Abstract
The use of physics-based simulations of manufacturing processes for the prediction of material properties and defects is increasingly widespread in industry. Such simulation tools help answer “what-if” questions, and a materials engineer may have to conduct a number of simulations for decision making. In practice the engineer is often seeking a solution to an “inverse problem”, i.e., prediction of inputs/process parameters for a desired outcome. Such an inverse problem is often solved by formulating it as a constrained-optimization problem. Extensive simulation in the input-parameter space when performing the optimization is avoided by approximate response surfaces iteratively constructed using simulations executed while traversing the design space. In this paper, we present a case study on the application of machine learning techniques to address such inverse problems. Specifically, using data from physics-based simulations, we explore the use of two different kinds of models constructed by machine learning. The first approach constructs a “generative” model (a Bayesian network), from which input values can be obtained directly from output values, without the need of an optimization step. It does however need additional knowledge in the form of conditional (in)-dependences between process parameters, intermediate state variables, and outputs. The second is a purely predictive machine learning model capturing complex non-linearity followed by the use of optimization methods (simulated annealing) for inverse prediction. We present results for modelling of a heat treatment process chain involving carburization, quenching and tempering. Our findings are as follows: For the range of output-values we examined, the predictive model performs better than the generative model. However the generative model has the ability to discover multiple solutions to the inverse problem, unlike in the traditional response-surface-based design of experiments. Thus the generative approach may prove more useful for exploratory industrial practice in the long run.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
National Research Council, “Integrated Computational Materials Engineering: A Transformational Discipline for Improved Competitiveness and National Security,” The National Academies Press, Washington, D.C., 2008.
National Science and Technology Council, “Materials genome initiative for global competitiveness,” 2011.
K. Rajan, “Materials Informatics,” Materials Today, 8 (10) (2005), 38–45.
S. Singh, H. Bhadeshia, D. MacKay, H. Carey and I. Martin, “Neural network analysis of steel plate processing,” Iron-making and Steel-Making, 25 (1998), 355–365.
A. Agrawal, P. D. Deshpande, A. Cecen, G. P. Basavarsu, A. N. Choudhary and S. R. Kalidindi, “Exploration of data science techniques to predict fatigue strength of steel from composition and processing parameters,” Integrated Materials and Manufacturing Innovation, 3 (8) (2014).
A. J. Keane and P. B. Nair, Computational Approaches for Aerospace Design: The pursuit of Excellence (West Sussex: John Wiley & Sons Ltd., 2005).
D. Koller and N. Friedman, Probabilistic Graphical Models: Principles and Techniques, Cambridge (The MIT Press, 2009).
M. Scutari, “Learning Bayesian Networks with the bnlearn R package,” 35 (3) (2010).
A. Patil, D. Huard and C. Fonnesbeck, “PyMC: Bayesian stochastic modelling in Python,” Journal of Statistical Software, 35 (4) (2010).
T. Zhang, R. Ramakrishnan and M. Livny, “BIRCH: An Efficient Data Clustering Method for Very Large Databases,” Proceedings of the 1996 ACM SIGMOD international conference on Management of data, 25 (2) (1996).
C.C. Chang and C.J. Lin, “LIBSVM: A library for support vector machines,” ACM Transactions on Intelligent Systems and Technology, 2 (3) (2011).
“AIMA-Java Home,” [Online]. Available: https://code.google.com /p/aima-java/.
Burl, Michael C, and Esther Wang. “Active learning for directed exploration of complex systems.” Proceedings of the 26th Annual International Conference on Machine Learning (ACM, 2009).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 TMS (The Minerals, Metals & Materials Society)
About this paper
Cite this paper
Shah, S., Reddy, S., Sardeshmukh, A., Gautham, B.P., Shroff, G., Srinivasan, A. (2015). Application of Machine Learning Techniques for Inverse Prediction in Manufacturing Process Chains. In: Poole, W., et al. Proceedings of the 3rd World Congress on Integrated Computational Materials Engineering (ICME 2015). Springer, Cham. https://doi.org/10.1007/978-3-319-48170-8_31
Download citation
DOI: https://doi.org/10.1007/978-3-319-48170-8_31
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-48612-3
Online ISBN: 978-3-319-48170-8
eBook Packages: Chemistry and Materials ScienceChemistry and Material Science (R0)