Loading [a11y]/accessibility-menu.js
Machine learning-based metamodels for sawing simulation | IEEE Conference Publication | IEEE Xplore

Machine learning-based metamodels for sawing simulation


Abstract:

We use machine learning to generate metamodels for sawing simulation. Simulation is widely used in the wood industry for decision making. These simulators are particular ...Show More

Abstract:

We use machine learning to generate metamodels for sawing simulation. Simulation is widely used in the wood industry for decision making. These simulators are particular since their response for a given input is a structured object, i.e., a basket of lumbers. We demonstrate how we use simple machine learning algorithms (e.g., a tree) to obtain a good approximation of the simulator's response. The generated metamodels are guaranteed to output physically realistic baskets (i.e., there exists at least one log that can produce the basket). We also propose to use kernel ridge regression. While having the power to exploit the structure of a basket, it can predict previously unseen baskets. We finally evaluate the impact of possibly predicting unrealistic baskets using ridge regression jointly with a nearest neighbor approach in the output space. All metamodels are evaluated using standard machine learning metrics and novel metrics especially designed for the problem.
Date of Conference: 06-09 December 2015
Date Added to IEEE Xplore: 18 February 2016
ISBN Information:
Electronic ISSN: 1558-4305
Conference Location: Huntington Beach, CA, USA

References

References is not available for this document.