Ram Prakash, Sandeep Singhal and Ashish Agarwal
The research paper presents analysis and prioritization of barriers influencing the improvement in the effectiveness of manufacturing system. The purpose of this paper is to…
Abstract
Purpose
The research paper presents analysis and prioritization of barriers influencing the improvement in the effectiveness of manufacturing system. The purpose of this paper is to develop an integrated fuzzy-based multi-criteria decision-making (F-MCDM) framework to assist management of the case company in the selection of most effective manufacturing system. The framework helps in prioritizing the manufacturing systems on the basis of their effectiveness affected by the barriers.
Design/methodology/approach
In this paper, on the basis of experts’ opinion, five barriers have been identified in a brain-storming session. The problem of prioritization of manufacturing system is a multi-criteria decision-making (MCDM) problem and hence is solved by using the F-MCDM approach using dominance matrix.
Findings
Manufacturing systems’ effectiveness for Indian industries is influenced by barriers. The prioritization of manufacturing systems depends on qualitative factor decision-making criteria. Among the manufacturing systems, leagile manufacturing system is given the highest priority followed by lean manufacturing system, agile manufacturing system, flexible manufacturing system and cellular manufacturing system.
Research limitations/implications
The selection of an appropriate manufacturing system plays a vital role for sustainable growth of the manufacturing company. In the present work, barriers which influence the effectiveness of manufacturing system have been identified. On the basis of degree of influence of barriers on the effectiveness of the manufacturing system, five alternative manufacturing systems are prioritized. The framework will help the management of the case company to take reasonable decision for the adoption of the appropriate manufacturing system.
Practical implications
The results of the research work are very useful for the manufacturing companies interested in analyzing the alternative manufacturing systems on the basis of their effectiveness and their sensitivity toward various barriers. The management of Indian manufacturing company will take decision to adopt a manufacturing system whose effectiveness is least sensitive toward barriers. Effectiveness of such manufacturing system will improve with time without having retardation due to barriers. With improved effectiveness of the manufacturing system, the manufacturing company would be able to survive with global competition. The result of the present work is based on the inputs from the case company and may vary for the other manufacturing company. In the present work, only five alternative manufacturing systems and five barriers have been considered. To obtain the better result, MCDM approach with more number of alternative manufacturing systems and barriers might be considered.
Originality/value
The research work is based on the fuzzy analytic hierarchy process framework and on the case study conducted by the authors. The work carried out is original in nature and based on the real-life case study.
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M. P. Jenarthanan, A Ram Prakash and R Jeyapaul
The purpose of this paper is to develop a mathematical model for optimizing the metal removal rate (MRR) through Response Surface Methodology (RSM). The developed model helps us…
Abstract
Purpose
The purpose of this paper is to develop a mathematical model for optimizing the metal removal rate (MRR) through Response Surface Methodology (RSM). The developed model helps us to analyze the influence of individual input machining parameters (cutting speed, feed rate, weight percentage) on the responses in machining of Al-TiB2 composite.
Design/methodology/approach
RSM is used to optimize the MRR by developing a mathematical model. Three factors, three-level box Behnken design matrix in RSM is employed to carry out the experimental investigation. The “Design Expert 8.0” software is used for regression and graphical analysis of the data are collected. The optimum values of the selected variables are obtained by solving the regression equation and by analyzing the response surface contour plots. Analysis of variance (ANOVA) is applied to check the validity of the model and for finding the significant parameters.
Findings
The response surface model developed, helps to calculate the MRR at different input cutting parameters with the chosen range with more than 95 per cent confidence intervals.
Originality/value
The effect of machining parameters on MRR during machining of Al-TiB2 composites using RSM has not been previously analyzed.
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M.P. Jenarthanan, A. Ram Prakash and R. Jeyapaul
The purpose of this paper is to develop a mathematical model for metal removal rate and surface roughness through Taguchi method and analyse the influence of the individual input…
Abstract
Purpose
The purpose of this paper is to develop a mathematical model for metal removal rate and surface roughness through Taguchi method and analyse the influence of the individual input machining parameters (cutting speed, feed rate, helix angle, depth of cut and wt% on the responses in milling of aluminium-titanium diboride metal matrix composite (MMC) with solid carbide end mill cutter coated with nano-crystals.
Design/methodology/approach
Taguchi OA is used to optimise the material removal rate (MRR) and Surface Roughness by developing a mathematical model. End Milling is used to create slots by combining various input parameters. Five factors, three-level Taguchi method is employed to carry out the experimental investigation. Fuzzy logic is used to find the optimal cutting factors for surface roughness (Ra) and MRR. The factors considered were weight percentage of TiB2, cutting speed, depth of cut and feed rate. The plan for the experiments and analysis was based on the Taguchi L27 orthogonal array with five factors and three levels. MINITAB 17 software is used for regression, S/N ratio and analysis of variance. MATLAB 7.10.0 is used to perform the fuzzy logics systems.
Findings
Using fuzzy logics, multi-response performance index is generated, with which the authors can identify the correct combination of input parameters to get higher MRR and lower surface roughness value with the chosen range with 95 per cent confidence intervals. Using such a model, remarkable savings in time and cost can be obtained.
Originality/value
Machinability characteristics in Al-TiB2 MMC based on the Taguchi method with fuzzy logic has not been analysed previously.
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It was December 13, 2010. The Government of Uttar Pradesh announced their plan to urbanize the entire area along the Yamuna Expressway (YE) in order to prevent haphazard growth of…
Abstract
It was December 13, 2010. The Government of Uttar Pradesh announced their plan to urbanize the entire area along the Yamuna Expressway (YE) in order to prevent haphazard growth of urban sprawls on the flanks of the YE. The YE was conceived in 1997 as a dream project of Ms Mayawati, the then Chief Minister of Uttar Pradesh, with the idea of reducing the travel time between Delhi (and the larger National Capital Region) and Agra. It was a 165 km long expressway and was proposed to run from Greater Noida to Agra via Mathura. Amidst issues concerning land acquisition, and various protests and litigations, the deadline for completion of the project had extended beyond its original completion date of February 8, 2010 to April 2013. Meanwhile, the project cost had escalated from Rs 2500 crore (cr) in the year 2000 to about Rs 10,000 cr as of December 2010. By then, about 80% work on the expressway had been completed. The project was finally expected to be completed around April 2011. This was, however, subject to the pending court judgements and mitigation of risks as perceived by Jaypee Infratech, the concessionaire of the project.
Details
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Keywords
Ratnesh Kumar, K. Chandrashekhar Iyer and Surya Prakash Singh
In construction management, risks and claims are treated separately, but several studies tacitly acknowledge a strong link between the two. In this context, this research intends…
Abstract
Purpose
In construction management, risks and claims are treated separately, but several studies tacitly acknowledge a strong link between the two. In this context, this research intends to investigate whether risks and claims have a causal relationship? Based on this causal relationship, a claim-based risk assessment model (C-RAM) is developed to quantify occurrences and cost implications of risks using project data.
Design/methodology/approach
First, the causal relationship between risks and claims is established through a conceptual framework for content analysis of the literature on risk management (RM) and claim management (CM). Then, a C-RAM is developed based on the content analysis of 234 claims from 24 settled arbitration awards.
Findings
Risks and claims are found to be two stages in the same chain of uncertain events that affect projects, subsequently revealing a causal relationship between risks and claims. Due to this causal relationship, claim documents become a potential source of risk information from past projects. Proposed C-RAM quantifies occurrences of risks with three parameters: number of projects in which a risk occurs, number of ways in which a risk occurs, and number of claims a risk causes if it occurs. Also, cost implications of risks are quantified as percentage of contract sums for interpretation as tangible values.
Research limitations/implications
Though C-RAM is applicable to all types of claims, the results in this paper are based on impacts of risks in past projects that caused claims and reached to arbitration stage.
Practical implications
The causal relationship between risks and claims will encourage integration of knowledge on RM and CM which is currently treated separately. Practitioners can now visualize claims as cost implications of risks that occurred in projects. Further, C-RAM makes risk assessment (RA) more objective by quantifying the cost implications of risks as percentage of contract sums which can be readily used for contingency estimation.
Originality/value
The relationship between risks and claims, and the potential of claim documents as a source of project risk information, can initiate a new paradigm in RM research based on project data.
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This study aims to examine sociodemographic characteristics, levels and patterns of mortality experiences amongst Indian prisoners over the past two decades (1998–2018).
Abstract
Purpose
This study aims to examine sociodemographic characteristics, levels and patterns of mortality experiences amongst Indian prisoners over the past two decades (1998–2018).
Design/methodology/approach
This study used prison statistics in India to analyze occupancy rate, percentage distribution, annual/decadal change, male–to–female ratios, prison mortality rate and causes of natural/unnatural deaths.
Findings
During 1998–2018, prisons in India grew by 18% and prisoners by 69%, leading to overcrowded jails. Males outnumbered female prisoners. Seventy percent of prisoners had an educational attainment level lower than 10th grade. In 2018, over 14 per 1,000 prisoners suffered from a mental illness and 384 per 100,000 died. Unnatural deaths accounted for 8%–11% of all prisoner deaths; 84% were by suicide. Illness accounted for 95% of all natural deaths in 2018; one–quarter was due to heart diseases.
Research limitations/implications
The study did not establish an association between sociodemographic characteristics with mental illness and mortality due to the non-availability of data.
Social implications
The pattern of a deteriorating living environment, rise in mental illnesses and mortality among Indian prisoners calls for immediate action from the authorities to protect them. Almost all unnatural deaths were by suicide (mostly by hanging). This detailed study would help authorities to take corrective measures for prisoner safety and well-being. There is also a need to develop a scientific database for this population.
Originality/value
To the best of the authors’ knowledge, this is the first study to examine morbidity and mortality experiences of the prisoner population using national statistics.
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Lokesh Singh, Rekh Ram Janghel and Satya Prakash Sahu
The study aims to cope with the problems confronted in the skin lesion datasets with less training data toward the classification of melanoma. The vital, challenging issue is the…
Abstract
Purpose
The study aims to cope with the problems confronted in the skin lesion datasets with less training data toward the classification of melanoma. The vital, challenging issue is the insufficiency of training data that occurred while classifying the lesions as melanoma and non-melanoma.
Design/methodology/approach
In this work, a transfer learning (TL) framework Transfer Constituent Support Vector Machine (TrCSVM) is designed for melanoma classification based on feature-based domain adaptation (FBDA) leveraging the support vector machine (SVM) and Transfer AdaBoost (TrAdaBoost). The working of the framework is twofold: at first, SVM is utilized for domain adaptation for learning much transferrable representation between source and target domain. In the first phase, for homogeneous domain adaptation, it augments features by transforming the data from source and target (different but related) domains in a shared-subspace. In the second phase, for heterogeneous domain adaptation, it leverages knowledge by augmenting features from source to target (different and not related) domains to a shared-subspace. Second, TrAdaBoost is utilized to adjust the weights of wrongly classified data in the newly generated source and target datasets.
Findings
The experimental results empirically prove the superiority of TrCSVM than the state-of-the-art TL methods on less-sized datasets with an accuracy of 98.82%.
Originality/value
Experiments are conducted on six skin lesion datasets and performance is compared based on accuracy, precision, sensitivity, and specificity. The effectiveness of TrCSVM is evaluated on ten other datasets towards testing its generalizing behavior. Its performance is also compared with two existing TL frameworks (TrResampling, TrAdaBoost) for the classification of melanoma.
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Lokesh Singh, Rekh Ram Janghel and Satya Prakash Sahu
Automated skin lesion analysis plays a vital role in early detection. Having relatively small-sized imbalanced skin lesion datasets impedes learning and dominates research in…
Abstract
Purpose
Automated skin lesion analysis plays a vital role in early detection. Having relatively small-sized imbalanced skin lesion datasets impedes learning and dominates research in automated skin lesion analysis. The unavailability of adequate data poses difficulty in developing classification methods due to the skewed class distribution.
Design/methodology/approach
Boosting-based transfer learning (TL) paradigms like Transfer AdaBoost algorithm can compensate for such a lack of samples by taking advantage of auxiliary data. However, in such methods, beneficial source instances representing the target have a fast and stochastic weight convergence, which results in “weight-drift” that negates transfer. In this paper, a framework is designed utilizing the “Rare-Transfer” (RT), a boosting-based TL algorithm, that prevents “weight-drift” and simultaneously addresses absolute-rarity in skin lesion datasets. RT prevents the weights of source samples from quick convergence. It addresses absolute-rarity using an instance transfer approach incorporating the best-fit set of auxiliary examples, which improves balanced error minimization. It compensates for class unbalance and scarcity of training samples in absolute-rarity simultaneously for inducing balanced error optimization.
Findings
Promising results are obtained utilizing the RT compared with state-of-the-art techniques on absolute-rare skin lesion datasets with an accuracy of 92.5%. Wilcoxon signed-rank test examines significant differences amid the proposed RT algorithm and conventional algorithms used in the experiment.
Originality/value
Experimentation is performed on absolute-rare four skin lesion datasets, and the effectiveness of RT is assessed based on accuracy, sensitivity, specificity and area under curve. The performance is compared with an existing ensemble and boosting-based TL methods.
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Chandra B. Khatri and Satish C. Sharma
The aim of the present paper is to study the combined influence of textured surface and micropolar lubricant behaviour on the performance of two-lobe hole-entry hybrid journal…
Abstract
Purpose
The aim of the present paper is to study the combined influence of textured surface and micropolar lubricant behaviour on the performance of two-lobe hole-entry hybrid journal bearing system. The bearing performance parameters of the textured circular/two-lobe hole-entry hybrid journal bearing system have been computed against the constant vertical external load supported by the bearing.
Design/methodology/approach
In this work, Eringen’s micropolar fluid theory has been used to derive the governing Reynolds equation. The consequent solution of the governing Reynolds equation has been obtained by using finite element method (FEM) numerical technique.
Findings
The present study indicates that the use of the textured surface, two-lobe profile of bearing and micropolar lubricant, significantly enhances the bearing performance as compared to non-textured circular journal bearing.
Originality/value
The present study concerning the influence of surface texturing on the behaviour of the two-lobe hole-entry hybrid journal bearing lubricated with micropolar lubricant is original. The theoretically simulated results of the present study will be useful to design an efficient journal bearing system.