메뉴 건너뛰기




Volumn 25, Issue 6, 2014, Pages 478-493

Data mining driven DMAIC framework for improving foundry quality-a case study

Author keywords

CART; casting defect; CHAID; data mining; decision tree; DMAIC; foundry; six sigma

Indexed keywords

CART; CASTING DEFECT; CHAID; DMAIC; SIX-SIGMA;

EID: 84896784936     PISSN: 09537287     EISSN: 13665871     Source Type: Journal    
DOI: 10.1080/09537287.2012.709642     Document Type: Article
Times cited : (47)

References (36)
  • 3
    • 84896745353 scopus 로고    scopus 로고
    • Development of quality cost system in cast iron foundries
    • Arasu, M, and, Gandhinathan, R. 2010. Development of quality cost system in cast iron foundries. Metal Casting Technologies, 56 (3): 48-54.
    • (2010) Metal Casting Technologies , vol.56 , Issue.3 , pp. 48-54
    • Arasu, M.1    Gandhinathan, R.2
  • 4
    • 84884312812 scopus 로고    scopus 로고
    • A Framework for integrated analysis of quality defects in supply chain
    • Aravindan, S, and, Maiti, J. 2012. A Framework for integrated analysis of quality defects in supply chain. Quality Management Journal, 19 (1): 34-52.
    • (2012) Quality Management Journal , vol.19 , Issue.1 , pp. 34-52
    • Aravindan, S.1    Maiti, J.2
  • 5
  • 6
    • 0036474654 scopus 로고    scopus 로고
    • Data mining for improving a cleaning process in the semiconductor industry
    • Braha, D, and, Shmilovici, A. 2002. Data mining for improving a cleaning process in the semiconductor industry. IEEE Transactions on Semiconductor Manufacturing, 15 (1): 91-101.
    • (2002) IEEE Transactions on Semiconductor Manufacturing , vol.15 , Issue.1 , pp. 91-101
    • Braha, D.1    Shmilovici, A.2
  • 8
    • 59349091147 scopus 로고    scopus 로고
    • Promoting customer satisfactions by applying six sigma: An example from the automobile industry
    • Chen, SC, Chen, KS, and, Hsia, TC. 2005. Promoting customer satisfactions by applying six sigma: an example from the automobile industry. The Quality Management Journal, 12 (4): 21-33.
    • (2005) The Quality Management Journal , vol.12 , Issue.4 , pp. 21-33
    • Chen, S.C.1    Chen, K.S.2    Hsia, T.C.3
  • 10
    • 33845660695 scopus 로고    scopus 로고
    • Data mining for yield enhancement in semiconductor manufacturing and an empirical study
    • Chien, CF, Wang, WC, and, Cheng, JC. 2007. Data mining for yield enhancement in semiconductor manufacturing and an empirical study. Expert Systems with Applications, 33 (1): 192-198.
    • (2007) Expert Systems with Applications , vol.33 , Issue.1 , pp. 192-198
    • Chien, C.F.1    Wang, W.C.2    Cheng, J.C.3
  • 12
    • 71749101988 scopus 로고    scopus 로고
    • Process control strategies for a steel making furnace using ANN with Bayesian regularization and ANFIS
    • Das, A, Maiti, J, and, Banerjee, RN. 2010a. Process control strategies for a steel making furnace using ANN with Bayesian regularization and ANFIS. Expert Systems with Applications, 37: 1075-1085.
    • (2010) Expert Systems with Applications , vol.37 , pp. 1075-1085
    • Das, A.1    Maiti, J.2    Banerjee, R.N.3
  • 13
    • 77951098182 scopus 로고    scopus 로고
    • A hierarchical process monitoring strategy for a serial multi-stage manufacturing system
    • Das, A, Maiti, J, and, Banerjee, RN. 2010b. A hierarchical process monitoring strategy for a serial multi-stage manufacturing system. International Journal of Production Research, 48 (8): 2459-2479.
    • (2010) International Journal of Production Research , vol.48 , Issue.8 , pp. 2459-2479
    • Das, A.1    Maiti, J.2    Banerjee, R.N.3
  • 14
    • 64949146030 scopus 로고    scopus 로고
    • Guest editorial: Statistical thinking and experimental design as dual drivers of DFSS
    • Goh, TN. 2009. Guest editorial: statistical thinking and experimental design as dual drivers of DFSS. International Journal of Six Sigma and Competitive Advantage, 5 (1): 1-9.
    • (2009) International Journal of Six Sigma and Competitive Advantage , vol.5 , Issue.1 , pp. 1-9
    • Goh, T.N.1
  • 18
    • 0000661829 scopus 로고
    • An exploratory technique for investigating large quantities of categorical data
    • Kass, G. 1980. An exploratory technique for investigating large quantities of categorical data. Applied Statistics, 29 (2): 119-127.
    • (1980) Applied Statistics , vol.29 , Issue.2 , pp. 119-127
    • Kass, G.1
  • 19
    • 34548218639 scopus 로고    scopus 로고
    • Improving foundry process control: An investigation of cluster analysis and path model
    • Khanzode, VV, and, Maiti, J. 2007. Improving foundry process control: an investigation of cluster analysis and path model. International Journal of Productivity and Quality Management, 2 (4): 404-422.
    • (2007) International Journal of Productivity and Quality Management , vol.2 , Issue.4 , pp. 404-422
    • Khanzode, V.V.1    Maiti, J.2
  • 20
    • 79957992809 scopus 로고    scopus 로고
    • A review of data mining applications for quality improvement in manufacturing industry
    • Koksal, G, Batmaz, I, and, Testik, MC. 2011. A review of data mining applications for quality improvement in manufacturing industry. Expert Systems with Applications, 38: 13448-13467.
    • (2011) Expert Systems with Applications , vol.38 , pp. 13448-13467
    • Koksal, G.1    Batmaz, I.2    Testik, M.C.3
  • 21
    • 33745604543 scopus 로고    scopus 로고
    • Implementing the lean Sigma framework in an Indian SME: A case study
    • Kumar, M. 2006. Implementing the lean Sigma framework in an Indian SME: a case study. Production Planning & Control: The Management of Operations, 17 (4): 407-423.
    • (2006) Production Planning & Control: The Management of Operations , vol.17 , Issue.4 , pp. 407-423
    • Kumar, M.1
  • 27
    • 4544223922 scopus 로고    scopus 로고
    • An exploratory study of object-oriented software component size determinants and the application of regression tree forecasting models
    • Pendharkar, PC. 2004. An exploratory study of object-oriented software component size determinants and the application of regression tree forecasting models. Information & Management, 42: 61-73.
    • (2004) Information & Management , vol.42 , pp. 61-73
    • Pendharkar, P.C.1
  • 28
    • 57049145560 scopus 로고    scopus 로고
    • Statistical and visualization data mining tools for foundry production
    • Perzyk, M. 2007. Statistical and visualization data mining tools for foundry production. Archives of Foundry Engineering, 7 (3): 111-116.
    • (2007) Archives of Foundry Engineering , vol.7 , Issue.3 , pp. 111-116
    • Perzyk, M.1
  • 30
    • 84896792191 scopus 로고    scopus 로고
    • Virtual DOE, data mining and artificial neural networks
    • Pyzdek, T. 1999. Virtual DOE, data mining and artificial neural networks. Quality Digest, 7: 26
    • (1999) Quality Digest , vol.7 , pp. 26
    • Pyzdek, T.1
  • 31
    • 33744584654 scopus 로고
    • Induction of decision trees
    • Quinlan, JR. 1986. Induction of decision trees. Machine Learning, 1: 81-106.
    • (1986) Machine Learning , vol.1 , pp. 81-106
    • Quinlan, J.R.1
  • 32
    • 16244386248 scopus 로고    scopus 로고
    • A comparative predictive analysis of neural networks (NNs), nonlinear regression and classification and regression tree (CART) models
    • Razi, MA, and, Athappilly, K. 2005. A comparative predictive analysis of neural networks (NNs), nonlinear regression and classification and regression tree (CART) models. Expert Systems with Applications, 29: 65-74.
    • (2005) Expert Systems with Applications , vol.29 , pp. 65-74
    • Razi, M.A.1    Athappilly, K.2
  • 33
    • 34249783911 scopus 로고    scopus 로고
    • Capability enhancement of a metal casting process in a small steel foundry through Six Sigma: A case study
    • Sarkar, BN. 2007. Capability enhancement of a metal casting process in a small steel foundry through Six Sigma: a case study. International Journal of Six Sigma and Competitive Advantage, 3 (1): 56-71.
    • (2007) International Journal of Six Sigma and Competitive Advantage , vol.3 , Issue.1 , pp. 56-71
    • Sarkar, B.N.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.