Wood bonding strength sensitivity estimation and power consumption prediction in wood machining process by artificial intelligence methods

Abstract

Purpose

The paper aims to present an investigation of wood bonding strength as a very important indicator for effective using in further manufacturing processes.

Design/methodology/approach

In this study, the wood bonding strength sensitivity was estimated based on grain orientation, feed speed, heating time and temperature, temperature and type of adhesive. Artificial intelligence methods were applied for this analysis because it is strongly a nonlinear process.

Findings

It was shown that the artificial intelligence tools could be useful, reliable and effective for the wood bonding strength sensitivity estimation. Afterwards the power consumption in in solid wood machining process is analyzed and estimated by the artificial intelligence tools.

Originality/value

Results shown that the wood bonding strength is the most sensitive for type of adhesive. Thus, the results of the present research can be successfully applied in the wood industry to reduce the time, energy and high experimental costs.

Citation

Jovic, S., Golubovic, Z. and Stojanovic, J. (2017), "Wood bonding strength sensitivity estimation and power consumption prediction in wood machining process by artificial intelligence methods", Sensor Review, Vol. 37 No. 4, pp. 444-447. https://doi.org/10.1108/SR-06-2017-0119