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Micro-EDM is an important process in the field of micro-machining. Especially, the μEDM is one of the technologies widely used for manufacture of micro-parts, micro-tools and micro-components, etc. The accuracy and repeatability of the μEDM process is still highly dependent on the μWEDG process. The electrode generation and regeneration is considered a key enabling technology for improving the performance of the μEDM process. Many engineers considered the Taguchi technique as engineering judgment during multiple response optimizations. This paper aims to focus on the use of micro-WEDG process to generate a micro-tool (electrode) with minimum surface roughness and higher metal removal rate (MRR).

In this research work, the Taguchi quality loss function analysis is used to examine and explain the influences of three process parameters (feed rate, capacitance and voltage) on the output responses such as MRR and surface roughness. Further, the optimized machining parameters were determined considering the multiple response objective using Taguchi multi-response signal-to-noise ratio.

Based on the experimental result, it was concluded that the Taguchi technique is suitable for the optimization of multi-response problem.

This paper presents an alternative approach using Taguchi's quality loss function. In most of the modern technological situations, more than one response variable is pertinent to the success of an industrial process. In this research work, the influence of feed rate, capacitance and voltage on the MRR and surface roughness (multiple responses) is investigated.

Value creation in management education has been argued to happen throughout an academic programme. This study adopts a learner-centric value-perspective approach that advances the view that value creation is not a unitary occurrence but accrues in different forms in each phase of the learner journey process. As learners progress through a management programme, we focus on four key phases in the learner’s journey – pre-admission, pre-programme commencement, programme duration, and post-programme completion. These phases correspond to the learner’s transitioning from a prospect to having secured admission to actively attending classes to having graduated. Accordingly, in each phase of the learner journey in an Indian management education programme, this study (a) maps the institute’s actions, (b) identifies the learner touchpoints, and (c) evaluates the potential learner engagement levels from the respective touchpoints. This study categorizes the emergent value in each phase in the learner journey as the exploration of value, the commencement of value, the formation and augmentation of value, and the accrual of value. The study then concludes with identifying future research areas. Such an approach to deconstructing value can aid management institutions in nurturing talented and engaged learners.

The purpose of this paper is based on the response surface method, the authors determined the conditions for achieving the optimum rubber-sealing performance by using the maximum contact stress as the response value.

A two-dimensional model of a compression packer rubber was established by finite-element analysis software. Under the single axial load of 53.85 MPa, the four single factors of the end-face inclination angle, subthickness, height of rubber and friction coefficient of the rubber were analyzed.

Results show that the optimum sealing performance of the rubber tube is achieved when the end-face angle is equal to 45º and the thickness of the rubber tube is 9 mm. The response surface designed by Box–Behnken shows that the sealing performance of the rubber tube is the optimum when the end-face inclination angle is 48.1818°, the subthickness is 9 mm, the height of rubber is 90 mm and the friction coefficient is 0.1. Verification test results show that the model is reliable and effective.

Packer operations are performed downhole, and research on real experiments is limited. In this work, the feasibility of such experiments is determined by comparing finite-element modeling with actual experiments, and the results have guiding significance for actual downhole operations.

The framework of Six Sigma Define-Measure-Analyze-Improve-Control was applied in this study, and various tools and techniques were used at different stages to implement lean measures to ensure quality. The purpose of this paper is to develop a decision-making framework that assesses key quality performance to ensure that practitioners improve quality and control by modeling and optimizing production processes.

A model of a quality performance index system was established. The weights of factors and sub-factors, which were estimated using an FAHP, were used as a reference for the decision maker under fuzzy uncertainly to make a decision, and thus, results present the bottlenecks in processes. Furthermore, any other factors that may affect the key process bottlenecks must be considered. The critical to quality characteristics were determined, and factor levels were set. The interaction between the factors was analyzed, their significance was studied using the Design of experiments and the parameters were predicted. Finally, quality improvement decisions were made through failure mode and effects analysis.

The implementation results of this research prove that the proposed model could successfully determine the key processes and focus on the improvement of critical quality factors under limited resources.

This study establishes a set of performance appraisal methods for production systems, which can be used for improving productivity and quality.

This chapter explores the landscape of higher education in Singapore as the nation positions itself to emerge stronger after the pandemic. A focus on three major issues facing higher education − digital transformation, job readiness of graduates, and access to quality education, describes education-based initiatives for addressing these challenges in Singapore. Local autonomous universities are woven into the tapestry of sustainable growth and development within Singapore’s economy. Connections between the development of education-based initiatives are made to illustrate how these institutions of continuous learning are positioning for relevance considering current developments and to suggest some areas for enhancement for promoting lifelong learning opportunities for all.

The purpose of this paper is to address three key objectives. The first is the proposal of an enhanced multiobjective optimisation (MOO) solution approach for the mean and mean-variance optimisation of multiple “quality characteristics” (or “responses”), considering predictive uncertainties. The second objective is comparing the solution qualities of the proposed approach with those of existing approaches. The third objective is the proposal of a modified non-dominated sorting genetic algorithm-II (NSGA-II), which improves the solution quality for multiple response optimisation (MRO) problems.

The proposed solution approach integrates empirical response surface (RS) models, a simultaneous prediction interval-based MOO iterative search, and the multi-criteria decision-making (MCDM) technique to select the best implementable efficient solutions.

Implementation of the proposed approach in varied MRO problems demonstrates a significant improvement in the solution quality in worst-case scenarios. Moreover, the results indicate that the solution quality of the modified NSGA-II largely outperforms those of two existing MOO solution strategies.

The enhanced MOO solution approach is limited to parametric RS prediction models and continuous search spaces.

The best-ranked solutions according to the proposed approach are derived considering the model predictive uncertainties and MCDM technique. These solutions (or process setting conditions) are expected to be more reliable for satisfying customer specification compared to point estimate-based MOO solutions in real-life implementation.

No evidence exists of earlier research that has demonstrated the suitability and superiority of an MOO solution approach for both mean and mean-variance MRO problems, considering RS uncertainties. Furthermore, this work illustrates the step-by-step implementation results of the proposed approach for the six selected MRO problems.

The purpose of this paper is to illustrate the successful application of Six Sigma at a small and medium scale plastic parts manufacturing unit. Overall operational excellence is one of the foundations of global competiveness. Indian industries are also keeping up with achieving and maintaining operational excellence through different improvement tools and methodologies. Plastic parts manufacturing industries in India are also on the move to increase their overall quality, productivity and profitability. However, it appears from the available literature that application of Six Sigma, one of the most effective breakthrough improvement strategies having direct impact on bottom line of the organization, is not being explored to its full potential, especially at plastic parts manufacturing industries in India. This study was thus undertaken at plastic products manufacturing plant to introduce Six Sigma to them by applying the same to their chronic problems and drawing improvements in quality, productivity and profitability.

This paper illustrates the real-life case study of improving quality and productivity of injection molding process by phase wiz application of define, measure, analyze, improve and control, the process improvement methodology of Six Sigma.

The critical defects, such as short molding, contamination, injection point and flash are reduced from the process leading to annual savings of INR 10.80 lacs. This is a considerable amount for a small concern in question.

Because this was the pilot project and the firm was of small and medium size, data collection was the major issue, which consequently took considerable time and efforts at define and measure phases. Injection molding is a very salient process for plastic products manufacturing. Almost one-third of plastic products are made by this process. Thus, improving quality of products made out of injection molding process is of paramount importance. The paper is an attempt to exhibit how a small-scale plastic injection molded parts manufacturing unit can put fruitful efforts to achieve competitive advantage through Six Sigma.

From the review of literature, it appears that application of Six Sigma among plastic parts manufacturing units, especially small and medium, is very rare, not in India but across the globe. This case study has opened up the direction to small- and medium-scale plastic parts manufacturing units to implement Six Sigma and to move a step forward toward achieving competitive advantage.

The aim of this paper is to review the literature on the prediction of cutting tool life. Tool life is typically estimated by predicting the time to reach the threshold flank wear width. The cutting tool is a crucial component in any machining process, and its failure affects the manufacturing process adversely. The prediction of cutting tool life by considering several factors that affect tool life is crucial to managing quality, cost, availability and waste in machining processes.

This study has undertaken the critical analysis and summarisation of various techniques used in the literature for predicting the life or remaining useful life (RUL) of the cutting tool through monitoring the tool wear, primarily flank wear. The experimental setups that comprise diversified machining processes, including turning, milling, drilling, boring and slotting, are covered in this review.

Cutting tool life is a stochastic variable. Tool failure depends on various factors, including the type and material of the cutting tool, work material, cutting conditions and machine tool. Thus, the life of the cutting tool for a particular experimental setup must be modelled by considering the cutting parameters.

This submission discusses tool life prediction comprehensively, from monitoring tool wear, primarily flank wear, to modelling tool life, and this type of comprehensive review on cutting tool life prediction has not been reported in the literature till now. The future suggestions provided in this review are expected to provide avenues to solve the unexplored challenges in this field.

This study aims to answer an important question of how brand-related communications, including advertising and sales promotion contents, which are disseminated on social media platforms, can enhance positive brand image and evoke favourable behavioural intention from consumers.

This study surveys 615 consumers of automotive brands across Malaysia. The data were analysed with AMOS, which was used to specify both the measurement and structural models. The mediating effect of both hedonic and functional brand images was tested using the bootstrapping approach in AMOS and Sobel test.

The findings reported in this research demonstrate that there are positive and significant relationships between social media advertising content, social media sales promotion content, hedonic brand image, functional brand image and behavioural intention. Both hedonic and functional brand images have significant mediating effects on social media advertising content, social media sales promotion content and behavioural intention. However, the relationship between social media advertising content and behavioural intention is insignificant.

This paper proffers insights to brand managers and marketers on how to leverage on social media contents by unearthing the roles of traditional marketing communications such as social media advertising and promotional information in enhancing brand preference and improving consumers’ purchase intention.

This research advances the discussions in the realm of social media communications and branding by examining the mediating effects of both hedonic and functional brand images of automotive brands. In addition, this study focuses on two essential marketing communications, advertising and sales promotions, which are commonly disseminated by brand managers on social media platforms.

There is growing evidence of a knowledge gap in the association of maintenance with production activities in bottling plants. Indeed, insights into how to jointly optimise these activities are not clear. In this paper, two optimisation models, Taguchi schemes and response surface methodology are proposed.

Borrowing from the “hard” total quality management elements in optimisation and prioritisation literature, two new models were developed based on factor, level and orthogonal array selection, signal-to-noise ratio, analysis of variance and optimal parametric settings as Taguchi–ABC and Taguchi–Pareto. An additional model of response surface methodology was created with analysis on regression, main effects, residual plots and surface plots.

The Taguchi S/N ratio table ranked planned maintenance as the highest. The Taguchi–Pareto shows the optimal parametric setting as A4B4C1 (28 h of production, 30.56 shifts and 37 h of planned maintenance). Taguchi ABC reveals that the planned maintenance and number of shifts will influence the outcome of production greatly. The surface regression table reveals that the production hours worked decrease at a value of planned maintenance with a decrease in the number of shifts.

This is the first time that joint optimisation for bottling plant will be approached using Taguchi–ABC and Taguchi–Pareto. It is also the first time that response surface will be applied to optimise a unique platform of the bottling process plant.