scispace - formally typeset
Search or ask a question
Institution

Jadavpur University

EducationKolkata, India
About: Jadavpur University is a education organization based out in Kolkata, India. It is known for research contribution in the topics: Population & Schiff base. The organization has 10856 authors who have published 27678 publications receiving 422069 citations. The organization is also known as: JU & Jadabpur University.


Papers
More filters
Journal ArticleDOI
TL;DR: In this article, a DEA-based framework is proposed to study the combined effect of sustainability oriented innovation and lean practices on the supply chain sustainability performance of SMEs, and the results reveal that combined lean and sustainable oriented innovation helps achieve SMEs' supply-chain sustainability.

99 citations

Journal ArticleDOI
TL;DR: The results indicated that the antioxidant enzymes, catalase, SOD and microsomal NADPH-DT diaphorase in digestive gland of S. cucullata could be useful biomarkers of PAHs contamination and should be incorporated into interpretation of biomonitoring studies by the use of appropriate controls and identical treatment in analysis of polluted and non-polluted samples.

99 citations

Journal ArticleDOI
TL;DR: In this article, the synthesis and characterization of p-CuO/n-ZnO heterojunction nanocomposites and their application as a broad spectrum photocatalyst were reported.

99 citations

Journal ArticleDOI
TL;DR: A vast population of the block may have arsenic body burden and cases of Bowen's disease and cancer have been identified among adults who also show arsenical skin lesions and children in this block are also seriously affected.

99 citations

Journal ArticleDOI
01 Dec 2008
TL;DR: In this paper, a TOPSIS-AHP-based expert system is proposed to select the most appropriate NTM process for a specific work material and shape feature combination, while taking into account different attributes affecting the NTM processes selection decision.
Abstract: With the introduction and increased use of newer and harder materials such astitanium, stainless steel, high-strength temperature-resistant (HSTR) alloys, fibre-reinforcedcomposites, and ceramics in the aerospace, nuclear, missile, turbine, automobile, tool, and die-making industries, a different class of machining processes has emerged. Instead of employingthe conventional cutting tools, these non-traditional machining (NTM) processes use energy inits direct form to remove materials from the workpiece. Selection of the most suitable NTMprocessformachininga shapefeatureona givenworkmaterialrequiresconsideration ofseveralfactors. A combined method using the ‘technique for order preference by similarity to idealsolution’ (TOPSIS) and an analytical hierarchy process (AHP) is proposed to select the mostappropriate NTM process for a specific work material and shape feature combination, whiletakinginto account different attributes affecting the NTM process selection decision. This paperalso includes the design and development of a TOPSIS-AHP-method-based expert system thatcan automate the decision-making process with the help of a graphical user interface and visualaids. The expert system not only segregates the acceptable NTM processes from the list of theavailable processes, but also ranks them in decreasing order of preference. It also helps the useras a responsible guide to select the best NTM process by incorporating all the possible error-trapping mechanisms.Keywords: non-traditional machining process, multi-attribute decision-making, TOPSIS,AHP, expert system, graphical user interface1 INTRODUCTIONWith the development of technology and evolutionof new materials, the challenging problems faced byscientists and technologists in the field of manu-facturing are increasing. Hence, there is a need formachine tools and processes which can easily andprecisely machine to intricate and accurate shapesmaterials such as titanium, stainless steel, high-strength temperature-resistant (HSTR) alloys, fibre-reinforced composites, ceramics, refractories, andother difficult-to-machine alloys, which have higherstrength,hardness,toughness,lowmachinability,andother diverse properties. Traditional edged cuttingtool machining processes are uneconomical for suchmaterials as the attainable degree of accuracy andsurface finish are quite poor. The application of non-traditional machining (NTM) processes has pavedthe way for new developments in machining hard-to-machine and advanced materials, both now and inthe future. The use of these NTM processes isincreasingly requested on the shopfloor. Such pro-cesses were developed and came into use during the1980s: they are called non-traditional because con-ventionaltoolsarenotemployedforthemetalcutting[1, 2]. Instead, the energy in its direct form is used toremove materials from the workpiece. Newer NTMprocesses such as magneto-rheological flow finishing(MRFF), abrasive flow machining (AFM), photo-chemical machining (PCM), electrochemical turning(ECT), electrochemical honing (ECH), and otherhybrid processes such as electrochemical dischargemachining (ECDM), electrochemical arc machining(ECAM),andabrasivewaterjetmachining(AWJM)arenow being used in various industries for precisionmaterial machining. Some of the NTM processes

99 citations


Authors

Showing all 10999 results

NameH-indexPapersCitations
Subir Sarkar1491542144614
Amartya Sen149689141907
Susumu Kitagawa12580969594
Praveen Kumar88133935718
Rodolphe Clérac7850622604
Rajesh Gupta7893624158
Santanu Bhattacharya6740014039
Swagatam Das6437019153
Anupam Bishayee6223711589
Michael G. B. Drew61131524747
Soujanya Poria5717513352
Madeleine Helliwell543709898
Tapas Kumar Maji542539804
Pulok K. Mukherjee5429610873
Dipankar Chakraborti5411512078
Network Information
Related Institutions (5)
Indian Institutes of Technology
40.1K papers, 652.9K citations

96% related

Indian Institute of Technology Kharagpur
38.6K papers, 714.5K citations

95% related

Indian Institute of Technology Kanpur
28.6K papers, 576.8K citations

94% related

Indian Institute of Technology Bombay
33.5K papers, 570.5K citations

94% related

Indian Institute of Technology Delhi
26.9K papers, 503.8K citations

93% related

Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202385
2022332
20211,949
20201,936
20191,737
20181,807