Experimental investigation on electro-discharge surface modification phenomenon of P20+Ni die steel using green P/M composite electrode

In the present work, attempts have been made to optimize the EDM process with an aspect of surface modification of AISI P20+Ni die steel using powder metallurgy (P/M) electrode. Experiments have been performed according to central composite rotatable (CCD) design using response surface methodology (RSM). Effects of compaction pressure (Cp), peak current (Ip), pulse-on time (Ton), and duty cycle (τ) were correlated with surface roughness (SR) and microhardness (MH). Adequacy of mathematical model has been checked by performing ANOVA. Composite desirability approach was used to obtain optimal set of parameters for minimum SR and maximum MH. The errors between the predicted and experimental value of responses at the optimal set of parameters for SR and MH maintain within 5.26% and -3.64% respectively. Scanning electron microscope and energy dispersive spectroscopy analysis confirmed the transfer of P/M electrode material on the work surface. The result indicates three times improvement in microhardness of EDMed surface.

Preparation, Optimization and Evaluation of Chitosan-Based Avanafil Nanocomplex Utilizing Antioxidants for Enhanced Neuroprotective Effect on PC12 Cells

(1) Introduction: in recent decades, interdisciplinary research on the utilization of natural products as “active moiety carriers” was focused on due to their superior safety profile, biodegradability, biocompatibility and the ability for sustained or controlled release activity. The nano-based neuroprotective strategy is explored as an imperative treatment for diabetic neuropathy (DN). Avanafil (AV), that selectively inhibits the degradation of cGMP-specific phosphodiesterase, thereby increasing the levels of cGMP, makes a decisive mediator for cytoprotection. (2) Methods: AVnanocomplex formulations were prepared by a modified anti-solvent precipitation method and the method was optimized by Box–Behnken design. An optimized formulation was characterized and evaluated for various in vitro parameters; (3) results:based on the desirability approach, the formulation containing 2.176 g of chitosan, 7.984 g of zein and 90% v/v ethanol concentration can fulfill the prerequisites of optimum formulation (OB-AV-NC).OB-AV-NC was characterized and evaluated for various parameters. The neuroprotective mechanism of AV was evaluated by pretreatment of PC12 cells with plain AV, avanafil nanocomplex (NC) without antioxidants (AV-NC) and with antioxidants (α-Lipoic acid LP; Ellagic Acid EA), AV-LP-EA-Nanocomplex has also shown considerable attenuation in intracellular reactive oxygen species (ROS) and lipid peroxidation with a significant increase in the PC 12 viability under HG conditions in comparison to pure AV; (4) conclusion: the nanocomplex of AV prepared to utilize natural polymers and antioxidants aided for high solubility of AV and exhibited desired neuroprotective activity.This can be one of the promisingstrategy to translate the AV nanocomplex with safety and efficacy in treating DN.

Parametric investigation of tool wear rate in EDM of Fe-based shape memory alloy: microstructural analysis and optimization using genetic algorithm

Purpose This study aims to experimentally investigate the influence of considered process parameters, i.e. pulse on time, pulse off time, peak current and gap voltage, on tool wear rate (TWR) in electrical discharge machining (EDM) of iron (Fe)-based shape memory alloy (SMA) through designed experiments. The parametric optimization for TWR has also been attempted using the desirability approach and genetic algorithm (GA). Design/methodology/approach The response surface methodology (RSM) in the form of Box–Behnken design has been used to scheme out the experiments. The influence of considered process inputs has also been observed through variance analysis. The reliability and fitness of the developed mathematical model have been established with test results. Microstructure analysis of machined samples has also been evaluated and analyzed using a scanning electron microscope (SEM). SEM images revealed the surface characteristics such as micro-cracks, craters and voids on the tool electrode surface. SEM images provide information about the surface integrity and type of wear on the surface of the tool electrode. Findings The input parameters, namely, pulse on time and pulse off time, are major influential factors impacting the TWR. High TWR has been reported at large pulse on time and small pulse off time conditions whereas higher TWR is reported at high peak current input settings. The maximum and minimum TWR values obtained are 0.073 g/min and 0.017 g/min, respectively. The optimization with desirability approach and GA reveals the best parametric values for TWR i.e. 0.01581 g/min and 0.00875 g/min at parametric combination as pulse on time = 60.83 µs, pulse off time = 112.16 µs, peak current = 18.64 A and gap voltage = 59.55 V, and pulse on time = 60 µs, pulse off time = 120 µs, peak current = 12 A and gap voltage = 40 V, correspondingly. Research limitations/implications Proposed work has no limitations. Originality/value SMAs have been well known for their superior and excellent properties, which make them an eligible candidate of paramount importance in real-life industrial applications such as orthopedic implants, actuators, micro tools, stents, coupling, sealing elements, aerospace components, defense instruments, manufacturing elements and bio-medical appliances. However, its effective and productive processing is still a challenge. Tool wear study while processing of SMAs in EDM process is an area which has been less investigated and of major concern for exploring the various properties of the tool and wear in it. Also, the developed mathematical model for TWR through the RSM approach will be helpful in industrial revelation.

Modeling of the machining parameters in turning of Al-5052/TiC/SiC composites: a statistical modeling approach using grey relational analysis (GRA) and response surface methodology (RSM)

PurposeMetal matrix composites (MMC) has been a section which gives an overview of composite materials and owing to those exceptional physical and mechanical properties, particulate-reinforced aluminum MMCs have gained increasing interest in particular engineering applications. Owing to the toughness and abrasive quality of reinforcement components such as silicon carbide (SiC) and titanium carbide (TiC), such materials are categorized as difficult materials for machining. The work aims to develop the model for evaluating the machinability of the materials via the response surface technique by machining three distinct types of hybrid MMCs.Design/methodology/approachThe combined effects of three machining parameters, namely “cutting speed” (s), “feed rate” (f) and “depth of cut” (d), together with three separate composite materials, were evaluated with the help of three performance characteristics, i.e. material removal rate (MRR), cutting force (CF) and surface roughness (SR). Response surface methodology and analysis of variance (ANOVA) both were initially used for analyzing the machining parameters results.FindingsThe contours were developed to observe the combined process parameters along with their correlations. The process variables were concurrently configured using grey relational analysis (GRA) and the composite desirability methodology. Both the GRA and composite desirability approach obtained similar results.Practical implicationsThe results obtained in the present paper will be helpful for decision-makers in manufacturing industries, who work on metal cutting area especially composites, to select the suitable solution by implementing the Grey Taguchi and modeling techniques.Originality/valueThe originality of this research is to identify the suitability of process parameters combination based on the obtained research results. The optimization of machining parameters in turning of hybrid metal matrix composites is carried out with two different methods such as Grey Taguchi and composite desirability approach.

Experimental investigation of machinability and geometrical behavior of Titanium Alloy (Ti–6Al–4V) using Abrasive Waterjet Circular cut using Response Surface Methodology-Desirability approach

In the current study, the research explored the effect of the process parameters on the Titanium Alloy (Ti–6Al–4V) to improve the machining, surface and geometric characteristics of the circular cut-off profile by determining the optimum parameters for the Abrasive Water Jet Machining (AWJM). The input parameters considered are the Abrasive Flow Rate (AFR), Stand-off Distance (SoD), and Traverse Rate (TR). There are various input parameters to evaluate output parameters like Circularity, Cylindricity, and Surface Roughness (SR) of the circular cut profile. The experiments are conducted using Central Composite Design (CCD) in the Response Surface Methodology (RSM). Analysis of variance (ANOVA) is carried out to define most influenced process parameters and percentage of contribution. The RSM is used to predict the mathematical models for formulating the objective function using experimental results. RSM desirability approach is included in the method for determining optimum levels and discerning impacts on response variables of machining parameters. Confirmation tests with an optimum level of machining parameters have been completed to determine the adequacy of the RSM. In addition to that, the cutting profiles are also analysed using Scanning Electron Microscope (SEM). The Atomic Force Microscope(AFM) is often used to verify the minimum Surface Roughness of the AWJM machined surface.

Optimization of the Indirect Extrusion Process of Copper-Clad Aluminum Rods by Methods of Statistical Experimental Designs and Numerical Analyses

The success of composite extrusion is influenced by multiple process parameters. In order to investigate the significance of specific parameters during indirect extrusion of copper-clad aluminum (CCA) rods, statistical methods were applied and a central composite experimental design was implemented. The runs of the experimental design were modeled with the finite element method based software DEFORM 2D and evaluated with respect to product quality, described by four response variables. Using variance and regression analyses, as well significant linear and quadratic effects of the five investigated process parameters as interactions between them were identified. Based on a statistical model, an overall optimum setting for the process parameters was predicted utilizing the response surface methodology with a desirability approach. By applying the output of the statistical analysis to an extrusion trial, the extrusion of a high quality CCA rod was achieved. Moreover, the results of the statistical analysis could be verified by comparing predicted and experimentally determined values of the investigated quality characteristics.

Cottonseed Kernel Powder as a Natural Health Supplement: An Approach to Reduce the Gossypol Content and Maximize the Nutritional Benefits

Cottonseed is one of the important by-products of the cotton crop. Researchers claim that cottonseed with less than 0.45% of gossypol is quite good for human consumption and animal feeding because it is a rich source of protein, edible oil, and energy. Total and free gossypols are the influencing parameters that reduce the edible nature of the cottonseed. In the present work, multiple quadratic regression models have been prepared to predict the reduction in the free and total gossypol percent. This response surface method (RSM)-based approach was applied to investigate the combined effect between input parameters such as acetone level, time of extraction, liquid-to-solid ratio (LSR), and the number of extraction cycles, whereas output responses are free and total gossypol reduction percentage. Analysis of Variance (ANOVA) has been performed to determine the highly significant parameter. The optimum combination of input parameters was determined using the RSM-based desirability approach, and confirmatory experiments were performed to validate the combination. Results revealed that the number of extraction cycles and liquid-to-solid ratio significantly affects the reduction of free and total gossypol levels. The values of r-square were found above 0.9, which indicates that the developed models are suitable and reliable for predicting free and total gossypol reduction percentage.

Application of response surface methodology for color removing from dyeing effluent using de-oiled activated algal biomass

Background Conservation of the ecosystem is a prime concern of human communities. Industrial development should adopt this concern. Unfortunately, various related activities release lots of noxious materials concurrently with significant leakage of renewable resources. This work presents a new biosorbent activated de-oiled microalgae, Chlorella vulgaris, (AC) for biosorption of Acid Red 1 (AR1) from aqueous solution simulated to textile dyeing effluent. The biosorption characteristics of AC were explored as a function of the process parameters, namely pH, time, and initial dye concentration using response surface methodology (RSM). Results Optimization is carried out using the desirability approach of the process parameters for maximum dye removal%. The ANOVA analysis of the predicted quadratic model elucidated significant model terms with a regression coefficient value of 0.97, F value of 109.66, and adequate precision of 34.32 that emphasizes the applicability of the model to navigate the design space. The optimization depends on the priority of minimizing the time of the process to save energy and treating high concentrated effluent resulted in removal % up to 83.5%. The chemical structure and surface morphology of AC, and the dye-loaded biomass (AB) were characterized by Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), energy-dispersive X-ray analysis (EDX), and transmission electron microscope (TEM). The activation process transforms the biomass surface into a regular and small homogeneous size that increases the surface area and ultimately enhances its adsorption capacity Conclusion The optimization of the process parameters simultaneously using RSM performs a high-accurate model which describes the relationship between the parameters and the response through minimum number of experiments. This study performed a step towards an integrated sustainable solution applicable for treating industrial effluents through a zero-waste process. Using the overloaded biomass is going into further studies as micronutrients for agricultural soil.