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10. Contributors

Author

Abedin, Md Anwarul, primary, Abijith, Devanantham, additional, Ahmad, Mokbul Morshed, additional, Aluru, Manjunath, additional, Bahinipati, Chandra Sekhar, additional, Banerji, Rajarshi, additional, Birkmann, Jörn, additional, Biswas, Soubhik, additional, Buranajarukorn, Panu, additional, Cavuilati, Ratu Apenisa W.C., additional, Chakraborty, Dibakar, additional, Dadhich, Gautam, additional, Das, Satabdi, additional, Datta, Satabdi, additional, Deka, Paresh Chandra, additional, Dias, B.A.R.H., additional, Dissanayake, D.A.T.W.K., additional, Djalante, Riyanti, additional, Dwivedy, G.K., additional, Suresh, E.S.M., additional, Ghosh, Arka, additional, Ghosh, Tuhin, additional, Guha, Indrila, additional, Hasan, Md. Shahidul, additional, Hassan, Mohd Sayuti, additional, Hosenuzzaman, Md, additional, Ichikawa, Yutaka, additional, Islam, M. Rafiqul, additional, Islam, Sheikh Tawhidul, additional, Jana, Sujoy Kumar, additional, Jayawardana, J.M.C.K., additional, Jennifer, Jesudasan Jacinth, additional, Kamanga, Tamara Faith, additional, Kanasan, Vishanthini, additional, Kapuria, Preeti, additional, Khadgi, Vijay, additional, Kibria, Mohammad Golam, additional, Krishnakumar, Kavya, additional, Kumar, Vijay, additional, Lokuhetti, Chinthaka Samarawickrama, additional, Mahmood, Swarnali, additional, Malavipathirana, S., additional, McMillan, Joanna M., additional, Miah, Md. Giashuddin, additional, Mitra, Debashis, additional, Miyazaki, Hiroyuki, additional, Mondal, Krishna Prosad, additional, Mukhopadhyay, Anirban, additional, Mwale, Faidess, additional, Narain, Vishal, additional, Pal, Anushree, additional, Pal, Indrajit, additional, Panja, Subhash Chandra, additional, Papp, Bendegúz, additional, Parthasarathy, K.S.S., additional, Parven, Afshana, additional, Patra, S.N., additional, Plangoen, Pheerawat, additional, Poddar, Sanghmitra, additional, Poontirakul, Porntida, additional, Pramanick, Niloy, additional, Pramanik, Malay, additional, Rahman, Khan Ferdousour, additional, Rainasiri, Janaka, additional, Raj, Vinit, additional, Roy, Joyashree, additional, Saleh, Abul Fazal M., additional, Sankriti, Ramanarayan, additional, Saqib, Shahab E., additional, Saranya, Thiyagarajan, additional, Saravanan, Subbarayan, additional, Sekac, Tingneyuc, additional, Sen, Subir, additional, Shah, Ashfaq Ahmad, additional, Shaikh, Furqan Ali, additional, Sharma, Ashish, additional, Sharma, Sunil Kumar, additional, Shaw, Rajib, additional, Shrestha, Mandira Singh, additional, Shrestha, Sangam, additional, Singh, Leelambar, additional, Singh, Nongmaithem Bragy, additional, Sukwanchai, Kullanan, additional, Syed, Md Abu, additional, Szabo, Sylvia, additional, Tangwanichagapong, Siwaporn, additional, Tantanee, Sarintip, additional, Terney Pradeep Kumara, P.B., additional, Thanvisitthpon, Nawhath, additional, Tsusaka, Takuji W., additional, Udayakumara, E.P.N., additional, Udmale, Parmeshwar, additional, Vanama, Venkata Sai Krishna, additional, Varo, Joeli, additional, Veerappan, Ramesh, additional, Madhu Mohan, Velapgy, additional, Vidanage, Shamen Prabhath, additional, and Viswanathan, P.K., additional

Published
2021
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17. Experimental Finalization of Levels of Predictor Variables for Accelerated Degradation Testing of Cutting Tools.

Author

Das, Monojit, Naikan, V. N. Achutha, and Panja, Subhash Chandra

Subjects
CUTTING tools, TOTAL quality management, SUPPLY chain management, MANUFACTURING processes, INTERNET of things, INDUSTRY 4.0
Abstract

During accelerated degradation testing (ADT), the component is exposed to higher levels of parameters that cause it to fail faster than it would under normal operating conditions. This approach saves time and reduces expenses associated with tool life tests for valuable workpieces. The fundamental assumption of ADT is that failure modes observed at elevated levels of parameters are similar to those under recommended usage conditions. In this study, tool failure modes are utilized to determine the appropriate levels of predictor variables, like cutting parameters and tool nose radius, for ADT of PVD-coated carbide tools during dry machining of Inconel 800. To study tool failure modes, experiments are done at three levels of cutting speed (Vc), feed rate (f), depth of cut (ap), and nose radius (r) by following the Taguchi L9 orthogonal array. Notably, a favourable tool failure pattern is observed when employing the middle level of the nose radius and operating at relatively higher cutting speeds while ensuring that f and ap fall within the recommended range. In the future, with the parameters’ levels established through this study, full-scale ADT will be performed and extrapolated to predict the reliability function of the cutting tool under normal conditions. The suggested method holds potential for various applications, including estimating the reliability of cutting tools and monitoring tool condition, among others. [ABSTRACT FROM AUTHOR]

Published
2023

35. A CASE STUDY ON IMPLEMENTATION OF MAHALANOBIS-TAGUCHI SYSTEM FOR MULTIVARIATE MANUFACTURING PROCESS CONTROL.

Author

SIKDER, SAGAR, MUKHERJEE, INDRAJIT, and PANJA, SUBHASH CHANDRA

Subjects
MULTIVARIATE analysis, PROCESS control systems, STATISTICAL correlation, PRODUCT quality, MANUFACTURING processes
Abstract

In manufacturing process, quality of an end product or component is often assessed by multiple critical output characteristics (or responses). These responses are often correlated or interacting. Change in process setting condition, with an intension to improve a particular response characteristic, may adversely affect another response. In this context, researchers had proposed various parametric and nonparametric Multivariate Statistical Process Control (MSPC) charts for monitoring of correlated responses and detecting out-of-control signal. However, there is little evidence of nonparametric MSPC research to identify influential response(s) for any out-of-control situation in a manufacturing process. In this paper, Mahalanobis-Taguchi System (MTS), based on Mahalanobis Distance (MD) scale and Taguchi's Orthogonal Array (OA) design, is explored for detecting out-ofcontrol state and identify influential response(s) for an out-of-control state. Suitability of the approach is verified using a manufacturing process case study. [ABSTRACT FROM AUTHOR]

Published
2018
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40. A Mahalanobis Taguchi System-based Approach for Correlated Multiple Response Process Monitoring.

Author

Sikder, Sagar, Mukherjee, Indrajit, Panja, Subhash Chandra, and Bera, Sasadhar

Subjects
TAGUCHI methods, VIGILANCE (Psychology), QUALITY control charts, STRUGGLE, INDUSTRIAL management, NEURAL circuitry, LOGISTIC regression analysis
Abstract

Developing a single control chart for monitoring correlated multiple quality characteristics (or responses) is always a challenging research endeavor. In this context, various classical approaches, such as X² and Hotelling T2 control chart are recommended by researchers. However, interpretation, understanding of out-of-control state, and developing a control strategy for such charts are quite difficult. Difficulty is significant due to lack of understanding (knowledge) at the operational (worker) level. Monitoring each response individually and overall concluding on incontrol state may be erroneous in case there is evidence of correlation between responses. In this paper, an easy-tointerpret integrated approach, based on Mahalanobis Taguchi system (MTS), binary logistic regression and neural network, is proposed. The approach adopts MTS to define a normal group and the Mahalanobis space, considering Hotelling T2 control limits. Subsequently using unknown observations and nonlinear regression approach, a systematic approach to define cutoff value is proposed to classify normal and abnormal group. Logistic regression model is observed to provide more conservative cutoff as compared to a neural network. The overall integrated approach is verified using a real life industry case. The results clearly indicate the suitability of the approach as compared to classical approach at operations level. [ABSTRACT FROM AUTHOR]

Published
2014

43. Failure Mode and Effect Analysis of Indian Railway Signalling System.

Author

Panja, Subhash Chandra and Ray, Pradip Kumar

Subjects
COMMUNICATIONS industries, COMPLEXITY (Philosophy), RAILROADS, STREET signs, PUBLIC utilities, CABLES, POWER transmission
Abstract

Signalling, a complex system is one of the most important infrastructures of railway service. The performance of railway signalling system depends on the functioning of a number of subsystems, such as panel, cable, relay, point and point machine, trackcircuit, signal, back indication, point-drive card, signal-drive card, and level crossing gate. Railway signalling system plays an important role for safe running of trains by preventing occurrence of accidents and minimizing the risk of the passengers, operating personnel, and railway infrastructure. Failure mode and effect analysis (FMEA) is applied to analyse the risk of these subsystems during the operating phase of railway signalling system. Risk is measured in terms of Risk Priority Number (RPN), which is a product of occurrence, severity, and detection. It is observed that point and point machine, signal, and trackcircuit are the critical subsystems of railway signalling system. [ABSTRACT FROM AUTHOR]

Published
2009

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