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The purpose of this paper is to optimize the welding parameters: rotating speed and plunging depth of carbon steel and pure copper joints using friction stir spot welding (FSSW) with the aid of the design of experiments (DOE) method.

Carbon steel and pure copper sheets were welded using the FSSW technique with a cylindrical tool and without a probe. The welding parameters were: rotating speed: 1,120, 1,400 and 1,800 RPM and plunging depth: 0.2 and 0.4 mm. The welding process was carried out both with and without pre-heating. The welded specimens were analyzed using a shear tensile test. A microstructural investigation at the optimum conditions was carried out. The results were analyzed and optimized using the statistical software Minitab and following the DOE method.

Pre-heating the sample and increasing the rotating speed and plunging depth increased the tensile shear force of the joint. The plunging depth has the biggest effect on the joint efficiency compared with the rotating speed. The optimum shear force (4,560 N) was found at 1,800 RPM, 0.4 mm plunge depth and with pre-heating. The welding parameters were modified so that the samples were welded at 1,800 RPM and at plunging depths of 0.45–1 mm in 0.05 mm steps. The optimized shear force was 5,400 N. The fractured samples exhibited two types of failure mode: interfacial and nugget pull-out.

For the first time, pure copper and carbon steel sheets were welded using FSSW and a tool without a probe with ideal joint efficiency (95 percent).

This paper analyses the effect of circular economy practices on sustainable supply chain performance. The study explores the impact of mediating variables such as supply chain flexibility and capabilities and the moderating role of supply chain integration in the relationship between circular economy practices and sustainable supply chain performance in Indian manufacturing firms. The study builds on the stimulus-organism-response (S-O-R) model to conceptualise circular economy practices that influence supply chain capabilities, integration and flexibility, impacting sustainable supply chain performance.

This study adopted an online survey questionnaire distributed to managers of Indian manufacturing firms adopting circular economy practices. The data were analysed using SPSS Amos 25 and PROCESS macros.

The results suggest a positive impact of circular economy practices on sustainable supply chain performance in manufacturing firms. In addition, a supply chain manager's relationship with retailers is improved in the presence of supply chain capabilities and flexibility. Supply chain integration further strengthens this relationship as a moderating variable.

By examining the literature on circular economy practices and sustainable supply chain management, this study contributes to bridging the gap between supply chain capabilities, integration and flexibility using the S-O-R model. This study is possibly among the first to explore and provide empirical evidence on how circular economy practices in manufacturing firms can impact supply chain managers' experiences and thus help to improve environmental well-being. Both academics and business professionals might find these contributions interesting.

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.

The automotive industry extensively uses switched reluctance motors (SRM) because of their excellent performance. The main purpose of this article is to investigate the design of a particular type of SRM called doubly salient outer rotor switched reluctance motor (DSORSRM) for electric vehicle application in this paper.

Different configurations of DSORSRM motor such as long flux path SRM, reduced flux path mutually coupled SRM and short flux path SRM (SF-SRM) are considered for investigation. The best configuration based on average torque is selected for further investigation by conducting an electromagnetic analysis. Also, in the proposed design, laminating material with low iron loss and superior performance characteristics is selected by doing electromagnetic analysis for SRM with M19, M660-50D, M-19 and M800-100A non-oriented laminating core material. Because vibrations are produced in DSORSRM devices as a result of changing induction, a mechanical analysis was performed to estimate the natural frequencies of vibration and the amplitudes that may lead to acoustic noises.

SF-SRM configuration with three-phase, 12/10, 250 W, 48 V, 1,000 rpm is selected with the impact in the elimination of flux reversals and also has various salient features such as singly excited, no rotor windings, no permanent magnet, pure in construction and high starting torque. Still, this SRM suffers from vibration owing to changing induction. In lamination material selection, M19 is chosen as optimized material to obtain vibration reduction. Vibration analysis was performed for the optimized 12/10 SF-SRM with M19 lamination material, and the corresponding modes for the machine to operate with reduced vibration are analyzed. The current and speed characteristics of the prototype model for the DSORSRM motor are obtained and validated with finite element analysis (FEA) results.

The performed FEA result shows that the proposed DSORSRM with short flux path configuration produces a high average torque of 1.915 N m. The M19 lamination material gives a minimum iron loss of 9.056 W. The modal frequencies are estimated and validated with numerical equations.

This paper aims to present a friction stir molding (FSM) method for the rapid manufacturing of metal tooling. The method uses additive and subtractive techniques to sequentially friction stir bond and then mill slabs of metal. Mold tooling is grown in a bottom-up fashion, overcoming machining accessibility problems typically associated with deep cavity tooling.

To test the feasibility of FSM in building functional molds, a layer addition procedure that combines friction stir spot welding (FSSW) with an initial glue application and clamping for slabs of AA6061-T651 was investigated. Additionally, FSSW parameters and the mechanical behavior of test mold materials, including shear strength and hardness, were studied. Further, scanning electron microscopy (SEM)/elemental map analysis (EDS) of the spot weld zones was carried out to understand the effect of FSSW on the glue materials and to study potential mixing of glue with the plate materials in the welded zone.

The results indicate that FSM provides good layer stacking without gaps when slabs are pre-processed through sand blasting, moistening, uniform clamping and FSSW using a tapered pin tool. The tensile shear strength results revealed that the welded spots were able to withstand cutting forces during machining stages; however, FSSW was found to cause hardness reduction among spot zones because of over-aging. The SEM/EDS results showed that glue was not mixed with slab materials in spot zones. The proposed process was able to build a test tooling sample successfully using AA6061-T651 plates welded and machined on a three-axis computer numerical control (CNC) mill.

The proposed FSM process is a new process presented by the authors, developed for the rapid manufacturing of metal tooling. The method uses additive and subtractive techniques to sequentially friction stir bond and then mill slabs of metal. The use of FSSW process for materials addition is an original contribution that enables automatic process planning for this new process.

The aim of this paper states that total productive maintenance (TPM) is an improvement tool which employs the effective utilization of employees in order to enhance the reliability of the equipment in consideration.

This paper identifies and evaluates the factors accountable for the adoption of TPM methodology in manufacturing organizations. Twenty-four factors affecting the TPM implementation are explored and categorized into five significant categories. Afterwards, these identified TPM factors have been evaluated by using a most popular Multi-criteria decision-making (MCDM) approach namely fuzzy pivot pairwise relative criteria importance assessment (F-PIPRECIA).

In this paper, through application of F-PIPRECIA, “Behavioural factor” is ranked first while “Financial factor” the last. Considering the sub-factors, “Top management support and commitment” is ranked first while “Effective use of performance indices” is ranked the last. A further sensitivity analysis indicates the factors that need higher level of attention.

The result of current research work may be exploited by the top administration of manufacturing enterprises for assessing their TPM adoption status and to recognize the frail links of TPM application and improve accordingly. Moreover, significant factors of TPM can be identified and deploy them successfully in their implementation procedure.

The conclusion obtained from this research enables the management to clearly understand the significance of each considered factor on the adoption of TPM in the organization and hence, provides effective utilization of resources.

In the current scenario, new materials are gaining popularity due to higher specific properties of strength and stiffness, increase in wear resistance, dimensional stability at higher temperature, etc. Subsequently, the need for precise machining has also been increased enormously. The purpose of this paper is to study the surface roughness during the turning of Al-10%SiC and Al-5%SiC-5%Gr composites under different cutting conditions.

Artificial neural network (ANN) has been effectively employed in solving problems with effortless computation in the areas such as fault diagnosis, process identification, property estimation, data smoothing and error filtering, product design and development, optimisation and estimation of activity coefficients. Response surface method is also used to analyse the problems involving a number of input parameters and their corresponding relationship between one or more measured dependent responses. Using Design Expert.8 evaluation software package, a simpler and more efficient statistical RSM model has been designed. RSM models are created by using 27 experimental data measurements obtained from different turning conditions of aluminium alloy composites.

In this work, the surface roughness during turning of Al-10%SiC and Al-5%SiC-5%Gr composites under different cutting conditions has been studied. The surface roughness value is proportional with the increase in feed rate and depth of cut while inversely proportional with the cutting speed. In all turning conditions, Al-10%SiC composite has lower surface roughness values than Al-5%SiC-5%Gr hybrid composite. An ANN and response surface models have been developed to predict the surface roughness of machined surface. The experimental results concur well with predicted models.

In the present trend, new materials are gaining popularity due to higher specific properties of strength and stiffness, increase in wear resistance, dimensional stability at higher temperature, etc. Subsequently, the need for precise machining has also been increased enormously. In this work, the surface roughness during turning of Al-10%SiC and Al-5%SiC-5%Gr composites under different cutting conditions has been studied.

The purpose of this study is to determine the enablers of the quality of work life (QWL) of employees working in the Garment industries.

The study was carried out in a fivefold step. In the first step, the enablers of QWL were identified through an exhaustive literature survey, in the second step identified vital few components through Pareto analysis. Then the third step was followed by exploratory factor analysis (EFA) to further, to identify the precise components and validate the same using confirmatory factor analysis in fourth step. The final step included interpretive structural modeling and Cross-Impact Matrix Multiplication Applied to Classification analysis to model the validated components and determine the interrelationships and linkages.

Predominant QWL enablers of employees working in the garment industries are training and development, satisfaction in job, compensation and rewards, relation and co-operation, grievance handling, work environment, job nature, job security and facilities.

In this study, the interpretive structural model is designed based on the opinion of the experts who are working in the garment industry considering the responses from employees in garment sectors. The framework can be extended further to the other sectors.

In future, the researchers in QWL may develop a model to quantify the level of employees’ QWL who are working in different sectors. Enablers of QWL are essential, and based on this further statistical analysis can be carried out. This study will provide limelight to the researchers in choosing the valid and reliable set of enablers for the empirical studies. Organizations can get benefit by implementing the outcome of this research for the enhancement of the QWL of employees.

The study was carried out in 133 garment industries where 851 workers constituted the final valid responses that were considered for analysis. The outcomes from the study help administrators, policy and decision-takers in taking decisions to enhance QWL.

The purpose of this paper is to address the determination of crack location and depth of multiple transverse cracks by monitoring natural frequency and its prediction using Artificial Neural Networks (ANN). An alternative to the existing NDTs is suggested.

Modal analysis is performed to extract the natural frequency. Analysis is performed for two cases of cracks. In first case, both cracks are perpendicular to axis. In second case, both cracks are inclined to vertical plane and also inclined with each other. Finite element method (FEM) is performed using ANSYSTM software which is theoretical basis. Experimentation is performed using Fast Fourier Transform (FFT) analyzer on simply supported stepped rotor shaft and cantilever circular beam with two cracks each.

The results of FEM and experimentation are validated and are in good agreement. The error in crack detection by FEM is in the range of 3-15 percent while 5-20 percent by experimentation. The database obtained by modal analysis is used to train the network of ANN which predicts crack characteristics. Validity of method is investigated by comparing the predictions of ANN with FEM and experimentation. The results are in good agreement with error of 7-16 percent between ANN and FEM while 9-21 percent between ANN and experimental analysis.

It envisages that the method is capable. It is an effective as well as an alternate method of fault detection in beam/rotating element to the existing methods.

This paper aims to define a hierarchical and multi-criteria framework based on pillars of the Modernization of Higher Education to evaluate European Accounting Doctoral Programmes (EADE-Model).

The authors applied a quali-quantitative methodology based on the analytic hierarchy process and the survey approach. The authors conducted an extensive literature and regulation review to identify the dimensions affecting the quality of Doctoral Programmes, choosing accounting as the relevant and pivotal field. The authors also used the survey to select the most critical quality dimensions and derive their weight to build EADE Model. The validity of the proposed model has been tested through the application to the Italian scenario.

The findings provide a critical extension of accounting ranking studies constructing a multi-criteria, hierarchical and updated evaluation model recognizing the role of doctoral training in the knowledge-based society. The results shed new light on weak areas apt to be improved and propose potential amendments to enhance the quality standard of ADE.

Theoretical and practical implications of this paper are directed to academics, policymakers and PhD programmes administrators.

The research is original in drafting a hierarchical multi-criteria framework for evaluating ADE in the Higher Education System. This model may be extended to other fields.