Open Access
Issue |
E3S Web Conf.
Volume 430, 2023
15th International Conference on Materials Processing and Characterization (ICMPC 2023)
|
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Article Number | 01244 | |
Number of page(s) | 18 | |
DOI | https://doi.org/10.1051/e3sconf/202343001244 | |
Published online | 06 October 2023 |
- P. H. Yang, W. S. Hwang, “The development of a computerized diagnostic system for casting defects,” American Foundry Society Transactions, (1990), 90-185, 855-858. [Google Scholar]
- Robert T. Plant, Qing Hu, “The development of a prototype DSS for the diagnosis of casting production defects,” Computers Industry Engineering, Vol. 22, No. 2 (1992), 133-146. [CrossRef] [Google Scholar]
- Shinichiro Fujikawa, Kos Ishii, Taylan Altan, “A diagnostic expert system for defects in forged parts,” J. Intelligent Manufacturing, Volume 6, (1995), 163-173. [Google Scholar]
- Shinichiro Fujikawa, Kos Ishii, Taylan Altan, “A diagnostic expert system for defects in forged parts,” J. Intelligent Manufacturing, (1995), 6, 163-173. [Google Scholar]
- R. S. Ransing, M.N. Srinivasan, R.W. Lewis, “ICADA: Intelligent Computer-Aided Defect Analysis for Casting,” J. Intelligent Manufacturing (1995), 6, 29-40. [CrossRef] [Google Scholar]
- Petra Perner, “Why case-based reasoning is attractive for image interpretation,” Case-Based Reasoning Research and Development, Springer Verlag (2001), Inai, 27-44. [CrossRef] [Google Scholar]
- D.B. Karunakar, G.L. Datta, “Modeling of green sand mold parameters using artificial neural networks,” Indian Foundry Journal, Vol. 49, No. 12, (2003), 27-36. [Google Scholar]
- R. G. Chougule, B. Ravi, “Casting process planning using case-based reasoning,” American Foundry Society Transactions, 111, (2003), 1321-1330. [Google Scholar]
- B. Bakir, I. Batmaz, F. A. Gunturkun, I. A. Ipekci, G. Koksal, N. E. Ozdemirel, “Defect cause modeling with decision tree and regression analysis,” World Academy of Science, Engineering and Technology, 24, (2006), 1-4. [Google Scholar]
- T.R. Vijayaram, S. Sulaiman, A.M.S. Hamouda, M.H.M. Ahmad, “Foundry quality control aspects and prospects to reduce scrap rework and rejection in metal casting manufacturing industries,” J. Materials Processing Technology 178, (2006), 39–43. [CrossRef] [Google Scholar]
- D. B. Karunakar, G.L. Datta, “Controlling green sand mold properties using artificial neural networks and genetic algorithms – A comparison,” Elsevier J. Applied Clay Science, Vol. 37, (2007), 58-66. [CrossRef] [Google Scholar]
- M. Perzyk, “Statistical and visualization data mining tools for foundry production,” Archives of Foundry Engineering, ISSN (1897-3310), Vol. 7, issue 3, (2007), 111-116. [Google Scholar]
- N. Nagurbabu, R.K. Ohdar, P.T. Pushp, “Application of intelligent techniques for controlling the green sand properties,” Proceedings of 55th Indian Foundry Congress (2007), 177-186. [Google Scholar]
- D. B. Karunakar, G.L. Datta, “Prediction of defects in castings using backpropagation neural networks,” Inderscience International J. Modeling, Identification and Control, Vol. 3, No. 2, (2008), 140-147. [CrossRef] [Google Scholar]
- D. B. Karunakar, G.L. Datta, “Prevention of defects in castings using back propagation neural networks,” Springer International J. Advanced Manufacturing Technology, Vol. 39, (2008), 1111-1124. [CrossRef] [Google Scholar]
- R. Sika, Z. Ignaszak, “Implementation of KMES quality system for acquisition and processing data in the chosen foundry,” Archives of Foundry Engineering, ISSN (1897-3310) Vol. 8 Special Issue 3/(2008), 97 – 102. [Google Scholar]
- A. Chokkalingam, S. S. Mohamed Nazirudeen, “Analysis of casting defect through defect diagnostic study approach,” J. Engineering, Annals of Faculty of Engineering, Hunedoara, (2009), ISSN 1584 – 2665, 209-212. [Google Scholar]
- Hathibelagla Roshan, Rajesh Ransing, “Process knowledge and product characteristics gained using casting process optimization,” Foundrymag, October (2010). [Google Scholar]
- V. V. Mane, Amit Sata, M.Y. Khire, “New approach to casting defect classification and analysis supported by simulation,” 59th Indian Foundry Congress Transactions, Chandigarh, February (2010), 87-104. [Google Scholar]
- Z. Gorny, S. Kluska Nawarecka, D. Wilk Kolodziejczyk, “Attribute-based knowledge representation in the process of defect diagnosis,” Achieves of Metallurgy and Materials, Vol. 55, issue 3, (2010), 819-826. [Google Scholar]
- Z. Ignaszak, R. Sika, “Specificity of SPC procedures application in foundry in aspect of data acquisition and data exploration,” Archives of Foundry Engineering, DOI: 10.2478/v10266-012-0108-8, ISSN (2299-2944) Vol. 12, Issue 4/(2012), 65 – 70. [CrossRef] [Google Scholar]
- Harshwardhan Pandit, Uday Dabade, “Application of historical data in foundry for casting parameter optimization,” Proceedings of 27th National Convention of Production Engineers, National Seminar on Advancements In Manufacturing - Vision 2020, (2012), BIT, Mesra, Ranchi, Jharkhand. [Google Scholar]
- Harshwardhan Pandit, Amrita Mangarulkar, Uday Dabade, “Development of new prototype interactive system for casting defect identification and analysis,” Applied Mechanics and Materials, Vol. 197, (2012), 433-437. [CrossRef] [Google Scholar]
- A. G. Thakare, D. J. Tidake, “Data mining for casting defect analysis,” International J. Engineering Research & Technology (IJERT), ISSN: 2278-0181, Vol. 2, Issue 12, December 2013, p. 2153-2157. [Google Scholar]
- Mu-Yen Chen, Min-Hsuan Fan, Chia-Chen Chen, Siang-Yu Jhong, “Integrating artificial neural network and Taguchi method on constructing the real estate appraisal model,” World Academy of Science, Engineering and Technology International J. Economics and Management Engineering, International Scholarly and Scientific Research & Innovation 8(9) (2014), 3010 – 3018. [Google Scholar]
- Hardik Sheth, Kushal Shah, Divyesh Sathwara, Rushik Trivedi, “Investigation, analysis of casting defect by using statistical quality control tools - Introduction concept of lean six sigma and feedback system,” International J. Engineering Development and Research, Vol. 3, Issue 4, ISSN: 2321-9939, (2015), 247 – 254. [Google Scholar]
- Harshwardhan Pandit, “WebCADAS: A New Online Education System for Casting Defect Identification, Analysis and Optimization of Parameters,” International J. Latest Trends in Engineering and Technology (IJLTET), Vol. 5 Issue 4 July (2015), ISSN: 2278-621X, 246 - 253. [Google Scholar]
- Jitendra A Panchiwala, Darshak A Desai, Paresh Shah, “Review on quality and productivity improvement in small scale foundry industry,” International J. Innovative Research in Science, Engineering and Technology, (An ISO 3297: 2007 Certified Organization), Vol. 4, Issue 12, December (2015), ISSN (Print): 2347-6710, DOI:10.15680/IJIRSET.2015.0412027, 11859-11867. [Google Scholar]
- Raghwendra Banchhor, S.K.Ganguly, “Modeling of molding sand characteristics in disamatic molding line green sand casting process,” Proceedings of BITCON-2015 Innovations For National Development National Conference on Innovations in Mechanical Engineering for Sustainable Development, E-ISSN 2249–8974, International J. Advanced Engineering Research and Studies, IV/II/Jan.-March, (2015), 227-230. [Google Scholar]
- Beeresh Chatrad, Nithin Kammar, Prasanna P. Kulkarni, Srinivas Patil, “A study on minimization of critical defects in casting process considering various parameters,” International J. Innovative Research in Science, Engineering and Technology (An ISO 3297: 2007 Certified Organization) Vol. 5, Issue 5, May (2016), ISSN (Print): 2347-6710, DOI:10.15680/IJIRSET.2015.0505326, 8898 – 8902. [Google Scholar]
- Rashmi Mishra, Nitin Gupta, Durgesh Joshi, “Prediction of moulding sand properties using multiple regression methodology,” J. Advanced Computing and Communication Technologies (ISSN: 2347 - 2804) Vol. No.4, Issue No. 1, February (2016). [Google Scholar]
- A. Rodziewicz, M. Perzyk, “Application of Time-Series Analysis for Predicting Defects in Continuous Steel Casting Process,” Archives of Foundry Engineering, ISSN (1897-3310) Volume 16 Issue 4/(2016), 125 – 130. [CrossRef] [Google Scholar]
- Gukendran R, Parameshwaran R, Sambathkumar M, Sasikumar KSK, “Process Parameter Optimization in Green Sand Casting Using ANN,” International J. Scientific and Technology Research. Volume 8, Issue 11, November 2019 ISSN 2277-8616. [Google Scholar]
- Harshwardhan Pandit, Anand Deshpande, “Theory of combined imbalance for quality improvement in green sand moulded castings,” Materials Today: Proceedings, Volume 47, Part 10, (2021), 2315-2321, ISSN 2214-7853, https://doi.org/10.1016/j.matpr.2021.04.294 . [CrossRef] [Google Scholar]
- N. Mehta, A. Gohil, K. Dave, V. Patel, “Development of casting defect analysis module through integrated approach for small and medium scale industries,” Materials Today: Proceedings, Volume 38, Part 5, (2021), ISSN 2214-7853, 2935-2942. [CrossRef] [Google Scholar]
- I. Rajkumar, N. Rajini, “Effectiveness of Feeding System for Enhanced Product Quality in Sand Casting Industries,” International J. Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8 Issue-4S2, December (2019). Retrieval Number: D10481292S219/2019 DOI:10.35940/ijrte.D1048.1284S219. [Google Scholar]
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