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The purpose of this paper is to minimize the mean flow time in open shop scheduling problem (OSSP). The scheduling problems consist of n jobs and m machines, in which each job has O operations. The processing time for 50 OSSP was generated using a linear congruential random number.

Different evolutionary algorithms are used to minimize the mean flow time of OSSP. This research study used simulated annealing (SA), Discrete Firefly Algorithm and a Hybrid Firefly Algorithm with SA. These methods are referred as A1, A2 and A3, respectively.

A comparison of the results obtained from the three methods shows that the Hybrid Firefly Algorithm with SA (A3) gives the best mean flow time for 76 percent instances. Also, it has been observed that as the number of jobs increases, the chances of getting better results also increased. Among the first 25 problems (i.e. job ranging from 3 to 7), A3 gave the best results for 13 instances, i.e., for 52 percent of the first 25 instances. While for the last 25 problems (i.e. Job ranging from 8 to 12), A3 gave the best results for all 25 instances, i.e. for 100 percent of the problems.

From the literature it has been observed that no researchers have attempted to solve OOSPs using Firefly Algorithm (FA). In this research work an attempt has been made to apply the FA and its hybridization to solve OSSP. Also the research work carried out in this paper can also be applied for a real-time Industrial problem.

The paper aims to develop a mathematical model for delamination and surface roughness during end milling by using response surface methodology (RSM) and to determine how the input parameters (cutting speed, depth of cut, helix angle and feed rate) influence the output response (delamination and surface roughness) in machining of hybrid glass fibre reinforced plastic (GFRP; Abaca and Glass) composite using solid carbide end mill cutter.

Four-factor, three-level Taguchi orthogonal array design in RSM is used to carry out the experimental investigation. The “Design Expert 8.0” is used to analyse the data collected graphically. Analysis of variance is carried out to validate the model and for determining the most significant parameter.

The feed rate is the cutting parameter which has greater influence on delamination (88.39 per cent), and cutting speed is the cutting parameter which has greater influence on surface roughness (53.42 per cent) for hybrid GFRP composite materials. Both surface roughness and delamination increase as feed rate increases, which means that the composite damage is larger for higher feed rates.

Effect of milling of hybrid GFRP composite on delamination and surface roughness with various helix angles of solid carbide end mill has not been analysed yet using RSM.

Sustainable end-of-life (Sus-EoL) practices can be achieved through manufacturing of sustainable products, and recovery and recycling after the use phase. To achieve Sus-EoL, the manufacturing organizations should handle their products after their EoL. The recovery of used products is achieved through the design of the collection location. However, the first step is to understand and identify the barriers (e.g. lack of awareness among people, lack of technology, etc.) which prevent the implementation of Sus-EoL practices. The paper aims to discuss these issues.

This paper is about the 18 barriers responsible for the poor success of Sus-EoL practices of used plastic parts. By applying the DEMATEL method and by incorporating experts’ knowledge, a prominence and causal relationship diagram was developed through which the influential strength among barriers was studied.

The α value is computed as 0.068, and the values lower than α were eliminated to obtain the digraph. Poor curbside pick is identified as the most dominant barrier in implementation of Sus-EoL practices in plastic parts with an influential score of 3.96.

The research is conducted in the Indian scenario which could be extended to global context by selecting the suitable barriers.

The results from the study can be used by the managers of organizations to enhance the possibility of Sus-EoL practices by incorporating suitable strategies which is the significant contribution of this study.

In the past, few authors discussed about the barriers of Sus-EoL practices; however, the analysis of complex interrelationship does not exist. Thus, the global and group interrelationship has been studied which is expected to pave way for future research in the direction of elimination of barriers and so on.

This paper aims to develop a mathematical model for analysing surface roughness during end milling by using response surface methodology (RSM) and to determine how the input parameters (cutting speed, depth of cut and feed rate) influence the output parameter (surface roughness) in the machining of hybrid glass fibre reinforced plastic (GFRP; Abaca and Glass) composite by using solid carbide end mill cutter.

Three factors and a three-level Box–Behnken design in RSM were used to carry out the experimental investigation. Handysurf E-35A was used to measure the surface roughness of the machined hybrid GFRP composites. The “Design Expert 8.0” was used to analyse the data collected graphically. Analysis of variance was carried out to validate the model and determine the most significant parameter.

The response surface model was used to predict the input factors influencing the surface roughness of the machined surfaces of hybrid GFRP composite at different cutting conditions with a chosen range of 95 per cent confidence intervals. Analysis of the influences of the entire individual input machining parameters on the surface roughness carried out using RSM.

The effect of the milling of hybrid GFRP composite on the surface roughness with solid carbide end mill by using RSM has not been analysed yet.

This paper aims to develop a mathematical model for delamination during end milling by using response surface methodology (RSM) and to determine how the input parameters (cutting speed, depth of cut and feed rate) influence the output response (delamination) in machining of hybrid glass fibre reinforced plastic (GFRP; abaca and glass) composite using solid carbide end mill cutter.

Three factors, three levels Box–Behnken design in RSM is used to carry out the experimental investigation. Shop microscope Mitutoyo TM-500 is used to measure the width of maximum damage of the machined hybrid GFRP composites. The “Design Expert 8.0” is used to analyse the data collected graphically. Analysis of variance is carried out to validate the model and for determining the most significant parameter.

The RSM is used to predict the input factors influencing the delamination on the machined surfaces of hybrid GFRP composite at different cutting conditions with the chosen range of 95 per cent confidence intervals. Analysis on the influences of the entire individual input machining parameters on the delamination has been carried out using RSM.

Effect of milling of hybrid GFRP composite on delamination with solid carbide end mill has not been analysed yet using RSM.

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.

This study aims to optimize the parametric combination of injected slub yarn to achieve least abrasive damage on fabrics produced from it.

Single base injected slub yarn structural parameters, vis-à-vis slub length, slub thickness and slub frequency, were varied during preparation of yarn samples under this research work. A total of 17 yarn samples were produced according to the Box and Bhenken design of the experiment. Subsequently knitted and woven (using injected slub yarns in the weft only) fabric samples were prepared from these yarns. Yarn and fabric samples were abraded with standard instruments to see the impact of yarn structural parameters on abrasive damage of fabric in terms of fabric mass loss and appearance deterioration. From the test results, empirical models relating to slub parameters and fabric abrasion behavior were developed through a backward elimination regression approach. Subsequently, a set of optimal parametric combinations was derived with multi-objective evolutionary algorithms by using MATLAB software. This was followed by ranking all optimal solutions through technique for order preference by similarity to idle solution (TOPSIS) score analysis.

The injected slub yarn’s structural parameters have a strong influence on the abrasive damage of knitted and woven fabric. It is seen that the best suitable parametric combination of slub parameters for achieving the least abrasive damage is not the same for knitted and woven fabric.

The spinner can explore this concept to find out the best suitable parametric combination during pattern making of injected slub yarn through MATLAB solution followed by TOPSIS score analysis based on their priority of criteria level to ensure better abrasion behavior of fabric produced.

Optimization of parametric combination of injected slub yarns will help to ensure production of fabric with most resistance to abrasion for specific applications. The studies showed that the optimal solution for woven and knitted fabrics is different. The result indicates that in the case of knitted fabric, comparatively lesser slub thickness is found to be suitable for getting better fabric abrasion resistance, whereas in the case of woven fabric, comparatively higher slub thickness is found suitable for the same.

The purpose of this paper is to find out the optimum machining parameters using Taguchi technique with principal component analysis (PCA) during end milling of GFRP composites.

In multi-objective optimization, weight criteria of each objective are important for producing better and accurate solutions. This method has been employed for simultaneous minimization of surface roughness, cutting force and delamination factor. Experiments were planned using Taguchi’s orthogonal array with the machining parameters, namely, helix angle of the end mill cutter, spindle speed, feed rate and depth of cut were optimized with considerations of multiple response characteristics, including machining force, surface roughness and delamination as the responses. PCA is adopted to find the weight factors involved for all objectives. Finally analysis of variance concept is employed on multi-SN ratio to find out the relative significance of machining parameter in terms of their percentage contribution.

The multi-SN ratio is achieved by the product of weight factor and SN ratio to the performance characteristics in the utility concept. The results show that a combination of machining parameters for the optimized results has helix angle of 35°, machining speed of 4,000 m/min, feed rate of 750 mm/rev and depth of cut of 2.0 mm.

Effect of milling of GFRP composites on delamination factor, surface roughness and machining force with various helix angle solid carbide end mill has not been analysed yet using PCA techniques.

The purpose of this paper is to develop a mathematical model for delamination through response surface methodology (RSM) and analyse the influences of the entire individual input machining parameters (cutting speed, 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 polycrystalline diamond (PCD).

Three factors, three levels face-centered central composite design matrix in RSM is employed to carry out the experimental investigation. Shop microscope is used to examine the delamination of GFRP composites. The “Design Expert 8.0” software was used for regression and graphical analysis of the data collected. Analysis of variance is used to check the validity of the model and for finding the significant parameters.

The developed second-order response surface model is used to calculate the delamination of the machined surfaces at different cutting conditions with the chosen range of 95 per cent confidence intervals. Analysis of the influences of the entire individual input machining parameters on the delamination has been carried out using RSM.

Influence of solid carbide end mill coated with PCD on delamination of bi-directional GFRP composite during milling has not been analysed yet using RSM.

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.