Defect Area and the Regions a Production Surrogate Represents
Abstract
The question concerns fault area by fault category and plate characteristics. The secondary ordinary least squares analysis contains 1,941 observations and uses HC3 covariance. The coefficient on Steel_Plate_Thickness is 0.0013, with a 95 percent interval from 2.682e-04 to 0.0023. Logged recorded defect area is an outcome surface rather than an acceptance decision. Category and plate characteristics can organize observed variation, but a partition search would change which regions receive attention. Independent checking is needed where a surrogate selects a favorable candidate. The present fit neither discovers new process settings nor measures their effect on defects. The estimates describe the released collection and its conditioning set. No new data collection, simulator experiment, or deployment-policy evaluation is claimed.
References
- F. Gao, S. Gao, H. Xiao, and Z. Shi, “Advancing Constrained Ranking and Selection with Regression in Partitioned Domains,” IEEE Transactions on Automation Science and Engineering, vol. 16, no. 1, pp. 382–391, 2019.
- S. Gao and W. Chen, “A Partition-Based Random Search for Stochastic Constrained Optimization via Simulation,” IEEE Transactions on Automatic Control, vol. 62, no. 2, pp. 740–752, 2017.
- M Buscema, S Terzi, W Tastle. Steel Plates Faults. UCI Machine Learning Repository, 2010. Dataset. https://doi.org/10.24432/C5J88N.
- White, Halbert. “A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity.” Econometrica, vol. 48, no. 4, 1980, pp. 817–838. https://doi.org/10.2307/1912934.
- Cameron, A. Colin, and Douglas L. Miller. “A Practitioner’s Guide to Cluster-Robust Inference.” Journal of Human Resources, vol. 50, no. 2, 2015, pp. 317–372. https://doi.org/10.3368/jhr.50.2.317.
- Rasmussen, Carl Edward, and Christopher K. I. Williams. Gaussian Processes for Machine Learning. MIT Press, 2006. https://gaussianprocess.org/gpml/.
- Ankenman, Bruce, Barry L. Nelson, and Jeremy Staum. “Stochastic Kriging for Simulation Metamodeling.” Operations Research, vol. 58, no. 2, 2010, pp. 371–382. https://doi.org/10.1287/opre.1090.0754.
- Kim, Seong-Hee, and Barry L. Nelson. “A Fully Sequential Procedure for Indifference-Zone Selection in Simulation.” ACM Transactions on Modeling and Computer Simulation, vol. 11, no. 3, 2001, pp. 251–273. https://doi.org/10.1145/502109.502111.
- Frazier, Peter I., Warren B. Powell, and Savas Dayanik. “A Knowledge-Gradient Policy for Sequential Information Collection.” SIAM Journal on Control and Optimization, vol. 47, no. 5, 2008, pp. 2410–2439. https://doi.org/10.1137/070693424.
- Bertsimas, Dimitris, and Melvyn Sim. “The Price of Robustness.” Operations Research, vol. 52, no. 1, 2004, pp. 35–53. https://doi.org/10.1287/opre.1030.0065.