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The purpose of this paper is to optimise the process parameters, namely, point angle, spindle speed and feed rate in the drilling of glass-reinforced stainless steel mesh polymer (GRSSMP) composites using grey relational fuzzy logic.

Based on the full factorial design, the experiments were conducted. The output responses considered are thrust force, torque, delamination and diameter deviation. Based on responses, the optimised process parameter was selected using grey-fuzzy reasoning analysis (GFRA).

The percentage contribution of the drilling parameters is analysed using analysis of variance (ANOVA), and the result shows that feed rate is the most influential factor in the drilling of GRSSMP composites.

The optimised drilling parameters have been used for drilling of polymer composites in the production industry.

Optimisation of process parameters during the drilling of GRSSMP composites using GFRA has not been performed previously.

In this study, TiO2 is used to enhance the mechanical properties of the composite material containing agave Americana fiber and polyester resin.

Agave Americana fiber was first treated with 5% of NaOH, and the composition of treated and untreated fiber was kept constant, whereas the particulate and resin were alternatively used. The handlay method is used to fabricate the composite plates. The morphology of the composites was studied using scanning electron microscopy (SEM).

The composite was composed of 30% treated agave Americana, 10% of TiO₂ particulates and 60% of a polyester resin for better and enhanced mechanical properties.

The composite can be used for aero-structural components, automobile components and other areas where light-weight components are required.

A new type of agave Americana fiber with TiO₂ and polyester resin composite was fabricated and investigated.

The purpose of this paper is to apply a framework for value stream mapping (VSM) integrated with fuzzy quality function deployment (QFD) for enabling scientific prioritization of improvement proposals to improve leanness.

The literature was reviewed from the perspectives of VSM, QFD and fuzzy logic applications. The current state map was developed for the case component; fuzzy QFD was used for prioritizing improvement proposals and prioritized proposals were incorporated in the future state map.

The approach enabled the scientific mapping of wastes with improvement proposals and thereby enabling systematic implementation of improvement proposals. The conducted pilot study resulted in 4 percent reduction in cycle time. As the lean implementation is a continuous process, furthermore improvements are expected in near future.

The study was conducted in an Indian camshaft manufacturing organization. The improvements in terms of leanness parameters were quantified.

The findings determined from the study has practical relevance. Besides, managerial implications were also discussed.

The study presented in this paper was conducted in a real time manufacturing environment. Hence the contributions of study are found to be valuable among academic and practicing communities.

The purpose of this paper is to determine the optimum level of geometrical parameters such as helix angle, nose radius, rake angle and machining parameters such as cutting speed, feed rate and depth of cut to arrive minimum surface roughness and tool wear during end milling of Al 356/SiC metal matrix composites (MMCs) using high speed steel end mill cutter.

L27 Taguchi orthogonal design with six factors and three levels is employed for conducting experiments. Analysis of variance (ANOVA) is carried out using Minitab16 software to find the influence of each input parameter on output performance measure. Grey-fuzzy logic multi optimisation algorithm is used to find the optimum level of the input parameters for minimum surface roughness and tool wear simultaneously.

It is found that optimal combination of helix angle 40°, nose radius 0.8 mm, rake angle 12°, cutting speed 90 m/min, feed rate 0.04 mm/rev and depth of cut 1.5 mm have generated minimum surface roughness of 0.4063 µm and tool wear of 0.0375 mm. From ANOVA analysis, it is found that cutting speed influence is more on output performance followed by helix angle and rake angle compared with other machining and geometrical parameters.

The influence of tool geometry during end milling of MMC using Grey-fuzzy logic algorithm has not been explored previously.

Milling is a flexible creation process for the manufacturing of dies and aeronautical parts. While machining thin-walled parts, heat generation during machining essentially affects the accuracy. The workpiece temperature (WT), as well as the responses like material removal rate (MRR) and surface roughness (SR) for input parameters like cutting speed (CS), feed rate (F), depth-of-cut (DOC), step over (SO) and tool diameter (TD), becomes critical for sustaining the accuracy of the thin walls.

Response surface methodology was used to make 46 tests. To convert the multi-character problem into a single-character problem, the weightage was assessed using the entropy approach and the grey relational coefficient (GRC) was determined. To investigate the connection among input parameters and single-objective (GRC), a fuzzy mathematical modelling technique was used. The optimal performance of process parameters was estimated by grey relational entropy grade (GREG)-fuzzy and genetic algorithm (GA) optimization.

SR was found to be a significant process parameter, with CS, feed and DOC, respectively. Similarly, F, DOC and TD were found to be significant process parameters with MRR, respectively, and F, DOC, SO and TD were found to be significant process parameters with WT, respectively. GREG-fuzzy-GA found more suitable for minimizing the WT with the constraint s of SR and MRR and provide maximum desirability of 0.665. The projected and experimental values have a good agreement, with a standard error of 5.85%, and so the responses predicted by the suggested method are better optimized.

The GREG-fuzzy-GA is a new hybrid technique for analysing Inconel625 behaviour during machining in a 2.5D milling process.

The purpose of this paper is to find out the optimal level as well as the influence of end mill cutter geometrical and machining parameters while machining metal matrix composite. End milling is carried out on Al 356/SiC metal matrix composites (MMC) using high-speed steel (HSS) end mill cutter. The optimum level of input parameters such as helix angle, nose radius, rake angle, cutting speed, feed rate and depth of cut are calculated for minimum temperature rise.

L27 Taguchi orthogonal design, signal-to-noise (S/N) ratio, are applied for conducting experiments, and to find the optimal level of input parameters for minimum temperature rise, respectively. Analysis of variance (ANOVA) is used to analyze the significance of input parameters on temperature rise.

It is found that the optimal combination of helix angle 400, nose radius 0.8 mm, rake angle 80, cutting speed 30 m/min, feed rate 0.04 mm/rev and depth of cut 0.5 mm have generated minimum temperature rise. From ANOVA analysis, it is found that rake angle influence is more on output performance followed by cutting speed and nose radius compared with other machining and geometrical parameters.

The influence of geometrical parameters such as helix angle, nose radius and rake angle of end mill cutter on temperature rise while machining MMC has not been explored previously.

This paper aims to address the production of biocomposite nanofibers using luffa natural fibers and polyaniline conductive polymer/polyethylene oxides (PANI/PEO).

In this study, luffa natural fibers are extracted by chemical method. After mixing the treated luffa (TL) with the PANI/PEO solution, TL/PANI/PEO nanofibers were produced by electrospinning (ES) method under different ES parameters to examine the optimal conditions for nanofiber production. Then TL/PANI/PEO biocomposite nanofibers prepared in different weight ratios were produced to analyze the effects of luffa in the morphology and thermal properties of the biocomposite nanofibers. The characterization analysis of TL/PANI/PEO biocomposite nanofibers was performed by scanning electron microscopy, Fourier transform infrared spectroscopy (FTIR) and differential scanning calorimetry (DSC) analysis methods.

The analysis shows that different weight ratios of TL to PANI/PEO changed the morphology of the membrane. When increasing the weight ratio of TL, the morphological structure of TL/PANI/PEO transformed from nanofiber structure to thin film structure. The appearance of O—H peaks in the FTIR results proved the existence of TL in PANI/PEO nanofibers (membrane). Moreover, an increase in the weight ratio of luffa from 2% to 7.5% leads to an increase in the peak intensity of the O—H group. Regarding DSC analysis, biocomposite nanofibers improved the thermal properties. According to all results, 2%wt TL/PANI/PEO showed optimal morphological properties.

Plant cellulose was extracted from the luffa, one of the natural fibers, by method of alkali treatment. A new type of biocomposite nanofibers was produced using TL blend with PANI via electrospinning method.

The purpose of this paper is to determine the influence of the shape of a cutting edge on high-performance milling high-performance cutting. The main purpose of the test was to determine the possibility of increasing the efficiency of machining AlZn5.5CuMg alloy, which is used mainly for the thin-walled structural aerospace components.

In all, eight cutters for machining aluminum alloys with different shape of the cutting edge (1 – continuous, 4 – interrupted, 3 – wavy) were tested. The influence of different shapes of a cutting edge on cutting force components and vibration amplitude was analyzed. Furthermore, the impact of a chip breaker on the form of a chip was determined.

The conducted test shows that using discontinuous shapes of a cutting edge has impact on the reduction of the cutting force components and, in most cases, on the increase of vibration amplitude. Moreover, using a chip breaker caused significant chip dispersion. The optimal shape of a cutting edge for cutting AlZn5.5CuMg alloy is fine wavy shape.

Potential practical application of the research is high-performance milling of AlZn5.5CuMg alloy, for example, production of thin-walled aerospace structural components.

Different shapes of a cutting edge during high-performance milling of aluminum alloy were tested. The influence of tested geometries on HPC process was determined. The most favourable shape of a cutting edge for high-performance cutting of AlZn5.5CuMg alloy was determined.

The weld joint mechanical properties of friction stir welding (FSW) are majorly reliant on different input parameters of the FSW machine. The study and optmization of these parameters is uttermost requirement and aim of this study to increase the suitability of FSW in different manufacturing industries. Hence, the input parameters are optimized through different soft computing methods to increase the considered objective in this study.

In this research, ultimate tensile strength (UTS), yield strength (YS) and elongation (EL) of FSW prepared butt joints of AA6061 and AA5083 Aluminium alloys materials are investigated as per American Society for Testing and Materials (ASTM E8-M04) standard. The FSW joints were prepared by changing the three input process parameters. To develop experimental run order design matrix, rotatable central composite design strategy was used. Furthermore, genetic algorithm (GA) in combination (Hybrid) with response surface methodology (RSM), artificial neural network (ANN), i.e. RSM-GA, ANN-GA, is exercised to optimize the considered process parameters.

The maximum value of UTS, YS and EL of test specimens on universal testing machine was measured as 264 MPa, 204 MPa and 14.41%, respectively. The most optimized results (UTS = 269.544 MPa, YS = 211.121 MPa and EL = 17.127%) are obtained with ANN-GA for the considered objectives.

The optimization of input parameters to increase the output objective values using hybrid soft computing techniques is unique in this research paper. The outcomes of this study will help the FSW using manufacturing industries to choose the best optimized parameters set for FSW prepared butt joint with improved mechanical properties.

The purpose of this paper is to identify the ideal process parameters to be set for the drilling of hybrid fibre-reinforced polymer (FRP) (kenaf and banana) composite using High-Speed Steel drill bits (5, 10, 15 mm) coated with tungsten carbide by means of statistical reproduction of the delamination factor and machining force using Taguchi–Grey Relational Analysis.

The contemplated process parameters are Feed, Speed and Drill Diameter. The trials were carried out by taking advantage of the L-27 factorial design by Taguchi. Three factors, the three level Taguchi Orthogonal Array design in Grey Relational Analysis was used to carry out the trial study. Video Measuring System was used to identify the damage around the drill region. “Minitab 18” was used to examine the data collected by taking advantage of the various statistical and graphical tools available. Examination of variance is used to legitimize the model in identifying the most notable parameter.

The optimised set of input parameters were found out successfully which are as follows: Feed Rate: 450 mm/min, Cutting Speed: 3,000 rpm and Drill Diameter of 5 mm. When these values are fed in as input the optimised output is being obtained. From ANOVA analysis, it is apparent that the Speed (contribution of 92.6%) is the most influencing parameter on the delamination factor and machining force of the FRP material.

Optimization of process parameters on drilling of natural fibres reinforced in epoxy resin matrices using Taguchi–Grey Relational Analysis has not been previously explored.