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Article
Publication date: 18 May 2021

Baneswar Sarker and Shankar Chakraborty

Like all other natural fibers, the physical properties of cotton also vary owing to changes in the related genetic and environmental factors, which ultimately affect both…

Abstract

Purpose

Like all other natural fibers, the physical properties of cotton also vary owing to changes in the related genetic and environmental factors, which ultimately affect both the mechanics involved in yarn spinning and the quality of the yarn produced. However, information is lacking about the degree of influence that those properties impart on the spinnability of cotton fiber and the strength of the final yarn. This paper aims to discuss this issue.

Design/methodology/approach

This paper proposes the application of discriminant analysis as a multivariate regression tool to develop the causal relationships between six cotton fiber properties, i.e. fiber strength (FS), fiber fineness (FF), upper half mean length (UHML), uniformity index (UI), reflectance degree and yellowness and spinning consistency index (SCI) and yarn strength (YS) along with the determination of the respective contributive roles of those fiber properties on the considered dependent variables.

Findings

Based on the developed discriminant function, it can be revealed that FS, UI, FF and reflectance degree are responsible for higher YS. On the other hand, with increasing values of UHML and fiber yellowness, YS would tend to decrease. Similarly, SCI would increase with higher values of FS, UHML, UI and reflectance degree, and its value would decrease with increasing FF and yellowness.

Originality/value

The discriminant functions can effectively envisage the contributive role of each of the considered cotton fiber properties on SCI and YS. The discriminant analysis can also be adopted as an efficient tool for investigating the effects of various physical properties of other natural fibers on the corresponding yarn characteristics.

Details

Research Journal of Textile and Apparel, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1560-6074

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Article
Publication date: 24 July 2020

Subham Agarwal, Santonab Chakraborty and Shankar Chakraborty

Due to several unique characteristics, such as high tensile strength, low extensibility, high frictional resistance, biodegradability, eco-friendliness and cheapness, Jute…

Abstract

Purpose

Due to several unique characteristics, such as high tensile strength, low extensibility, high frictional resistance, biodegradability, eco-friendliness and cheapness, Jute ranks second just after cotton with respect to its worldwide consumption and production. To overcome the difficulties of the existing Jute grading procedure, this paper aims to focus on the application of decision-making trial and evaluation laboratory (DEMATEL) and multi-attributive border approximation area comparison (MABAC) methods for evaluation of 10 Tossa Jute fiber lots based on strength, defects, root content, color, fineness and bulk density properties.

Design/methodology/approach

The DEMATEL method divides all the six physical properties of Jute fiber into cause and effect groups. The most influencing property is also identified. On the other hand, the considered Jute fiber lots are ranked using MABAC method along with the identification of the strengths and weaknesses of each of them.

Findings

This combined approach would provide a more scientific and realistic way of Jute grading and evaluation based on various properties of the considered Jute fiber lots. The positions of the superior and the inferior Jute lots perfectly match with those as identified by the earlier researchers.

Originality/value

It is concluded that the adopted combined decision-making tool can be effectively applied for grading and evaluation of other natural fibers with diverse heterogeneous physical properties.

Details

Research Journal of Textile and Apparel, vol. 24 no. 4
Type: Research Article
ISSN: 1560-6074

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Article
Publication date: 4 July 2016

Shankar Chakraborty and Kanika Prasad

Availability of accurate quantity of materials, at correct place and at right time is extremely critical for increasing production effectiveness of any manufacturing…

Abstract

Purpose

Availability of accurate quantity of materials, at correct place and at right time is extremely critical for increasing production effectiveness of any manufacturing organization. This can be achieved through employing an appropriate material handling equipment (MHE) capable of performing the desired operation. Therefore, choosing a right MHE from the available options is a key concern for the success, growth and competitiveness of a manufacturing organization. The purpose of this paper is to describe the design and development of an expert system based on quality function deployment (QFD) methodology in Visual Basic 6.0 for selecting the most appropriate industrial truck which is a commonly practiced MHE in any manufacturing organization.

Design/methodology/approach

A QFD-based approach is adopted to incorporate customers’ needs into the evaluation criteria on the basis of which industrial truck selection is carried out. The applicability of the developed expert system in solving industrial truck selection problems is demonstrated using two illustrative examples.

Findings

While applying this QFD-based model, CPCD 80x manufactured by Heli is recognized as the most suitable forklift truck for transporting unitized loads within a manufacturing unit with some spatial constraints, and for loading/unloading packages/boxes/cartons and place them at the desired locations in a manufacturing unit, ETV 216 manufactured by Jungheinrich evolves out as the most suitable reach truck.

Originality/value

Till date, numerous research articles have been published suggesting the applications of various mathematical models, multi-criteria decision-making methods and knowledge-based systems for solving MHE selection problems, and it is intriguing to note that none of the previously adopted methods has proposed a systematic procedure for selection of the evaluation criteria and interrelated the needs of customers with the technical specifications of MHEs while identifying the best alternative for performing a specified operation. These issues can be addressed through application of this developed QFD-based expert system, which can translate customers’ needs into organizational functions that are implementable in the decision-making/selection procedure.

Details

Journal of Manufacturing Technology Management, vol. 27 no. 6
Type: Research Article
ISSN: 1741-038X

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Article
Publication date: 8 February 2019

Shankar Chakraborty and Ankan Mitra

The purpose of this paper is thus to develop a hybrid decision-making model for optimal coal blending strategy. Coal is one of the major resources contributing to…

Abstract

Purpose

The purpose of this paper is thus to develop a hybrid decision-making model for optimal coal blending strategy. Coal is one of the major resources contributing to generation of electricity and anthropogenic carbon-dioxide emission. Being formed from dead plant matter, it undergoes a series of morphological changes from peat to lignite, and finally to anthracite. Because of non-uniform distribution of coal over the whole earth and continuous variation in its compositions, coals mined from different parts of the world have widely varying properties. Hence, it requires an ideal blending strategy such that the coking coal having the optimal combination of all of its properties can be used for maximum benefit to the steel making process.

Design/methodology/approach

In this paper, a multi-criteria decision-making approach is proposed while integrating preference ranking organization method for enrichment of evaluations (PROMETHEE II and V) and geometrical analysis for interactive aid (GAIA) method to aid in formulating an optimal coal blending strategy. The optimal decision is arrived at while taking into account some practical implications associated with blending of coal, such as coal price from different reserves.

Findings

Different grades of coal are ranked from the best to the worst to find out the composition of constituent coals in the final blending process. Coals from the mines of two different geographical regions are considered here so as to prove the applicability of the proposed model. Adoption of this hybrid decision-making model would subsequently improve the performance of coal after blending and help in addressing some sustainability issues, like less pollution.

Originality/value

As this model takes into account the purchase price of coals from different reserves, it is always expected to provide more realistic solutions. Thus, it would be beneficial to deploy this decision-making model to different blending optimization problems in other spheres of a manufacturing industry. This model can further accommodate some more realistic criteria, such as availability of coal in different reserves as a topic of future research work.

Details

Journal of Modelling in Management, vol. 14 no. 2
Type: Research Article
ISSN: 1746-5664

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Article
Publication date: 3 July 2017

Shankar Chakraborty and Soumava Boral

Subtractive manufacturing process is the controlled removal of unwanted material from the parent workpiece for having the desired shape and size of the product. Several…

Abstract

Purpose

Subtractive manufacturing process is the controlled removal of unwanted material from the parent workpiece for having the desired shape and size of the product. Several types of available machine tools are utilized to carry out this manufacturing operation. Selection of the most appropriate machine tool is thus one of the most crucial factors in deciding the success of a manufacturing organization. Ill-suited machine tool may often lead to reduced productivity, flexibility, precision and poor responsiveness. Choosing the best suited machine tool for a specific machining operation becomes more complex, as the process engineers have to consider a diverse range of available alternatives based on a set of conflicting criteria. The paper aims to discuss these issues.

Design/methodology/approach

Case-based reasoning (CBR), an amalgamated domain of artificial intelligence and human cognitive process, has already been proven to be an effective tool for ill-defined and unstructured problems. It imitates human reasoning process, using specific knowledge accumulated from the previously encountered situations to solve new problems. This paper elucidates development and application of a CBR system for machine tool selection while fulfilling varying user defined requirements. Here, based on some specified process characteristic values, past similar cases are retrieved and reused to solve a current machine tool selection problem.

Findings

A software prototype is also developed in Visual BASIC 6.0 and three real time examples are illustrated to validate the application potentiality of CBR system for the said purpose.

Originality/value

The developed CBR system for machine tool selection retrieves a set of similar cases and selects the best matched case nearest to the given query set. It can successfully provide a reasonable solution to a given machine tool selection problem where there is a paucity of expert knowledge. It can also guide the process engineers in setting various parametric combinations for achieving maximum machining performance from the selected machine tool, although fine-tuning of those settings may often be required.

Details

Benchmarking: An International Journal, vol. 24 no. 5
Type: Research Article
ISSN: 1463-5771

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Article
Publication date: 2 May 2019

Shankar Chakraborty, Prasenjit Chatterjee and Partha Protim Das

To meet the requirements of high-dimensional accuracy and surface finish of various advanced engineering materials for generating intricate part geometries…

Abstract

Purpose

To meet the requirements of high-dimensional accuracy and surface finish of various advanced engineering materials for generating intricate part geometries, non-traditional machining (NTM) processes have now become quite popular in manufacturing industries. To explore the fullest machining capability of these NTM processes, it is often required to operate them while setting their different controllable parameters at optimal levels. This paper aims to present a novel approach for selection of the optimal parametric mixes for different NTM processes in order to assist the concerned process engineers.

Design/methodology/approach

In this paper, design of experiments (DoE) and technique for order preference by similarity to ideal solution (TOPSIS) are combined to develop the corresponding meta-models for identifying the optimal parametric combinations of two NTM processes, i.e. electrical discharge machining (EDM) and wire electrical discharge machining (WEDM) processes with respect to the computed TOPSIS scores.

Findings

For EDM operation on Inconel 718 alloy, lower settings of open circuit voltage and pulse-on time and higher settings of peak current, duty factor and flushing pressure will simultaneously optimize all the six responses. On the other hand, for the WEDM process, the best machining performance can be expected to occur at a parametric combination of zinc-coated wire, lower settings of pulse-on time, wire feed rate and sensitivity and intermediate setting of pulse-off time.

Practical implications

As the development of these meta-models is based on the analysis of the experimental data, they are expected to be more practical, being immune to the introduction of additional parameters in the analysis. It is also observed that the derived optimal parametric settings would provide better values of the considered responses as compared to those already determined by past researchers.

Originality/value

This DoE–TOPSIS method-based approach can be applied to varieties of NTM as well as conventional machining processes to determine the optimal parametric combinations for having their improved machining performance.

Details

Journal of Modelling in Management, vol. 14 no. 2
Type: Research Article
ISSN: 1746-5664

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Article
Publication date: 7 August 2017

Shankar Chakraborty, Debapriyo Paul and Puneet Kumar Agarwal

Quality education is a key requirement of a burgeoning country, like India as it aims to establish a sustained growth. However, the current situation of Indian education…

Abstract

Purpose

Quality education is a key requirement of a burgeoning country, like India as it aims to establish a sustained growth. However, the current situation of Indian education system is extremely poor. Although efforts are being made nationwide to improve the present situation, it is incontrovertible that different complications ail different Indian states. Some states suffer from a poor gross enrollment ratio, while others have an extremely high student-teacher ratio. The purpose of this paper is to compare the educational performance of 28 Indian states in order to identify those which require immediate attention.

Design/methodology/approach

For fulfilling this objective, a multi-criteria decision-making (MCDM) framework utilizing preference ranking organization method for enrichment of evaluations and geometrical analysis for interactive aid methods is adopted.

Findings

The results indicate that the educational performance of Goa is the best amongst all the considered alternative states, while Bihar is the laggard in this direction.

Research limitations/implications

From the results, the states which fare to be the worst can easily be identified along with the specific areas/criteria, where they are falling behind. Based on these findings, necessary remedial actions can be undertaken so as to improve the educational performance of the ailing states.

Originality/value

This paper employs a novel geographic information system (GIS) method and a hue-saturation-value color coding scheme in order to determine the influence of individual criterion on the overall state rank, thereby representing an integration of MCDM and GIS which has never been applied before for educational performance evaluation.

Details

Benchmarking: An International Journal, vol. 24 no. 6
Type: Research Article
ISSN: 1463-5771

Keywords

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Article
Publication date: 29 November 2018

Shankar Chakraborty, Rajeev Ranjan and Poulomi Mondal

A road network provides arterial arrangement to facilitate business, transport, social integration and economic progress of any nation. During the last seven decades after…

Abstract

Purpose

A road network provides arterial arrangement to facilitate business, transport, social integration and economic progress of any nation. During the last seven decades after independence, road transport infrastructure in India has expanded manifold, both in terms of spread (total length and density of road) and capacity (number of on-road registered vehicles, and volume of passenger and freight traffic handled). But, with the enrichment of road transport network in India, the number of traffic accidents and total cost for maintaining the road infrastructure also keeps on increasing. It becomes necessary to evaluate state-wise performance of the Indian roads using some mathematical tools. The paper aims to discuss this issue.

Design/methodology/approach

In this paper, using preference ranking organization method for enrichment of evaluations (PROMETHEE) and geometrical analysis for interactive aid (GAIA) approaches, an attempt is made to appraise the state-wise performance of Indian roads based on 12 critically important criteria. A geographic information system method and a hue-saturation-value color coding scheme are also employed to identify the influence of individual criterion on the overall rank of 29 Indian states.

Findings

It is observed that amongst all the considered states, the road conditions in the states of Mizoram and Arunachal Pradesh are really satisfactory, whereas Bihar and Uttar Pradesh are the lagging states requiring governmental intervention and support to enhance their road network infrastructure.

Practical implications

This analysis would help the decision makers to identify the strengths and deficiencies of each Indian state with respect to its road conditions so that proper promotional and growth actions can be implemented.

Originality/value

From the review of the existing literature, it is quite evident that till date, no research work has been conducted in order to evaluate the performance of roads, and their conditions and characteristic features in the Indian context. In this paper, the state-wise performance of the Indian roads is appraised based on several identified parameters using a combined PROMETHEE-GAIA approach.

Details

Benchmarking: An International Journal, vol. 25 no. 9
Type: Research Article
ISSN: 1463-5771

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Article
Publication date: 5 February 2018

Shankar Chakraborty, Partha Protim Das and Vidyapati Kumar

The purpose of this paper is to exploit the fullest potential and capability of different non-traditional machining (NTM) processes, it is often recommended to operate…

Abstract

Purpose

The purpose of this paper is to exploit the fullest potential and capability of different non-traditional machining (NTM) processes, it is often recommended to operate them at their optimal parametric combinations. There are several mathematical tools and techniques that have been effectively deployed for identifying the optimal parametric mixes for the NTM processes. Amongst them, grey relational analysis (GRA) has become quite popular due to its sound mathematical basis, ease to implement and apprehensiveness for multi-objective optimization of NTM processes.

Design/methodology/approach

In this paper, GRA is integrated with fuzzy logic to present an efficient technique for multi-objective optimization of three NTM processes (i.e. abrasive water-jet machining, electrochemical machining and ultrasonic machining) while identifying their best parametric settings for enhanced machining performance.

Findings

The derived results are validated with respect to technique for order preference by similarity to ideal solution (TOPSIS), and analysis of variance is also performed so as to identify the most significant control parameters in the considered NTM processes.

Practical implications

This grey-fuzzy logic approach provides better parametric combinations for all the three NTM processes with respect to the predicted grey-fuzzy relational grades (GFRG). The developed surface plots help the process engineers to investigate the effects of various NTM process parameters on the predicted GFRG values.

Originality/value

The adopted approach can be applied to various machining (both conventional and non-conventional) processes for their parametric optimization for achieving better response values.

Details

Grey Systems: Theory and Application, vol. 8 no. 1
Type: Research Article
ISSN: 2043-9377

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Article
Publication date: 4 September 2017

Shankar Chakraborty and Siddhartha Bandhopadyay

In spinning industries, selection of the most appropriate fibre for yarn manufacturing plays an important role for achieving an optimal mix of several yarn…

Abstract

Purpose

In spinning industries, selection of the most appropriate fibre for yarn manufacturing plays an important role for achieving an optimal mix of several yarn characteristics, like maximum tenacity, elasticity and spinning ability; and minimum unevenness and hairiness. Identification of the best suited cotton fibre from a set of available alternatives in presence of different conflicting physical properties is often treated as a multi-criteria decision-making (MCDM) problem. The paper aims to discuss this issue.

Design/methodology/approach

In this paper, the preference ranking organisation method for enrichment of evaluations (PROMETHEE) and geometrical analysis for interactive aid (GAIA) methods are integrated to solve a cotton fibre selection problem. The PROMETHEE II method ranks the alternative cotton fibres based on their net outranking flows, whereas GAIA acts as a visual aid to strongly support the derived selection decision. The weight stability intervals for all the considered fibre properties (criteria) over which the position of the top-ranked cotton fibre remains unchanged are also determined.

Findings

The clusters of cotton fibres formed in the developed GAIA plane act as a yard stick for their appropriate grading to aid the blending process. The ranking of 17 cotton fibres as achieved applying the combined PROMETHEE-GAIA approach highly corroborates with the observations of the past researchers which proves its immense potentiality and applicability in solving fibre selection problems.

Originality/value

Two MCDM methods in the form of PROMETHEE II and GAIA are integrated to provide a holistic approach for cotton fibre grading and selection while taking into consideration all the available cotton fibre properties.

Details

International Journal of Clothing Science and Technology, vol. 29 no. 5
Type: Research Article
ISSN: 0955-6222

Keywords

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