(* indicates current or former student; ** is current or former postdoc)
Wu, D.**, Wei, Y.*, and Terpenny, J., “ Predictive Modeling of Surface Roughness in Additive Manufacturing Using Machine Learning,” International Journal of Production Research.
Li, Z., Wu, D.**, Hu, C., and Terpenny, J., “ An Ensemble Learning-based Prognostic Approach with Degradation-Dependent Weights for Remaining Useful Life Prediction,” Special Issue on Impact of Prognostics and Health Management in Systems Reliability and Maintenance Planning, Reliability Engineering & System Safety.
Wu, D.**, Jennings, C. *, Terpenny, J., Gao, R., and Kumara, S.,“A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Application of Random Forests to Tool Wear Prediction,” Journal of Manufacturing Science and Engineering 139 (7), April 2017.
Kulkarni, A.*, Jennings, C.*, and Terpenny, J., “Clustering Design Structure Matrix: A Review of Methods and Evaluation Metrics,” Proceedings of the 2018 Industrial and Systems Engineering Conference, May 19-22, 2018, Orlando
Jennings, C.*, Wu, D.**, and Terpenny, J., “Forecasting obsolescence risk and product lifecycle with machine learning,” IEEE Transactions on Components, Packaging, and Manufacturing Technology ( Volume: 6, Issue: 9, Sept. 2016 ).
Jennings, C.*, Wu, D.**, and Terpenny, J., “Forecasting Obsolescence Risk using Machine Learning,” Proceedings of the ASME 2016 International Manufacturing Science and Engineering Conference, (MSEC 2016), June 27 – July 1, 2016, Blacksburg, Virginia.
Jennings, C.*, Wu, D.**, and Terpenny, J., “Forecasting Product Obsolescence Using Machine Learning,” Industrial and Systems Engineering Research Conference (ISERC), May 21-24, 2016, Anaheim, California.
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