The condition of cutting tools used in a wood disintegration process has a large impact on the quality of the products. Today we don’t know how long the intervals for replacement of worn out tools should be. This project aims at gaining knowledge for how process parameters for wood disintegration are related. By using optical techniques, wireless on-rotor sensing and multivariate modeling we want to find out the most feasible and cost efficient way to monitor these parameters online.

Background

Forest, pulp and paper is the industry branch which is generating the largest net export income to Sweden. Trees are harvested and refined into e.g. furniture, houses, pulp and paper or paper based sanitary of these products. The machinery used at some of the manufacturing steps of these products, disintegrates wood by using cutting tools. Example of such disintegration processes are drilling, sawing, slicing, splitting or chipping. It is known by experience that condition of cutting tools used in a wood disintegration process has a large impact on the quality of the products after processing. What we don’t know is how long the intervals for replacement of worn out or damaged tools should be to maintain an even and good quality of the output products. If we also consider physical properties of the input wood raw material and consumed mechanical power, the question on when to replace tools becomes even more complex.

We conclude that we in general know very little about details regarding the wood disintegration processes. This is mainly because no realistic constitutive models exist for wood. In addition, today’s wood processing machinery lack important measurement techniques for online monitoring of crucial process parameters. Extensive parameter data is necessary for development and proof of theoretical/numerical models.

This project aims at gaining knowledge and understanding for how process parameters for wood disintegration are related. Moreover, by means of optical techniques, wireless on-rotor sensing and multivariate modeling we want to find out the most feasible and cost efficient way to monitor these parameters online.

Goal

This project aim to give answer to the following multi disciplinary research questions:

• Can tool conditions such as sharpness and damages be measured by machine vision techniques and if so, how can we describe tool status with a set of parameters?
• Can tool conditions indirectly be measured by correlation with other process parameters such as tool forces and/or local temperature rise?
• When considering system complexity, feasibility and possible correlation between any process parameters, which parameters are the most relevant and cost efficient to measure online?
• What constitutive model will describe the wood material in the best way and how do measured process parameters correlate with the calculated ones?
• In particular, how do the physical properties of wood raw material and condition of tool affect the power consumption and the quality parameters of processed wood?

Partners

Andritz Iggesund Tools AB
Iggesund Paperboard AB
PulpEye AB

Financiers

Knowledge Foundation (HÖG 13)