barrel temperature (95–160 °C) on specific mechanical energy (SME), bulk density (BD), water absorption index (WAI), water solubility index (WSI), expansion ratio (ER), breaking strength (BS) and instrumental color (L*, a*, b*) was evaluated.

All the system and product responses were significantly (p < 0.01) affected by independent variables. Regression models obtained were highly significant with high coefficient of determination (R² = 0.992). The optimum extrusion conditions obtained by numerical optimization for development of snacks were moisture content of 12%, screw speed of 350 rpm and temperature of 133 °C. The vitamin B₁ content of brown-rice-based snacks was 0.45 mg/100 (50% of RDA) whereas no vitamin B₁ was detected in white-rice-based snacks used as control.

The developed snacks contain 0.45 mg/100 g of vitamin B₁. If a person on an average consumes 150 g of snacks in a day, 50% of RDA (1.2 mg/day) for vitamin B₁ can be sufficed. Therefore, developed snacks can prove to be a viable vehicle to reduce the vitamin B₁ deficiency burden among the target population. Large-scale production and consumption of such type of snacks could improve the nutritional status of vitamin B1 deficient people. Furthermore, it can also provide a good opportunity for snack industry to develop nutritious snacks through utilization of brown rice.

Brown rice flour contains nutrients such as iron, calcium, zinc, sodium and vitamin B₁ in appreciable portions and was thus explored for development of nutritious snacks. Moreover, developed snacks recorded an overall acceptability of 4.70 out of 5, which depicts it is acceptable for mass production and consumption.

The purpose of this paper is to present a new method to optimize the electro chemical machining process parameters for titanium alloy (Ti6Al4V).

The desirability function analysis (DFA), fuzzy set theory with trapezoidal membership function and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method are used to optimize the electro chemical machining process parameters for titanium alloy (Ti6Al4V). In recent years, the utilization of titanium and its alloys, especially of Ti6Al4V materials, in many different engineering fields has undergone a tremendous increase. The ECM process has a potential in the machining of Ti6Al4V. The machining parameters such as electrolyte concentration, current, applied voltage and feed rate with multiple responses such as material removal rate (MRR) and surface roughness (SR) are considered. Experimental work is carried out on Ti6Al4V using second order central composite rotatable design. The two responses are converted into global knit quality index using DFA. Fuzzy set theory with trapezoidal membership function is used to convert all machining parameters and responses into fuzzy values. Then a TOPSIS approach which determines the optimal machining parameters in terms of higher closeness coefficient is proposed to optimize the machining parameters of ECM for titanium alloy. Finally, ANOVA is performed to investigate the significance of each machining parameter and to identify the most influencing factor which affects the process responses.

The optimal machining parameters for ECM process are determined using desirability function analysis, fuzzy set theory and TOPSIS.

A new method is proposed to optimize the electro chemical machining process parameters for titanium alloy.

The extraction of bioactive compounds such as pigments from natural sources, using different solvents, is a vital downstream process. The present study aims to investigate the effect of different variables, namely, extraction temperature, mass of fermented rice and time on the extraction process of orevactaene and flavanoid pigment from Epicoccum nigrum fermented broken rice.

Central composite rotatable design under response surface methodology was used for deducing optimized conditions. The pigments were extracted under conditions of extraction temperature (40-70°C), mass of fermented rice (0.5-1.5 g) and time (30-90 min), using water as the extraction media. The experimental data obtained were studied by analysis of variance. Data were fitted to a second-order polynomial equation using multiple regression analysis.

The optimum conditions generated by the software for aqueous extraction process, i.e. extraction temperature of 55.7°C, 0.79 g of fermented matter and extraction time of 56.6 min, resulted in a pigment yield of 52.7AU/g orevactaene and 77.2 AU/g flavanoid.

The developed polynomial empirical model for the optimal recovery of the orevactaene and flavanoid pigments could be used for further studies in prediction of yield under specified variable conditions.

The response surface methodology helped in optimizng the conditions for the eco-friendly low-cost aqueous extarction process for orevactaene and flavanoid pigments, produced by Epicoccum nigrum during solid state fermentation of broken rice. This optimization can provide the basis for scaling up for industrial extraction process.

This paper focuses on optimizing the extraction conditions to get the maximum yield of orevactaene and flavanoid pigments, using water as the extracting media. No literature is available on the optimization of the extraction process of Epicoccum nigrum pigments, to the best of the authors’ knowledge.

Pickling or salt curing is one of the major traditional processes to increase the shelf life of bitter gourd in India. No information is available about optimization of salt curing of bitter gourd and the related changes in its quality. Moreover, specific investigations are needed to evaluate individual susceptibility of fruit and vegetables to osmotic dewatering with pre-treatment to obtain new minimally processed food products. The purpose of this study is to optimize the salt curing process with blanching as pretreatment for bitter gourd.

A study was conducted to optimize the blanching and other process factors of salt curing (solution concentration and treatment time) based on mass transfer and quality factors of bitter gourd using response surface methodology. Experimental design was made using central composite rotatable design with different time of blanching, solution concentration and treatment time. The colour, firmness, water activity and other mass transfer kinetic parameters were used for optimization.

Blanching had significant effect (p < 0.001) on water loss (WL), weight reduction (WR), solid gain (SG), water activity and firmness of bitter gourd. Mass transfer kinetic parameters like WL, SG and normalized solid content increased and normalized moisture content was found to decrease with increase in solution concentration and curing time. Relationships between process variables and quality factors were established in either quadratic or linear form with higher R² values. A 15 per cent solution concentration for the period of 5 h with the blanched samples at 800°C for 5.26 min was found to be the optimum condition for osmotic dehydration to achieve maximum WL and SG, minimum water activity and minimum changes in firmness.

The optimized combinations for the salt curing process would be more helpful for the processors and other stakeholders involved in the pickling process by reducing energy and other input resources.

Mostly in India, the pickling process is carried out at micro- and small-scale level and in an unorganized way. This study would help those involved to reduce their input resources and to organize the process, thus leading to more dividends to the stakeholders and optimum price to the end-users. For the medium- and large-scale processing units, this study would give insight to automate the whole process in an efficient manner.

This study was performed using sophisticated and higher-end instruments. The data were observed meticulously and analysed with proper statistical tools, increasing the credibility of the study. This study gives concrete results which are directly useful to the stakeholders.

The purpose of this paper is to deal with the experimental data related to the friction stir welding (FSW) of marine grade Al-Mg4.2 alloy. Mathematical models are developed to study the individual and interaction effects of input variables on the performance characteristics of joints. FSW parameters are optimized to maximize the yield strength and weld nugget microhardness of the welded joints.

Response surface methodology is applied to establish the mathematical relationship between six input factors, namely, tool rotational speed, transverse speed, tool shoulder diameter, tool material hardness, tilt angle and pin profile; and two response variables, namely, yield strength and weld nugget microhardness. Six factors–five-level rotatable central composite matrix is used for the design of experiments. The quadratic model is used, as suggested by the design expert software, to express the response parameters as a function of investigated input parameters. The competence of the developed models is verified through analysis of variance.

The present investigation clearly indicates that the studied input factors have a significant effect on the quality of the joints. The optimal combination of input factors is determined to achieve the desired responses.

This paper teems a new look on tensile and hardness properties of Al-Mg4.2 joints by relating the microstructure, fractrographs and grains distribution with the dynamic recrystallization and plasticized material movement during the FSW process. The outcome of this research will help in seizing the opportunities of joining Al-Mg4.2 alloy using FSW, in the offshore and marine applications.