Projects / Programmes source: ARIS

Morphology of mechanicaly formed - machined wood surfaces

Research activity

Code Science Field Subfield
4.01.00  Biotechnical sciences  Forestry, wood and paper technology   

Code Science Field
T130  Technological sciences  Production technology 
roughness, wood surface, mechanical woodworking, surface morphology, cutting process
Evaluation (rules)
source: COBISS
Researchers (5)
no. Code Name and surname Research area Role Period No. of publicationsNo. of publications
1.  01392  PhD Bojan Bučar  Forestry, wood and paper technology  Head  2002 - 2005  174 
2.  10340  PhD Dominika Gornik Bučar  Forestry, wood and paper technology  Researcher  2002 - 2005  240 
3.  11925  PhD Marijan Medič  Forestry, wood and paper technology  Researcher  2002 - 2005  89 
4.  22063  PhD Miran Merhar  Forestry, wood and paper technology  Researcher  2002 - 2005  99 
5.  17847  Anton Šolar    Technical associate  2002 - 2005  14 
Organisations (1)
no. Code Research organisation City Registration number No. of publicationsNo. of publications
1.  0481  University of Ljubljana, Biotechnical Faculty  Ljubljana  1626914  66,962 
Surface roughness, which is used to determine and evaluate the quality of a product, is one of the major quality attributes of machined wood surfaces. Several factors will influence the final surface roughness in wood machining operations. The final surface roughness might be considered as the sum of two independent effects. First one is the ideal surface roughness which is a result of the geometry of tool, spindle speed and feed rate. The second one is a natural surface roughness which is the result of the material machining characteristics and irregularities in the cutting process based on interaction between the tool tip and relevant local material properties. Factors such as spindle speed, feed rate, and depth of cut that control the cutting operation can be set-up in advance. However, factors such as tool geometry, tool wear, chip loads and chip formations, or the material properties of both tool and workpiece are uncontrolled. One should develop techniques to predict the surface roughness of a product before machining in order to evaluate the fitness of machining parameters such as feed rate or spindle speed for keeping a desired surface roughness and increasing product quality. It is also important that the prediction technique should be accurate, reliable, low-cost, and non-destructive. Therefore, the purpose of this study is to develop one surface prediction technique, which is termed the multiple regression prediction models, and then evaluate its prediction ability. The aim of our research is also wish to find an answer to the fundamental questions arise from the consideration we made. Firstly, which type of measuring technique is most capable of giving reliable measurements of wood surfaces and secondly, which parameters are most appropriate for wood surfaces.
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