A novel online defect detection method based on X-ray images of railway castings

Shen, Kuan; Cai, Yufang

doi:10.1784/insi.2012.55.7.360

Cited by 2 publications

(1 citation statement)

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“…In this sense, several authors have been working with quality control tools to identify casting defects and to improve the quality of casting products. For more information, see the following: Siekanski and Borkowski (2003); Dobrzański et al (2006); Li et al (2006); Dobrzański et al (2007); Saikaew and Wiengwiset (2012); Shivappa et al (2012); Shen and Cai (2013); Kassie and Assfaw (2013); Dabade and Bhedasgaonkar (2013); Joshi and Jugulkar (2014); Kinagi and Mench (2014); Chaudhari and Thakkar (2014); Vante and Naik (2015); Kandel et al (2016).…”

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confidence: 99%

Goal programming and multiple criteria data envelopment analysis combined with optimization and Monte Carlo simulation: An application in railway components

et al. 2021

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This work has been developed in a large steel industry in Brazil, which produces railway and industrial components, and whose aim was to reduce casting defects. Usually, in industrial processes, identifying the causes of defects and their control are relatively complex activities, due to the many variables involved. In this context, the production processes of seven products, involving 38 process variables (inputs and outputs), have been evaluated adopting a new and innovative procedure. Initially, using a Weighted Goal Programming ‐ Multiple Criteria Data Envelopment Analysis (WGP‐MCDEA) model, we identified the most relevant input and output variables, and the studied company validated the results. Next, using the multiple regression technique, empirical functions were constructed for two response variables chosen by the company – number of external cracks and number of internal cracks. Then, to model the real processes adequately, we introduced the occurrence of uncertainty on the coefficients of these functions, considering them as random variables, according to triangular probability functions. Finally, applying the optimizer Optquest, optimization via Monte Carlo simulation (OvMCS) was performed, and with the Ordinary Least Square technique, we obtained the best fit for the two response variables. Specialists from the company validated the proposed procedure. They found that the values of input and output variables obtained by OvMSC, as well as the values of the response variables, belonged to the database available in the ERP system of the company. These results showed that the procedure proposed herein provided feasible and useful solutions to improve the industrial processes under study.

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